MongoDB Aktie

Welcome to MongoDB's 2026 Investor Day. Thank you so much for joining us, whether you're here in person with us at the Nasdaq MarketSite or joining us online via the webcast. My name is Jess Lubert, Head of Investor Relations at MongoDB. We have a packed agenda. We're excited to talk about our market opportunity, some of our new innovations, hopefully, you read about earlier today as well as to provide you with the opportunity to hear directly from some of our senior leaders.

Before we get started, a few quick housekeeping items. We expect today's program to run about 4 hours, concluding around 3:15 p.m. We will have 2 breaks. We'll have a 30-minute lunch break around noon, and we'll have a 10-minute bio break following our partnership session and before the financial update. We're going to hold 2 Q&A sessions: one to address yesterday's announcement upfront, and then we'll do a longer session at the end following the presented content. Finally, the full presentation deck will be posted to the Investor Relations website shortly following our prepared remarks.

Now for a brief legal disclosure. Our remarks today will include forward-looking statements for purposes of the safe harbor provision under the Private Securities Litigation Reform Act of 1995, including statements regarding our market opportunity, strategy, product innovation and financial targets. These statements reflect our views as of today only and are subject to risks and uncertainties that could cause actual results to differ materially. For a full discussion of these risks, please refer to our SEC filings, including our most recent Form 10-Q for the period ended July 31, 2026. Additionally, we will discuss non-GAAP financial measures today. A reconciliation of these measures to their most directly comparable GAAP metrics is included in the appendix of today's presentation on our IR website.

So here's our schedule for today. We're going to kick it off by addressing what you read about yesterday, followed by a short Q&A session on the topic. After that, here's what you'll hear. Dev Ittycheria, our Interim President and CEO, is going to talk about our market and opportunity to drive durable growth. Ben Cefalo is going to talk about core products and some of the new innovations to Atlas and EA you've hopefully read about today. Pablo Stern-Plaza is going to talk about our AI capabilities and Atlas Agent Engine. You're going to hear from Emergent Labs, which is one of our AI customers, about how they use our products. Ryan Mac Ban will talk about our go-to-market strategy. Erica Volini will talk about partners as a scale engine, and then Mike Berry will come on the stage and tie it all together.

So I want to make a few acknowledgments. A tremendous amount of work went into preparing for today. So we'd like to extend a sincere thank you to all the internal teams who built today's content. You'll also hear from some of our customers today. So we'd like to thank our customers for taking time out of their schedule to help you understand how they use our products. And then we'd like to thank the Nasdaq for hosting us here at this fantastic site.

With that, I'd like to have Mike Berry come on the stage for some opening remarks before we dive into the agenda. Mike?

Thank you, Jess. Great to see everybody. Thank you for joining us. Well, during my presentation, I'll stand over here so you can see the slide. It doesn't really matter, so I'm going to stand in the middle. So before we get started on the event today, and hey, folks, we got a lot of great stuff for you to go through. We wanted to address the news that came out yesterday. We'll do a few -- I'll do a few prepared remarks, and then I'll ask Dev to join me, and we'll do a short Q&A session focused on the transition.

We really can't say anything more than is in the 8-K. And today, we're focused on the plan going forward. As you know, CJ resigned last Friday, and Dev was named as the Interim President and CEO. We wish CJ all the best in his new role at Meta. He had a lot of great sayings, as most CEOs do. And one of his favorites was, don't look back, solve forward. And that's what we're doing, folks. We are solving forward today. So going forward, the Board has named Dev as Interim President and CEO. Dev is certainly well-known to employees and the investment community, and he's uniquely qualified to leave MongoDB -- lead MongoDB during this transition. He remained on the Board over the past year, and I can say he was certainly very involved in the running of MongoDB during that time.

The Board has already launched a search for a new CEO, and we're very confident they will bring aboard a great new leader for the business. During the transition, folks, it is business as usual at MongoDB. The business is doing very well, and we have a lot of exciting announcements to discuss today and tomorrow at our .local New York event. To the business of -- it's -- to the point of it's business as usual, it's important for you to know that the whole management team is here today, and we are fully aligned with the direction and plans that we're going to show you today. In addition, we have several members of our Board of Directors as well. So I'm not going to introduce the folks that are going to present. They're going to get enough of the spotlight, but I would love the management team of the non-presenters to stand up, if you would, please.

So last year, you heard from Jim, our CTO; and May, our CMO. In addition, you have Deepa, who's our Chief Information Officer; Andrew, who's our Chief Legal Officer; you have Harsha, who's our Chief People Officer; and you also have Ashish right behind Andrew there, who's our Technical Fellow. So they are here as well as the folks presenting. I want to make sure and introduce you to them. Folks, this is a team event. We are all in front of you today. And then if I could ask the 4 Board members to stand up, please, because it's super important they're here to support us as well. You have Roelof, Hope, Chip and Tom. So they are here to support us as well.

The goal, folks, is, and I've gone back and forth on this, and I'm just going to say it. This is not about one person. There's about 5,700 people that work every day to move MongoDB forward. And what you're going to hear today is the plan of all of the employees, okay? So our goal is to make sure the transition goes very smoothly. We as a team are going to continue to drive the business forward. We take our responsibilities to customers, employees and shareholders very seriously, and you have all of our commitments, all of ours that we will stay focused and drive execution. We have a great business and a lot of momentum, and our job is to keep it going.

So with that, I'd like to invite our former and our new President and CEO, Dev Ittycheria, to join me for a short Q&A.

So I'll take a few questions here. Raise your hand when we call on you, be sure to state your name and your firm's name. Keith, do you want to kick it off?

Keith Bachman from Bank of Montreal. Welcome back officially. I wanted to ask a question on the transition, and that is to say, Jess and I were talking in the hall, what do you think CJ did particularly well over the last 9 months in a short period of time to help position MongoDB? And then when you think about your leadership, however long that may last, what are you really focused on in terms of priorities? And then the third part is, as you think about what you're looking for, for your next leader, anything you want to call out that you think is particularly important, whether it's the R&D, go-to-market, et cetera?

Yes. So thanks for the question. First, I would say, I think what CJ did well was he really elevated the company to be able to sell at the C-level. He has a pretty big Rolodex coming from ServiceNow, where he knew a lot of senior decision-makers. And historically, with infrastructure software, you have to sell both at the end user level because you have to get people buying because no one is going to try and shove infrastructure software down end users' throats unlike application software. And then you have to still sell high to kind of get the economic buyers bought into whatever the compelling reason is.

And I think he -- especially in some key accounts where he had deep relationships, he helped us, I would say, accelerate deals. But what I'll also tell you is that -- and I've seen this myself, I show up at a C-level, we shake hands and a deal happens. It wasn't because of that meeting. It's all the work, groundwork that happened before that meeting to set the stage for us to kind of consummate the deal. And so I don't want to suggest that, that was a one-man show. It was really the go-to-market team who really helped galvanize the momentum in those accounts. But clearly, CJ added some value on key accounts where he helped push the deals over the line.

On the second question, what am I going to focus on? One is really driving momentum. I think we have a lot of momentum. I think you're going to be quite pleased in terms of what we're going to share, sharpening and executing on our priorities. Obviously, a lot of focus on product and a lot of focus on go-to-market and also just focusing on our people and our culture. I think one of our assets has always been our culture. That's something I'm really focused on. And then in terms of -- and I'll speak more about this in my talk.

When I -- in terms of who we're looking for, obviously, we want someone who can help scale it. We think the opportunity here is enormous. And I did obviously resign because at some point, after 11 years, you feel like you want to hand the baton to someone else. But we want to find someone who's really passionate about where to take this business forward. I think this business has a chance to become a very, very large business. And let me just explain why. With the move to AI and agentic workloads, as people start to get more sophisticated, agents need to understand what's happening, right? They need to be able to identify and find information, and that information has to be fresh, not stale.

It can't be from a week ago, it can't be even from a few hours ago about what's going on. Then you need to be able to -- through that information, you need to find the right relevant information because you can have a lot of fresh information. But if you pick the wrong information, you can make a bad decision and reason about something poorly. Then you need to be able to scale elastically because by definition, these workloads are very unpredictable, and you could have agents -- people talk about agent swarms and stuff like that. So the traffic could be very unpredictable. We have an architecture designed for that. And then you also need to manage state. And so I think we are -- and so we'll talk a lot about all this a little later, but I think we are really well positioned to truly be -- help customers through this agentic journey and be a beneficiary of that.

Sanjit Singh, Morgan Stanley. Dev, great to see you again. I'm so glad you're back to head up Mongo on this transition. My main question is about when I look forward over the next several quarters, just the magnitude of change. In CJ's tenure, he did make a lot of changes. You mentioned the go-to-market changes. Product-wise, there was a doubling down on advanced. A lot of people left MongoDB that was formerly working with you and a lot of new people have come in.

And so when we think about the next couple of quarters, and there will be a new CEO at some point as well, and that also represents change, right? And so in terms of managing all this organizational change, what's your strategy for that? And the newer team members that have come over in the last year, are you reaching out to them to make sure that they stay on board? How are you just thinking about managing that process?

Yes. So thanks for the question, Sanjit. What I would tell you is for the Board members who've been here a long time like Chip and Tom and Roelof, actually, Hope has also been here a very long time. We've seen a lot of executive changes. So we went through a lot of people. And one of the things we realized is as the business scales, not every executive scales the same rate. So I wouldn't suggest that the change we've seen over the last year is something more profound. The organization has gone through many changes in the past. In fact, we replaced our CRO right after we went public. I was a temporary CRO for -- a few people remember, like for like almost a year before we promoted someone in that role. So I think the organization is -- ability to handle change is quite good.

I would also say that change is a constant. I mean, we're in an environment where things are changing so rapidly. And so I think it's just par for the course now to deal with change. And I would say to the third point about someone new coming, I would say, clearly, we would want someone who really understands the business and not just make change for the sake of change. Some of the changes that CJ made, people were just tired. Some of my team who was working with me, they had also been on the journey for a long time. So it wasn't like they suddenly need a new skill set. They were just tired. It was a long time, so they want to take a break. So I'm pretty confident that we're going to navigate this through this well. And I would just say that change is something that we're very used to.

Dev, Rishi Jaluria, RBC. So good to have you back. Maybe one that I'd love to continue on that thought process, like one of the big changes that we saw at CJ was this refocus on EA, bringing some of what was Atlas-only features into EA, especially for these large enterprise AI workloads. Can you maybe talk about like that strategy, kind of some business continuity on that? It seems to be reflected in the updated targets of not just an Atlas growth but an overall growth number. And maybe what more can you do to build on that success that we've seen with EA, especially for AI workloads in large, regulated industries?

Yes. So actually, I didn't touch on that and Sanjit asked the same question. What I'll tell you is those decisions that were made to invest in EA happened when I was here, right? It wasn't like all of a sudden, we suddenly decided to double down. You're going to hear from Ben and Pablo later, but we were making concerted effort because we had heard from customers, a lot of organizations you represent, a lot of large financial institutions still want to run workloads on-prem. And now obviously, what we're seeing is the market is shifting and buying behavior is shifting, especially with AI that people don't necessarily want to run everything in a hyperscaler.

They may want to run on-prem. They want to run a neocloud, and they're also very, very concerned about IP protection. So they may want to use open source models. So having that flexibility really strengthens our run anywhere story. We had clearly put a lot of momentum around Atlas, and obviously, that showed up in our results, but we heard loud and clear from a lot of large customers and financial services represents a large EA estate for us that, hey, they want to see more investments. So we had already started that work before I left.

All right. We'll take our last question on this session of the day here.

Dev, Alex Zukin from Wolfe Research. Glad to have you back, and I apologize, it's probably an unfair question, but I think it's important. And it's basically if you had to think about given the rate of change and the pace of change, how long do you anticipate this process to take to find a new leader? And what's the prototypical leader in your mind? Is this somebody that's highly technical? Is it more sales oriented? Is it more West Coast-based to reclaim the Bay? Just give us kind of your wish list.

Yes. And I want to be clear, it's not going to be just solely my decision. The Board will be involved. And obviously, I suspect many members of the executive management team will be part of the process to evaluate candidates. What I would say, and again, it's obviously only been a couple of days, but what I'd say is that I don't think we're looking for one particular domain expertise. I think we want someone who can -- has experience kind of building software companies and also understands both building and go-to-market because we have a large organization. We serve a lot so many different organizations. We have a fairly sophisticated go-to-market organization with an enterprise sales force a commercial organization, a self-serve business and a partner channel, and you'll hear all about that a little later today.

So we want someone who really understands. And my preference is, in fact, I wrote this when I just recently joined Sequoia was like when I like people who can understand technology, but equally understand what it takes to acquire customers and keep them, right? So I think you need to have a mix. You can't just be purely one-dimensional. But we're going to look at a lot of candidates. My phone has already been ringing off the hook. There's -- and so I don't doubt that we'll have a lot of good candidates to choose from.

All right. Okay. So thank you for that. Thank you for keeping it short. Now what did Ed Sullivan say? On to the show. So now we're going to walk you through the story. Hey, folks have got a lot of great things. So thank you for joining us. It's going to be a long day. It will get a little bit hot in here, but we'll take breaks. And Dev, on behalf of everyone at MongoDB, welcome back.

Thank you. And I do want to say -- I just want to double-click what Mike just said. There's a great team here. Mike is a world-class CFO, and this business was not run by one person. There was a real team here and not just the team that reported to CJ, but the team under them. It's -- so I'm privileged to represent and support that team, but they're the ones really running the business. Okay. So I will try and cover a few things quickly. Can we -- can you get the slides going?

So first and foremost, like what we are doing is we are essentially trying to become an intelligent data platform. And I just want to go back. When I joined MongoDB, the challenge then was could we prove ourselves to be viable for mission-critical use cases. Could customers really conceive of using a document database to run truly mission-critical workloads. We did that. The second job was like could we build a cloud business because people were fairly skeptical that we go head-to-head with people like Amazon and Google and GCP, and win. We actually proved that. The third leg of the stool now is really expanding the breadth of capabilities we can offer to be able to essentially address a broader set of customer needs. And so we came out with things like Vector Search. We came out with just Full-Text Search.

And then with the Voyage acquisition, we do things like embeddings and re-rankings. What we've also seen is that our business has grown very, very quickly, right? The market is massive. This is one of the largest markets in enterprise software. This market is going to essentially double from 2024 in 6 years to nearly $200 billion in terms of size. And if you look at our business, we've grown the business 5x since 2020, and we expect the business, based on our guidance, to hit $3 billion by the end of this year. So the key point is our market is large and growing, and we're growing much faster than the market because of our strong execution. So a couple of things we've done is also since our -- we've grown our revenue 5x, our customer count has grown by 3x. So what that says is not only are we acquiring lots of customers, but we're also expanding more deeply inside those organizations to acquire more workloads and address a broader set of use cases.

And we serve some of the most demanding and sophisticated customers across nearly every vertical industry, every geography and for almost every conceivable use case. So when it comes to AI, I think it's very important for all of you to understand why AI is a tailwind for 3 basic reasons: one, more workloads. With AI, you can produce far more software, far more quickly. So by definition, more software applications require more databases and more data persistent stores. So that's point number one. And you're going to hear a lot more about this. Two, new workloads. We're going to address a broader set of workloads around agents, memory, knowledge work, the breadth of use cases expands not just from software, but taking over knowledge work that people are doing manually. And the third is a brand-new set of customers. We've already expanded into the Frontier Labs. We're seeing a lot of AI native companies starting to use MongoDB. So there's a whole ecosystem of new genre of customers who are not just customers of ours, but trying to become larger and larger customers.

So what do these customers really need? So whether you're a Fortune 500 company or an AI-native company or a Frontier Lab, there are some things that are common across all these customers. One, you need real-time context. AI, as I mentioned earlier, is only as good as the data it sees. If it reasons on bad data or stale data, you're going to have bad answers. Two, even if you have fresh data, you need to precisely retrieve that data accurately and succinctly. Third, you need to be super elastic and highly scalable, right? Because the unpredictable nature of these agentic workloads is something people have not seen before and traditional SQL databases are not designed to scale well.

And the fourth thing is that you also need trust. No one is going to trust an AI system or an AI application if they one can't guarantee the quality of the answers or that potentially something else may go wrong. It's not reliable, it's not available, it's not secure. And so security, governance, availability and reliability are super, super important. And last but not least, and I touched on this, you need to be able to run anywhere. I think it's very clear with AI that customers are taking a very different approach than, say, with the cloud when anyone was rushing to the cloud, they're starting to be much more discriminating about which workloads they may run in the cloud, which workloads they may run on-prem. Sometimes they may consider a hybrid environment for -- and it's all about IP protection, data residency and sovereignty and a bunch of other reasons.

So what that says is that the more options a customer has, the better it is for MongoDB because we can run anywhere and you don't have to change the line of code. Where -- what's interesting, and you're going to hear from this from Ben, even with Postgres, if you go from AlloyDB to an AWS offering like Aurora, you actually have to change a lot of code. And that's the misconception a lot of people have is that Postgres is a portable technology that can be run anywhere. So on top of that, you're going to hear a lot about this today, so I'm not going to really belabor this. But as I said, is the document model and the capabilities are coming out with agent memory is going to be really interesting. 9.0 and Infinite is something that I'll touch on a little bit. We've really doubled down on Queryable Encryption, something no one else has in the industry and the fact that we can run anywhere.

So let me just give you a couple of customer examples that I think you'll find interesting. This is one of the largest health care providers in the world today. Their business has grown with us by 22x in 3.5 years. One of the big use cases they used us for was to modernize their entire claims platform, which is probably their most mission-critical platform. It handled 15 million transactions per day at under 200 milliseconds, so super low latency. And on top of that, once we laid that foundation, they said, hey, we want to offer a chatbot for our customers to answer questions rather than trying to search for documents and so forth. So they built an AI chatbot on top of MongoDB. And with that, they've lowered their TCO by almost 80% and increased their accuracy of answers by almost 60%. So they're a very, very happy customer.

Another customer is a very large media company. They run their entire streaming business on top of MongoDB. That ARR went up by 3x in just 12 months. So they start with one app, and now they've grown to 20 different streaming services and streaming applications. And their user preference of record because they have different brands, users can pick their preferences and it's portable across all the streaming services that this company offers. All that was built on MongoDB. And now they're offering very sophisticated search where people can search on videos and not just titles, but also scenes and they're really trying to amp up the ability to search almost any content that they offer for people to use that in different ways.

And last but not least is a large European retailer. They have been up 7x in the last 3 years. And what they did was they moved a very complex ERP application off Oracle to MongoDB. And then on top of that, they replaced Elastic with Vector Search and basically are running their production system on MongoDB. Then on top of that, they built an AI chatbot to help customers address specific questions about our -- items available, the variations that they could offer and other details about their inventory. So what you see is that once we lay the data foundation for customers, it's very easy for customers to build upon that to offer new AI capabilities.

So this is just a small subset of some of the AI natives who are already customers of ours. We can't mention the Labs for all the obvious reasons, but you can probably predict who some of them are. And what's interesting is the Frontier Labs are using MongoDB for critical use cases like model training, managing their chat system, high-velocity product delivery, like feature testing, experimentation, analytics, et cetera. And what's also interesting is many of these AI natives are not just using one capability, 2/3 of them are using actually more than one capability. So they're really using the broad platform. So we find that super exciting.

And then these Frontier Labs are not just customers, but also becoming distribution partners. One of the interesting things we did about a little over a month ago was launched an MCP managed connector. Now we always had an open source MCP connector, but it required a lot more work on the customer to set it up and configure for them to use it on MongoDB. Well, we came out with a managed connector to connect to Atlas, and we've seen interest spike 8x over the last 6 weeks -- 6 to 7 weeks. And what it allows customers to do is with one button, have a one button connection to Claude Code, Codex, Grok Build, et cetera. And so they can connect to Atlas to either instantiate a new instance or be able to query the data as they're building applications. And we feel like we're just getting started.

And so the other interesting thing is a lot of you have asked, how can you tell if an end user is using Atlas versus an agent using Atlas? Well, not every agent uses the MCP connector today because people sometimes still direct connect to Atlas. This gives us a lot of visibility about agent traffic coming into Atlas and will allow us to get a sense of how we're doing in this new genre of workloads. Again, you're going to hear a lot more about this later, but we've made many meaningful investments over the last few years, things like, as I said, Vector Search, 8.0, Stream Processing and Voyage. We also announced a bunch of new capabilities earlier this year, both to deepen and broaden the platform as well as announce some new AI capabilities.

What we're really excited about is that you're going to hear some really interesting stuff. First and foremost, 9.0. The performance of 9.0 is the best we've ever delivered. And it's essentially the throughput is 2x faster, and you can do reads and writes 30% to 35% faster. And we know that 8.0 was a step function in performance over the prior releases. So we believe we are delivering the most performant platform in the industry. Second is Infinite. And this may be the most profound innovation we've launched since, I would say, ACID transaction support, which is very complex in a distributed database or even Atlas itself. What we have done is decoupled storage and compute, so you can scale storage and compute independently. So you can have very storage-intensive use cases or very compute-intensive use cases.

What it does allows you to scale up very quickly for agentic workloads, get better economics and you can tune the performance based on the type of the workload. You're going to get a lot more detail on this when Ben comes up here. And last but not least, we've built an agent memory runtime and governance stack for running agents on production. Remember, I said, once you lay the data foundation, it becomes easier for people to build agentic applications, and you're going to hear a lot about how we're tying this stack to enable customers to get started. And the advantage we offer, unlike some other people is, one, we're open to any model. If you're working with a model provider, you can't do that. We're open to any framework. And we're open to any cloud or open source model that you want to run on-prem. So it gives customers extreme optionality.

So like software, there's really 2 engines of the plane that really drive a software business. One is the product innovation we just talked about, but the other is the world-class go-to-market. And even my short time, when I left MongoDB day-to-day and then started working with VCs and more recently with Sequoia Capital, I will tell you that MongoDB's reputation from a go-to-market point of view is second to none. And if anyone knows my history, we've produced some world-class go-to-market leaders. And so this is something as much thought and effort we put into product, we also put into distribution. So we are spending a lot of time and a lot of investments in moving up the market to serving our enterprise accounts better and driving more market share inside those accounts. We're investing a lot in partnerships.

Eric -- Ryan is going to talk about our sales organization. Erica is going to talk about our partner organization. And we're also making a lot of investments in specific vertical organization. So you're going to hear a lot from our go-to-market leaders about how we're really investing and growing our go-to-market team to capture the big opportunity we see in front of us. So to kind of summarize where I'm focused, one is really to drive momentum. This is not a company that needs a lot of fixing or problem solving. The company is on strong footing. You'll see it when the other people present and then you'll see it in the numbers when Mike presents. Two, is to really execute sharply on product and go-to-market. And three, is to provide a seamless transition while I'm here. So that's what I'm focused on.

I'm going to hand it off now to Ben, who's our Chief Product Officer for Core, to talk about the innovations there. Thank you.

Thanks, Dev. Hi, everyone. Nice to see everybody. So again, I'm Ben. I lead the core products across our core platform, including the database, our Atlas managed service and our community and our enterprise self-managed products. So today, I'm going to, as well as my co-CPO, Pablo, will tell you all about why we have the right product and strategy to win now.

So I've been with MongoDB for nearly a decade. And in that time, I've had countless customer conversations. And lately, they all come back to the same theme. Customers are building applications that are more demanding than we've ever seen before with real-time operational data playing a crucial role. And our platform is built for this moment. That's because of a decision our founders made at the very beginning when they built MongoDB, and they created the first database to make JSON in the document model truly native. They saw that relational databases were holding companies back and relational schemas are inflexible by design.

Every new field, every structural change takes a significant amount of effort to migrate. So they didn't try to make a better relational database. They built something better. They built -- here's why that matters right now. AI agents need to work with complex and evolving data on the fly, and JSON is perfect for this. It maps naturally to how applications and now agents want to model data. And that's why JSON is a default language of AI. A question I hear constantly though and a lot from this community is how does native JSON support stack up against JSONB and Postgres? And I get why. On paper, JSON's in the name, right? But having native support versus a bolt-on hack is fundamentally different.

The difference should show up in multiple ways in how we handle large documents more effectively. Updates are faster because we're not rewriting the entire JSON blob on every change. And our query language is designed for JSON, making it easier on developers and also now agents. At the end of the day, if you're using Postgres and JSONB, you are forcing flexible data into a rigid table, canceling out all of the flexibility benefits customers need from JSON. This kind of flexibility isn't a nice to have in the AI era. It's a prerequisite. And it's our durable differentiator and it's more valuable than ever.

And we are delivering on innovations built around the document model that strengthens this position. And I'm going to focus on 3 key areas today. First, we are doubling down on our Enterprise Advanced and our run anywhere strategy. Regulation, data sovereignty, AI intellectual property concerns means customers can't accept limits on where to run. Second, we're hardening our foundation with 9.0, so it's always ready for mission-critical workloads. And third, we're delivering extreme elasticity with Atlas Infinite, so customers can scale rapidly for unpredictable events and agent-driven demand.

Okay. So let's get to it. First, we're going to dig into run anywhere. In nearly every enterprise conversation I have, customers tell me they need the option to run across multiple cloud environments and clouds and self-managed environments. They need the control over where workloads run and where data lives for 3 reasons. First, regulation. And in many industries and countries dictate how data can be stored and where. Enterprises want control and are concerned about intellectual property. They need to know exactly where their data lives and how it is handled. And now with AI in the mix, they don't want their proprietary data exposed to risks around AI access, training or ownership. Plus, business continuity and resiliency means enterprises need a safety valve. They can't be locked into a single environment. If there is an outage at a data center or they want to change vendors, they need their applications to be truly portable.

And we're seeing these reasons play out with our customers today. For example, Nationwide U.K., the second largest provider of household mortgages and savings in the U.K. runs across both EA and Atlas to meet their regulatory requirements while giving members real-time access to their accounts and the transactional data. A major U.S. bank uses EA to serve more than 100 production applications across payments, fraud detection and account services. By self-managing, they keep the sensitive customer data and conversational data inside of their own environment. And then lastly, Zoom runs EA deployed across dozens of environments globally to deliver low latency, highly available communications at scale and wherever their business needs dictate. This need for flexibility is exactly what our run anywhere strategy is built for. And what makes run anywhere even more powerful is having EA as our self-managed option alongside of Atlas in the cloud.

Last month marked the 10-year anniversary of MongoDB Atlas. Back in 2016, we were convinced that the cloud would fundamentally change how organizations build and use the database. So we built Atlas, a fully managed database as a service with all the benefits of cloud-native infrastructure. And since then, we've delivered a steady stream of innovation. From our initial launch and support for the 3 major hyperscalers to client-side field-level encryption to the advanced networking options, we've stayed ahead of the curve and defined what a world-class managed database should be. Today, Atlas can run across AWS, Azure, GCP in 130 cloud regions. Customers can even run a single cluster across multiple clouds simultaneously.

And Atlas is also available in all the cloud marketplaces. So regardless of what hyperscaler our customers choose, they can transact through their cloud provider with. But let's not forget that this is the same database that you can run in any environment. This is not something you can easily do with Postgres. Since there are so many flavors out there and each behaves just a little differently. As Dev said, Amazon Aurora is not the same as Neon, which is not the same as Supabase, which is not the same with open source Postgres. You think you're betting on one database, but you're really limiting yourself to that specific flavor. This means applications are not portable.

With MongoDB, our database can run on a developer's laptop in self-managed enterprise environments and even in air gap deployments for the most sensitive workloads like public sector or regulated industries. This is the definition of run anywhere. Because of this innovation and flexibility, Atlas has been used by nearly 20 million users and processes more than 3 trillion queries each day. To put that into a little perspective, that's 35 million queries a second and about 340 queries for every man, woman and child on earth per day. Let's leave that for a second. It's more than double what it was just a few years ago. But let's not forget, Atlas is also a great way for builders to get started. There is this perception out there, and you all ask me about it every time we talk, but the data tells a different story. Not everyone starts with Postgres. On average, we see 500,000 free tier clusters each quarter, and this has been growing steadily each year. And when you combine that with 150 million community downloads, that's a huge number of builders that begin their journey with us.

Putting it all together, we believe that our customers deserve the same capabilities no matter where they choose to run. And it's this portability that really matters. Run anywhere gives our customers unique flexible advantages and is a huge competitive moat for us. That is why we're also continuing to invest in EA. In June, we GA'd Search and Vector for self-managed customers. And as a reminder, we introduced Vector Search in 2023 in Atlas, which was actually before the ChatGPT launch, we were in private preview with our Vector Search. And today, we see customer workloads with billions of vectors. Vector Search is a core building block for AI applications. And customers told us clearly they needed it to run in their own self-managed environments, not just in our cloud.

And with that, teams end up stitching together with that -- out that -- excuse me, teams would end up stitching together a database and a vector store themselves. And this is what they have to do today with relational databases, which adds complexity and an infrastructure tax teams cannot afford when trying to ship AI applications fast. So by making search available for Enterprise Advanced and customers get the core database and vector search running anywhere they need without the complexity. The demand came in immediately and across different use cases. A large multinational bank is simplifying its AI architecture by bringing together operational data, Vector, embeddings and Full-Text Search on top of Enterprise Advanced. And another large multinational financial services company wants MongoDB to serve as a system of record for all the customer information using our search capabilities. Enterprise choice matters more than ever. Our customers want to know we will keep investing in our self-managed options, which we've made that commitment, and we don't plan to stop there.

Now all of that only matters if the core database can carry the load, which brings us to MongoDB 9.0. Since our inception, we've never taken the foot off the gas when it comes to the database innovation. As we move deeper into the enterprise, the applications we power grew more demanding. We match that growth with a constant drumbeat of innovation, staying ahead of the customers' needs so they could keep building on us at any scale. With that innovation, I just want to remind you that we were one of the first distributed databases to support transactions and that we've natively supported horizontal scaling, which is how you distribute data across machines to support a larger scale from the very beginning.

Our 8.0 release went GA 2 years ago right around now, which delivered meaningful performance gains over the previous release. This release has experienced faster adoption with nearly 80% of the Atlas fleet is already on 8. Since launch, we're also seeing higher customer satisfaction and improvements in customer retention. So clearly, our customers have been pleased with what 8.0 has delivered. And now this brings us to today with the release of 9.0. 9.0 is the best release of MongoDB we've ever shipped. We continue to harden the core to give the most demanding enterprises a strong foundation. And with 9.0, we're setting a new bar for security, durability, availability and performance.

We've made a significant improvement in the way we look at performance and scale, up to 20% higher transactional throughput, 35% faster reads, 30% faster updates and 2x more throughput on larger instances. So to put it a little bit in perspective is 9.0 is faster than 8, 8.0, which was faster than 7. And if you use the release before 2023, we're about 100% faster than we were back then. The customers have also noticed this. This has made MongoDB a better fit for more opportunities. All of this adds up to greater efficiency, which means greater consumption. And we saw this pattern with 8.0, better performance and scale means customers save more -- serve more users, transactions and AI-driven activity.

In other words, it's Jevons paradox in action. When we introduce better price performance, we've seen stronger growth in subsequent periods. Our customer, Coinbase, has been putting 9.0 through its paces at the real production scale, so let's take a look at their video.

[Presentation]

Thanks to Coinbase for that. Awesome collaboration. Can we switch the screens in the back, please? Thank you. So performance and scale are nonnegotiables for their business, and that's exactly what 9.0 has been built to deliver. In 9.0, we've also enhanced our industry-first and industry-only Queryable Encryption capability. Last year at our Investor Day, our CTO, Jim talked about Queryable Encryption, which is a powerful security capability that only MongoDB has. In most databases, the data remains in the clear, so it can be queried. Queryable Encryption lets customers keep the data encrypted and still query it. So let's say a customer wants to find a name that starts with the beginning parts or find a credit card number that ends with 6789 or a medical record note that contains fever. They stay -- they can run these queries while the data stays encrypted and the query itself is also encrypted.

This is why Queryable Encryption makes MongoDB really suitable for applications with super highly sensitive data. For example, the France's Internal Digital Force manages and secures digital communication systems for law enforcement. They needed a defense in-depth strategy, and Queryable Encryption let them encrypt the data knowing that they would only -- they're the only people that could ever decrypt it. And to meet regulatory requirements, a leading financial institution needs to retain all the customer notifications about their financial products. They leverage Queryable Encryption to protect the PII data contained in those communications, which helps safeguard their customer identities. And like everything else we do, we always are thinking about run anywhere. And again, you can use Queryable Encryption no matter where you're running.

9.0 is the best release we've ever delivered for our customers. The demands of modern and AI applications require us to keep building the most performant and fastest database we can. But there's another level of demand that's on the horizon, and we're skating to that puck today. We're bringing even greater elasticity and scale to our platform with Atlas Infinite, making it easier to handle the bursty nature of AI agents. Infinite is Atlas' biggest architectural innovation since its inception in 2016. We just heard from Coinbase about how important scale is as they bring more and more agents online. What makes agent scale requirements different from those we've seen in the past? Let's take a look.

User-driven applications can be more predictable based on timing or seasonality. Their intensity also scales with the number of users in the system. So think about Cyber Monday. You know the traffic is coming, you can plan for it. On the other hand, agents don't generate steady, predictable traffic. They can sit idle for long stretches and then suddenly introduce massive unpredictable demand that hits the database all at once. And even with users interacting with the application, agents will also make purchases, adjust prices, manage inventory and handle fulfillment on that same Cyber Monday. So you need a database designed for this unpredictable and intense agentic activity. This is what Infinite delivers, the ability to handle both of these workload shapes simultaneously.

It's built for extreme elasticity. Infinite is designed to scale up and scale down rapidly. So when agents run in an autonomous loop or fan out and spawn additional agents, the database is able to respond. This is the exact shape of workload Infinite was designed for from the ground up. As an application scales, customers want more performance per dollar and pay only for what they use. So it offers tunable performance. But to be clear, this is not about spending less. It's about making sure businesses aren't paying for idle capacity that could be used elsewhere. Plus, it removes all the guesswork out of capacity planning by scaling up and down to meet the customers' demands in real time.

And to be very, very clear, this is not a whole new database to migrate to. It provides the same Atlas experiences customers already love. Infinite is a new deployment option within Atlas that current customers can adopt seamlessly. No application code changes are required. The current edition of Atlas database isn't going anywhere. We're renaming it to Atlas core database, and we're going to continue investing in it as well. Infinite delivers all of this with a very high security posture. And at launch, we are -- we already have SOC 2, ISO and PCI and more to come later on. Architecturally, we are encrypting the data before it leaves the compute layer, and that compute always runs in a secure environment dedicated to each customer. Neither the cloud providers nor we can see any data that a customer stores in Atlas.

We've been running a private preview for Infinite with some of our largest customers, and the results have been excellent. One of our preview customers, Icon Solutions, processed more transactions per second and another one, PicPay, ran 4x its normal peak traffic. And tomorrow, Okta is going to join me on stage at our local event to share their experience with Infinite as well. But right now, let's hear what Carrier had to say. Go ahead and play the video, please.

[Presentation]

Our thanks to Glenn and the team at Carrier for their partnership. As Glenn said, efficient scaling directly impacts his customers and the Carrier business. Atlas Infinite can deliver elastic scaling because we've separated the compute from storage. So let's take a couple of moments, and we'll walk through how that works. When tuning for performance and scale, you can adjust compute and storage in multiple ways. First, there's vertical scaling. This is when you just buy a bigger box. Then there's horizontal scaling when you add more servers into the system. In both approaches, compute and storage are coupled. So scaling up often means adding both compute and storage together, whether you need both or not.

So how does Atlas Infinite get extreme elasticity? This is where separating the compute from the storage comes in. This architecture unlocks the next level of performance. When an application suddenly needs more compute to process agentic activity, the database can respond in an instant. Extreme elasticity and tunable performance at scale are what we hear from our customers, and they need it and expect it from a database. And it's exactly what Infinite delivers. Changes like this don't happen overnight. You can't easily implement this architecture in an OLTP database. With our large enterprise customer base, you need to do it in a way that guarantees minimal disruption without lowering the bar for security and durability. And that's why we've been working on Atlas Infinite for over 2 years.

This expands the set of opportunities that we can compete for, especially ones where scaling compute and storage are -- separately are critical, such as bursty unpredictable nature of agents. We've been putting Atlas Infinite through its paces and the results have been pretty impressive. These gains help customers scale faster and support a higher ceiling as more and more agents come online. And as I mentioned earlier about Jevons paradox, we expect better price performance to translate into greater usage as our customers build bigger and more ambitious applications. Atlas Infinite is a major innovation in our core database, and we're perfectly suited for the agentic applications that require high performance with rapid scaling. It also puts us in a position to win even more customers where compute and storage have to scale independently. This means that Atlas Infinite will be a win for workloads that drive more growth for MongoDB.

So let me bring this back to where I started. Our founders bet on JSON because they saw that the real world does not fit neatly into rigid tables. And that bet has proven to be a great one over time. And when we have the opportunity to educate customers on the benefits of MongoDB, they choose us over relational alternatives. So everything I've walked through today builds on that original advantage. Run anywhere means we can compete for more because customers can choose where to run. 9.0 means our foundation is strong and earns enterprise trust because our security, durability, availability and performance keep getting stronger release after release. And Infinite means we will win when the demand is unpredictable. And to be clear, with agents it will be because our platform can respond in an instant. Individually, each of these is a meaningful investment. But together, they're the reason we believe MongoDB is the ideal platform for the AI era.

So with that, I thank you. And I get to say lunch is going to be in the foyer, and we're going to be back here in 30 minutes, right? Sweet. So thank you all for paying attention.

[Break]

Please welcome to the stage, Pablo Stern-Plaza.

Great to see you. I'm Pablo Stern-Plaza, and I run AI and emerging products. And it's really good to see a bunch of familiar faces here in the audience. Today, I'll cover why MongoDB will be a winner in AI. But I want to start with what we're seeing and hearing from customers. We have lots of customers that are building on us from AI natives to enterprises. And I've met with nearly 100 of them in the 6 months that I've been here. And one of the clear messages is in order to deliver AI outcomes, at production scale, you need the right stack.

We've been talking about the latent power of data for decades. And AI is changing that. It's providing insight and AI needs real-time data to be performant. MongoDB is that platform. And as you'll hear in a bit, Emergent Labs, an AI native, picked MongoDB over Postgres as they look for an adaptive and flexible model to be able to build and scale their data or one of the U.S.'s leading hiring platforms is switching from Postgres to MongoDB because of its struggles with nested JSON. To show you how MongoDB wins in AI, I'm going to walk you through this in 3 parts. First, I'm going to show you how we're showing up for builders. Then I'm going to talk a little bit about how our platform is perfect for AI and building AI workloads. And then I'm going to talk about some of the stuff we covered a little bit earlier today in terms of how we're bringing customers into production faster for agentic workloads.

So let's start with the builders. The way builders find and choose technology has changed a lot. Builders are learning about exploring and choosing technology in widely different ways than they have in the past. From answer engines to coding agents, we show up in many different places and the tooling matters underneath them. And we're investing to make sure that MongoDB shows up well across all of these. For answer engines, as they are the conversational tools like a chatbot, our focus has been twofold. First, we're showing up in the answer. And then secondly, making sure that you're driving accuracy. We've invested heavily, and we're seeing very strong results here. Over the past year, we've improved accuracy by over 50% in prompts to test high fit scenarios for MongoDB, such as if you're looking for a managed database service. It's important to show up and to show up accurately.

Now think about the AI frameworks. This is really about having the right integrations to the development frameworks that are using to build code on top of. It's important to be in the right place for these because those end up being the selection for the rest of the stack as you build new workloads. Here, we have deep integrations with the popular frameworks like LangChain and Mastra. And we're continuing to add new ones as new emerge and gain popularity. You'll hear a bit more about this from Erica in a little bit. Our coding agents. This is an important one. They're critical to how applications are getting built and how they're going to be built in the future. To make it easier for coding agents, we introduced Frontier marketplace plug-ins into Codex, Claude, Grok Build, Devin, Cursor at our Build Fest in mid-August. And we're seeing really good traction on those. And then lastly, AI platforms. So these are the prompt engineering platforms that more and more developers are choosing to build on.

Earlier this month, we announced an integration to Vercel v0. And tomorrow, we're going to talk about Monk, Retool and Emergent as new integrations, enabling developers and builders to build AI apps using Atlas. As you heard from Dev, a big unlock for many of these capabilities was our managed MCP. As Dev said, we have a local MCP that we released last year, and that's getting good traction, but it requires overhead to manage, operate and install. With managed MCP, it's one click. We remove the friction. And that's important for builders and developers as they're trying to get and build those applications quickly. And because of all this innovation, we've seen tremendous traction since launching this just a few weeks ago.

Now the second thing I want to hit on is why we're perfect for AI. And I'll start building on some of what Ben laid out before me. We're built on the JSON document model. And as Ben said, this is how AI thinks. It's how it interacts, uses a document model. And on top of that document model, we have native retrieval capabilities. And that's really critical because without accuracy, there's no trust. And without trust, you're not going to put stuff into production. And our AI retrieval capabilities, Full-Text Search, Vector Search, embeddings and re-rankings are what drives that accuracy. So let me take these in turn. As you know, many people start with Full-Text Search, the exact keyword match. It's fast, it's precise. It gets you the answer for what you're looking for. But Vector Search took that a step further because it understands user intent. So you can find information based on semantically similar meanings for terms.

So for example, if I'm looking for a car repair, then the exact words match the query. And those 2 things are really important to drive value. But the unlock really is around getting into embeddings and reranking, which is part of our Voyage AI acquisition from February 2025. This is about driving the quality of those results. And Vector Searches are only as good as the embeddings behind them. Our Voyage models consistently rank at the top of Hugging Face's retrieval embedding benchmarks, consistently beating models from Google, Cohere and OpenAI.

And our rerankers take those results and improve the accuracy and relevance. So if you think about it, if you're in a trawler in the sea and you're fishing, you grab a whole host of fish, that's where the embedding helps you out. But picking out the best fish out of that net is what the rerankers do. And because of our investments in research and in our Vector Search capabilities, we've seen a 4x increase in the customer count using Vector Search in the past 2 years, and our Voyage customer base is doubling quarter-over-quarter and has for the last 2 periods. We're seeing that momentum with customers.

So take Financial Times, one of the market leaders in journalism. They use MongoDB as their content management system. And they've taken our Vector Search and the automated embeddings that we just released 7 weeks ago, and they're using it to find the right articles at the right time for their AI searches. But it's not just enterprises. Take AI natives, too, Gravity AI, one of the AI native ad networks that's helping drive relevant ads in AI responses. They picked Voyage to surface the most relevant responses quickly for any marketing campaign.

And what CTO told me was their internal benchmarking showed when they tested Voyage against other models, we were more accurate than the others, and that was their key selection criteria. What I love about these capabilities is that they allow us to have new conversations with AI customers. And it creates this virtuous flywheel. Existing database customers extend into Vector Search and Voyage. Voyage customers need Vector Search and then look at the database and enable us to have database conversations with customers. But the thing that's really exciting about all these capabilities on the platform is that they are also the natural home for memory.

So first, let me do a little grounding on how agents think and how memory works. On their own, agents are like goldfish. You ask them something. The next time you ask them, they're back to the basics. They don't remember anything. What changes that is memory. And when an agent perceives what's going on, it does so in real time, and it shapes what it sees. And those perceptions inform a plan. And then the agent takes that plan and acts on it. The outcomes of the plan and the actions then are fed back into memory to build a knowledge base and does so and it can think better the next time. And that process starts over and over again. Perceive, plan, act, repeat. It creates an agentic loop. And memory is what makes that loop coherent across sessions, time and agents. It's memory that enables agents and agentic systems to make smarter decisions and take better action over time.

Memory is powerful, and it needs to live close to the data. But it's an extension of everything that you heard about from Ben and from me. It changes often. So it's perfect for JSON in the document model. And compared to Postgres and JSONB, as you heard, we performed better in memory cases around large documents and we're much easier to work with. The right memory requires the right moment with the right accuracy and precision, which is truly about retrieval. As an example, Dev talked about one media company that's using us. Well, another large U.S. media company that's using us for memory is taking their entire catalog of movies, series and is embedding them using MongoDB and is using that to then go and find and quickly surface the right and relevant use cases where something appears in one of those videos, they can then use either for an ad placement or for doing some research, or Metal, an AI native that many of the audience may be familiar with that's building context layer for investors, uses us for memory, context, including all past messages, all user preferences and all context traces.

You see, one thing that technology-forward companies are realizing is that agent memory is critical. It's going to become one of the core systems of record of the enterprise. It's a core source of knowledge, and it's evolving source of knowledge. And that positions us perfectly because memory itself is evolving. And so we're staying ahead of the curve. And we're introducing some new capabilities for agents. And this is where I'm going to spend the rest of the presentation. Today, we announced Atlas Agent Engine. This is one of the largest expansions of our platform in company history. Agent Engine is our open, secure, governed agentic infrastructure built for builders and designed to run anywhere.

When we started talking to customers about building our agentic stack, we heard 3 major unsolved challenges in the market. An Agent Engine is specifically designed to address those. First, as I discussed, memory is critical and having an operational enterprise-grade system that can store the data is crucial and a gap in many of the solutions out there today. Our solution automatically enables long and short-term memory to bring the right context at the right time.

Today, if you look into the market, many customers need to build the memory logic themselves or connect multiple building blocks in order to enable that. We give them a system that does it all. They own their data and they have full visibility into it as well. It truly enables them to build knowledge and a system of record. Second, from a security perspective, one of the things that we heard consistently was many of the systems today don't have the right level of security isolation for their workloads and the data. This puts a lot of onus on the developers and risk into the company in terms of ensuring that you're building the security correctly. Atlas Agent Engine uses Atlas' enterprise-grade security framework as a starting point that is already trusted by the world's largest enterprises. And we've extended it to include governance and guardrails built in on first principles as a design point into the system so it can run safely and fully auditably.

And thirdly, as Dev said at the beginning, many systems are closed because they focus on their cloud, their LLM, their framework. We've taken a completely open approach. We enable customers to run across any cloud or self-managed. We let customers choose which model they want to use. And we are enabling them to use any framework or integrate into any observability provider or solution that they want and back into any data source that they want to use. So to summarize, Atlas Agent Engine is a complete enterprise-grade data governance and run time to bring AI agents into production. It has persistent memory with context, built-in security guardrails from the start, and it lets the builders choose their LLM, their cloud and their data sources.

Now as I'm on stage here today, we've already had many customers in our design partnership across multiple different industries, giving us feedback, and the feedback has been tremendous. Take Paysafe. They're streamlining their payment processing business with agents that investigate payment surfacing errors and bring them to analysts to explore, or CDK, an automotive retail software company that's cutting their reliance on third-party data sources by having the agents extract rates, rebates, lease terms to be able to calculate car deal payments directly. And so far, the feedback in the preview in the design partnership has been great and tremendous.

But we're also building alongside our partners. And we've got global systems integrators that are coming to our customers and bringing customers to us as we design this and as we bring it out into the market. One of our big partners is Accenture. Let's hear from them.

[Presentation]

So we really thank Accenture for their partnership. And you heard those 3 themes come out in terms of being able to have that memory in context, the security guardrails and governance and then being able to run anywhere.

But we work with many other partners, including Infosys, and Infosys actually brought one of their customers to us as a design partner for Agent Engine, and they're joining us live today. And so, we have Citizens Bank, one of the largest financial institutions in the U.S., who is using Atlas Agent Engine to be able to drive outcomes.

And to talk about it, I want to welcome to the stage, Vikas Agarwal, SVP and Head of Data Platforms. Vikas, welcome.

Thank you. Thanks for having me here.

So let's start off. Why don't you introduce yourself and the bank?

So I'm Vikas Agarwal, Head of Data Platform and Streaming Services for the bank. So I always say this that the data flow through me, basically. So my responsibility is to provide faster and better platform and so that we can justify all the requirements coming for the data, and we provide the data as faster and accurate like Dev said at the beginning, the most key important thing is the quality of the data and how we make sure that we provide that quality of data. And MongoDB is a great partner of us. We are in the fifth year of the partnership. So we finished 5 years with MongoDB. Personally, I'm working with MongoDB from 10-plus years.

So I love MongoDB as a database. We are using Mongo Atlas. So for me, MongoDB is not just a database. So when I use Mongo Atlas, it's complete framework for a faster delivery and better resiliency and also best performance provided database like in the system. So we build our technology around it. We are using it in so many places. The database itself is used for many Tier 1 applications in the bank. So pretty amazing.

Yes. And I think one thing that a lot of what you hit on is what Ben talked about in terms of that foundation that we've built on top of as we thought about Atlas Agent Engine. Talk a little bit -- you guys are a tech-forward bank and you're constantly looking at the market. And so why don't you talk a little bit about how you guys are looking at the agentic landscape?

Yes. So like other banks, we are also going very deep into this agentic landscape. We are building lots of agents, lots of agents for -- whether it's for our customers or for internal use. For me, the primary goal is to build the agents for my platform so that I can deliver faster and provide better support for our end users and the business. And like we are building so many solutions, which is going for patent as well into that space. And I can talk -- I think it's already out for the patent. So I think I can talk about it as well.

So we are building a multi-agent framework. We are kind of the first bank who are building that multi-agent framework where agents are talking to each other internally. And what they are doing is if they are catching all the events in the platform. And then they will -- then multiple agents will work together to identify the problem and they will go and provide the solution to a human and then human can go and fix the problem pretty fast. So that way we are putting more resiliency into the platform. And we saw that it reduces our overall like incidents and things moving faster. Anybody coming up with the change, it's going faster because of those agents. So those kind of use cases is going on, and that's where we are with the agents.

So when you and I first talked about Atlas Agent Engine, you were really excited. You want to share like what excited you given the fact that you've been looking at this landscape for some time?

Yes. It means we are building or I -- or my team is building the agents in the bank, it's going on from quite a long time, but me and my team have started looking into that direction in last 1 year. And then the one thing which we realized -- so if you think about the agents, it evolved too much in last 1 year. So even last night, I was sitting with some people, we were talking about it. 3 months back, I was sitting with the same people. We were talking about the Claude is the best LLM. And then today, my statement will be like Codex is the best LLM.

So it's always going back and forth between these LLMs. So everybody, what they are doing in the technology is basically -- they want to build something which is LLM agnostic. So you can switch the LLM depending on what you need and what LLM works best for your need. Whether it's Claude or OpenAI or Copilot and then the Meta is coming out of this news as well. So based on that, we are also trying to do the same thing. And on top of it, we built some SLMs as well on top of it so that we can use our internal models for a small task or the task, which we try to do with where we have knowledge and we know what we have to do instead of going to the LLM all the time. And when I was looking into that Atlas Agent, I was still in touch with Magenta.

Code name at the time.

Yes. So when I start looking into it, the one thing resonated with me was the memory feature, which we are also using in our other system with MongoDB. I think we've built something similar like 4 years back with MongoDB and when it came out into the -- with the LLM, it becomes really useful because what I feel is, like I said, the knowledge is there. We have those sort of knowledge. We don't need to go to LLM all the time. The things which we can do within the memory that works perfectly well for us. And that's where I think it provides more value because the performance is pretty good because when you go and hit the LLM, it always takes some time to come back with the answers. But when you go and directly hit to the memory layer, it's pretty faster. It's quite faster than hitting to the actual LLMs. So I think that makes perfect sense to use for that memory layer.

The second thing is it's platform agnostic. So since everybody have this question in their mind, I think people sitting here also have this question that every single tool outside or the software company outside in the market have something similar, right? Or they are saying that they are building something similar. So how this is different. The difference is it's not tied to the MongoDB. It's not like you have to use MongoDB. It's a completely agnostic framework where you are using a vector search of MongoDB as like a store. And no matter what tool you use today or what AI platform you use today, you need to build something as a vector store or the store where you can store your data. So even we, as a bank, we also use MongoDB for vector store for our existing ones. So I think using that agent...

Agent Engine lets you enable that use case. And it lets you get like across multiple different data stores, you're able to have that memory in context. It comes straight. And I think the other thing that you mentioned also is like platform agnostic pieces so you can run air gap, you can run it in the cloud.

So the containment of the agent is pretty much required the security, the guardrails we are using like 3 layer of security when we build the agent, so it will not touch any of my production data. It will not go and do anything in my production data and there are 3 different set of guardrails. So that's why the reaching to that point is always tough for the agent. But when it goes in, the air gap memory layer, which we built with the Mongo, going in there, getting the answer will become more easier and quicker in that way. So it's become more easy. And also the heterogeneous platform side, it can go and -- the MongoDB itself can go and connect -- and collect the information what we need, and then it can feed into the agent if required.

And maybe one last question for you. So Infosys introduced us. Can you talk a little bit about how you see the partnership between MongoDB, Citizens and your partner, Infosys?

Yes. So Infosys is a great partner, and they are like -- our partnership is going almost 10 years plus with the bank, and they are our strategic and implementation partner in the bank. So we use them for any new thing we onboard and we do the partnership together. It's our like tight partnership that provide a lot of value in it. They first -- for us what -- we don't want to spend a lot of time into the new tech because there is a lot of new tech coming to the market. So first, they help us to identify whether that tech is useful for us or not. So instead of we go and find out whether that tech is useful for us or not, they do their own research. They can always come back with a great research and tell us that whether we should invest in this tech or not. And that's how the partnership goes.

Yes, it's a great plug for Erica's section coming up a little bit in terms of what we're doing with partners. Well, Vikas, thank you so much. Thanks for the partnership. We're really excited to work together to build our agentic future.

Yes, absolutely. Thank you.

Thank you. So it's great to hear the value that Agent Engine is bringing to customers, but we're just getting started. Tomorrow, you're going to [indiscernible], you'll hear from CDK. You'll hear from partners like Infosys and our ISV partners as well like Fireworks and a fireside chat. So right now, you're probably thinking 2 things. How is this going to contribute to growth? And how does it stack up against the alternatives?

Now the beauty of Agent Engine is that all existing Atlas customers are already entitled to it. No new contract, no new sales motion, all Atlas builders can start using it today. That's tens of thousands of Atlas customers who just got access to our public preview. And it isn't just one feature, it's a stack of agentic capabilities and components. That means that it enables you to build, deploy and run these agents. And as each component solves a problem, it also creates a monetization opportunity. We have new opportunities to monetize our run time and our memory. But it also pulls through our data foundation, which will drive consumption of those products as well.

There are, of course, many technology solutions out in the market. However, we believe Agent Engine is different because we've looked at the market, and we built it on first principles from what we saw. First, I want to emphasize, we are building for our core persona, the builder. Now as you heard from Vikas, memory is critical. In an operational data layer, OLTP, is truly critical path for agentic applications. Agent data grows and agents need to access their data in real time and at scale. That's why they need a proven and mature OLTP database right at the center. Layered on top, our advanced retrieval powered by Voyage enables that memory and context that you heard Vikas talk about.

Now security was also a first principle for us. Many systems today don't have the right guardrails for both agents and the data that they access. Agent Engine addresses that. And then as you heard, the AI landscape is moving incredibly fast and lock-in is a risk. Anything you get from a hyperscaler will be tied to their cloud. From an LLM provider will be tied to their models. We don't lock into any cloud, any model or any environment. We truly let you run anywhere.

We believe Atlas Agent Engine becomes the agentic run time for building, deploying and scaling AI agents in production. So we close where I started. Why MongoDB wins in the AI world? It's 3 things. One, we're investing to show up for builders. We've always been a builder platform, and we're going to continue to invest to ensure builders love us. Second, we have the perfect platform for AI from JSON to document model, our run anywhere flexibility, the real-time operational core that we have and built-in retrieval that's proven from AI natives to enterprises. And third, as you heard, we're expanding our agentic stack with Agent Engine, combining memory with full agentic infrastructure, including security, governance and an openness for enterprises and AI natives to put agents into production. Thank you.

With that, I'm going to welcome Ben to the stage to have a little customer conversation.

Thanks Pablo. I think we have a slide.

Yes, we do.

Cool. So we love talking about -- talking with our customers. So we have another customer here from Emergent. Madhav, please come up on stage and have a talk.

Thank you so much.

So before we get into the questions, why don't you take a moment to introduce yourself to our friends here?

Yes. I'm Madhav. I'm the Co-founder and CTO of Emergent. At Emergent, we are a platform, which lets nontechnical users build production-ready full stack applications, both mobile applications and web applications. So even if you don't know programming, you can come and build apps on Emergent and deploy it and then run your business on it.

Yes. And that's great. And you've seen the tremendous growth in the last few months. Why don't you tell us a little bit about that?

Yes, just to give you some scale. So we have 15 million users, 18 million apps have been built on our platform. And we use MongoDB. All the apps that get deployed on Emergent uses the MongoDB, around 150,000 active paid deployed apps on Emergent right now.

That's amazing. And so before you arrived at MongoDB, even before we talk about like how you looked at your database selection or even your entire stack selection, what was your approach when you originally founded the company? Like how did you -- what did you think? And how did that change over time?

Yes. So I think like a lot of discussion in this room had the word AI in it, and you probably already tied up [ something ] to AI, AI. But the thing is like AI doesn't do anything for you if you -- if it's not friendly, right? Imagine somebody who's a domain expert, but doesn't know any programming and they still want to build an app, how do you help that person? How do you help this business -- small business owner? So our approach was, hey, how can we make a platform where users do not have to be experts in coding, they have to be experts in their own domain and still build their business-critical apps.

And so we extracted a lot of them -- a lot of these things from them. And one of the design decisions we took, we said that, hey, we're going to use MongoDB for our database because many of these users who come to our platform, they do not have a well-formed idea what they want to build. As they are building, they evolve their idea, they decide what new features to add. And MongoDB is very flexible in that, right? Like your schema can evolve with your new features. And so it was with your idea. It's very friendly for this kind of coding. And then it also scales well. Like once you deploy the AI platform, it scales well. So it's best of both worlds.

Well, we obviously thank you for being a customer. We love working with you, but you didn't start out on Mongo, right? So when you first started the company, what did you start out on? And then what did you learn and made you come to MongoDB?

Yes. So I mean -- and remember, we started like -- we started as a research lab like 2 years back, our product launched 1 year back. But as a research lab, we were experimenting with like we're building our own coding agent harness from the ground up. And so we started with typical Postgres setup. And what we were realizing was that it becomes very difficult for agent to sort of like -- and this is Claude 3.5, so intelligence hasn't yet caught up. So Claude 3.5 back in the day, if you get like a lot of the migrations related problem, you'll run into these kind of migration issues where it has to kind of maintain the database as you go along, right? So it's not just a programmer that you have to bring in a DB admin, you have to bring in like encode all those persona in it.

So what we realized very early on that a lot of those headaches go away if you switch to a document-based database, and part of the reason is that conceptually an app very much is like these JSONs, right? Like if you're familiar with technologies like React, they talk and think in terms of JSON, right? And so the translation layer is very simple. Whatever your app functionality is in thinking in terms of JSON, the same data representation sits in your database. And it is also very friendly for like backup, if you want to like backup, if you want to like bring it back in. So we are a very eval-driven company. We ran our evals. We figured out, you know what, MongoDB is the way to go, and that's how we took the decision.

Yes. I think there's a perception out there that agents, because they're just agents, don't necessarily care about complexities of stacks, but I think you've proven that wrong.

Yes. And I think like a lot of people missed that in this world, agents is definitely important, but like there's a human component to it as well, right? Like so a programmer using an agent is different from a nonprogrammer using an agent, right? So a programmer might be able to say, "Hey, you know what, that migration did not go through, can you roll it back." But a nonprogrammer will not be able to articulate these things, right? So keeping the audience in mind who are like nontechnical users, they are building these agents, how do we make it friendly for them. That is where like [indiscernible] decision came for us.

Yes. Well, and a lot of your customers aren't developers, right? They are typical people that are just saying, like, I have this idea, it's great. But that creates all that flexibility or the inflexibility you talked about before. But then you also have customers that 300,000 lines of code like big actual enterprise stack. So how are you approaching just your tech stack decisions or even managing the database now because you have to think about the [ few-dollar ] customer all the way up to the million-dollar customer?

No, no, absolutely. I was just like asking one of my engineers, what are some of the scalable apps built on our platform? Like there are users who are building apps, which is like scaling like 5 GB per week on MongoDB. These are like -- they have like 15 million documents. So we have users who have built pretty scalable apps on MongoDB. And so the whole idea is that it has to scale with your ambition. I mean, we built Emergent with this concept in mind that it's not just for UI prototyping, it's not just for building toy apps and demo apps, it's for building production-ready, business-critical apps that you would actually ship to your end users. And that's where the enterprise nature of MongoDB comes in. We have the great fortune of like collaborating with MongoDB on various like platform features. So for example, how do you have APIs to spin up a database or to bring them down. And with those platform features, we're able to like scale our platform.

That's amazing. As you think about agents and better memory and retrieval inside of the apps that people are building, how has your collaboration with Mongo been able to help with that? And are you seeing a dramatic difference from like what the market sees from a retrieval perspective?

Yes. No. I think like a lot of people like -- I think fundamentally, it's like when people start building these things, right, one of the design principles that we have is we want to avoid complexity, right? So you don't want to bring in like 5 different technologies to accomplish the same thing. And if your database already has things like vector DB search, et cetera, then you don't have to bring in a third integration to make that happen, right? And this simplicity allows for agents to work well, the humans to sort of keep everything in their head and then scale as well. And so we are like literally like making use of all the new features that are coming on MongoDB and like basically making them available for our customers.

That's great. And we're super excited about that as well. And as your deployment rates have been increasing and you start looking at what next year is going to bring for you, what do you need from an infrastructure partner or a vendor? What's the most important thing to you?

No. I mean I think like one of the things that is very important for us is just the like platform capabilities of meeting the uptime, meeting the scale requirements, meeting -- the cost is also important for our customers, some of them like they want to make sure that it can scale down to 0 if it's not being used and things like that, right? So I feel like with the partnership with MongoDB that we have, we are like kind of pushing each other. We have some requests that we bring it to you and you have some requests that you bring to us, like we are like very fortunate to be in a situation where we can like push each other in this AI-native world.

Yes, I agree. We're super lucky to have you. So last question, AI agents are writing more and more of the world's software. What does that actually mean for the database from your vantage point, right? So you're in a unique position. You see all this inbound. You see all this activity. Are you prepared for what's coming next?

I mean, we're very excited. I think like anybody who's in this room, like they should just like go and maybe sign up for Emergent and just like build an app. So you get this kind of like not just see it in theory, but see it in practice. And like it's kind of mind-boggling, the kind of apps that people are building. We have people who are building mobile apps, which they've never written a single line of code. And like just prompting and like building mobile apps, shipping production-ready apps. So very excited to be in this world.

Well, that's so great. And thank you for sharing your story with us, and I appreciate you coming. Thank you.

Thanks.

So now I believe we're going to welcome our CRO, Ryan, up to the stage. Ryan?

Thank you, Ben. It's so great to be here this afternoon and also to see some familiar faces. For those of you who I have not met before, I've spent my career building and scaling enterprise go-to-market systems across consumption and cloud-native platforms alike. So the real question I'm sure you have for me today is why MongoDB and why now? There's really 3 key reasons behind my decision to join MongoDB. It's people, tech and timing. On the people front, we have one of the strongest operating benches in enterprise software today. Everyone on this leadership team has built and scaled global platform companies. And together, we have over 100 years of experience.

On the tech side, not only does our JSON document-based model align well with the frontier labs, but specifically the way that we store and retrieve application context, it closely aligns with how agents consume information. Last, we are trusted for mission-critical workloads across financial services, health care, public sector and AI digital natives. And whether it's JPMC, Wells Fargo or Santander for payments or delivering global services for governments around the world, we are the trusted platform of choice.

And the last is timing. AI tooling with partners like Cognition unlocks the modernization and migration of legacy Oracle, SQL and relational database workloads. We can do this now in days, not weeks and years. A year ago, these capabilities were just getting started. And now they're real. And I have to tell you, they are very real. Not only are we winning new AI workloads, but AI opens up a new TAM to over $10 billion to our teams to go after. For the reasons above, I truly believe this is the best time to not only be a MongoDB customer, as you heard on stage here, but equally be a leader at MongoDB. We are driving parity between Atlas and EA. We're delivering more core platform capabilities and products and doing so with greater velocity and greater reliability than ever before.

And as many of you know, I was also the CRO at Confluent previous to the IBM acquisition. And while there are many similarities, specifically around consumption, and the equivalent of an EA or self-managed capability, workloads on this side, streaming projects on that side, this is a much larger opportunity. And without question, the market is coming to us especially with AI after a decade.

Before we talk about the investments for the future, let's quickly take a look at the progress we've made to date. With Q2 earnings behind us, we have now delivered 6 quarters of consecutive Atlas growth at 29% year-over-year. Our self-serve continues to thrive, adding 25,00 to 3,000 customers a quarter, and we had a record of net new customers in Q2, totaling over 70,000 customers at MongoDB. I never had this at Confluent. And 17% year-over-year growth in our 100,000-plus ARR customers and our $1 million-plus ARR customers. I want to be super clear. This is a story of strength and momentum. We've seen tremendous traction over the previous 6 quarters, and now it's time to build on that momentum the team has created.

To drive the next wave of durable revenue growth, we're making 3 strategic investments. We will focus our go-to-market efforts on the enterprise and executive buyer and our AI and digital natives. We're going to evolve our go-to-market system as we extend our capabilities and our platform with new products by adding product specialization, all those resources across all 3 global regions, and we'll be doubling down on our application modernization platform. And with our AI partners, you heard me mention, we will continue to identify and alleviate the modernization and migration pain that has plagued all of our customers for years.

The path to $5 billion plus is through the enterprise executive suite. We are now evolving beyond a developer-led bottoms-up motion and I want to be super clear here as well. This is an and, not a replacement by adding equal focus on the enterprise suite that is driving enterprise-wide AI buying decisions. We will drive greater executive alignment by being laser-focused on our segmentation. We'll drive executive briefings and events, increase our forward invest for some of the new capabilities you heard about today as well as we will increase our incentives to our sellers to drive larger, more complex deals. We will go deeper in our accounts with our vertical depth, financial services, health care, public sector and there, again, a special focus on AI and digital natives.

Regionally, we'll be investing in U.S. Fed. We're actively pursuing FedRAMP high, and we'll have some announcements shortly. At only 10% penetration today, we have nothing but headroom to grow. We'll be hiring a new U.S. Fed General Manager to accelerate the recent acquisition of Clarity. And we'll be making strategic geo investments in APJ and doubling down in Japan. We are establishing a new entity and hiring a new Japanese country leader, fourth largest economy, and it's been somewhat untapped by MongoDB, although you would be surprised at the current ARR without even having feet on the street. We expect this to be accelerated by a strong partner motion with my partner, Erica, as well.

Now we also need to build our go-to-market muscle around product specialization as we extend our capabilities of the platform with the addition of new products. Specialization will allow us to go from incubation to scale with every new product without slowing our acceleration that we've had of Atlas and EA. We will maintain a strong core sales motion while driving the sale and adoption of new platform capabilities and products with the specialized team. We have developed the model now with specialists focusing on products like vector search, embeddings and re-rankings, that will allow us to repeat this and scale with every new launch. As you heard today, with the announcement of Atlas Engine, we will also increase our investment here as well.

There's more to this than just adding resources. Specialization allows us to fine-tune our value messaging, our demos, our pricing and packaging and equally to ensure we have a fast feedback loop to our product and engineering teams as we compete. And here, there's no better prospect than an existing customer. And with over 70,000-plus customers, we have an enormous upsell, cross-sell opportunity for us to capitalize on.

And lastly, modernization and migration. This represents a massive opportunity for this team. AI tooling lowers the cost and risk of legacy migration. We can do this now, as I mentioned, in weeks and not years, and it doesn't require armies of bodies as it has in the past. Equally, this modernization challenge has plagued our enterprise customers, as I mentioned, for years. We are actively migrating Oracle, SQL and other relational workloads to MongoDB via our application modernization platform and with partners like Cognition. Our path to driving durable and consistent growth to $5 billion and beyond is clear, engaging with the executive buyer, leaning into our vertical strength and geo-specific investments. We'll be strengthening our commercial muscle through platform specialization and capturing the modernization, migration wave all of our customers have been waiting for. We have the right leadership, now the right go-to-market system to drive the next wave of durable growth here at MongoDB.

And before I turn it over to Erica, Dev is correct. We have the world's best go-to-market machine. And I have absolutely no question in our ability to drive and capture the next wave of growth for MongoDB.

I'm now going to turn it over to Erica to go over our strategy with our partners.

Okay. Ryan, thank you. I'm thrilled to be here with all of you at Nasdaq. For those who I have not met, my name is Erica Volini, and I am the Chief Customer Officer here at MongoDB. In this role, I am responsible for ensuring an outstanding customer experience from technical support to customer success, to professional services, to our partner ecosystem, and that's going to be my focus for today. Now by way of background, I spent over 2 decades at Deloitte running 1 of their 5 global consulting businesses. In that role, I had a front-row seat to understanding what it takes for customers to truly drive digital and business transformation. After Deloitte, I pivoted from consulting into the world of tech joining ServiceNow, where I was responsible for scaling the partner ecosystem and, ultimately, their global go-to-market at a very strategic inflection point as they went from $5 billion to $10 billion.

The through line of both of these experiences has been a deep focus on why and how customers make the decisions that they do. And that's something that I hold front and center as I focus on ensuring that we deliver an outstanding customer experience for our over 70,000 customers.

Now part of bringing that remit to life is building a strong, trustworthy and innovative partner ecosystem because we know that ultimately, no one can win the AI era alone. We need MongoDB in every room where enterprise decisions are being made, no matter where a customer is on their agentic journey, whether they are modernizing their legacy systems and data, whether they're building agentic features into their applications, integrating agents, data and systems so that they can act in concert or scaling that capability across the business. Partners are guiding their customers at every step in the journey. And our goal is to be the platform they consistently rely upon. To do so means intimately understanding the expectations of our customers, expectations that have fundamentally shifted in the last few years.

Let's start with value. Customers no longer measure a company by the output they deliver. Today, customers measure us by how we impact their business outcomes. That means being deeply ingrained in their business, in their technology landscape and being core to driving their AI transformations. Customers value business outcomes in large part because their AI initiatives aren't pilots anymore. In production, AI has to work. It has to be secure, durable, available and performant, delivering the business outcomes our customers are after. That's what our platform was built for.

Our differentiators, though, they can't just be about technology because transformation today means more than a new system or a new platform. It means an entirely new way of working. To navigate this landscape, customers can't just rely on a single platform or a service provider. They need an ecosystem working together to help them succeed. That's why a strong partner motion here at MongoDB is nonnegotiable. That's not because we can't grow alone. We've proven we can, but because the fastest path to scale runs through partners enterprises already trust.

We will see partners influence our revenue, our reach and our innovation through 4 primary levers. First, embedding our platform in every aspect of the AI ecosystem; second, becoming the default embedded data layer inside the agentic stacks of the leading system integrators; third, co-innovating with hyperscalers and Neo cloud; and fourth, becoming the intelligent data platform for industry ISV solutions. No matter what partner our customers choose, we want MongoDB to be the platform that powers their success. Our first lever is integrated across the full AI ecosystem. This is about making MongoDB easily and readily available within the companies that are redefining today's technology landscape. As you heard from Dev earlier, we look at the AI ecosystem across 4 major categories: coding agents, AI platforms, answer engines and AI frameworks. Across them all, we are investing in showing up wherever a builder needs us, whether that's an agent or a human. Like our connection with Devin, the AI coding agent from Cognition.

With Devin, a team staring down a decade of accumulated legacy code doesn't spend the next 6 months manually mapping every table and dependency. Instead, Devin leverages MongoDB to work against live data instantly to transform the code in days or weeks, not months or years. Vercel is one of the largest distribution channels in the industry for how applications get built. Millions of developers use Vercel's v0 today. Well, MongoDB Atlas is now a native option in that flow, thanks to our managed MCP provisioned in a single click. So the agent building the app can query live data directly as it works. Mastra, fastest-growing TypeScript framework for AI agents now runs all of their data retrieval and memory on a single MongoDB Atlas cluster. That means a team can ship a production-ready agent without assembling and maintaining a patchwork of infrastructure. Each of these partnerships helps us embed MongoDB as the preferred data platform from day 1.

LangChain is a perfect example of how this motion translates into both revenue and reach. Our customers can build generative AI and RAG applications on MongoDB through LangChain today. Now the partnership is still relatively young, but the revenue tied to LangChain is up more than 460% year-over-year. And builders are reaching for MongoDB inside LangChain projects 220,000 times each week. Now as important as each of these partnerships are individually, their true strength comes from the ecosystems that each of them has built, including our next lever, system integrators. All of the leading SIs, as you know, have built their own agentic stacks, often positioned as frameworks that incorporate the best-of-breed technologies required to drive a customer's transformation end to end.

So when MongoDB is embedded as the data layer in these stacks, we get invited to transformation driven by the top of the house. Together, these top 6 systems integrators touch nearly every Fortune 2000 company on the planet. And Gartner estimates that enterprise AI engagements with SIs will increase by 15% by 2029. That's the access that these partnerships hand us. And that is why I am so excited that all 6 of these organizations have not only named MongoDB as a strategic partner for agentic delivery, but have already started to deploy Atlas Agent Engine internally, and the results are showing.

One of these partners has already seen 984% higher query speed and a 63% latency drop when testing Agent Engine against sovereign AI offerings. Those are metrics that matter. But these partners don't just see the value of MongoDB for their internal use. They have also integrated Agent Engine into their agentic stacks that they're bringing to their customers. And they have identified use cases that span the entire enterprise from an agentic assistant for the oil and gas industry to an agentic business loan platform to agentic ticket management and technical support and agentic knowledge hubs that can search across policies, procedures and even medical documentation autonomously. Listen, I could go on.

But what we know about these use cases is they span in all industries, and they're going to play a critical role in helping us drive that vertical depth and going deeper and wider across the enterprise, 2 of our go-to-market priorities that Ryan just spoke about.

Now if you want to know why, why are these partners believing that MongoDB needs to be an essential part of their agentic stack, please look no further than the quotes right here. These are quotes not just from individuals at these partners, but the leaders of their global AI practices. They have become not just business partners, but close collaborators as we build and scale for this next era. And that leads me to our third lever, hyperscalers and neo clouds, the compute power that this next era relies upon.

Nearly every workload an enterprise runs, every model they train, every transaction that they process sits on someone's cloud. Now Ben talked earlier about our Run Anywhere strategy, and that's what gives customers the flexibility to control where their workloads run and where their data lives, which means we need strong relationships across cloud providers of all types.

With so many customers running Atlas on hyperscaler infrastructure, our teams are constantly collaborating on joint solutions and products. Roughly half of all MongoDB revenue is supported by a hyperscaler partner, and we're seeing marketplace revenue growing faster than overall revenue year-over-year. And that success shows up in the recognition we've earned. We've recently been named a Partner of the Year by AWS, GCP and Microsoft. At GCP specifically, we received Partner of the Year recognition for the past 7 consecutive years, and we're one of the top 3 Azure partners. Now I'm committed to continuing and expanding this streak. But as we all know, hyperscalers are not the only cloud business in town.

The cloud provider market has expanded exponentially. We already work with many of the names you're seeing behind me, and we are actively pursuing relationships with others to ensure that wherever a customer's capacity lives, MongoDB meets them there.

Specialty and neo clouds are reshaping the economics of AI infrastructure and moving up the stack. In MongoDB, we are uniquely positioned to support the scale, the speed and the data requirements of AI workloads. Our strategy is not only to launch new neo cloud relationships, but to evolve with existing partners as their platforms mature. As DigitalOcean, for example, expands from GPU infrastructure into an integrated AI native cloud spanning compute, inference, data and agents, MongoDB can act as the foundational data layer for these emerging AI applications. Our goal with hyperscalers and neo clouds is not just to integrate with them, but it's to co-build and co-innovate alongside each other, like with AWS, our largest cloud partner today.

We're building with AWS and our own application modernization platform known as AMP to make legacy modernization dramatically simpler for enterprises stuck on decades-old systems. We are also, and I'm very excited about this in discussion with both AWS and Microsoft about leveraging the memory layer that Pablo talked about of Atlas Agent Engine directly into their AI platforms. Having this type of robust set of cloud partnerships, this really comes to life when we see it at our customers.

Now Ben, I don't know where he is, talked earlier about Nationwide and their regulatory requirements. But what I want to talk about with this customer is what was happening behind the solution that got them where they needed to be. Nationwide needed to modernize their payments with 0 downtime risk. Now working at this scale, it doesn't happen with a single vendor. They needed the ecosystem, MongoDB as the data platform, Accenture as a systems integrator and multiple cloud providers running a single stretched MongoDB cluster across both simultaneously. Nationwide now moves GBP 1.5 billion in transaction volume every 24 hours, handling 3 million payments at peak, and we went from 0 code to fully in production in under 8 months. That's the nature of an ecosystem win. We make each other stronger and drive better customer outcomes than any of us could do alone.

Now as much focus as there is on developers building applications, we also know that many enterprises out there are buying applications to meet their needs. And that's why the fourth and final lever is becoming the default data platform for industry ISV solutions because getting embedded in industry-specific solutions means that a customer that's won by the ISV is a customer won for MongoDB, and our installed base grows even further.

Let's talk about what that looks like in practice. A security team leveraging BigID doesn't start their morning worrying where sensitive data might be hiding across a decade of acquisitions and half-documented systems. With MongoDB embedded, they run one search and see all of it, structured and unstructured before an auditor or an attacker finds it for them.

A medical technician using software from one of the industry's best health care technology providers no longer stitches together one system to run a test and then another to manage the data behind it. It's one platform powered by MongoDB as the data foundation to keep it real time, accurate and, of course, safe. A customer messaging into an AI-powered support line has no idea what's happening in the background. They just get a better answer than they did last week. But behind the scenes, the model is learning from real conversations in real time. And because of our queryable encryption, we never actually see what was said. So we're able to keep customer privacy paramount.

Someone with no engineering background opens Emergent Labs who you just heard from. They describe the app that they want to build in really plain English language, and they get a working product back. Grounded in, you guessed it, MongoDB's data platform. In 4 months, that's happened nearly 2 million times with more than 50,000 of these apps now in production. This is what winning through the ISVs look like. We are the batteries powering the Flashlight. Partners with deep industry expertise embed MongoDB in their solutions, so every customer bought into them is bought into us.

So now we've talked about these 4 distinct levers and how each one works to expand our reach and expand our revenue. But to me, the story really isn't about each of these levers individually. It's about how they come together into one ecosystem. See, a customer on their agentic journey needs a full team behind them. They need a systems integrator. They need cloud providers. They need unique industry-specific solutions, and they need the best data platform in the world to underpin it all.

Partners have the relationships, the trust, the industry expertise. But what powers all of it, that's ours. We aren't just building the products enterprises want. We're becoming the platform that partners build their own AI and modernization strategies on. That's the bet we're making on partners. And it's the exact same bet I made when I took the role of Chief Customer Officer. We don't scale by doing more ourselves. We scale by leveraging the best people, products and partners in the market to deliver what our customers need when they need it.

Now hopefully, that all resonated because we're going to take a quick 10-minute break. And when you return, Mike is going to talk about what all this goodness looks like in the numbers. Thank you.

[Break]

Thank you for your patience, putting up with the warm room a little bit. I had to take my jacket off. It got a little too hot. Before I get started on the safe harbor and go through some stuff, just 2 things. One, as you know, I like to pace. So if I'm in the way of the slide, just say get out of the way, and I will try to move. Jess did talk about, hey, these will get posted right when we're done, so you don't have to take a picture, you'll have access to it.

The other thing, as Jess said it at the beginning, I just want to reiterate, folks, this is a big event for us, and it takes a lot of time, people and effort. It truly takes a village to use that term from Rita and Megan at the front that you saw there to the finance team that does all kinds of great work to the legal team that puts up with all of us. Oh, no, we can say that, right? To the marketing team who helps put this on. And then, of course, Jess and Will and the investor team. Thank you very much. It's truly a huge effort.

Okay. So before we get into the financial update, once again, just on the safe harbor. As we said before, we will be making forward-looking statements during today's presentation. These statements reflect our views only as of today and are subject to a variety of risks and uncertainties. Please refer to our Form 10-Q for the quarter ended July 31, 2026, and other filings that we may make in the -- with the SEC for more details on the risks. So thank you for listening to that.

Okay. Hey, I'm going to key off what Dev and everybody else said. And hopefully, you heard a lot of great things today. I know you did. But let me highlight a few of the things that you heard from the team. Dev's presentation talked about we compete in a very large growing market, and we have bless you, true discernible differences in the JSON native document model and our Run Anywhere strategy. We talk to you folks a lot about that. That's what really wins us business when we're in there, especially with the enterprises.

Ben and Pablo did a great job sharing the exciting updates for our new innovations around Atlas, EA and AI. Ryan and Erica gave a great overview of the go-to-market priorities as well as our plans to significantly expand our partner ecosystem. So my job is to pull it together, what does it mean for financial metrics, and we could possibly talk about the long-term model as well.

Okay. As I go through my presentation today, I'm going to break it into 5 sections. I try to do 3, but I had to do 5, so you'll have to bear with me. We're going to talk about the strong track record of results. Then we're going to shift to our growth that why we believe it is real and durable, I'm the CFO. So of course, I'm going to talk about operating margin and leverage, which is mainly driven by revenue growth. Then I'm going to talk about our AI focus and how we expect those product innovations to reflect in the financial model. And then after 3.5 hours, we'll talk to you about our financial targets.

Okay. Hitting on our commitments. Hey, folks, we talked about it at the beginning. We, as a management team, take this very seriously. And credibility is built one quarter at a time and every quarter matters. And we take our commitments very, very seriously. So what we are going to show you is what we showed you last year and then how did we perform against those. So many of you are going to recognize this slide. So we've included the commitments from last year as well as how we did for '26 and then what the guidance is for fiscal '27. So last year, we talked about Atlas growth, 20% plus. In fiscal '26, rounded up, we grew by about 29% and our guide for this year is approximately 27%.

While we did not specifically guide for EA and other, the implied growth in the guidance that we gave was low to mid-single digits. In '26, we grew at 7%. And in '27 guide, we've now increased that to 11%. We noted on the earnings call, this is the first time in 3 years that we are projecting double-digit growth for EA. That equated to total revenue growth commitment in the high teens. For fiscal '26, 22.8% and the guide for fiscal '27 is 21% to 23%. This will be the second straight year of acceleration of total revenue coming in at the high end. And then, of course, my next 2 favorite, operating margin commitment, and we'll talk about cash as well.

So our commitment was, on average, 100 to 200 basis points a year. We went from 14.9% in '25 to 18.5% in '26, and we're guiding to approximately 21%. That was 360 basis points growth from '25 to '26 and 250 basis points for this year. We were super clear last year, the 100 to 200 basis points was not a ceiling. And we've talked a lot about the thing that I loved about Ryan's presentation is, man, there was a lot of investment in that discussion. It's going to result in great revenue at some point. We are continuing to invest in the business, and I'll walk you through a slide on that. It's got to -- we have a great business model. And when we drive revenue growth and still invest, it all falls to the bottom line.

And then finally, free cash flow conversion. Jason, you've known me for a long time, Kirk, as you know, I love cash. It's my favorite. And cash flow conversion has been exceptionally good the last couple of years. We talked about 80% to 100%. Keep in mind that this is -- the business model is dependent on it in terms of how you collect from your customers. There's also this thing called working capital. And the team has done a great job managing both of those pieces. So fiscal '26, the cash conversion, which is non-GAAP operating income -- or I'm sorry, free cash flow divided by non-GAAP operating income, 108% year-to-date in '27, 108%, and we guided to the upper end of the range, okay? So as you can see -- sorry, I missed that one. We've hit every single one of our commitments. And again, we take this very seriously as a team to make sure that we do that.

In addition to hitting our commitments, we also listened. I think in the first 2 months of my role here, we spent a lot of time on calls with you folks saying, what are the things that you would like to see better from MongoDB. And 2 things came up. Please give us a view of what you're guiding Atlas when you do your guidance. Don't make us do the math. So starting in Q2 of last year, we started to give that view. The other ask was, please share the ARR growth for EA and other because of the multiyear revenue, it jumps all over and please share that. So we started that in Q2 of last year, those 2 pieces. And then, of course, we introduced the long-term model, which hopefully has helped, especially you long-only investors, get a view of where we think the business is headed over the long term.

Okay. Let's shift to growth, something we all love. Dev had this same slide that he showed earlier, and we showed it last year. It's a very large market. IDC estimates it's going to reach $200 billion by the year 2030, and it's growing at a compound annual growth rate per IDC of 12%. There are very few markets that are this big that grow this fast. And it was one of the things that I loved when I had my first conversation with Dev about joining MongoDB. It is a huge market. Both of these -- it includes both OLTP, which is obviously where we focus and OLAP. We look at really the OLTP market mainly, but we also look at OLAP as potential areas to invest in the future. We still have a very small share of the TAM today. Based on our guide, it's about 2.2%. There is a ton of room to run in this market. And as Dev, Ben and Pablo discussed, we believe we have the best data platform for the AI era, and we're very confident that we have a long runway ahead of us in this market.

Okay. Let's shift to Mongo. This is the last several years of our revenue broken out between Atlas and EA. So over the last 5 years. Total revenue compounded growth rate, 28% on a market that's growing 12%, we are clearly gaining share, and that's a big part of our strategy. As you can see, the revenue growth has been strong and consistent. You can also -- one of the great results of this is a metric we talk a lot about, which is the 122% net dollar retention rate. And again, that's total company across both of these products.

Our view is we have 2 independent healthy growth engines. Atlas, which has grown at a compound growth rate of 36% over this time frame and EA and other, and we added services in there to get the full picture, growing at a compound growth rate of 14%. Both of those over the last 5 years have grown double-digit growth. It is not a single growth engine story, and we feel it is durable because we have 2 strong growth drivers.

This is my -- one of my favorite charts after cash. We talked about this last year, which is even though Atlas in fiscal '25 was growing very nicely, 27% year-over-year, we had flatlined as it relates to growing incremental dollars year-over-year. And for me, when somebody says the word acceleration, this is the slide I think about. We have now, for 6 straight quarters, added more dollars year-over-year each quarter. And that is a great testament to not only the sales team, to the customer success team, to the technical services team.

And I'm going to show you a lot of stats on our customer growth that plays directly to this. And this growth has been across multiple vectors in our business. One is we do not believe it has been negatively impacted by the growth of VA, and we've been super clear about that. We've also seen growth across both self-serve, our wonderful product-led go-to-market motion as well as our sales sold. And as we've discussed, the majority of the growth in Atlas in the last several quarters has been driven by the larger enterprises, specifically in the U.S. and EMEA. And Erica touched on this, the marketplace offerings are a big part of our growth as well. And all 3 of the hyperscalers are growing very nicely, and it now represents approximately half of the total revenue of Mongo.

This is a critical point because we get this question a lot. How do you compete against the hyperscalers? When someone deploys in the marketplace, everybody wins. We win and we get a customer, the hyperscaler gets consumption and the customers are able to burn down their commits. So that's one of the reasons why it continues to grow. Atlas is the biggest driver of growth. Guess what? EA also transacts in the marketplace as well. So it's a big driver of growth. As you look at that slide, those are the things continuing to push that up to the left.

Okay. Let's shift to EA and Other, our self-managed product. This is over the last 10 quarters, the same time frame I just showed you on Atlas. As you can see, we were really averaging mid-single-digit growth until we saw an inflection in Q3 '26, and it's gone nothing but up at that point. Dev talked a lot about the industry drivers. Ben talked a lot about the product enhancements that we've made. Very importantly, all the new stuff we just built, it just rolled out a couple of months ago. So it has not really been a big driver of this. This has always been a strong product in regulated industries, but now we're starting to see other segments, especially technology as they're trying to deal with all the shifts in the industry that they want to run it on their own and self-manage it. The ARR growth has now been double digit for the last 3 consecutive quarters. And again, that was before we added search and vector search in Q2 of this fiscal year.

We get this question a lot, so let's just get it out there. There's also a significant overlap with Atlas. If you look at EA customers with greater than $100,000 in ARR across Mongo, approximately 2/3 of them also use Atlas. So this issue of, hey, there's a cannibalization, and we see -- and we talked about on the last earnings call, we see that cohort actually growing faster than the total. So that it more Mongo is better across either EA or Atlas, okay?

So now let's take a look at some customer metrics, and I'm going to move over so you can see these. Again, folks, you'll get a view of these. This is our total customer growth. Compound annual growth rate, 23%, driven mainly by Atlas growth, over 70,000 customers. Now let's break that down into the specific cohorts.

If you look at customers that grow above $100,000 in ARR, it's grown largely in line with the total and a little bit more, but not much. Now let's go to the larger customers. If you look at customers, and Ryan talked about this as well, with greater than $1 million in ARR, you can see that, that cohort is actually growing much faster at 34%. So this isn't -- I'd like to do this because I'm going to give you a couple of new pieces of information, and this is one of them.

This is the growth of the cohorts above $5 million in ARR, and it's growing at 57% compound annual growth rate. When I first started, this was the move that Dave and Cedric made to move upmarket to drive more growth, and we'll talk about it.

So we get a lot of questions about, hey, can you walk us through the workloads and how they're scaling? And my answer to that is typically that doesn't matter, that does. Because they may deploy a workload, our job is to cross-sell, upsell and expand that customer, and this is what the move-up market has done. Now you say, great, Mike, hey, that's customer numbers. What does it mean for revenue? So this is another new one. I'm going to let you look at it. This is ARR starting in Q2 of fiscal '18 to the quarter we just finished. And it shows you the growth in ARR by customer size. And you can see the durable growth, especially in the larger customers, continue to drive the revenue growth. This is the move up market. This is the platform play. This is AI starting to take shape in enterprises that's driven such a big growth. And our job is to continue to get them to expand with Mongo even if their early workloads start to plateau. This is a customer play, not a workload-by-workload play, okay?

I just need to get a drink, hold on a second. It's a great chart, isn't it? We had a bunch of different colors on there, and May said, no, Mike, they're going to throw up if you throw a purple on there. I put purple on there because it's my granddaughter's favorite color. And she said, "Hey, Grandpa, you're going to put purple on there, I'm like, no, sorry, I got to go with green.

So we have a lot of conversations with you folks. The #1, actually #2 ask for metric, give us total ARR. You tell us what EA is. I've said repeatedly, Atlas growth largely mimics revenue growth mimics ARR growth and the ask was very simple. I cover a lot of companies, don't make me do the math. We did the math for you. This is subscription ARR ending Q2 '25, '26 and '27, okay? And as you can tell, it has accelerated each year, and they're both accelerating. Atlas growth consistent with revenue growth. We've given you this number before from a growth rate perspective, but there are the dollars. Spoiler alert, you're not going to see this every quarter, you're going to see it every year. So take a hard look at it. We'll talk about growth in revenue. But again, folks, Atlas revenue growth, largely consistent with ARR growth, and we give you the EA number as well, but those are the dollars. So hopefully, that helps.

For us, this is a big part of the Run Anywhere story and the importance of self-managed because it is another growth driver, and it helps us drive total revenue growth, which is what I care about because it funds everything that we do.

Okay. What does that mean for margin? This is another great slide. This is the revenue growth versus the operating margin. And during this time, we've increased and we talked about it, 360 basis points last year, at the upper end of the guide, 250 basis points growth. That revenue growth drives all of the operating growth. At the same time, we've invested in the business. So last year, OpEx was up, call it, mid-teens. This year in the guidance, it's almost 20% growth. And R&D is 30% in our guidance. The result of that 30% is what you just heard from Ben and Pablo in terms of the product innovation. Over time, we'll talk about this when we guide '28. There are areas of optimization, AI, offshoring and other things. But our goal is to continue to invest in growth because revenue drives operating margin expansion.

So what are those areas we're going to invest in? Ryan went through a bunch of these in his, and you heard this as well from Ben and Pablo. Most of it has been on the core database and meeting our customers where they are as they make their AI journey. So no surprises here in terms of what we're investing in. We haven't talked a lot about Voyage, but we're also obviously investing in Voyage as well. This is rolling out 9.0. It's Atlas Infinite. It is our MCP -- managed MCP server and then, of course, new products like Atlas Agent Engine.

I can see -- so used to saying EA, now I have to say AAE. So Atlas Agent Engine. And that's a big part of the investment. And then on the go-to-market side, Ryan went through a good bit of this, adding quota-carrying reps, especially in the enterprises to drive the data I just showed you, a stronger muscle around specialization for new products, expansion into specific geos. Asia Pacific and U.S. Fed are the 2 we've called out, investing in the partner ecosystem. Also and super importantly, folks, we continue to invest in developer awareness and supporting our AI initiatives. That -- go back to Reclaim the Bay is a big piece, and we're going to continue to invest in that as well. So a lot of this is in the -- all of this is in the 100 to 200 basis points growth that I showed you about 10 slides ago. So it's all baked into that.

Okay. A small thing but super important for us is, hey, you want to be a business in my mind, you need to be GAAP profitable. And the company has invested for all the right reasons. We guided to be GAAP profitable, $0.77 at the high end of our guide. It's a part of it. A lot of this growth has been driven by the revenue growth, but also we've gotten a lot better about managing our stock-based comp, and I know a lot of you care about that as well. 30% 5 years ago, year-to-date, 20% as a percent of revenue. We're not committing to a range other than to tell you we know it's important. We will continue to manage dilution, and we expect this to go down. It may bump around a little bit if we do acquisitions or we want to invest everywhere else. We want the flexibility to drive growth. And everybody knows, especially for engineers, is a big part of their comp. So we want that flexibility, but we're mindful of the dilutive aspect of equity plans.

Okay. Now my favorite chart, cash flow. Hey, when I started, and I looked at Mongo, and I struggled as to why the cash conversion was only about 50%. You generate all this profit, why isn't it going to cash? And there's 2 big drivers. One is the business model and product mix normalized. And as we went from EA to Atlas, the payment cycles there are just different. And you saw that in the deferred revenue, look at the cash flow, that was a negative impact to operating cash. That's largely modulated as that has normalized. The other part is what we can all, and I give all 5,700 employees at Mongo a lot of credit. We can manage our working capital a lot better. I never get a vendor anymore that says I'm going to pay you in net [ 30% ]. No, we're going to pay you a lot longer.

And it's just like everybody else. So it's been the normalization of the business as well as managing working capital. Folks, we generated almost $500 million of cash last year. And our goal and our desire is to continue to generate meaningful cash flow. It gives us a lot of flexibility around the share buyback as well as using our cash to grow. So huge slide for us.

Okay. Before I get to the financial targets, I do want to hit AI and new product innovations. So we've had a lot of debate about this, and we get a lot of questions from you folks. Tell us how big AI is in your business. So this is a point in time we will define for you exactly how we define AI natives and frontier labs. Here's my -- I'm almost assured when we stand up here or whatever next year, that definition will change the market is moving too fast. So it is a point in time. Please don't expect we're going to update it every quarter. But we want to give you a view of how big it is now with the full provide, though, which is we make no promises to keep the same definition. Don't yell at me if we move it around, the market is moving too fast.

How do we define AI native and frontier labs? Those companies that model R&D, whose core product is their own foundation, i.e., frontier labs. That's one. AI infrastructure companies whose core product is the physical or software infrastructure that AI models run on. And number three, AI-native software companies whose software application would not exist without generative or deep learning AI. Those are the 3. We do our best to audit it. Everybody says they're an AI company, fantastic. We use outside services as well. This is our best guess as we sit here today. So all these metrics key off of that definition.

Okay. Let's jump into it. On the left, you see, and we've talked about this, what is the percent of customers that are using multiproduct? And all these products are focused almost always on an AI use case, search or Vector Search, and we've talked about that 34% to 48%, basically from 1/3 to 1/2. These are for customers with more than $100,000 of ARR. So these are the biggest customers. And then you look at Vector Search, which is a big piece of it, and Pablo talked about this as well. Customers that use Vector Search in the last 2 years have grown by 4x. It's a huge growth in the customer base. Vector Search, as you know, powers AI agentic use cases such as REG, semantic search and agent memory is a big piece of it. So lots of good signs in the customer base.

Last year at Investor Day, we talked about what percent of Atlas ARR customers have at least one AI use case. And we define this. Are they using Vector? Have they raised their hand and said, we want to be part of your programs? Last year, it was 30%. This year, it's 40%. And this is when we talk to you folks about, hey, we're starting to see enterprises deploy AI at least internally, and these are some of the use cases that we see, and it's across a mix of enterprise and AI native customers.

So we get this question a lot. What are the top use cases you see in your customer base deploying AI? So I'll give you the 3. Giving their systems accurate real-time context from their company's own data, making sure the data can support their AI initiatives. Number two is running autonomous AI agents. And number three is AI-powered customer support. Those are the 3 big use cases that we see today. That's going to change over time, but that's where we are today, specifically focused around enterprise customers.

All right. I'm going to give you a second to stare at this chart. On the left -- your left is the number of customer count in the AI native cohort that I just defined for you. On the right is the revenue for Atlas and the percent growth. As we sit here today, AI natives make up less than 5% of Atlas revenue. I'm just going to cut the question off. When you think of that, think of 3% to 5% as we sit here today, okay? But look at that incredible growth, 65 (sic) [ 67% ], 129%, 165%. We talk a lot about this, which is it's still small, but it's growing very, very quickly. And this is why we're comfortable with this continuing to drive growth in the future, and it's off of a small base, but it's super powerful.

We showed you the names, you know who some of the customers are. And a lot of our product innovations are designed to drive that number up. So total customer count doubling in the last 2 years, revenue for Atlas growing at 165% year-over-year, and it's about 3% to 5%. So I never have to answer. We don't -- it's just a small number. There it is. There it is. Okay. And we're spending a lot of time and effort extending our lead in the AI universe.

Okay. We introduced 3 new big things today, new features on EA, Atlas Infinite and Atlas Agent Engine. We've talked a lot about EA features and how we monetize that is incremental EA monetization through new search and Vector Search licenses. If you want that functionality, you need to get -- you need to basically sign up for it. We'll give it to you, but you're going to pay for it. Early revenue contributions should start in fiscal '27, but build much more in fiscal '28. On Atlas Infinite, it is so well suited to agentic workloads. And as Ben shared, hey, these are spiky, bursty workloads and Infinite is a great solution for that. It delivers extreme elasticity, which again is perfect for those workloads. It just went into public preview. We expect at some point in '28 or in early '28 that it goes GA, but it's going to build throughout '28, okay? There's a lot of development that still needs to happen. So don't look for meaningful revenue in '28, but certainly in '29. With all of these new products, we hope we go faster, but that's the base case today.

Atlas Agent Engine, and Pablo talked a lot about this, we feel really good about the product positioning. And to reiterate some of those points, it delivers the capabilities that we think is needed in the market. It provides agent memory so agents can learn. It has the governance and guardrails that keep agents safe and compliant and it's open and flexible. And it's for builders, which is who we market to. So this is a new component. The monetization of this is it's going to drive more Atlas consumption.

As Pablo talked about it, everybody can use it in the public preview now. The expectation is that GA is either later this fiscal year or early next fiscal year, and we should start to see some momentum in fiscal '28, okay? So those are the 3 new products. That's how we view those today. We haven't guided '28. We'll certainly let you know how these play in. I think the number you'll see move the most is EA and then the other 2 following as we go through '28.

You've been super, super patient. Let's jump into it. These are the long-term targets that we showed last time, and let me just run through them. Importantly, folks, we're in a 3- to 5-year time frame. That was our first long-term model. So this is from last year, this is not new. We were making sure to give you a view of where we thought the business was moving, and we picked 3 to 5 years. And again, total revenue growth, high teens, Atlas revenue growth, 20% plus, EA and other implied mid-single digit and then operating margin of 100 to 200 basis points, not a ceiling and then 80% to 100% free cash flow. We did not talk about GAAP profitability, but we will today.

So in the new model, we have shortened the time to 3 years. Folks, 5 years is a long, long time. This gives us the capability to be a little bit more pragmatic about what we think the next 3 years look like, and we don't have to have the hedging of 5 years is way, way too long. So that's important because it narrows the time frame. So let's start at the top.

Our new long-term target for total revenue growth is 20% plus. We feel very confident that we have 2 growth drivers. And we'll talk about each one of them, Atlas and EA, and we are very comfortable saying that we expect to grow total revenue north of 20% for Atlas. We have increased our long-term target to mid-20%, okay? This is an important metric. So let's talk about this a little bit.

Our old target was 20% plus. Our new target is mid-20s. Keep in mind, we are using the same guidance framework that we've talked about the last 3 quarters. If we've been clear about one thing, it's the guidance framework that we have for Atlas and for EA, and we've not changed that. We continue to feel very good about Atlas. It is a consumption product, and we'll always be prudent in our projections, and we've told you that. And again, it's a little bit shorter time frame, but it is a meaningful jump in terms of how we think Atlas should grow.

And as we've said, folks, we're going to be prudent in our forecasting and guidance. That being said, we get a lot of questions about how can Atlas grow faster? And what we've listed here are what those targeted sources of upside are. And we've talked a lot about this. Better enterprise AI adoption is probably #1. AI native contribution, it's 3% to 5% now. Boy, we would love that to be much, much higher. The new products, if they get going faster and we start to see better traction, that will help. App modernization is the goal mine we're all chasing. And now with our new app product, we feel much better about that. That is an upside and then all the great partnership motions that Erica talked about as well. So in our base case, these are all here, but they're all -- if they do better, then hopefully, we can grow Atlas faster, okay? So that's Atlas.

Let's shift back and talk about EA and other. We all know the nuances of the multiyear revenue impact to EA. And I've said from the time I started, I'm not going to lean over and bet on a multiyear deal because the minute you do that, you don't get it and I look like a fool in front of all of you. We are basing our long-term targets on ARR because it mitigates the multiyear impact. But what we're committing to is 10% plus growth in EA. That is not a ceiling either. We expect double-digit growth if all the new products pick up and the world continues to move, hopefully, that's even higher. But at a minimum, we expect to grow ARR over the 3-year time frame at 10% plus.

Operating margin is the same at 100 to 200 basis points. And it is not a ceiling folks. What I want to make sure you realize is we walked you through the business model. We want to invest to drive growth in product innovation and in go-to-market. And I don't think we have a margin problem. We want to drive better growth. We expect the business model to continue to drive up margins, but we also want to be able to invest in the business. So we're going to leave it at 100 to 200 basis points. The last year, 2 years ago, it was 350 basis points, 260 basis points, it's not a ceiling. And the model has great leverage. And the same thing for cash flow, we're going to leave it at the same 80% to 100% because, again, so much of this is driven by the pricing and the collections from customers and folks, we don't want to go down that path. We feel good about where we are in our pricing, and we expect the business model and the product mix to be relatively normalized. And then very importantly, we fully expect to be GAAP profitable in '27 and in the future.

So you add all that up, this year on the Rule of 40, we've guided to the Rule of 44. Here's what we say. If we do what we say we're going to do and maybe a little bit better, gosh darn, we get pretty close to the Rule of 50 exiting the 3-year. That is our goal. I'm not going to commit to it because, again, I want the flexibility for those things to move around a little bit. So we feel really good about this. We get a lot of questions, how do you feel about the business? We feel really good about the business. And that's a commitment that we're making over the next 3 years.

Okay. Before we go to Q&A, let me talk about capital allocation, just to close this off. We continue to expect to drive material free cash flow. That then we will use to help manage dilution from the equity plans. It's not going to completely offset it, but we do expect to manage dilution through the cash flow. And then we have over $2 billion of cash on the balance sheet. It's not burning a hole in our pocket. We will look to do targeted acquisitions that are focused around product road map or open market adjacencies. And I missed one thing, which is I'm very happy to announce that the Board of Directors has approved another $1 billion in our repurchase plan. So thank you for that. That's all part of the stock buyback, and we are announcing that today. So we will target disciplined acquisitions that are either tech and talent or bring new adjacencies to the business like we did with Voyage a couple of years ago.

Okay. Key takeaways from the day from my perspective. It's a huge market opportunity. We have strong momentum in a large and growing market, and we still have a very small share. Our core products, we are confident in the durability of growth of both of them. AI is early. New innovations are early, but momentum is building and the new innovations strengthen our right to win. The business model continues to generate operating margin expansion and fully supports the new long-term targets. And potentially most important after all the questions in the last 2 days, folks, we think we have -- we know we have the team to execute. We have the right team in the right place to hit our commitments. We take very seriously our commitments to 70,000 employees, 80-plus million shares outstanding and our awesome 5,700 employees, and we are proud to stand in front of you today and tell you the plan for all of those folks.

So we're excited about Mongo. And at that, it's time for Q&A, and I'm going to ask Jess to moderate. If I could ask the management team to come up, that would be great. Thank you.

We've got about 45 minutes for Q&A here. I'll just give a run of show here while the team comes up, direct your questions to either Dev or Mike, and then they'll chunk them up with respect to the answers.

Jason, do you want to kick us off here? State your name and firm name.

All right. Jason Ader with William Blair. I wanted to ask about the Atlas Agent Engine. And in particular, like what does this compete with today? Is it replacing something? Just maybe talk about what people are doing today in terms of that agent narrative.

Yes. So what we see when we talk to a lot of customers, especially in the early days is they're using a host of different solutions. I mentioned some of them on the slide, you'll see hyperscalers SaaS solutions, some of the LLM providers and SaaS solutions and then there are also some that are verticalized around analytics or SaaS. What we've heard from customers is in a lot of these different solutions, the building blocks, like if you're using a hyperscaler, as example, who provides this, they provide you with all the building blocks, but you have to stitch this all together and put a lot of onus on the builders around some of the requirements for security, for being open, which a lot of the hyperscalers and LLM providers don't provide that flexibility.

And so what we've seen is it really comes down to 3 things from a competitive landscape where we think we have a differentiated solution. As you heard from Vikas, memory and getting memory to context, which we do natively and configurably for our customers is number one. The second one is we don't lock you into a model or we don't lock you into a cloud. So we really do provide that open framework. And then because we had a chance to listen to customers who are playing around with different solutions early on as we were going through design, we took security as the first principle and actually built it into the system. So you don't have to actually build security on top or bolt it on top. It's actually part of the system. And that's really important for customers, especially a customer like Citizens Bank that's in a regulated industry.

Tyler Radke from Citi. Great presentation and I appreciate, Dev, you being here in person. Maybe I'll direct this at you, and it probably applies to the broader group. But one of the things that we've heard you guys talk about as it relates to AI and why it's maybe a little bit earlier in the journey for Mongo versus some of your peers is many of the AI applications built were more employee-facing kind of back office facing. I wanted to ask you now that we've seen a lot of momentum, Meta Muse, some of these consumer-facing and customer-facing agents really rise into production.

How do you see Mongo's role within those use cases? I know Meta, for instance, uses PostgreS, but is that an opportunity to potentially win that in some type of partnership? Just how do you think about that opportunity broadly?

Thanks, Tyler. And I'll start, and I'll ask the team to jump in if I miss anything. So I think I said this right at the beginning. I think, one, when you use an agent to reason about a question or a decision, they need to have access to fresh data. So the freshest data in your system is live operational data, information about your business, what your transactions you're executing, what orders you're taking, what analytics -- not analytics, but sensor information you're getting about your business. And so one is like you need to have fresh data. MongoDB is an OLTP data store. Two, just because you have fresh data, it doesn't mean you can find the right information because you can have a lot of data. So being able to precisely retrieve that information based on what's being asked is super important.

So like the fact that we have a very sophisticated retrieval mechanism with Voyage and using embeddings and rerankings to basically -- think of it as like if you told someone, I want you to find information about something and go read the library. If they spend -- read every book in the library, that would be a very expensive process. But if you tell them, go into this section, it's about chemistry, it's about organic chemistry, and I want you to find this particular topic, all of a sudden, very quickly, you find the relevant information. So finding information quickly and precisely is important. We are world-class at that today. Three is scale. Like these -- as we talked about and as Ben talked about, the unpredictability of these workloads is very high. And so -- because now you don't know when the agents work, like obviously, for users after everyone goes off -- goes home, you see traffic come down.

You have a kind of a good pattern of traffic. But agents could be working all night in an unpredictable manner. So you need a platform that can elastically scale. With 9.0 and Infinite, we think we are the most performance and scalable OLTP in the marketplace. And then last but not least, you also need to know state because you need to understand what happened and when and keep understanding the history and what you're supposed to do next. And if you don't understand state, things can go awry. And obviously, with the memory capabilities we're rolling out. So we have all the ingredients to really position ourselves well to go after this market. Again, all these things have come out recently. So -- but we think there's going to be a ton of other exciting potentially new AI workloads that we can really go after.

Maybe I'll just say 2 things. One, with some of the frontier labs. One, we have seen, as they get into production scale, needing both the operational real-time data and also being able to drive it at scale, moving from solutions like PostgreS onto us, both across pre-training and post-training. So I think there's a journey for them. And the second thing I'll say, which we hear from customers is a lot of customers look at these pieces as composable blocks. And so they may use a cloud agent from one of the frontier labs or from a hyperscaler, but we are providing all of Atlas Agent as composable components. So if you want to own your data, if it's core to your knowledge, you can actually put it into one of those systems and we'll just work natively within that cloud. So it gives customers some flexibility.

Rishi Jaluria, RBC. This is super helpful with all the information. Mike, in your remarks, when you're pointing at the TAM slide, you kind of hinted about maybe wanting to invest potentially more into OLAP as we think about the TAM. I know in the past, you had Atlas Data Lake, I think that's now called Atlas Data Federation or been folded into there. Maybe can you talk about what could that look like? Is there -- is that kind of a demand that's starting to come from customers? And the flip side of that question that's always going to get asked is we've seen some of the OLAP vendors try to get into OLTP via M&A. Not too much has happened out of that, but maybe just how should we be thinking about both of those forces?

Yes. Thanks, Rishi. I'll start, and I'll ask my friend, Ben, to jump in as well. So we are focused on OLTP. That's in building out that platform. That's the majority of the R&D. That's what we're focused on. When we look at M&A for tech and talent, it's largely road map accelerators in there. That being said, there's a big market there. There is now starting to be a little bit of a gray area in that. So we'll look at it. But -- we also don't want to get diffused outside of our core focus. We think the market is so big in our core market and the growth is there. So we'll look at it. We'll be disciplined, but that would be a big step for us to step outside that. And again, we've got great sellers. We want to keep them focused, and that's the big piece. But you want to talk about our friends coming into our group?

Yes. And for everyone who's listened to me talk for the last 12 months, you know I describe this as cute that OLAP players think they could be credible in the OLTP space. And it took us a lot of years to do that. And I think, if anything, then we've been saying from the very beginning of the AI journey that OLTP was the higher ground for AI. then getting into OLTP is just asserting what we knew from the very beginning.

Ryan MacWilliams, Wells Fargo. Good to see another Ryan Mac. So really great to see the growth since from Mike in the AI customer cohort in Atlas, like really strong growth in that segment. So what are you seeing in both your AI Atlas customers and your core Atlas customer segment? Those 2 segments that help give you confidence in the new multiyear Atlas market.

Yes. So as we've said it for the last several quarters, we feel really good about the Atlas business. It's growing at 29% consistently. And a lot of what we've seen is not only the incremental adds. It really goes back, Ryan, to the customer piece that we talked about in terms of the growth within our existing customer base, the expansion there. We get a lot of questions about workloads. And yes, workloads will plateau. But the team, Erica's team and Ryan's team have done such a wonderful job of cross-selling and upselling. We're adding to the platform less still of half of our greater than $100,000 ARR customers using multiproducts. And we've talked about this. There's a lot of product adoption. There's not a ton of revenue generated yet because it's going to be lower than that. That is an upside as well.

All the new products we talked about, even though we have marketplace in Japan, we should be a lot bigger. U.S. Fed has a huge growth opportunity. So you look across that whole area and Voyage, and that's why we have confidence in the multiyear outlook. And the other piece is, and I just want to address it, look, we didn't have a CRO for 6 months, and now we do. And CJ, bless his heart, really had to jump into that void. Now we've got him. And the great part about that is Dev has the same relationships, awesome, but now we have a guy to execute on it. And that's the other thing. We feel much better about where go-to-market is sitting here today than we did last year because of the coming transition.

Siti Panigrahi from Mizuho. Congratulations on that Atlas Infinite launch. It was great to see the architecture, how you decouple memory and storage. So the question is, how are you going to position? There are certain applications can be both agentic and user workloads. So how are you going to differentiate that positioning? And as customers do some kind of spend optimization, do you expect some of the Atlas Core customer switching to the Infinite?

Yes. I think from -- on the positioning side, first of all, remember what we announced today is preview, right? Just on AWS. We have a ways to go. We're going to add the other clouds and everything. So it's still early. As I said, Infinite is definitely designed to handle the bursty and unpredictable nature of agents and scale. That doesn't mean that it's only for that. We see different levels of scale all the time depending on customers' workloads. Sometimes it's a steadier peak. It all kind of depends. What my expectation is that, one, because it's preview, and two, since Atlas Dedicated clusters has been out now in the market for 10-plus years, I don't see a mass movement of existing customers moving to Atlas Infinite. They're happy where they are. It's a predictable pricing model. There's no complaint, right?

As they start working with data in agentic workloads, my guess is those workloads will start out earlier on Infinite. And because we're giving the customers' flexibility of going back and forth, however they want to. Remember, guys, portability, #1 thing I've been saying, go on-prem, go into self-managed, go into Atlas. There's going to be movement and that's what the flexibility we're providing. So I don't think this is going to be like, "oh, sh**, I can optimize. I'm just going to go fast forward there." I think it's going to be slow, and I think it's going to be allow customers to prototype more and allow them to move data around.

Ivan Feinseth, Tigress Financial Partners. What do you see as driving your biggest competitive wins? Is it new application development? Is it data migration, data consolidation? And what do you think are your advantages that are driving that? And then also, what do you think is on the horizon that's the next big thing that also you're best positioned to benefit from?

Look, we've done a really good job of getting into one line of business with a customer. And now we have a seat at the table at a more executive level, and they're looking at it across now multiple lines of business. I would tell you that's a huge strength for us to go upsell and cross-sell. As you heard from Mike, too, and the team, adding new platform capabilities, they're looking at our road map and saying, wow, okay, this is where I want to be.

And I also mentioned I've been doing this long enough to where there are so many customers that have so much technical debt that they've had backlogs that they want to modernize and migrate and they're taking those projects on because now the AI tooling and capability is there for them to do so and being able to migrate in literally days and weeks instead of years. So it's all of those things together, I will tell you.

Patrick Colville here from Scotiabank. Lots of juicy, fantastic metrics. To me, the most interesting was the 3% to 5% from AI natives. I guess to me that metric speaks to the success, but also the opportunity because I compare it to another infrastructure company headquartered in the city, and they're at 3x that level, right? So I guess maybe for you, Mike, and also for the broader team, what would it take for that metric to get to the double digits, the mid-teens point contribution from AI natives?

Yes. Thanks for the question, and I'll let you ask them all that piece of it. For us, look, it is -- we've said it, Patrick, the whole time, which is it's still a relatively small piece. It is workload by workload in there. And if you look at AI natives and frontier labs, they're going to be different. We love the AI natives. They're all still pretty small. You heard of one today, Emergent, which is doing a nice job, and they're growing. So for us to get that in double digits, it's really 2 things. It is more workloads in the larger labs, clearly. And then that AI native cohort starting to scale and starting to grow as well because those are the workloads that we should scale with them as they grow their business.

And continued customer acquisition. We've talked about this. This is where the new customer metric comes in. You saw that double the number of customers that are AI native. That's going to expand as well. So it is still small, growing very quickly. For us, that's a relatively good thing because we also don't have dependency on any one, and we do expect that to continue to drive growth.

Kirk Materne with Evercore. I think this is probably for Ryan, but you all are obviously pursuing sort of a dual-pronged strategy, obviously, getting bigger with your biggest customers at the same time going after builders. How do you think about just monetization and pricing along those 2 different tracks? Obviously, your bigger customers have to think about security, statefulness. Smaller customers just might want to go. And I was just kind of curious how you think about making sure that you're thinking about monetization the right way across those 2 vectors and anybody else that wants to add to that?

That's a great question. Look, it comes up all the time, and it's something that Erica and I continually look at, as well as with the engineering team. First, it starts by listening, right? Listening to our customers, whether it be one that comes through self-serve and is somewhat small and from our largest customers. The flexibility that we have, though, with both Atlas and EA today is pretty helpful as we see more customers focusing on a hybrid approach, I will tell you. And then it's really just listening. So we're going through that, and we will continue to evaluate that, and we'll make sure that we adjust accordingly, right, with keeping an eye on margins.

Yes. I think, Ben, maybe you can just talk a little bit about how our pricing approach is much simpler than some of the other infrastructure providers.

Yes, for sure. So a couple of things is that a lot of the other infrastructure providers doesn't matter which one, they bill customers, especially in cloud as by read units and write units. We don't do that, right? Customer comes in, and I'll just talk about Atlas Dedicated because it's the GA product. You pick your capacity, you pick your storage. If you don't exceed that capacity, your bill is the same every month, and it's predictable. Customers love predictability. Enterprise customers love predictability. But what this also means is as enterprises start to prototype different types of workloads or they add a little bit more data, unless they exceed those bounds of that -- the box, so to speak, we're not going to charge them more.

So on the weekends, they want to try an agentic workload and throws 100,000 queries at it. They didn't exceed their allocate. Great. And so I think it's really important to understand that like one of the reasons why enterprises love us so much besides the security and the governance and compliance and all these things, is that we provide them a very predictable surefire way that they can actually understand their spend, understand their usage, and they're not worried about the runaway bills that we've heard from the past.

Raimo Lenschow from Barclays. The big debate in the market is people start working on AI and the database you start with is the starting point. And then if someone comes in later and says they have the better database, that becomes a database migration, which is kind of a headache that we all know. Like -- so in that respect, like where are we on that winning back the base? What are the proof points that you see? Pablo, that's probably you, sorry. But where are we on that journey?

Sure. Raimo, it's good to see you again. Look, as I said, one of the key things for us that's super important is making sure that we are where the builders are. And I think as you know this, like we've always been a builder company. And so I went through what we're doing to show up from an awareness and from also an accuracy perspective. And we're seeing good early wins in terms of driving both of those. That's sort of the start of the funnel. We also need to be in all the right places for customers as they're building those applications. And frankly, like we are just getting into a lot of those marketplaces. And so -- and then it's creating a lot of that opportunity. So the coding agents can actually pick the best platform for the job where we are as it relates to JSON and the document model and also our integrated vector search and capabilities like Voyage. So that's a starting point.

But the other thing I'll also say is when we've seen this with some of the at-scale customers, like we've had frontier lab that migrated to us in 4 weeks off of a PostgreS database for conversation history. And so the other thing also to think about it, as Dev said earlier, it's a new workload, and there's a lot of those that are coming up and spinning up. And so I think we are participating in that market, and it is in the enterprise and in production still pretty early. And the other part of this is we're also working very closely with a lot of the builder communities like the Emergent to make sure that we drive that awareness early on.

But Raimo, you've heard us talk and they were at our .local in London, ElevenLabs, where they started on Firestore and they just got to a point of scale as they saw their market opportunity increase and they said, we need to get off and got on MongoDB. And there's a whole host of other those that as a start-up, you're just trying to make your next paycheck, trying to find your product market fit. And then when they hit a scale point, they tip over really, and we're there to help them.

Let me just add one other thing, right? The way I like to think about this is the developers of today are different from the developers of 3 years ago when the tech stack was determined for them and then they started writing the code, right? Now Vibe coding, Claude Code, whatever makes it so they don't care about the tech stack until it's maybe too late. But if you think about the selection that happens in that tech stack decision, the model makes or the agent or whatever, pick your poison, it makes 2 decisions, one of them really cool and one of them sh****. One of them that's cool is it's always modeled on JSON, right? That makes our migration easier later on.

The mistake that the models make is that it picks PostgreS on JSONB most of the time, which to Pablo's point, that's what we're trying to change. So when they scale and reach some sort of exit velocity just like Emergent did, right, easy migration, and we'll get them up here. So I'm happy with the enterprise. I'm happy at the mid-tier, we'll migrate them every single time, no problem. And now Pablo and the team were focused on the long tail of the funnel as well.

Matt Martino, Goldman Sachs. Dev, great to see you again. This question is for you. I wanted to talk about the modernization opportunity in AMP specifically because it started under your tenure. I guess what I'm curious about is because it seems like we went through a bit of a quiet period on AMP over the last kind of 9 to 12 months. So I'm curious what's changed from a technical and commercial standpoint where you can actually see some follow-through in the numbers around the modernization opportunity broadly?

Yes. So just to level set with everyone, what I started talking about maybe like 3 years ago was the fact that when we had lots of customers who felt trapped on legacy platforms and it was easy to move the data, it was very hard to rewrite the application code. And that was the big tax. And because these were a lot of old brittle systems, a lot of stored procedures, it was a very, very heavy complex job. With AI, we thought that, that problem would get solved. But what we underestimated, candidly, was that the maturity of the AI models took longer than we had expected. Obviously, we've seen rapid innovation of model capability and performance. And so Mike being the great CFO, he's good at managing expectations says, investors are a little tired about hearing AMP. So next time we talk about AMP, let's have something really hard to deliver.

I would tell you that we're really excited about what we're seeing. We made an announcement of Cognition this morning, who's now our partner, who's helping us essentially work with us to help migrate and they've become very successful as a coding tool to help migrate customers and deal with that application logic problem. And obviously, we provide the tooling to migrate the data as well as put in all the guardrails to make that production system. So I think we didn't want to kind of overplay our hand today, but I think that's a really important growth vector. And as Mike talked about, it's one of the upside cases in our model.

Sanjit Singh, Morgan Stanley. I wanted to start with you, Dev, on -- I mean, we go back to the IPO and you were for years, consistently saying, we're about the developer, and we're focused on the developer. Obviously, the developer role is changing, right, from writing the code to orchestrating a harness. Do you see risk in that sort of changing roles of the developer and how MongoDB's value proposition ultimately changes? And what are you doing to mitigate that risk?

And then a follow-up question for Ben on the -- what was the question here? On memory, is that like a net? Have you sort of productized that capability? Because I kind of thought that was part of the core value proposition of having a document model. So with the new agent enterprise offering, have you sort of productized a memory capability? Just want to get some clarity on that.

Yes. So Sanjit, to your first question, there's no question in my mind that over the next, say, x number of years, whether that's 3, 5 or 10, humans will no longer write code. I mean that -- you see that coming and it's become very clear. I think there may be human in the loop. I think there'll be kind of checks and verifications and all that, but most code will be written by agents. So yes, the original value prop of MongoDB, one of them was that it makes the developer's life so much easier because it's so consistent with the way they code and they think, especially we had all the drivers to all the popular languages. I would say what also was one of our value propositions was the ability to essentially scale up and scale down. A lot of the AI workloads we talked about is the bursty traffic.

A lot of that traffic is ephemeral. We've always been a very scalable platform. So that plays to our strength. Ben mentioned JSON, the fact that we speak the language of AI. Everything -- you can even prompt in JSON. I don't know if people have actually done that, but you can prompt the LLM with JSON and it will respond because JSONs are -- I mean, LLMs are designed and built around JSON. So we think those attributes are just as important. And that's why we're building all this capability around like price performance, Infinite and Agent Engine to provide all the wrapping to enable agents. We recognize that we may have been a little slow like with other companies like Supabase who have done some work around making big much more agent friendly. Those are easy gaps for us to fill, and you'll see that us address that pretty quickly.

And just to then answer that. So there's memory of today, which I'll talk about, and then we'll talk about memory tomorrow or now today because we announced it today. Before today, when we talk about memory, you're 100% right. It was just another database workload for us. Agents are speaking JSON, great, stored in MongoDB. And that we saw that organically start happening at our customers as well as other context like chat conversations and other things. But the memory of today, what we announced today is a little different. It gets into more of the business logic.

Yes. So what we announced today, memory actually is a composable component. And as Ben said, we've got customers who have been using us for memory because they can and they've been building on top of it. But what we actually do -- and it's really -- it's both a memory and a compute problem. We actually bring in long-term memory. So all the stuff that comes in from previous conversations, stuff that gets inferred and brought back from the LLM. All that stuff comes in, and we actually break it down from a long-term perspective into 4 different types of memory. And so we do that under the covers for the customer. We break it down into procedural memory. So these are the steps that the agent will learn and memorize. Episodic memory, so the episodes and events that it's remembering.

Taxonomic memory, which is the taxonomy that may be for that agent, for that team or for that organization, the terms that are very specific to that team like Magenta, the code word that we had for Agent Engine before we launched it and then semantic memory, which is sort of the knowledge of the organization. That all happens in the system, we aggregate it. And then because we have Vector Search Voyage under the covers, we're able to bring back the right context for the agent at the right time, which both drives accuracy, meaning fewer turns and lower cost as they're trying to drive some of the token usage. And so that's all available to customers. We provided as an SDK, and we're externalizing also through MCP.

Alex Zukin with Wolfe Research. A lot of great numbers, especially like the AI native ARR and the -- but I was a little -- I think we're all a little surprised about the magnitude of the higher Atlas or the confidence in that mid-20s over the next 3 years. So I mean, maybe just dive in a little bit between those 2 AI natives versus core, kind of what's driving that confidence? Do you expect to kind of reaccelerate the non-AI native part of the business, given to your point, now you have a CRO, you didn't have one in the first 6 months. Maybe just help us understand that, and I've got a quick technology question.

Sure. So as we look at Atlas, and we've said it multiple times in the last several quarters, we continue to feel very good about the Atlas business. Technically, going from 3% to 5% to 3% was a big thing, Alex. It allowed us to say, hey, what do we really see, call it, in the near term? And we've seen all the growth that has been driven by the move-up market, which has been significant, that as we look at the geo expansion, the new products, as well as the platform. And that is going to, over time, hopefully start to monetize, again, good usage, but it needs to monetize more as we look at that. And then, of course, you have the AI natives.

So underneath the covers, I mean, look, at the AI natives are 3% to 5%, it means the core by definition as a percentage continues to call it, slow down, but the dollars continue to grow. And we look at that over the next 3 years, and we feel very good about growth in both of those. And we really haven't baked in a lot of upside for enterprise AI. It's the thing we talked to you folks the most about in the question earlier on when is that going to pick up? And at some point -- now keep in mind, AMP is great. They did a great job of going through that. We also want sales to be able to sell it. That's going to help both self-managed and Atlas but we do expect growth there. So the confidence is in the go-to-market engine, the growth we see in our larger customers, the new product and continued growth in AI natives.

If I could just add one other thing that I think it working, it also gives me confidence that I have the benefit of stepping back and now stepping back in is that when we launched 8.0, there was some concern both in the investor community, is that going to cannibalize some of your business, right? Because if you're providing a 30%, 40%, 50% faster machine, is going to need to use less gas. Well, it turned out that they wanted to drive that car more because they put more workloads on it because they saw so much benefit. And the fact that 80% of our fleet is already at 8.0 faster, 2x faster than any other prior release gives us confidence that, that same phenomenon will happen with 9.0 because 9.0 is that much more performant. And that's a pretty material data point.

Whether Atlas Infinite, where you feel like is there cannibalization risk concern? Is that completely net...

No. And let me address that. Ever since I've been at MongoDB, MongoDB has been viewed as a great technology. No one has ever questioned the caliber of the technology built. Some people have a question, are you -- some people use the term Ferrari or turbo sports car. It's great for certain workloads for the mainstream meat and potatoes, sometimes those internal workloads that don't need all the sizzle, maybe MongoDB is not the right profile for. By increasing the price performance, we make it just that much easier for people to put more and more workloads on MongoDB. So it helps our expansion to larger accounts because there's more workloads we can track. It helps our upsells and cross-sells in our existing customers. And obviously, it gets -- we can go after these new AI workloads as well.

[indiscernible] with Oppenheimer. Building on the enterprise AI opportunity, which you've talked about unlocking there, can you give us some perspective on what you think the system integrators can do for you when you start expanding there?

Yes. For me, the systems integrators are about credentializing us, giving us access to buyers that we would not have otherwise faced off against and helping us drive true transformation, right, at the business level. That's what the SIs are going to be able to help us do. And what's important for us, and you heard it from Vikas, right, and this is where it's actually coming true, so it is an example, is a lot of our enterprise customers are reliant on the system integrators to help them make the decision around what technology and what innovations they want to use for their AI transformations because there's so much innovation out there, it's complicated to make those decisions. And so getting with the SIs and getting embedded into their frameworks, they're giving us a natural entry point into those buyers, into those enterprises.

And we're credentialized from the start because they're walking in and saying, as part of our stack, we're embedding MongoDB. So for Infosys, we're part of Topaz's framework now. So when anyone walks into their innovation lab around the world, on the wall, it's going to say MongoDB. And when they show Topaz, it's going to say, we are the data platform for it. So that gives us access that we might not already have. And that's going to be true expansion for us in terms of new customers, new stakeholders and new revenue.

Yes. And I also want to add just like one of the things that surprised me, it kind of hasn't surprised me, but in some ways surprised me is that the gap between where the AI capabilities are and where the enterprises are is wide. there's a massive what people call the diffusion gap. And it kind of -- if you go back in history and look at like when PCs were deployed and networking was deployed, it took time for people to reengineer their work around this new technology and the productivity gains didn't really happen until 3, 4, 5, 6 years post the deployment of the technology. I definitely see having been at Sequoia and I was working with them over the summer, like this -- clearly, the AI natives are all AI pilled and they're being very, very aggressive.

But when you talk to the enterprises, they're still moving very slow. I mean the examples I gave you are some very sophisticated customers, they're just pleased to have an AI chatbot, right? And so it's going to take some time for them to really deploy and leverage the sophistication. In the last month, we've seen more AI models deployed with performance gains that are unbelievable. And each one of them would have been like a noteworthy front page of newspaper announcement 12 months ago. And now you just see them coming out almost every week. And these enterprises just can't keep up. So the SIs play an important role in kind of bridging that gap between where the capabilities are and where the customers are. And I think that's where they're going to add a lot of value.

It's Steven Koenig from Macquarie. I want to build on the good question that you answered earlier, Dev, about the nature of the developer changing in the future. And I want to bring together a couple of points that I've heard here. One is that the agents are currently like going with PostgreS with their JSON extensions instead of being aware of Mongo. And I want to also bring in your point about your working harder to move up the decision-making ladder in the enterprise. And then lastly, I just want to bring in the fact that like, Mike, when you were citing like 3 of the top use cases for agentic, the coding use case, which is huge in the enterprise and AI natives, wasn't one of those 3 use cases.

And so it smells like there's an opportunity here to enable the coding agents in creating applications and enabling them to decide to use Mongo and putting that in at an enterprise level. I don't know if I'm stretching this too far, but like how will Mongo -- is there an opportunity here? And if there is, how would you look to enable this?

Pablo, do you want to take...

Yes, I'll start. Look, I think as you know, in the enterprise, you have your enterprise approved solutions, and we fare very well in that space. One of the things that we hadn't been doing well is making sure that we show up in the coding agents, making it super easy for developers or for the -- in the coding agents, making it super easy for developers or for the executives to choose a framework. As you know, in enterprise, a lot of those decisions are getting made by enterprise architects in terms of like for their production at scale systems. And we've made that a lot easier for them. We made it a lot easier for those systems. And one thing I'll highlight in our partnership with Cognition, where they have -- they're getting a lot of reach in some of these enterprises, we're actually seeing that happening. And those choices both the coding agent and the platform is being made together.

When you think about it, in an enterprise, like they're not going to let developers build production scale applications using any single solution. Those typically go through the architecture team. And that, I think, one, we show up well there. But really, a lot of the stuff that we've been doing for both builder awareness, accuracy and being in those platforms is really helping us make it super easy for those teams to build on us.

Yanni Samoilis with Baird. So just going back to the enterprise AI transformations for a moment. Those can be super lucrative, but also they require a lot of handholding. The SIs can help with that, of course. But wondering if you think you need to send more of your technical folks in as well, embed them in those customers' operations to be able to help with some of that handholding or if the SI partnerships are sufficient enough?

It's going to be both is the answer. I mean we are building FDE capability right now, starting in our product teams, it will migrate over to professional services. We'll have that capability, and we'll be able to deploy that alongside our partners and our customers to help them accelerate their revenue. So we absolutely believe that's necessary. We know our customers, they have to set up their schemas, their indexes, everything correctly in order to make sure they get maximum value of the platform and to make sure that then they want to consume more. So that's going to be critical. It's a motion we're driving. And frankly, it's essential for delivering the kind of customer experience we want and getting the level of consumption we need.

And the thing I'll add to that, and this is true for both AMP and for Agent Engine where we do have FDE capabilities. It's not just about helping the customers. It also creates a super tight feedback loop for us as we've been in design partnerships and in our early previews with a lot of these customers, those FDEs are embedded. They're there, they're either physically there or in with those teams, and we're getting that feedback back super quickly, which is helping us reduce the cycle time for new innovation.

This is [indiscernible] for Miller Jump from Truist. So I just have one quick one on EA. And EA has been doing really well in the past few quarters, and it's benefiting a lot from Vector Search. Given the double-digit growth opportunity in the next 3 years, could you just talk about the opportunity? And where do you expect EA to continue benefiting from?

Yes. So as I articulated about the Run Anywhere strategy and vision, that is 100% something we're committed to. We're going to continue doing that, which basically means as we do more and more inside of cloud and other things inside of Atlas, where it makes sense, we'll continue making more parity decisions to bring those into the self-managed, I would say, ecosystem. But just from a market perspective, there's a lot of things going on. We talked about AI adoption in enterprises. Some of it is people, some of it is laws that haven't even been passed. They don't know what they're doing in France. They don't know what they're doing in England. How does GDPR going to apply to this? Does FDA have to approve this model for this particular use case?

Does it -- do we have to give an agent a Series 7 so they can look at financial data and give advice to your customers, right? We don't know what this actually looks like. That's -- when they figure out all that, that's when we're going to see enterprise AI like go really crazy. Until then, we're going to see what we're seeing as well as where EA plays into a part is, one, regulated industries where they can run it on inside of their fiefdom and then make it for internal -- more internal use cases. Two, public sector in like air-gapped environments because that's really important. They're not going to slow down.

And then three, there is plenty of geo locations in the world that doesn't have a cloud provider that either needs to be served by a Neocloud or an Equinix or pick your poison or just doesn't even have anything, so it has to go there. But the banks or the insurance companies still want to serve that area. So EA is going to play a part there. So EA really has a massive play in this thing, and it's not an or, it's 100% an and.

One thing I'll add to that, EA is a deployment option for us. As I talked about -- and Search and Vector Search was introduced recently in the last couple of months. So when I talked about the Vector Search growth over the last couple of years, it really was around our cloud. And the thing there, as Mike touched on, like those multi-feature use cases really do resonate with customers. And so one thing I think about is like we've seen the match and we've seen the market fit as we've deployed this in Atlas. And so as we go into EA, we're earlier in the journey, but we're very optimistic.

We're up on time. Thank you, everyone, for the great questions.

Thank you. Thanks, everybody.

Well, pleased to welcome our next -- I can't really call you a presenting company.

I think anyone here remembers the days of the presentations. I used to give a little canned presentation at these.

I remember that one, a long time way ago.

A long time ago, breakout after.

But our next fireside chat, MongoDB. With me to my immediate left, longtime friend, Mike Berry, who's the CFO. And then Ben is the CPO, that's in-charge of products. So we'll get into that a little bit as well. I didn't get this after my lab, excuse me.

And thanks for having us, Rob.

Absolutely. Absolutely. So I think the obvious question, mix of business and how we should think about it as investors. It seems to be a hyper focus around Atlas and what's happening with Atlas, but -- and customers are making a choice. And the EA business has been far better, I think, than you would have thought. So maybe just walk us through some of those different dynamics between EA and Atlas at this point and what's driving each and how investors should look at the business?

Sure. So again, thank you for having us. And you hit on it. This is all us meeting customers where they are. And what we've seen over the last, call it, 12 to 18 months is just a renewed interest in the self-managed part of the world. Do we invest and focus and obsess completely over Atlas? Absolutely. It's super important. In addition, what we've seen is really a pickup in growth in EA. It's always been a good, stable business, specifically around regulated industries, governments.

And I think what you've seen is, I'll say, 2 areas and then one reason why we like it so much is, hey, the run anywhere strategy is real. And for us to be able to run not only across the 3 public clouds, but also anywhere they want to deploy is super important for them. And then, now you bring all the additional regulations, you bring AI into it. Can they even deploy in a cloud given the capacity constraints? Economics matter.

So we've seen not only, I'll say, the core workloads where certain industries are going to run on-prem anyway, but now you've seen all the new things that they have to deal with, and that's really driven the growth in EA. And then for us, again, we -- is Atlas important? Absolutely, and we obsess over it every day. But we also love EA because they're our largest customers. They will typically -- the larger customers run both self-managed and Atlas.

And in that cohort, we talked about this at a conference last week when you look at the large customers that have self-managed and also Atlas, Atlas growth is actually higher than the total company average. So there's this perception, I think, Rob, which is, hey, it cannibalizes it. Our view is that simply hasn't been the case. And from our perspective, it also drives a boatload of profitability as well.

The other thing is, keep in mind, we have disclosed ARR for EA because of the revenue recognition, volatility. Keep in mind that, that's EA and other. Other is almost always just going to be OEM transactions. And this isn't the large Asia-Pac deal we've done before. But you'll see a little bit of that variability driven by other. If you look over the last 3 quarters, EA ARR has accelerated each quarter. And to the extent now that we actually guided double digits, I'm sure we'll talk about this in 2 weeks at our Analyst Day as well, but it's certainly now turning into a double-digit growth driver, and we feel really good about it.

Mike, is there any common characteristics of those going with EA? You mentioned large customers. But if you look internationally, are you seeing a push back to EA for nationalism or kind of running an on-prem versus being in the cloud and having risk of data? Is there any kind of common denominator you can speak to at this point?

Do you want to take that?

Yes, it's not really a common denominator. I just want to correct one thing that you said about like going back to EA. Customers aren't looking at it that way, although that is a security blanket for them. A lot of these laws and regulations and new certifications and everything else, they're still trying to figure these out. Some of them are -- like are talked about, but not even cemented into law yet. So customers like the flexibility and portability that we're able to give them that, quite frankly, not a lot of other technologies can give them.

And that's the fact that you build an application on MongoDB, you can start out in one place, move it to another place, bring it on-premise if you absolutely need to in self-managed or quite frankly, we have some customers that operate in some countries or some regions that don't have a cloud provider. So to give them a truly portable experience is really valuable to them. So that message of run anywhere and the portability of the application is really resonating.

But investors shouldn't have the view that it's a legacy type of customer or a legacy type of implementation.

Certainly not. And that's, what was it, 5 months ago now or 4 months ago now when we announced the GA of search and Vector onto the self-managed. We pioneered that in Atlas, and we had enough demand. This is all driven by customer demand. It wasn't a bet. Let's bring this to the self-managed world because they want to be able to have the flexibility of going both places, but also demand for the self-managed environment, workloads that just will never go to the cloud for whatever reason. They want the same functionality.

And if I could, Rob. It is Atlas important? Absolutely, and we focus on it a lot. I think there's a perception which is, we will only be an AI winner if Atlas accelerates. And we struggle with that greatly because that's not what our customers are saying. They're saying we want to deploy run anywhere. We might want to self-manage that.

And I think going forward, it's going to be really interesting. Hey, economics matter. if they need to absorb the incremental, call it, AI spend, how are they going to deploy that within the envelopes they all have. And that's why I think that part of the self-managed becomes a bigger piece of that, and we'll see how that plays out.

So I think this brings about 2 questions, and I'm sure I'm leading you into your Analyst Day. So your answers will be somewhat gauged and guarded, Mike, if I know you. But relative to the durability then of EA and your perspective of how that has changed, my sense is this is going to be a more durable growth driver possibly than it was before.

That's certain. We talked about that on the last earnings call. We haven't guided past this year, but we said we feel like it's -- we now have 2 growth engines, Atlas and self-managed.

And number two, just in terms of the customer economics between the 2 and monetization, the big deltas. Obviously, it's more profitable for you to sell a license to someone and they run it themselves and have that cost. But maybe walk us through the monetization opportunity on each and what the experience has been.

Sure. So -- and I think this goes a little bit to the lifetime value of that. Upfront, yes, you get the profitability. Over time, that license model will typically be a little bit more profitable, call it, gross margin. But when you go all the way down, keep in mind too, you still have to spend R&D, other dollars. So for us, we are excited about each dollar. The important part for us is that if somebody is doing self-managed and then they're running Atlas, that for us is great because over time, will stuff get deployed in Atlas and migrated to cloud? Maybe.

Are they going to deploy a net new workload? Hopefully, they do that in Atlas. So for us, there's also that synergy that comes with it. And keep in mind, and Ben can talk more about this, it's the same product. From an R&D perspective, there's very little difference. So it's not like we have to go spool up a ton of R&D. There's a little bit for the features, but it's also very efficient, and it serves both products.

So in terms of metrics that we should be looking at then, how relevant is cRPO to a leading indicator? It obviously spiked last quarter. But I know you gave us an ARR metric, and usually, we're going to triangulate between 3. But given your hybrid model, is cRPO a relevant metric for us to look at?

I think it's...

Or too much noise?

I don't think it's too much noise. I think it more goes to the go-to-market motion than anything else in terms of us, as we move upmarket into the enterprise, they like to have the longer commitments. There'll be requests that they have. There'll be requests that we have. And what you saw in Q4 of last year was just a much more growth in commitments. It was both across self-managed and Atlas.

And when you move up into the enterprise, that will be a part of the go-to-market motion. And keep in mind, people say, "Well, I want to correlate cRPO growth with revenue growth." If RPO is, call it, $1.4 billion and current is about half of that, $700 million, we guided somewhere around $3 billion. So it's a part of the revenue stream.

I would also ask investors, don't assume that's all incremental because it's not. You'll have existing customers that will renew a commitment. They'll do a long-term commitment, and then we'll put that in RPO as we need to. Also keep in mind, our RPO is everything above a year. We don't estimate below a year. So it's going to be a part of it. Is it important? Yes. Is it a leading indicator of growth? It's a part of it. It's not the biggest driver.

Okay. Then where do you think the most opportunity is within the core product set now? And when we think about the next incremental dollar of R&D, what are you putting it towards?

Yes. It's a good question. I think there's a couple of things. I mean, one, we've announced some new capabilities about 4 weeks ago around MCP server, which is essentially call it, the SDK or the interface that allows agents to talk a singular language to a bunch of different things behind it, right? So I think Terraform or CDK that's been out there for a really long time.

Now we have a better pathway for agents to communicate with our services. We're going to continue investing in there because that ultimately helps drive what we call unaided AI awareness, which means that the human is not potentially in the loop of those decisions and those recommendation engines from Frontier Labs. So I think that's one obviously big area.

I think number two, is as we're talking about self-managed, we pioneered a lot of different services in Atlas first. So because of the like how fast we can deploy into that environment that we fully control. We're going to constantly -- as now search and vector are now part of the self-managed ecosystem, what other things in the Atlas experience that we can continue to make that self-managed experience just as capable. We'll always be looking at that, too. So there's a lot of work going into that. So more information to come later, but that's my focus area right now.

Great. And CJ has spoken a lot about this vision to evolve Mongo into a data platform company. So from your perspective, what does that entail from a core product?

Yes, I -- it brings a couple of things. One, the way I think about it -- and I've been at Mongo for 9 years, the original thesis behind MongoDB and Atlas was -- we already have -- MongoDB is a good OLTP database. I think it's the best, but I'm biased, of course. And we have the system of record for all of the customers' revenue-generating applications. We want to provide the best developer experience to that real-time data as much as possible.

So if you look at what we've done with Atlas, we're not -- unlike other -- some competitors we have out there, we want to not make the customer have to duplicate their data just for another use case or just to onboard another service. Everything that we've ever done inside of Atlas is, okay, we've added search. You just turn that on. It's against that same data set. You want vectors. You just turn it on. We have Atlas Data Federation. We have Online Archive. We have all of these different robust services that now exist inside of the Atlas platform.

And why we think it's now -- intelligent data platform is the add-on of our Voyage acquisition to turn on auto embeddings. All of this is encapsulated into that -- that singular view of what the customer is already operating in, they're not copying their data outside of that boundary. They're not duplicating that data to just for another use case. So as we continue investing into that platform, we're going to continue that same thesis.

Okay. Great. And can you expand around how -- where the average enterprise is in terms of deploying custom AI use cases into production right now? And just -- maybe just a view of the last 6 months, how that's changing and where people are being held back to the extent they are right now?

Yes. I think it's certainly changing from the fact that like every enterprise is piloting, right? And I think the stakes are just different between what you do in OLTP, which is a lot of internal use cases, data warehousing use cases, reporting and analytical use cases versus the revenue-generating applications that are just natively the OLTP database. The stakes are just different. The difference of someone being MAD versus the headline of a Wall Street Journal article.

And so while all those pilots are still occurring, I think it's mainly security, governance laws, regulation, privacy, GDPR, all of these things combined are making the enterprises, especially the regulated industry enterprises just move slow. And it reminds me a lot of early on cloud adoption when the big 3 hyperscalers first started coming out where I was working for an automation company at the time. And one of our biggest drivers of growth was go discover where all of these people are using cloud where they're not supposed to.

And there was an idea that we have to be ancillary and keep it all in before we figure out how to like truly control it. And every article or essay from a CEO that comes out over a weekend makes people scared and be more conservative about like how do I want to control access to these things? And if I'm going to use these things, are we going to let them use it against our mission-critical revenue-generating data. So I think it's just a conservative approach and security and governance as that stuff gets figured out, then more enterprises will start using it in production.

That's great. And then there's been some early examples of Mongo being used as a memory layer for agents. Can you contextualize this opportunity a little bit more for us? And what's it mean for Mongo to be the at the memory layer per se?

Sure. So we're excited about this because it's just -- it's a natural extension for MongoDB. So agents speak -- from an input and an output perspective, they speak JSON. And what better place to store JSON inputs and outputs? Well, MongoDB, we're the only native JSON database ever built from the ground up.

So this happened organically with some customers where they were like, "Hey, this is the best place to stick this. Let's go -- we're going to use this. We already have MongoDB. We're going to just use this as our memory storage layer," and we see customers have. We've invested in the partnership with like LangChain being in part of that framework. That all has to do with being part of that agentic ecosystem of where our customers are developing their applications.

Great. Maybe address the top of the funnel issues that you guys had talked about previously and just in terms of Postgres, how you become the default right out of the gate versus, gee, this isn't a technology that scales Mongo is. Yes, how are you changing perception? How are you getting it at the top of the funnel?

Yes. So the first thing is I talked a little bit ago about MCP. That is part of that awareness activity. What we announced 4 weeks ago is now that our MCP server is part of all of the different connector libraries that exist inside of the Frontier Labs and Vercel v0 announcement, which was last week. It's all about meeting that developer where they are, but also allowing their interface of what the developer is working on, to seamlessly have access and provision resources both on MongoDB and inside of Atlas. That's certainly going to help from an awareness aspect of it.

The second thing we're doing is, we have to influence the training rounds that happen when these new models get trained. And that's by making our documentation more AI-friendly, our website, having new example applications in open GitHubs -- so people -- so all these models get trained on actual code and development against MongoDB.

All of that is to help influence those models. But it's lagging, right? There's not a new model that comes out every day. And so when we get -- see a new model, we play with it, we test it, make some tweaks. And then it's multiple days, sometimes weeks until the next training round and that model gets published. So it's a slow game, but it's obviously an area that we're paying attention to and investing in.

And related to that, we still are focused on developer awareness. That's a big piece. We talked about Reclaim the Bay, which we've now deployed, call it worldwide. So both, hey, we want agents to love us. We also want to make sure that developers continue to love us. And so it's a two-pronged approach.

And in true Mike-like fashion since you joined, operating margins were up 5 points, free cash flow margin up 15 points. So not surprising to someone who's known you as long as I have. But what changes have you affected internally to get there? Obviously, scale helps, but -- and how do we think about this business moving forward in terms of your typical enterprise company, if you will, Mike, in that journey to higher levels of margins without giving too much away from your upcoming Analyst Day?

Yes. So thanks for the question. So one of the things that when I joined Mongo that Dave and I talked about was, I think there was a perception, which is if you're going to grow revenue, you can't grow profitability. And I just fundamentally disagreed with that. And I give kudos to all 5,700 employees at MongoDB because it takes a village to do this. So scale is the #1 driver. What we did in '25 was we really took a step back and said, let's make sure we know exactly where we're spending our dollars and it's easy to spend money. It's a lot harder to say, am I getting a return on it.

We did a small restructuring in sales because we had added some capacity that wasn't driving productivity. And then that allowed us then to take a step back going into fiscal '26 to say, okay, I'm sorry, that was '26 into '27. Now we can start to spend money to drive innovation. And you see in the guidance, we are -- I mean, R&D dollars are up about 30%. We're investing a lot in that. Sales and marketing more like the mid-teens and then lower single digits for G&A.

That has allowed us to expand margin. And going forward, what we've talked about is, you'll continue to see us invest in R&D, probably at a lower percentage growth rate, but more -- but dollars. We do need to invest a little bit more in sales and marketing, make sure we have quota-carrying sales reps. Ryan is looking hard at, "Hey, I want to make sure I have the right specialist team." We want to make sure that we're focused on the AI natives. Also all the new Voyage folks, we want to make sure we get them converted. So we're going to likely pump up the dollars there.

Here's the great part, too, Rob, we've not really benefited from AI internally, and we've only started to roll it out. Our goal is, "Hey, we want to reallocate those dollars to drive growth." And then around working capital, hey, it's just discipline and focus in terms of how you manage your working capital, and the whole team has done a great job. It's -- we're super proud of the margins. Keep in mind, though, we are investing to drive growth. Growth will always be the #1 priority, but the business model allows us to invest a lot and still drive margins up.

At ultimate state, should a modern software company have better margins than we used to see? Or do you think that, that needs to get reinvested from a product velocity standpoint that maybe it's a little bit more at parity. Because you're going to save on sales and marketing. Clearly, there's G&A efficiencies that you're going to find. But how do you think about shaping this thing for the future?

So it's a great question. I think one thing different now than before is AI and the ability to automate. I think that, that's a big piece. Whereas before, yes, you could automate and get efficiencies, but it was still very people-centric. There are so many processes that I believe over the next couple of years are going to be much better run through AI. As an example, everyone is doing customer support. Hey, things like BDRs and stuff like that. You can do that automated. I think AI will change that game.

And the big question for lots of customers or companies is, do I want to reinvest that dollars -- those dollars or do I want to take it to the bottom line? What we've said is we want to reinvest those dollars to drive growth. And I think that creates the flywheel even more. That, I think, is the difference between now and before, which really says you should be able to drive better margins and still grow.

Great. Are there any questions? We got about 3 minutes left. If not, I've got 1 or 2 left. Maybe you can speak to customer acquisition and the strength that you've seen over the last year, some of the changes that have been put in place and how you're affecting that.

Sure. So Ben talked a lot about us wanting to make sure that agents love Mongo as much as the developers. A couple of years ago, we took a step back and said, "Hey, we probably took our eye off the ball as it relates to developers and AI natives." We reinvested a lot of dollars around marketing and developer awareness, specifically in the Bay Area. We call it Reclaim the Bay. And we also wanted to make sure that we were also driving the marketing awareness and doing all of the events there.

That drove really good top of funnel. We talk about our PLG, which is our product-led growth. That team does a great job driving that growth, converting from community, which, by the way, is a huge community. They use Mongo that touches us every day, moving them through the stages, and we've seen that in the net customer adds. The last 2 quarter inflection has really been driven by Voyage.

And net new customers in Voyage, we've acquired that business a little over a year ago. That's now starting to see the velocity in terms of customer adds. It has not shown up as a biggest -- as a percentage in revenue and it will, but Voyage has really been that driver. Now we need to convert them to Atlas customers and increase that revenue. That's been the biggest inflection.

Great. Questions? I'll ask one high-level one to wrap this up. With CJ at the helm, just changes in terms of the business, how it's functioning, direction. I think Dev did a fantastic job getting you guys here. But what's been the relative change, I guess, with CJ taking the helm?

Yes. And to your point, hey, Dev, what an awesome job he did, right? Get the company where it is. So kudos there. All companies go through changes. I think CJ has brought, I would say, 3 things. One is a myopic focus on customers, where Dave had it before, CJ spends the majority of his time talking to customers. It is truly a customer-led company. That's number one.

Number two is focus around the enterprise. He's very focused on driving that enterprise growth. That's probably the area he's talked publicly of, "Hey, I thought you folks would be further along in that, the C-suite thing." That's number two. And number three, and this is kind of both of us is -- the great part about Dev is he knew every part of the business. He built it. We're putting much more discipline into the rigor of running the business, what does that look like, monthly operating reviews, quarterly operating reviews, are we getting a return on investment? And that's been a change for the company.

So have your marching orders then changed as a result?

It's really that last piece, which is most companies do a great job, Rob, as you know, looking out the rearview mirror. What happened? Our focus is what are we seeing as leading indicators to allow us to change. Also being better from my perspective about giving the leadership team, here's how the quarters look, here's how the years look, what changes do we need to make intra-quarter that we didn't have before.

Great. Well, with that, I think we'll wrap. Thank you.

Thank you.

All right. CJ, let's start with you. You've now had nearly a year in the role, hundreds of customer conversations. What's become clear to you about the role MongoDB can play? And how does that change your ambition for the company?

Absolutely. First, it's great to be here. Thanks for inviting us. I would say, yes, on an average, the customer meetings I do tend to be around 10 to 12 a week and really trying to understand how do customers see us today and where do they see us in the future, right? And this is across AI natives, digital natives or Fortune 500 or Global 2000. The biggest learning I had is that we are definitely seen as a very modern database that can scale significantly for massive workloads, right? So to give an example, one of the Fortune 100 firms in North America told me that after they moved multiple of their workloads on MongoDB, they have now created us as a standard that any new application unless proven otherwise should be on MongoDB, okay, which is a massive thing in a highly competitive database world, as you know.

So one is that it is the most modern database. It is the right data platform when you think about creating AI workloads that are real time. It seems like it is a great technology or a great foundation. So that's my learning number one. Learning number two is the modernization opportunity that Dave and the team have talked about in the past, that is real as people are trying to get AI ready. And I know we are early and we said that this year, Mike and I shared with you guys that we are focusing on creating the right product and product-focused approach versus a services-focused approach.

But that opportunity, specifically in Fortune 100 and Global 2000 is very real for MongoDB as customers prepare themselves for AI. And then the third thing that I would share is my learning has been as MongoDB grew phenomenally workload by workload over the last many years now, including Atlas, the awareness in the C-suite has been low. So that's the, I would say, the thing that we can improve on is what I realized in having this conversation.

And the AI decisions, what is the AI architecture for the workloads, where do they fit -- see MongoDB in? Those are usually top-down decision made by a Chief Data Officer or Chief AI Officer or Enterprise Architect who -- so my realization was when I would tell them, "Hey, we have now vector integrated fully, we have this great best-in-class embedding model." So you should build your agents on top of MongoDB. They are like, 'Gee, we didn't know that. And with one very large financial services company 6 weeks ago, I had the conversation, and now they're doing a POC on top of Vector, Voyage and Mongo together. So I found that the awareness was low in the C-suite on MongoDB.

Yes. I think since day one, you've talked about maybe driving more top-down engagement. I guess where are we in that journey over the last kind of 12 months? Like how much higher is engagement around winning some of those newer workloads?

Yes. So sales cycles when you go top-down tend to be always long. But it is early, but it's working. So we are having a lot more strategic conversations with a large telecommunications firm or a large retail firm in Europe, and they are saying, okay, we will standardize one of the Fortune 100 companies in Texas once we engaged, they said CJ, the reason developers love MongoDB, but you have not served us in the past. So you are not one of the standards on our marketplace, let's fix that so that people can start building on MongoDB. So early, but the signs are very encouraging, and we are getting opportunity for newer workloads, including AI through those top-down conversations.

Okay. Great. Mike, let's bring you into the conversation. Atlas has been sustaining around 29% growth for the last few quarters. It seems to be largely led by large customers, while the newer AI cohorts remain a bit earlier. What is different about the workloads you're landing today? And how does that shape your view of the durability of the current trajectory and its potential to even strengthen over time?

Sure. So again, thanks for having us, Matt. So let's talk about the workload growth as it relates to the durability. So in answer to your first question, we've not seen very much -- there hasn't been a big difference between the workloads that we signed historically versus today. There's always some nuances by geo or by sector. What's really driving the growth is 3 things. One is, as we've continued to increase our go-to-market focus on the larger enterprises, as those workloads grow, we have 2 jobs. One is to get more workloads. And the more Mongo we have in there, it gives us the ability to expand our workloads. That's number one. Number two is the cross-sell, especially related to vector, search and now embeddings.

While almost half of our large customers have multiple products, the revenue contribution from that is still quite a bit lower. So that's the job to drive it up. And what underpins all of that, Matt, is, Hey, Jim Scharf and team have done a great job on the reliability and the performance of Atlas, and that has enabled us to limit the churn and contraction in that base. So we expect all of that to continue, which is why it gives us confidence in the durable growth. Again, not much difference in the workload trajectory, but that's really what's driving the growth in those large enterprises.

Okay. And then CJ and Mike, for you guys. AI demand is clearly building across the Frontier Labs, AI natives and a little bit on the large enterprise side as well, but each of them are on a different adoption curve. So can you walk us through the piece of new workload acquisition across those separate cohorts? And how you see each of those beginning to influence Atlas consumption?

Yes. I'll start and then Mike can contextualize in terms of how we think about the durability of it and what does it mean, is given some of the examples that I have publicly used with the permission, so for example, ElevenLabs, ElevenLabs is doing phenomenal. They started originally with a first-party service from a hyperscaler that could not scale as the number of agents were scaling, AI is their business on speech-to-text and text-to-speech and then they -- one of the founding engineers realize that this is not scaling the agentic workload for ElevenLabs, which is now doing north of $500 million in ARR, a very successful company and switched to MongoDB. That was a call made by the founding engineer in Europe.

And what they shared were 2 things. Number one is having search, vector search integrated into the operational layer made it simpler for them to scale before they were taking this data out of this first-party database, putting it somewhere else for search, data pipeline, fragmentation, all kinds of performance issue and outages issue. So one is now they have peace of mind. If you talk to Mati and the team, they say, always it works, okay? So that's one. Second thing on your question, a year ago, they were not even a customer, okay? Including some of the Frontier Labs example that I have shared a year ago exactly September, they were not a customer.

So we are early. And that's why Mike and I continue to say that we are early. We are optimistic and we see that some of these folks don't start with us. And some of the folks do start with us, but that's a minority. Most when they hit scale issues with Postgres or relational flavor whichever they are using, then they switch over to MongoDB. So that's why we continue to say, including the remarks last week that we are early.

Yes. And just to add to that. Thank you, CJ. We've talked about the growth vectors here. AI natives, we love that, lots of customers still early in the process. The labs, the same thing, very large customers. Our goal is to continue to increase that. For us, the big inflection point is when enterprises start to deploy AI and scale. So this is growing still relatively small, but we've seen great traction, and we do think it will continue to be a driver of growth. It's really that enterprise piece, which is the inflection.

Yes. And I think one last thing I do want to touch on is what really encouraged me over the last couple of quarters since I've been here is the new customer acquisition. So when we look at Voyage, predominantly that acquisition is coming via coding agents, right? So whether you go to Claude Code or Codex and you say, what's the best-in-class embedding model, even though OpenAI has their embedding model, the answer typically almost always says Voyage AI, which is helping us on the top of the funnel to be able to upsell and cross-sell Atlas. So that's number one.

And number two, getting 2,900 net new customers in Q2, these will become future customers of Atlas in a meaningful way, some of them and then the voice piece is important because the Voyage is almost always an AI workload. That's why they are using us, obviously, the embedding model for. And that we'll see how that plays out because Voyage acquisition only happened 18 months ago. But the number of logos and the quality of logos that I see that are signing up with Voyage is high.

Okay. Great. Then let's bring you in, CJ just referenced how Voyage is actually generating a lot of activity vis-a-vis the coding agents. But I think when you look at the broader ecosystem of new applications, a lot of that seems to be defaulting to Postgres, right? So how much progress has Mongo made in getting considered from day 1? And are you seeing that translate into more greenfield wins?

Yes. Thanks for the question. So I think a couple of things. So a few weeks ago, we had our Build Fest down here in San Francisco. We just announced another integration yesterday with Vercel, but what we announced a few weeks ago was we've always had an MCP server and the MCP is really like a gateway for the agent to talk to something else, right? And we made that significantly better to where the friction between like the sign-up provisioning and then actual usage is now doesn't exist. So when we announced that a few weeks ago as well as what we did yesterday, now the ability for the model or a coding agent to not only select us because it's the right technology decision, but we also will get benefit of the fact that there's no friction into that process.

So that's really going to be a big, I think, driver to -- from a discoverability perspective. Now we are there in the same spot as some other players. But at the end of the day, the models are making 2 decisions when they make a tech stack decision, the data model and then technology. They're making the right decision on the data model. They ingest JSON. They're making the wrong decision on the technology, and that's the content game that we've been talking about over the last few months, and there's a lot of investment going in there, reclaim the day you've heard us talk about. And we're going to continue investing in that awareness activity.

Yes. And I would say just -- we shared this on the call back last week is, currently between August 13 that Ben outlined in today, September 10. The data seems encouraging. Once we have this managed MCP across Codex, Claude and Grok Build and our intent is if we get more data points to be able to share at Investor Day, but this was critical piece of integration that we had to do with coding agents for them to know MongoDB as much as we may see.

The last thing I'll just add just real quick. Our MCP is unique in a sense where a lot of MCPs for database just provide data access. Ours actually allows you to do the data access, but also allows the coding agents to do provisioning of clusters. And so by having the MCP to be able to do both, it opens the door up for that for personal experience. So it's pretty unique in that case.

Okay. Perfect. CJ, one of the more interesting examples this quarter was a Frontier Labs using Atlas as a memory layer for inference. So if agents become more persistent and personalized like how significant could memory become as a distinct new workload for MongoDB?

I would say, first, when I really understood the use case with one of the labs, they started with one product to use as both short-term and long-term conversational memory that got saved in MongoDB. And MongoDB from unstructured data perspective is the best place to save it. So it kind of makes sense, both across read and write. And then the same lab for another emerging product also is using us for the same use case. This is something that customer drove the demand in this case. And now we are using that with enterprises to say, here is how a particular lab for this particular use case uses us for memory. So we are, I would say, early in positioning that correctly.

And then depending on the customer, they understand it. So I was with a large insurance firm and we said this is how MongoDB is used as a conversational memory and this particular lab is using it. And they said, "Oh, we have the exact same problem." The current solution we have is not working well, let us do a POC on that. So emerging use case we'll share more at the Investor Day around this whole memory phenomena, but that is definitely a clear MongoDB advantage right now.

Okay. I want to switch gears to EA. It seems for years, the infrastructure conversation was only moving in one direction toward public cloud. More companies are now looking at self-hosted and sovereign environments as deliberate choices. It's not MongoDB specific. We're seeing this across the broader software ecosystem. So I guess, CJ, the question for you is what's driving that change? And how durable do you think that will be?

Yes. So Mike and I speak about this all the time in what is driving this demand? That's the question we also got last week because 36% growth in a long time across industries on Enterprise Advanced self-managed was phenomenal. And for the first time, as you saw we raised the guidance in double digits for EA, 11% that we have not done in 3 years, right? Last 2 years was 7%. So one thing is we do want to meet customers where they are. So that -- I would start there first. And in my early days with MongoDB, the customer feedback, whether it's related to a customer, say, in France or the U.K. or whether it's in the United States or in Canada, for that matter, was for a variety of reasons, whether there were public cloud-related constraints or whether they wanted to build the AI layer in-house on-prem because if the memory prices are going up, clouds are going to charge you more. economics may not work.

So I'm going to now run this in on-prem. And our ability to run anywhere was a clear advantage. But what changed in 2026 from my standpoint, we are 9 months in is confluence of 3 things. One is public clouds definitely have capacity issues that they are talking to even regulated industries, whether it's banks and other places. Number two is certain workloads because of the cost-related issues besides the capacity-related issues, they want to run on-prem now. And then specific to AI, getting these workloads AI ready, they would rather run it in-house. And from a database standpoint and a modern database standpoint, we are the only one who can say that you can run that.

And one last example I would share, a large financial services firm right here on the U.S. West Coast told me they will move even more workload to Atlas if we can do a proper failover between Atlas and EA. So this is not coming at expense of Atlas. This is also helping us with Atlas because certain workloads will go to Atlas, but certain workloads, they like that we have this option. So that's how I see it. The initial demand is high. Ben and the team got search and vector search done on June 30, which has now created pipeline robust for AI workloads in self-managed environment. And that's why Mike and I felt comfortable last week to say we will grow double digit for the year. Mike, do you want anything.

Mike, maybe let me just double-click on this because I think if self-managed demand is becoming more structural than, let's say, episodic, like how is that going to change the longer-term profile of MongoDB?

So we think it will have a material impact on the growth. So in the past, if you look back a couple of years ago, and again, we'll talk about EA ARR to get the duration out of there. If that was growing in low single digits, now what we're talking about is ARR, 3 straight quarters above 10%. Well, it's not 29%. We understand that the ARR growth is significant. We now look at it that we have 2 durable growth drivers, between Atlas and EA or self-managed. And we do expect that to continue all the things that CJ just talked about.

The other thing I want to make sure we get this question a lot, which is if a customer uses EA does it come at the expense of Atlas. And our answer to that is a definitive no. If you look at the EA population, a good percentage of those also have Atlas, especially in the larger customers. If you look at the Atlas consumption growth in that cohort that has EA, it's actually higher than the total company. And that's what CJ is talking about. More Mongo is good. More Mongo, either Atlas or self-managed, we do not see it cannibalizing EA, I'm sorry, Atlas, we actually see it contributing to that growth. At some point, does some of those workloads move to the cloud, possibly. We'll see what happens, but we want more Mongo to drive both EA and Atlas, and that's what we're seeing in that cohort.

Okay. CJ, let's move to modernization. You talked about this at the top of the discussion. We're beginning to hear examples of AI compressing legacy migration time lines. Are you seeing the same thing? And how much could it accelerate the pace at which customers move to MongoDB.

So Mike and I shared in the beginning of the fiscal year that we have asked our engineering teams to focus this year on making sure we get the product right. And here is what I mean by the product. Is that the original approach that was taken rightfully so was a very people-centric delivery approach. We will look at the workload and then we'll move it to MongoDB, whether it's EA or Atlas or whatever the case might be. And the team, we gave the team the investment needed for them to focus on the product that does leverage AI, whether we leverage Claude or whether we leverage Devin, doesn't really matter. We will use AI. And our intent for this product team was very simple please work with 10-plus customers that they are currently working right now with 10-plus customers, create a great product by end of this fiscal year, which they are working on today.

And we want to reduce the modernization time line from years to months and months to weeks. So if you look at the workload, hey, originally, it may have taken 18 to 24 weeks, and we compress that to 4 to 8 weeks to fully modernize database as well as the app layer. And on the app layer, there's a lot of code, sometimes the code is in PL/SQL. At Oracle, I wrote a lot of SQL procedures when I was at Oracle. That's a code that works really, really well within Oracle's physical boundary. Can we move that over to whatever the customer wants to move that over.

So right now, we are seeing encouraging signs on leveraging AI to be able to get to the destination. Of course, we want the destination if the customer wants from an architectural perspective, Atlas, then could be Atlas. If they want one of the sovereign customers who is doing that in EA, that's fine too, because that's their decision. So this year is about creating a great product so that we can reduce the time lines from years to months and months to weeks. And then we are going to ensure that is something that Mike will figure out with the team, how do we articulate that opportunity when the next fiscal year starts. Mike, what do you say...

No, I would agree with that. And a lot of this is, hey, folks, we've done migrations since the beginning of Mongo. This is all about building the product and the tooling to actually help with the modernization of the applications. To CJ's point, we want that to be more tooling, less people and also a lot faster so that not only customers see benefit, but we do as well. So we took a step back in '27 to make the investment and we certainly hope to see that as we go into '28.

All right. Ben, let's shift to you. AI against most of the attention, but the core database has also become substantially faster and more efficient. Are those gains changing the kinds of workloads customers are willing to put on MongoDB?

I wouldn't say it's changing the type of workloads. We're always getting absolute critical workloads from any type of industry, whether it's financial services, insurance or any other regulated industry that we have the real revenue-driving keys to the kingdom, credit card applications, swipes of transactions coast to coast. So -- but what it is allowing them to do is provide a couple of different things. One, it gives them the flexibility to have more available capacity so they can handle their own spikiness in some sort of workload.

But number two, as enterprises are experimenting with how they're using their own AI experiences, whether it's chatbots or customer service apps and everything else, there's more activity that is hitting the database, hence, why we were so big on the real-time data access and the real-time data needs of AI. And so it gives them the flexibility to experiment and prototype what these experiences could be against that operational data without having to spin up playgrounds or move data around or have data pipelines off and then it becomes stale again. So that performance is really not about how much they spend on us. It's more about the fact that they are driving more utilization and more use cases using the same sets of data.

Okay. So CJ, let's shift to competition because I think what we seem to be seeing in the market right now is a lot of the analytical platforms are now encroaching on sort of the operational database category. So to the extent these categories do start to converge, where do you think customers will consolidate? And where will they continue to value an independent platform?

Absolutely. So it is very clear that analytical players see huge TAM on OLTP side. I mean, that has always existed, but they see TAM on the OLTP side and then they come up with a hybrid architecture to say, here is how we can do -- if you want to build your AI agents, you want transactional data, you want analytical data, batch data, doesn't really matter we'll give you that answer.

Even in my early days at Oracle, this was there. Oracle was great for OLTP and we try to go after analytical workloads late '90s didn't succeed, so ended up buying multiple companies in that space. So this whole thing of OLTP folks trying to do OLAP has been around, as you know, for a long time. What -- the way it plays out in customer conversations, which is where it matters to us the most is that where we are the standard or one of the standards, whether it's retail, manufacturing, health care or financial services, that doesn't come up as often to say, "CJ, we are going to move this massive workload on payments at a bank in Spain, real example, to something from this analytical player because we use that analytical database for data warehouse." So it's not playing out that in that sense.

Where it potentially plays out is somebody wants to just spin up a quick instance on OLTP to be able to do that because it comes from that particular company. That's where I see playing out, but Ben and I speak to customers a lot, and nobody has come in and said, "Hey, because of this HTAP architecture or whatever you want to call it, now we are thinking of moving from MongoDB to another version of Postgres that these analytical players have." Those have existed, do you agree with that?

Yes, I agree. I think the stakes are different, right? I think OLAP has its place for sure, especially long-term archival needs and anything like that, but the stakes are different. And that's why we remember like the early days of cloud, when Atlas first -- we first launched Atlas, I've been here 9 years, we launched it 10 years ago. Some of the first customer conversations, I remember having about when we were selling it to Atlas, it was so frustrating because they were using Snowflake and they're like we don't put data in the cloud. I'm like but you are. And they're like yes, yes, but that's OLAP data. This is critical OLTP data. So I just think the stakes are different. So maybe as CJ said, like for playgrounds and things like that, sure. But what really matters to be powering these agentic applications or these experience that all of these -- all of our customers want to be able to provide, they need access to the same data, the actual application is driving. And that's high stakes critical infrastructure for that application that could be generating billions of dollars for that company.

CJ, let's talk about Voyage for a moment. I'd love to get your perspective on sort of like what the pairing of Voyage's retrievable models with Atlas, like made that acquisition strategically compelling? And how do you see that combination changing the workloads Mongo can win and the way customers can expand on the platform. And Ben, obviously, you were here for Voyage. So I would love to get your perspective as well.

Why don't you start and I'll tell you where we are.

Sure. We actually launched the public preview of Vector search before ChatGPT launched, right? So we were looking forward as far as where the industry was going from an AI perspective, and that's why we built the Vector search and before we even thought about acquiring Voyage, we supported whatever embedding models that you wanted to be -- want to bring in. What made Voyage strategic for us was a couple of different things. One, from an AI awareness, brand perspective, Voyage was obviously super hot. They had the top-ranked embedding models they still do. We're investing a lot there.

But what we are also learning from our customers, which is really the same thesis that was always made -- that we always had with Atlas and why we added Atlas search the way we did, why we added Vector search the way we did is it's a singular platform with the same interface with no data pipeline, no copying data for just -- same use cases. So our thought was, could we do the same thing for vectors and could we do the same thing for AI and so why would they want to go out of the platform to actually do the embeddings. So the main thesis for acquiring Voyage was to get it into the platform? And then instead of the data leaving the boundary. And again, we're dealing with an insurance company, they don't want their proprietary data being moved all over the place. They want to have it stored and located into a single place that they can trust. So bringing Voyage into the fold, that entire closed loop, the entire use case including re-ranking now is all built in automatic in the same platform.

Yes. And the way I see it playing out with customers right now is we talked about a lot of self-service Voyage customer growth is coming via Claude Code and Codex. Right? We are getting some through our sales team, but the new customer growth on Voyage is mainly coming from coding agents, which is excellent because, like Ben said, these are the best-in-class embedding model. We have still ranked the top and people do their testing. Now I'll say how it is playing out in the enterprise, which is our bread and butter is I'll take 2 examples. One large media company that I spoke to and one large health care company all in the Fortune 50 range is they said, "Hey we needed to vectorize the data, we needed the right embedding models."

Of course, OpenAI recommended theirs. We use them for LLMs and so on. So besides retrieval and accuracy, which embedding models have, it also simplifies our data estate that Ben was talking about because it's -- all of this is in Atlas. And we really like that, and we have done all kinds of testing. I mean, Anthropic doesn't have it. They recommended us as in Voyage. But OpenAI by default will originally recommend theirs. And then basically embedding models, vector search or Symantec search and operational data, all working really well together with not many moving parts makes performance great. And that's why we used the example last week in the print about Financial Times. It completely changed and then our Atlas grew, our vector grew there.

And of course, they are now using embedding model. So that's how it's playing out in the enterprises. Right now, we have one of the largest health care companies running an AI workload using Voyage and one of the largest media companies is running their media workload using Voyage.

Okay. I'm going to try and sneak 2 quick ones in the last 3 minutes we've got here. Mike, for you, you've shown the model can deliver meaningful leverage. As you look ahead, where is it most important to keep investing? And how do you balance that against continued margin expansion?

Yes. So thank you. So margins not an unimportant topic. So in fiscal '26, and this is really an operating expense, we took a step back and said, "Hey, we need to rightsize and make sure we're spending it in the right place." We did a small restructuring in sales to be ready for that. In fiscal '27, we have continued to invest mostly in engineering, embedded in the guidance and the margin expansion is almost a 30% increase there. You're going to see a lot of the fruits of that labor in a couple of weeks when we get to Investor Day. And then sales and marketing, we've continued to invest in quota-carrying sales as well as marketing awareness, largely, call it, growing in that middle teens. As we go forward, you'll see us probably spend a little bit more in sales and marketing in really 3 areas.

One is, we need to drive quota-carrying reps. They've done a great job driving productivity, but we need to support that group. Also, as Voyage gets bigger and AI natives get bigger, we need a better team to go drive that growth. And then, "Hey, folks, we have a great partner network, but we can do better, and we want to make sure to invest in that." Super clear. And then you'll see R&D continue to grow probably at a lower percentage. That's really where AI will help as well. All of this with the margin constructs we've given you, the business model allows us to invest a ton of money because of the growth in the business as well as the gross margins and still drive margin improvement.

And that's why we made the point -- last week quickly, Matt, is that the growth of self-managed EA also helps us to invest because that's straight close to the bottom line and we absolutely love that. And that also helps us with the total revenue growth, which is accelerating for second year in a row.

Great. CJ, with the last minute we have here, as you look across everything we've discussed, what's the most important thing MongoDB still needs to prove to become the platform you believe it can be?

I think it's pretty simple. So one, what I would end with MongoDB still has massive potential in a large growing market. So that's number one. We are the most modern database. But when I look at the data platform with Vector integrated now embedding and few of the other exciting announcement that Ben and Pablo will make on September 29, I feel really good about the product because it has to be a great product, a great platform. And 3 things I'm watching out for is besides the growth in core and the AI natives that we just talked about, is managed MCP offering that we launched on August 13. We must have agents, coding agents love MongoDB and be able to work in Atlas the whole time through those coding agents. So that was the first right step in the right direction, and the team will do more. And then the third piece is how do we approach modernization using AI. This is a long-term strategic statement is also very important because many of these large customers of ours do want our help to modernize their workflows.

Excellent. That's a great place to leave it. Thank you so much for joining us all today.

Thank you, Matt.

Thanks for joining us, day 2 of Citi's TMT Conference. I'm Tyler Radke, Citi's Co-Head of U.S. Software. Excited to have MongoDB for the second session of the day. We have CEO, CJ Desai and the Head of Investor Relations, Jess Lubert. Thanks for making the appearance at the conference. CJ, it's great to see you.

I thought we'd just kick things off.

Just -- it's been almost a year since you've taken over the helm of Mongo. Just give us some of your observations about the business and what are your top priorities for the year ahead?

Absolutely. First, great to be here. Thank you for hosting us. We had a print last week on Q2. We felt that overall revenue acceleration and first time hitting 30% total revenue growth in a long time was just fantastic. And as I have been here for a year, there are 2 or 3 things that I'm seeing very clearly.

So number one is I wanted to make sure once I joined the company, where we have the biggest potential, right, where we have the biggest potential and the why behind the potential. And what I saw in last 10 months or so is that, number one, in enterprises, we are definitely becoming a standard for modern databases, whether these are financial services institution, insurance companies that have a lot of unstructured data and they want to modernize and move it to multi-cloud estate. We also saw over the last year or so that public sector demand is there. It's early, but public sector demand is there in large enterprise.

And then as I traveled in Tyler, Europe and Asia Pacific, India was very interesting to see a lot of new digital native companies building on MongoDB like Zomato and others. And then in Europe, sovereign cloud, us having that flexibility that you can run anywhere, so you can self-manage MongoDB was very prevalent in the U.K., France and a few other places. So that's in the enterprise environment.

The second learning I had was that many AI native companies are building on MongoDB, which was a new thing to me after I joined. And when I spoke to folks at Mercor or whether you look at ElevenLabs, and there are many others like that, that we shared even last week like EU, Harvey and others, these are AI native companies. And once they started hitting decent scale, we were the only answer. So that was very encouraging to see even though some of them did not start with MongoDB, they hit scale limits on Postgres or whatever first-party database from hyperscaler they were using and then switched to MongoDB. And now MongoDB is just a foundational operational data layer that they don't even think about. So that's my second bucket.

And then the third bucket that we shared last week is seeing early traction and early is an important word, early traction with the labs as in specifically Frontier labs and why are they using Mongo, -- what does that look like? Because they have many products, all these labs now have many products. So what are specific use cases they use us for varies by lab by lab. And again, fairly early. We don't have history with them for multi years. So that was very encouraging to see that one of the smartest teams at one of these labs said they have looked at every alternative, they thought they should be built something on open source and decided Mongo was the answer. And that just gave me a lot of conviction that this is a great technology for AI workload. So that's how I would classify it in 3 buckets, and that's the learning I have.

Yes. That's great. And I remember when Dave made the handoff to you a little under a year ago, obviously, you've had an impressive background at Cloudflare and ServiceNow kind of calling on the C level of the Fortune 500. And I think that's not something Mongo necessarily had in its DNA, kind of that top-down selling.

So like how have those conversations been going? How would you sort of characterize where we are in potentially some of those larger expansion opportunities? Maybe some of them were using Mongo departmentally. Where are we at in that adoption?

Yes. That is absolutely a focus where I wanted to leverage the relationships I had and the customer focus I had in my prior chapters of career and calling on to the CTOs, the CIOs, those are typically the 2 or line of business CIO at a bank or an insurance firm is it's going very well.

So one is they appreciate having that relationship to say, okay, Tyler, my biggest thing is that some of the large enterprises who are spending, whether it's 7-digit ARR or 8-digit ARR, didn't even know that they were spending that much and that they were using MongoDB for some of their mission-critical workloads. So once you establish that relationship and what I noticed is while MongoDB, like you said, was workload by workload and our sales team did a fantastic job with the product because developers love the product.

And now you come -- I don't want to say top-down, but at a C-suite level, the biggest learning I had was that AI decisions were being made top-down with the enterprise architect, Chief AI Officer, Chief Data Officer, pick a title or somebody like that. And that's when you go in and say, "Hey, do you know we have vector search integrated and text search, we have now Voyage embeddings." And almost all of those conversations, I do approximately 10 to 12 conversations a week. They are like, oh, we did not know this, right? So we are now gaining traction to, say, I'll just give an example, large biotech company, I meet the Chief Data Officer in South San Francisco. Next thing, he's like, talk to my enterprise architect, fast forward 4 weeks later, let's do a POC on our AI workload to see why we need real-time operational data via MongoDB. So that has started. So that's one.

And then the second thing we are discovering when I talk to the C-suite CTO, CTOs, CIOs, the modernization that they need to do to get AI ready in enterprises is a real thing and how can we help them? So that's where also we are a little early, but I'm like, hey, we can help you modernize from a particular relational database that's end of lifing or whatever and they're like, "Oh, I didn't know that, how fast can you do that? And my answer is that we believe that our teams can reduce it from years to months or months to weeks of modernization effort on the workload, and we have a set of tools and products for that. They're like, okay, let us get started there. So these are the things that make me really, really encouraged about the market size that MongoDB has and where we can play.

Okay. And going back to some of your points you referenced earlier around the AI natives and AI labs, I think it's sort of an interesting observation that maybe these companies didn't necessarily start on Mongo, right? But now you're actually seeing more traction, maybe a couple of years into the AI adoption cycle.

So -- like what are the use cases that you're seeing the labs, I guess, use you for? And I know some of the recent announcements like some of the wins that you announced in the most recent quarter, there was perhaps a win to kind of distance themselves from one of their larger partners that they use on the database side. So can you just talk about the use cases and kind of why you're winning and how you see the potential in those large labs?

I would say, first, the strengths of the platform is the reason we are winning, whether it's AI native or whether it's Frontier lab. And I'll tell you what specifically 3 things that always stand out to me when I speak to an engineer who makes a decision to use us for a particular workload. Document model and JSON and document model just make it super easy because that's the language of AI and us being JSON native is definitely an advantage. And that not being rigid relation -- I worked for Oracle for 8 years.

And in Oracle, the DBA has a lot of power still when you want to make a change in any database tables, queries, views, things like that. MongoDB is very agile. So the document model, combined with agility of it because your AI use cases change all the time. You're trying to move really, really fast. If you look at these labs, they are trying to ship products so fast compared to a typical software life cycle that we have seen in the past, right? They're almost like at a consumer company level. So agility of MongoDB definitely stands out.

Second is just scale. We can scale. We can scale out to massive, massive amount and not fall over because it's a scale-out architecture.

And then the third thing I would say is run anywhere. Run anywhere does help because one lab or one of the use cases, when they saw that we can run nodes across 2 hyperscalers, that was a big advantage to them because they were running out of capacity in one hyperscaler and they're like, okay, so are you saying that we can have redundancy in this other hyperscaler that is really meaningful to us. And so they choose for one of their workloads. So these are the 3 reasons, Tyler, that still why whether it's labs or AI native company chooses document model, scale and run anywhere.

And then in terms of use cases, sometimes if you're interacting with agent, you have lots and lots of conversations going on. Next time you interact with agent, it's almost like they forgot that you had that conversation with that agent. So sometimes for one particular product, one of the labs is using us as a conversational memory layer. For one of the AI native companies, we are completely what I would describe it as just the operational data layer.

And what they told me specifically, having search in a different -- having a pipeline for search, search in a different database operational data layer separate that they were using before. So they had multiple moving pieces. And for that 75 million agents, this is ElevenLabs growing like crazy, they just could not scale because they had to maintain the pipeline between the ETL the data out, put it for search, then sync it back in.

And now we are the underlying operational data layer for real-time agents. And that is -- and they do speech to text and text to speech, so a lot of unstructured data. So those are the kind of use cases where we stand out. There is like really no competition because even if you use -- I would say our competition for these kind of AI native companies where they are making an explicit or implicit decision is sometimes what a coding agent may tell you something and then you just choose that database or a first-party database from a hyperscaler and then eventually hit scale limits.

Right. So even where you are running into Postgres being used, it's not from a like large Postgres specific vendor, right? It's just kind of either open source or from...

Or the first party from a hyperscaler.

Yes, I see. Okay. And going back to your results last week, as you pointed out, total revenue growth, the highest in many years, north of 30%. I think you look at bookings, customer adds, net retention, all improved quarter-over-quarter. Yet I think investors focus on Atlas growth. It stayed consistent versus we look at some of your peers in the space are seeing more meaningful acceleration.

So how would you just encourage folks to kind of dissect those results? And I guess if you -- we were sitting here a year ago and Atlas is bending that growth curve going higher, what are the things that could make that go even faster?

Absolutely. First, what I want to touch on for our investors, and this really matters to us, this will be the second year that our revenue is accelerating -- total revenue. Revenue is accelerating. And that is meaningful to us and our operating margin, cash flow, all the numbers that we printed last week, fundamentally, the business is very good, right? The business is very good. When you look at the overall business, it's durable growth while profitability is increasing. That is always hard to achieve, but the teams are able to do that. So I'll touch on that from a total revenue comment perspective.

Second thing I would say is what would you need to believe? When people say, okay, CJ, your peer group of a data warehouse company, your peer group of an observability company, Tyler, I have done technology for a long time. Those are very different things than an operational database first, right? Understood, but we are put in that peer group, totally fair, and they will say, hey, AI is accelerating XYZ, which is totally fair and a lot of credit to them.

Most of the data warehouse use cases are employee-facing, right? MongoDB when a bank or an insurance company or an AI native company like ElevenLabs use us, they are almost always customer-facing use cases. And in customer-facing use cases, as companies, enterprises build more agents for customer-facing use cases, that's when you will see our growth curve starts to bend, right? And we still feel -- like I said earlier, I speak to banks, insurance firms, health care companies all the time, and they are still not doing customer-facing agents at scale.

When they do customer-facing agents at scale, you will see our consumption go up and so on, right, compared to right now, you do a query on data warehouse using one of the AI agents and you get better answers in natural language, great. That's great. But if the answer is not accurate, you don't have to deal with regulators and others for internal-facing application versus for external facing, the bar is very high, including your firm we talk to and many others. So that's number one. When will agents that are true customer-facing start to scale built on MongoDB. So that's one.

Second, I definitely feel that our vector integrated with now auto embeddings that we just released a few weeks ago will also be a tailwind. We are still early in that. But many of the banks I talk to, they have a different vector database. They have different operational database, then they are using embeddings from a frontier lab or somebody else. All of those being unified in Atlas is definitely going to help out when enterprises start -- that's where the TAM, big TAM is start building agents at scale. So that's second.

Third, from a growth perspective on Atlas is we just released fully managed MCP functionality for Claude Code, Codex and Grok Build, which was on August 13, not even a month ago. And we are seeing it's been only now 3.5-ish weeks. We are seeing higher traffic on Atlas once that we provided fully managed MCP capabilities on August 13, we launched it at GA. So we will provide additional updates on how that is going on Investor Day on September 29, which will have 6 weeks of data points on what we are seeing.

But I personally feel that, that is something super important that we had to do. We got it done. I would argue that we are a little behind on that compared to the other start-ups and others who did that. But now you can work in Atlas via Claude Code is a pretty big deal to us. Same thing with Codex, and that will be received very well by developers who want to either build, provision a cluster on Atlas and so on.

So it's early, but that was definitely necessary. We had to get done as it must have done and Pablo and the team did a great job to get it out there, including with Vercel v0, which we are behind. You had called this out with me many quarters ago and also Cognition Devin. We are really excited about our partnership with them, technical partnership with them and what they are trying to do to also modernize databases and so on.

Right, right. No, that's super interesting. So those 2 big product releases you called out, the auto embeddings, that sort of -- think of that as maybe more a tailwind for the enterprise customers and then fully managed MCP server, I mean, that could even be a tailwind for the self-serve.

That’s exactly correct. That's exactly right. And what is encouraging, you ask me, hey, what's your observation in the first year? Like to add a new customer, paying customer is really, really hard. And when we added these many customers in the last few quarters, over time, they will go -- they are almost all on Atlas, probably 99% of new customer adds all of them. Atlas, meaning in cloud. And Voyage has clearly been received really, really well because you know why? When we looked at why Voyage customers are being added at such a rapid pace, most of those customer adds came via referral from Claude Code and Codex. Which was not happening for Atlas, right? And that now acts as top of the funnel for us to potentially upsell Atlas.

So that new customer growth, when that also kicks in, when they start building workloads in a meaningful way, will also help Atlas.

I see. I see. Okay. And I guess as you just think about the -- we talked a lot about kind of your view on when we see the enterprise ramping and all that. I guess, like do you have it's difficult to put time frames on it and it varies by industry.

Certainly, I would say we're seeing customer-facing use cases ramp at the AI labs. I mean you talked about Harvey. We had Legora on stage yesterday. I mean these companies are really inflecting this agentic labor opportunity. Customer service is another big area. We just talked to Salesforce and you have companies like Sierra as well.

So it sounds like you're partially participating in the AI lab customer-facing use case. But where do you think the non-AI labs are in terms of that? And again, we understand like a big bank like Citi is regulated, but maybe something retail, e-commerce could be a little faster than bank. Like what's the general time?

Yes. I think that's a fair question. I one of the places where we are seeing higher enterprise rollout on MongoDB for customer-facing workload or product basically at the end of the day. So if you look at ElevenLabs, whether you argue that they are a lab or whether they are a product company, I mean, millions of agents running on MongoDB, they were Tyler, exactly a year ago, they were not a customer, okay? And now they are a customer that is growing very rapidly, right? So that's number one.

Number two, where I see higher traction among the industries, so bank would be behind from a customer-facing perspective is tech companies. So tech companies, cybersecurity companies that are built on MongoDB, whether they are cybersecurity companies out of Israel or cybersecurity companies, some out of United States of America, we are seeing them building agents on top of MongoDB and rolling that out for their customers, but we are the operational data layer.

So tech is a vertical where I have seen higher traction. It's still -- this year is when I saw the growth with some of the cybersecurity companies that are built on MongoDB because we do unstructured data very well. So like logs and things like that. So that's -- tech is a vertical. Retail, airlines, others, I have not seen massive customer-facing agents still being rolled out. Telecommunications company, some internal facing, but not external facing yet.

So when I look at that enterprise bucket, what I personally feel is that after tech, most likely, it's going to be retail and financial services that would follow, including insurance before like consumer product goods company or manufacturing go there.

Right, right. Okay. That's super helpful. I guess going back to the strength in the new customer adds, which, again, really impressive, I think, 2,900 new customers, the highest you've ever added. Many companies out there are talking about top of funnel, right? Maybe a little bit more on the application side.

But what are some of the things you're observing in what's driving such outsized top-of-funnel strength? And I think you alluded to this dynamic earlier where if you go on to Claude or ChatGPT, it might initially default you to recommend a Postgres. But I presume you've been working hard at getting Mongo more in this conversation. So just talk about the top of funnel, the things you're doing and how you see that durability of the strong customer adds.

Absolutely. So first bucket, I would say us getting new Voyage customers for embeddings, which are typically almost always AI use cases for the companies that use Voyage, right, obviously. And when we see that, that is -- I almost guarantee you mostly driven by Claude Code or Codex recommending please use Voyage, right?

They say it on Anthropic's website.

On Anthropic's website, they say it. But Codex, I mean OpenAI has their own embedding model. But if you say what's the best embedding model to use, you get pointed to Voyage. So I would say the cause and effect on Voyage customer adds is primarily driven by coding agents.

And then it is the opportunity for Ryan and the team, our sales team to now upsell them on Atlas. So that becomes a very meaningful top of the funnel for Voyage. Now when we are getting Atlas customers on top of the funnel, -- those customers are still very human-driven because we didn't have this managed MCP functionality that we just released 4 weeks ago. So human will say, I want to spin up Atlas and then they will use Atlas in the customer adds that we continue to get and then they scale over time.

I mean one of the labs told me that they actually came via self-service channel and then we noticed it and then we put team on it to make sure that they can scale nicely and so on. So with this managed MCP functionality, Tyler, I'm optimistic that we will continue to see new customer adds. Now you cannot game prompting, you just cannot with these agents. If you use certain words, of course, it will pick MongoDB in the prompt. If you don't pick certain words, it will default to a Postgres or things like that. So yes, the teams are working hard because we also want right use cases to be built on MongoDB.

But if it is some employee-facing apps with 1,000 users, we really don't want that workload. We want the workload that can scale a quality workload versus a quantity workload. So that's the focus we have.

Okay. And you brought up go-to-market a bit. I did want to touch on that just given some of the changes this year, you brought in Ryan Mac Ban. What's sort of the state of go-to-market? I mean, I think there's -- as we look at the commentary over the last few quarters, you've kind of consistently called out strength in North America. I'm sure there's areas that are not performing as well as North America. Like give us a sense on his priorities and how do you kind of see the productivity and the ramping of the reps that he's brought on?

Absolutely. I'll do that. Jess, you want to touch on regional performance, and then I'll go into the details.

Yes. I mean we've continued to see strength, particularly with our largest customers in North America. That's a very encouraging sign. North America, obviously, our largest region. There are some areas for opportunity for us to improve. So one is U.S. Federal. We're investing in FedRAMP High, and that will hopefully open up incremental opportunities.

That's one of the reasons we did the Clarity Business Services deal and then APAC. APAC is an area where there's an opportunity for improvement. Japan is a very large database region. It's one we're going after, still very early stages. And we definitely see opportunities to accelerate our success in Asia. That's an area that Ryan has made some changes, and we're optimistic that they'll have positive results on a go-forward basis.

And specifically, what I will tell you is when I look at the NRR number, and you cover, Tyler, software industry, you see that that's a very important metric on the retention rate, 122% NRR. But even when I look at NRR underneath for our self-managed as well as Atlas, both are very healthy.

It's not that one is driving the other. They both are very healthy. So the blended rate is 122%. In terms of Ryan and go-to-market, if Asia is a smaller percent of the total business, but it's a very important business because you have a lot of digital natives in India, you have digital natives in China, Korea, all these places. And most of those regions love MongoDB. But we also need to serve the large enterprises there really well. So that's some of the changes that he is making in Asia.

Okay. Okay. Got it. And as we think about the migration opportunity, right, we've talked a lot about these new use cases. And clearly, there's going to be way more new database use cases than existing. But migrations have been an important part of the story over time. And you've rolled out a number of initiatives there. You talked about partnership with Cognition, the MAAP program has been announced, I think, over a year ago. So how do you kind of see the migration opportunity today? Is that something you're investing more resources in? Or does it make more sense just to prioritize new workloads?

So being in the database industry for a long time, both as a customer and as a provider, I mean, Oracle is going to be 50 years old next year. I mean, think about it. So 50 years old company and database industry around 60 years. So we have the most modern database. So the opportunity, Tyler, just to answer, absolutely exists.

The change I made once I joined with Mike and the team is that we had taken this very people-centric approach on migration. Hey, we will help you migrate from Oracle to MongoDB for the right set of use cases or some relational database. So what we decided that this year, as in this current year, we are in the fiscal year, we are going to get the product right. And we want to provide a product to our customers that you can migrate from a relational database to MongoDB faster and the people portion is a smaller portion of that engagement.

When we announced it, it was very people-driven, services-driven approach. We are changing that to product-driven approach and AI definitely helps with that, whether it's Claude or Pick One as well as partnership with folks like Devin helps. And you do that and then you go to customers and say, now we can migrate faster. So we are going to put fuel into that in the next fiscal year once the product is ready. But the product, we are currently working with very large customers or very specific what we call design partners to make sure that our product really works so that they can migrate to Atlas.

Yes. And C.J., in sort of the closing question here, if we're sitting on the stage a year from now, which you definitely welcome back. What are the 2 or 3 things that you hope to have accomplished by then?

I would say you touched on this on the priority. First, we have to be the best database, whether it's self-managed, whether it's Atlas, it has to be the best database that can run anywhere, and we just want to continue to focus on that. The best product always wins in the market.

Number two, that I have shared, human developers love MongoDB. I want coding agents to love MongoDB. And managed MCP was the first big progress we made, but we'll continue to invest in that so that, hey, I want to do this. Of course, you should build on MongoDB. So that would be the second.

And then the third thing is you will see some of the exciting product announcements on September 29, which is 20 days from now, around what else we want to do so that we are the default real-time operational data layer for AI. I mean those are the 3 things.

Yes. Great. Well, let's wrap it there. C.J., Jess, thanks so much for coming to the conference, and appreciate everyone for joining the session.

Thank you. Thank you. Thanks, everyone.

Hello, and welcome to MongoDB's Second Quarter Fiscal '27 Earnings Call. [Operator Instructions]

I would now like to hand the conference over to Jess Lubert, Vice President of Investor Relations. You may begin.

Thank you, operator. Good afternoon, and thank you for joining us today to review MongoDB's Second Quarter Fiscal 2027 Financial Results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB; and Mike Berry, CFO of MongoDB.

During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, our opportunity to win new business, our expectations regarding Atlas assumption growth, the impact of EA and other business and multiyear license revenue and the long-term opportunity of AI, our financial guidance and underlying assumptions, including expectations regarding profitability and operating margin and our investments in growth opportunities in AI.

These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended July 31, 2026, filed with the SEC on September 1, 2026. Any forward-looking statements made on this call reflect our views only as of today and we undertake no obligation to update them, except as required by law.

Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures.

With that, I'd like to turn the call over to CJ.

Thank you, Jess, and thanks, everyone, for joining us today. I am pleased to share our very strong Q2 results. Total revenue of $772 million, up 30% year-over-year and representing the highest level of quarterly growth seen since fiscal year '24. Atlas revenue grew approximately 29% year-over-year for the fifth straight quarter driven by large enterprise customers and building AI momentum. EA and Other had a standout quarter, growing 36% year-over-year due to widespread strength driven by our run anywhere capabilities. We generated a non-GAAP operating margin of 24%, driven by the strong revenue growth we delivered. We ended the quarter with 70,600 customers, adding a record 2,900 net new customers in the period. Voyage customer count nearly doubled quarter-over-quarter and Atlas Vector Search adoption continues to outpace the growth of the rest of the company, showing our strong early momentum for AI workloads.

Our core business remains strong, and our [ run-anywhere ] advantage is a key differentiator for this quarter's growth across both Atlas and EA. Enterprises across financial services, health care, tech, are running their most demanding mission-critical workloads on MongoDB, and we are winning more workloads each quarter. Increasingly, these same enterprises as well as AI natives are choosing our platform for AI workloads evidenced by the adoption of Atlas Vector Search and Voyage embeddings. My team and I spent another quarter with the C-suite of our customers discussing our data platform for their most pressing core AI and modernization needs.

Our Q2 performance is exactly why I'm confident that we are emerging as the real-time intelligent data platform for modern application in the multi-cloud and AI era. I will begin with what I'm seeing in the enterprise. For customers that already run a large part of their data estate on MongoDB, building an agent on top of that data is a natural extension because the data and agent actually needs is live operational data not a stale copy sitting in a warehouse. Search, Vector Search and Embeddings are built in, not bolted on, so rather than agents connecting to many separate systems, they connect to one platform. We are seeing this show up across industries in a range of use cases, whether it's retrieval of internal knowledge, customer-facing chatbots and agents or fraud and identity workflows. It is still early, but we are seeing more of these workloads reach production, such as the Financial Times, which leverages us to power AI-driven discovery reaching millions of readers with interactive experiences at scale.

With Vector Search and Voyage, the Financial Times now unifies their operational data and Vector Embeddings on a single platform, building a hybrid full text and Symantec search solution, eliminating the complexity of sinking separate systems and accelerating time to production. By indexing content with a high accuracy Voyage for model and serving 100,000 daily queries on the cost-efficient voice for light model, the Financial Times has significantly cut attributable cost with minimal performance impact. What used to take weeks of manual index monitoring is now finished in a day.

Moving on to the momentum we are seeing with Frontier labs who are both customers and partners for us. Multiple leading labs leverage Atlas for workloads that are mission critical to how they ship their products. One lab uses us for inference and chat workloads, after moving away from post guess due to performance legs and outages affecting user experience. They migrated their chat memory system on to Atlas in just 4 weeks and now run at 10x faster REITs than [ post guess ]. Beyond that, labs uses for research workloads to store experimental results, evaluation data and training artifacts for model development. These relationships are still early and engagement varies lab-by-lab, but we are energized by the traction we are seeing with them.

As partners with these Frontier labs, we are enabling the developers and agents building on their platforms to leverage Atlas. Just recently, we launched a fully managed MCP server making it easier for developers and agents to connect directly to MongoDB when they are using Cloud Code, Codex and [ Grabill ] as well as popular coding tools like Cursor and Devon from Cognition. This is how we stay embedded in the AI supply chain for how new applications get built.

Paul Smith, Chief Commercial Officer at Anthropic, described our technology partnership and recent integration with Claude by noting the best AI applications need a strong database which is why we have long pointed to developers building on Claude to MongoDB Voyage for embeddings. More recently, demand from those developers drove MongoDB to build a new managed MCP server which has seen fast adoption since launch, and now lets developers explore query and manage their MongoDB data without ever leaving Claude.

The final piece of the AI opportunity is AI native. Companies whose data layer determines whether the product can support rapid scale. Some choose us from day 1, others start elsewhere like pro-driven development platforms and migrate to us as they hit scaling limits and real usage arise. That pattern is showing up in the numbers. We added a record 2,900 net new customers this quarter, and many of them are AI natives. [ Fireflies ], a unicorn AI-native start-up is building what it caused the #1 AI assistant for work, helping people unlock the knowledge, but it in their conversations. [ Fireflies ] serves more than 20 million users across 1 million-plus organizations and has processed over 7 billion meeting minutes. [ Fireflies ] choose Atlas from day 1 for its flexible document model over a rigid relational schema and totally runs today runs more than 40 micro services with change streams powering real-time pipelines for analytics and growth intelligence. That lean, scalable foundation has helped their -- fuel their hyper growth seamlessly.

We are also seeing strong traction with Voyage, our embedding and rebanking models, which consistently rank at the top of independent leaderboards. In August, we brought Automated Voyage Embeddings to Atlas for [ 1 click vector ] search setup, launched [ voice Cohort 4 ], a model purpose-built for Core and shipped an upgraded reranking API, all keeping Atlas retrieval accuracy for AI ahead of the market. Voice traction is showing up on both ends of the market. Some of our largest existing Atlas customers are beginning to adopt Voyage for the AI use cases, while a large majority of new voyage customers are AI native and have no prior relationship to MongoDB.

EU is one of them. Our Unicorn AI native that automates legal case intake, medical chronologis and demand letter drafting for plaintiff law firms. [ Eves ] Atlas embedding and reranking API part by Voyage AI [indiscernible] ranked 2.5 to surface the most relevant evidence from large sets of case documents. This improves retrial quality directly into use rag layer while simplifying the infrastructure needed to build and evolve these AI experience.

Turning to Enterprise Advanced. This quarter's strength was widespread across our installed base particularly within financial services, tech and the public sector. 2 patterns in how customers are using EA stand out and both point to why this business is strategic for us. The first is AI in governed self-managed environment. This quarter, we brought Search and Vector Search to EA closing a gap between our cloud and self-managed experiences. Demand came in immediately and across industries from customers looking to take a consolidated approach to building AI in their own govern, self-managed environment. A major U.S. bank shows what that looks like in practice. EA already serves as the standardized data platform for more than 100 production applications across payments, fraud detections, document processing, customer and account services.

This quarter, that bank extended that same environment to GenAI and [ Symantec Search ] for employee advisers chatbots, product search and document intelligence. By bringing operational data, Search and Vector retrievable together, self-managed with EA they keep sensitive customer and conversational data inside their own government environment without sending up separate systems. That gives them a practical foundation to expand AI and across the bank on the same platform already running their most critical operations.

The second is hybrid deployment, more and more of my customer conversations involve running across multiple clouds and self-managed environments at the same time. For customers on both EA and Atlas, it is an and, not an or. For example, one of the largest cybersecurity companies runs a substantial estate across both Atlas and EA, and both parts grew meaningfully in the quarter. Nationwide U.K., the world's largest building society is also a good example. They now run their growing speed layer application across both EA and Atlas simultaneously giving members real-time access to account and transaction data across every digital channel and supporting more than 24 million weekly app logins.

Splitting that workload across EA and Atlas gives a nationwide stronger operational resilience and help satisfy U.K. regulatory requirements while simplifying an estate that used to be far more fragmented. Nationwide already runs with us for faster payments, up to 3 million transactions and [ GBP 1.5 billion ] in value on a peak day on a self-managed dual Cloud EA cluster. Bringing AI self-managed opens net new demand for us and hybrid deployment often means that the strong estate opens the door to net new Atlas conversations within the same customers. EA's profitability also lets us invest more heavily in R&D and go-to-market furthering Atlas growth and our AI road map.

Finally, I feel great about the leadership team driving innovation across both Atlas and EA. Ben Cefalo owns core products, and Pablo Stern-Plaza owns AI and emerging products. And on the go-to-market side, Ryan Mac Ban has hit the ground running as our new CRO, giving me real confidence in our ability to capture the opportunity ahead.

Before I close, I would like to remind everyone that we will be hosting our Investor Day in New York City on September 29, and our [ Dorlocal ] New York user event on September 30. We look forward to seeing many of you there.

With that, I will turn it over to Mike.

Thank you, CJ, and good afternoon, everyone. I will walk through the second quarter fiscal '27 results and then turn to our outlook for the third quarter and the balance of the fiscal year. As always, I will be discussing both GAAP and non-GAAP results.

As CJ noted, we had another very strong quarter and came in above all of our guidance ranges. Given this performance and the strong momentum across the business, we are rolling the beat from Q2 and raising our second half fiscal '27 guidance, largely driven by strength in Atlas.

Before getting into the details, I want to highlight a few key takeaways for the quarter. First, total revenue growth accelerated to 30%, the first time we've reached that level since fiscal '24. Second, this is the fifth consecutive quarter with Atlas growth of approximately 29%. Third, EA and Other had an exceptional quarter, growing 36% year-over-year driven by EA's growing strategic importance to many of our largest customers and early traction from our Q2 launch of Search and Vector Search on EA. And finally, as a result of these trends, we significantly outperformed our operating margin and EPS guidance, reflecting the strength of our operating model.

Moving on to the results. Total revenue in the second quarter was $772 million representing 30% year-over-year growth compared to 24% growth in the year ago quarter. Turning to our product breakdown. Atlas revenue grew approximately 29% year-over-year and exceeded our guidance by approximately 300 basis points. Consumption was strong resulting in a third straight beat consistent with our guidance framework. This is the sixth straight quarter of year-over-year dollar growth in Atlas, adding a record $127 million in the quarter. Our main growth driver this quarter continued to be strength in North America and our largest customers, particularly those in the $100,000-plus ARR cohort consistent with the broader upmarket momentum we have discussed in recent quarters. This continued strength is reflected in our total company net ARR expansion rate, which increased to 122% for the quarter, compared to 119% a year ago and 121% last quarter.

The quarter-over-quarter increase in net ARR expansion rate was driven by strength in both Atlas and EA. We also continue to see momentum in the AI native cohort and across AI signals, including adoption of Vector Search, new Voyage customers and a continued increase in clusters connecting through MCP.

Turning to EA and other revenue. We saw very strong results with revenue growing approximately 30% year-over-year, our strongest quarter in 3 years. We saw early demand for the Search and Vector Search capabilities we launched on EA Q2, adding retrievable capabilities that enhance our ability to support AI workloads. This strength was broad-based reflecting momentum across a number of deals rather than any single transaction with particular strength in financial services, public sector and technology. This continued momentum highlights the strategic importance of EA as customers continue to expand their self-managed footprints to support both traditional and AI applications. EA and other ARR, which normalizes for the impact of duration grew approximately 11% year-over-year, the third consecutive quarter of double-digit ARR growth.

Moving down the P&L. Total non-GAAP gross margin was 75.9%, up approximately 210 basis points year-over-year, and subscription gross margin was 78.3%, up approximately 70 basis points year-over-year. The increase in subscription gross margin was primarily driven by the higher EA revenue mix in Q2.

Moving to profitability. We are excited that Q2 marks our third consecutive quarter of GAAP EPS profitability, and our full year guidance incorporates our expectation to be GAAP EPS profitable for fiscal '27. Non-GAAP income from operations was $186 million for an operating margin of 24% compared to [ 15% ] in the year ago period. We continue to be very pleased with our operating margin results, which benefited from the strong revenue performance this quarter.

Second quarter non-GAAP net income was $163 million or $1.90 per share based on 85.8 million fully diluted shares outstanding. This compares to net income of $87 million or $1 per share on 87.1 million fully diluted shares outstanding in the year ago period. Our remaining performance obligations, which we define as obligations for contracts with a duration greater than 12 months ended the quarter at $1.52 billion, representing year-over-year growth of 91% with the current portion growing 73%. We had a very strong quarter for new customers, adding approximately 2,900 customers sequentially, bringing our total customer count to $70,600, up from 59,900 in the year ago period. Growth continues to be driven primarily by Atlas, which had 69,300 customers at the end of the second quarter compared to 58,500 in the year ago period.

Within Atlas, Voyage customers roughly doubled quarter-over-quarter for the second consecutive quarter, continuing the encouraging signs of the demand for our AI embedding capabilities. We continue to feel good about the momentum we are seeing with new customers and would remind you that this metric will fluctuate from quarter-to-quarter. We ended the quarter with nearly 3,000 customers with at least $100,000 in ARR representing 17% year-over-year growth. Revenue growth from this cohort continues to be strong and outpaced total company revenue growth consistent with our move-up market. We also continue to see strong Atlas performance adoption. Of our Atlas customers generating at least $100,000 in ARR, 48% are leveraging 2 or more features on our platform, which is up from 42% in the year ago quarter, driven largely by Vector and tech search adoption.

Turning to the balance sheet and cash flow. We ended the second quarter with $2.4 billion in cash, cash equivalents and short-term investments. During the quarter, we allocated $100 million towards share repurchases and $59 million to settle taxes on employee [ RSUs ]. Operating cash flow was $142 million compared to $72 million in the year-ago period and free cash flow was $138 million compared to $70 million a year ago. We remain committed to driving meaningful and durable cash flow and through the first half of fiscal '27 we have generated $344 million in operating cash flow and $335 million in free cash flow.

Now I'd like to share some of the assumptions driving our third quarter outlook and provide some additional detail into how we're thinking about the rest of fiscal '27. As I mentioned earlier, we continue to be pleased with the strong and consistent Atlas growth. Our growth to date has been driven primarily by continued strength with our largest enterprise customers and we expect that to continue in the second half of fiscal '27. Based on this continued momentum, we expect Atlas growth of approximately 26% in Q3 and we are raising our full year growth expectation to approximately 27%, an increase of 300 basis points from the midpoint of our prior guidance.

Our second half guidance raise for total revenue is primarily driven by the strength we are seeing in Atlas. The strength in Atlas is highlighted by the sixth straight quarter of increasing revenue dollar growth year-over-year, strong net ARR expansion rate increasing multiproduct penetration and early signs of adoption of AI workloads. We discussed over the last several quarters in as Atlas has gotten larger, it has become more predictable and less sensitive to revenue movements by any individual customer or cohort. This can be seen in the consistency of the results we have delivered over the last 3 quarters where we have seen approximately 200 to 300 basis points of outperformance relative to our initial guidance. We used the same guidance framework for our Q3 outlook understanding that Q3 is our toughest compare of the year for Atlas.

For EA and Other, given the strength we saw in the first half, including the demand we are seeing for the Search and Vector Search capabilities we launched on EA Q2 we are raising our full year expectations for EA and Other revenue to approximately 11% growth in fiscal '27 up from our prior guidance of mid-single-digit growth. This is the first time in 3 years, EA and Other is projected to grow at a double-digit rate for the full year. Our second half guide is consistent with what we shared last quarter. We continue to expect EA and Other revenue to be approximately flat in the second half, with growth in the mid-single digits in the third quarter. Because multiyear deals are inherently hard to predict, we will continue to be prudent in how we guide the EA business. We are excited about the growth we are seeing in EA and would encourage you to focus on the full year growth rather than any single quarter since performance will naturally move around period to period.

Turning to profitability. You can see in the first half fiscal '27 results the leverage in the business model and the ability to drive incremental profitability while still investing in growth initiatives specifically engineering and product innovation. We remain committed to driving both revenue growth and improved profitability. We now expect to expand operating margin by approximately 250 basis points in fiscal '27, 100 basis points higher than the high end of our previous range. We will achieve this expansion while continuing to invest in key growth initiatives across both products and go-to-market. Our product investment remains focused on enhancing our AI and core database capabilities, including on EA, and you will hear more about our new product innovations at our upcoming Investor Day.

On the go-to-market side, we are investing in accelerating adoption of new product innovations and continuing to focus on our highest growth opportunities by geography and customer segment. We will also continue to invest in quota-carrying headcount, marketing programs and developer awareness. On cash flow, given our strong first half performance, we now expect full year free cash flow to conversion to be at the upper end of our long-term target range of 80% to 100%.

Now let's shift how this translates to guidance for the third quarter and fiscal '27. To reiterate, this second half raise is being driven mainly by strength in Atlas. For the third quarter, we expect total revenue of $756 million to $761 million representing 20% to 21% year-over-year growth. We expect non-GAAP income from operations of $152 million to $156 million for an operating margin of approximately 20.5% at the high end of guidance. We expect non-GAAP net income per share of $1.57 to $1.61, based on 87.1 million diluted shares outstanding.

For fiscal '27, we now expect total revenue of $2.99 billion to $3.03 billion representing full year growth of 21% to 23%, which would be the second straight year of total revenue acceleration at the high end of guidance. We expect non-GAAP income from operations of $616 million to $636 million for an operating margin of approximately 21% at the high end of guidance. With the combination of 23% revenue growth and 21% operating margin, we are targeting a Rule of 44 performance at the high end of our fiscal '27 outlook. We expect non-GAAP net income per share of $6.39 to $6.58 based on 86.4 million diluted shares outstanding.

In closing, I want to thank the entire MongoDB team for another quarter of strong execution. We are pleased with the results, confident in the durability of our growth and remain focused on driving long-term shareholder value as we continue to invest responsibly in the business. Last but not least, we look forward to seeing many of you later this month at our Investor Day. You can find more information on how to register for the live event or listen to the live stream on our IR website.

With that, operator, let's open it up for questions.

[Operator Instructions] Our first question comes from the line of Raimo Lenschow with Barclays.

Perfect. Congrats on the great quarter. The question I had was on Atlas. If I look at your -- if I listen to your guidance comments, the strength driven by Atlas. What are the factors that you're considering there? And what's driving your confidence?

Thanks for the question, Raimo. It's Mike. So as we talked about, we feel very good about the Atlas business. And what we look at is this was the fifth straight quarter of approximately 29% year-over-year growth, very consistent. We've increased the full year guidance by 300 basis points from the previous guide. And that is also buttressed by a record net new $127 million net new Atlas dollars as well as the increase in the net ARR expansion rate and now Atlas is almost a $2.3 billion run rate. So as we look forward, we continue to expect really good growth from our larger enterprise customers, especially in the U.S. We started to see some benefit from AI even though it's small, but we are excited about the momentum and we do expect consumption to continue to be consistent with what we've seen during the first half of the year.

Our next question comes from the line of Alex Zukin with Wolfe Research.

I guess maybe, CJ, if I look at the business, right, on the first half, clearly, Atlas accelerating subscription revenue growth is accelerating but it felt like 2Q, maybe it was a slight decel on Atlas, the guide for the rest of the year, particularly Q4 implies a pretty meaningful deceleration in Atlas. And I understand conservatism, but if we're kind of early and rolling down the hill with some of the AI natives and labs. What are some of the dynamics? Is it possible that EA is flipping some deals to Atlas like happened in Q4 of last year. What's kind of the dynamic that maybe we're not seeing?

Okay. So Alex, thank you. Let me address there are quite a few questions in there. I would say, first, to see consistent 29% growth in Atlas now, as Mike outlined, is extremely encouraging and that execution, whether it's in the enterprise or with AI-native cohort is overall very encouraging for us. And like you called out, we have seen the acceleration in the first half compared to what we guided in the beginning of March. So that's number one.

Number two, I want to be very clear that the growth of EA self-managed MongoDB is not coming at expense of Atlas. Atlas actually continues to grow, and we are Alex meeting customers where they are. When I originally joined, and I outlined in the first earnings call is that customers asked us that we want to run for these large massive workloads that they run on MongoDB EA. CJ, we want to get this AI ready and hence, the team should build Search and Vector Search on it because these kind of workloads for a variety of reasons, whether it's data solvent, whether they don't want to move it to public cloud for other reasons, will run in our self-managed environment. So we did that, and we delivered that on June 30, and we saw that, that was received really well in our customer base.

And as Mike called out this was a widespread stregth on our self-managed MongoDB, and it was not concentrated in a single customer and across industries. So point number one is that I feel very good about Atlas consumption trends in the first half going into the second half. Number two, EA growth that we are seeing from a self-managed perspective, whether they are running in neo clouds, whether they are running on-prem in their colors, whether there are sometimes some customers run EA in a public cloud in certain regions around the world feel very good that, that is not coming at expense of Atlas. And a couple of examples that I highlighted, we are actually seeing that from an operational resilience perspective, some of the large banks or government customers have said, that this is a strength of MongoDB data platform versus one over the other from Atlas on EA perspective.

Now in terms of guidance, I'll let Mike comment on it. But we raised the guidance by 300 basis points for the year on Atlas. You know where we started in March, and now we are at 27% growth. We are always going to be prudent about it. And for Q4 specifically, it is still in conduction dynamics that is still weighs away from our perspective. We need to see how things play out in the month of September, in month of October, which becomes the baseline then the holidays are coming in Q4, which does impact our consumption. So we are trying to be prudent in how we guide and I am optimistic on what I'm seeing, both from the cohort perspective on Atlas as well as what we are seeing on the AI native side? And Mike, do you want to comment on the guidance on Atlas?

Yes. Thank you. Great answer, CJ. I just want to underline what he said is our guidance philosophy, Alex, has not changed. In terms of how we guided the rest of the year, we'll always be prudent more than a quarter out, and that's what's reflected in the guidance. I also want to address your comment about Q4 and just be super clear on the call hey, there were no large bundled deals in the quarter. There was none of that Q4 dynamic this quarter.

[Operator Instructions] Our next question comes from the line of Matt Martino with Goldman Sachs.

CJ, for you, this is the second quarter you've highlighted strong momentum in Voyage customer count. And I think you made an interesting comment in the prepared remarks, where a variety of Voyage customers are net new to MongoDB. It seems like a great funnel to win some hypergrowth workloads among AI natives? How would you characterize the success in converting some of those customers to a broader platform sale thus far?

Yes. So Matt, I would approach this in 2 buckets, okay? Bucket number one, we are really, really energized by the new customer count for MongoDB that is coming via Voyage. And you are absolutely correct, and that's why those remarks were made explicitly that many of them are actually not MongoDB customers, okay? So that is absolutely true. And the acquisition, as you know, was done in February of 2025. So we are only 18 months into the acquisition.

Between the Voyage team that is making sure that we are best-in-class embedding model when you look at external data, benchmarks and so on. But most importantly, there are some customers who come in as voyage customers and they become Atlas customers, but it is still early because we just started making sure that we can now cross-sell upsell, whatever the right term you want to use. But I see this as a massive opportunity for Atlas long term that we are getting this Voyage customers and almost always, when I look at the names of the kind of customers we are getting, whether they are in San Francisco Bay Area, whether they're large enterprise, whether they are in London or Tel Aviv or Seattle, they tend to be driven by AI workloads. And when the team did analysis on where is the referral for our Voyage is coming, as you would have imagined, most of this referral is coming via coding agents.

Number one, codex, and -- sorry, Claude and #2 Codex is driving most of the referral traffic for Voyage. So we have like multiple things happening. Coding agents [indiscernible] Voyage they're recommending us, and we are getting this new customer cohort. Second is that becomes top of the funnel, like you said, that we will cross sell, upsell our [indiscernible] team have a plan for Atlas customers, and number three, almost always, these are AI workloads. So overall, early but super encouraging.

Our next question comes from the line of Karl Keirstead with UBS.

Okay. Great. Maybe I'll direct this one to CJ. CJ, you said that MongoDB is seeing some early momentum with AI workloads. As all of us try to monitor the timing and magnitude of the pending AI pull-through to Mongo. I'm just wondering if you could elaborate on what kind of AI use cases or workloads have you found have the greatest pull-through to Mongo, I was intrigued by a comment that Mike made intra-quarter where he flag customer-facing enterprise workloads. And I know in your prepared remarks, you mentioned customer support use cases. But perhaps you could elaborate a little bit on specific use cases that have the most powerful pull-through so we can all watch for those.

Absolutely. So what I have seen, and this is across many conversations Karl is I'm just going to first look at the bucket of enterprise, okay? Enterprises as in, whether you want to say Global 2000 or Fortune 500 or Fortune 100. When I look at those, MongoDB was always almost always database platform that was used for customer-facing workloads, whether it's say, insurance claims, health care policies, whether it's credit card transactions and getting the past data for the end users. MongoDB always shines when it is a massive workload that is customer-facing.

What I'm seeing is initially say you're a wealth manager at a bank, and there are lots and lots of knowledge-based articles that you want to vectorize, use our embeddings and then use as a chatbot for folks that are doing wealth management and talking to clients real time. That is one very specific example where a particular large bank is using MongoDB. There are also other examples where because there are lots and lots of documents, employee-facing use cases where knowledge-based articles so that employees can leverage, do a search because now search is fully integrated into the operational data and documents get loaded and then embedding make the vectorization better. That will be another large enterprise example where we are seeing use cases.

But the clarity that I got was that it was almost always, hey, we want to use MongoDB where the scale matters on the agents that we are trying to create for our customer-facing activities, whatever the customer-facing activities are. We are not seeing early traction with, hey, I created a [indiscernible] kind of a thing that appeals to a couple of hundred employees we are not seeing MongoDB being used because they are like, hey, this is too big. MongoDB, of course, gives us scale and all these other functionalities. So that's number one.

And Karl, the other thing I would say is when you look at AI natives, including I'm going to put Frontier labs in there, but you look at the example that I shared, which was on EU or whether it was Fireflies which are agents in production. But when you look at these agents in production, we have used 11 labs in the past and others, these are millions of agents in production that are doing something that is customer-facing, and they would say, we want to use MongoDB for scale, performance and of course, run anywhere, and that's where you're using it. So these are the 2 vectors that I'm seeing in enterprises are agents going into production where it makes sense on Atlas, okay, let's do that.

And on EA that I touched on in my prepared remarks, make no mistake as these regulated industries are trying to get their operational data AI-ready, was also the reason year growth was driven across the industry, including tech, where customer says hey, I'm building an AI agent for my tech, whatever technology platform uses MongoDB or technology company, and we saw the growth there. So that would be my overall summary on where we are seeing, and I'm going to have Mike comment anything additional, if you need it.

No. Great answer. Thank you.

Our next question comes from the line of Sanjit Singh with Morgan Stanley.

I appreciate you taking the question. CJ so I wanted to focus on Enterprise Advance? I think under your tenure, the EA growth profile has definitely been uplifted while Atlas growth has, to your point, sustained at a very attractive rate at 25%. And some of the things that we've been hearing from customers is that if Mongo wants these customers to ultimately get to Atlas, right, advancing EA's capabilities with Search and Vector Search and potentially Voyage as well, that's really important.

Do you have a perspective one on the time line on when you get these customers on the new AI features what the ultimate, if you want to call it, an upgrade or migration to Atlas, what that timing would look like? That's the first part of the EA question.

The second part of the EA question is which cohorts are implemental using it. So you mentioned kind of the large enterprises, the financial institutions [indiscernible] not the pricing. But do you see an opportunity -- I think you guys had hinted that neo cloud using EA as well. Do you -- is there a world where the AI have started to use EA because maybe under a theme of like data sovereignty or some other reason on why they become adopters of EA as well. So that's my few question of EA.

Sounds good. So I'm going to up-level this a little bit, Sanjit. And we are a very customer-driven company. And the reason we invested in EA road map that we outlined and it is nice to see it's working out is that customers said to us, many, many customers, even in my early days that you must invest in EA and if EA gets to being AI ready with Search, Vector Search and so on, they are asking, hey, can we also make a Voyage available in a self-managed type of an environment. that was very customer-driven, and we are meeting customers where they are, okay? So that's my number one thing.

Second thing, as Mike shared, 3 quarters of double-digit EA growth gives me optimism that now we have 2 growth drivers, Atlas and EA. And as I shared, not coming at expense of each other because that's a very important thing. Sometimes customers will say, I need to do this for operational resiliency. Sometimes customers will say, I don't see this workload moving to Atlas but given what you're doing, and I'm seeing it on Search and Vector Search being unified here is a new workload that we want to try it on Atlas. So our momentum on EA is also driving potential additional use cases that a bank or a public sector organization a government organization is using Atlas be very specific, in the second quarter, we have a customer in public sector who decided to then expand their usage of our self-managed MongoDB as an EA. But in addition, we are currently working with them because they see some benefits of MongoDB core base. And they're like, "CJ, if you guys are going to manage it and provide security patches and all other things, they currently have a pilot for Atlas in the government cloud that they are running."

So from my standpoint, having now 2 growth drivers on behalf of MongoDB Corporation for both Atlas and EA is very, very encouraging. Now in terms of the time line, one thing is that when we introduced this functionality for Search and Vector Search, which was driven by the AI demand, we are charging our customers extra for that feature set that we are providing in EA. So that's number one.

Number two, in terms of time to value, Sanjit, I would say the time to value is pretty fast. It's not like months, but it's weeks on the way we have release these features for our customers. It's just that they are self-managing versus when we manage in Atlas. Like what we saw on my financial times use case, that I shared that the time to value for them to leverage Vector Search and Embedding was in weeks, not in months and years to get AI ready for searches and others that happen. So that would be my overall perspective.

And then the last thing I would say is that specifically in banking and health care, what I'm also seeing is, "Hey, CJ, we are going to use Atlas, but we are going to potentially fail over to EA because of our resiliency that you guys provide, which is definitely world-class and our big advantage." So that's the summary that I see this as a durable growth driver for MongoDB Corporation. It is driven based on customer demand and the customer demand is we want to run anywhere, sometimes we'll self-manage. Sometimes it's MongoDB managed.

Our next question comes from the line of Ryan MacWilliams with Wells Fargo.

2-part question here. First one for CJ. Are you seeing customers come back to you ahead of their scheduled renewal and renew at a higher rate compared to a year ago? Like are they truing up sooner? Or are they seeing the consumption trends improve more strongly?

And then for Mike, I know your guidance philosophy hasn't changed, but given less history with quarterly Atlas guides. Getting some questions on the implied 4Q Atlas guide. Can you just help us with some inputs into that guide and how we should think about it as investors?

Yes. So the first thing, as Mike outlined, our NRR was very strong and high, and that was true across both Atlas and EA. So in terms of retention rate, and what I'm seeing even the dynamics on, hey, a customer may want to optimize the workload with our customer success teams and all that, the trends are very healthy and improving which is a great testimonial to our 8.0 release last year that customers feel very good about price performance and how their consumption is growing.

Now there are some customers who have outlined to me the large ones that our consumption is growing faster than we thought it would, and CJ, can we have a conversation on if we continue to grow at this rate should we relook at the contract. But that's not happening a lot. This is like onesies and twosies, maximum single digits but not widespread. So that's encouraging, meaning we are not getting the pushback as the consumption increases, that we want to renegotiate the contract or the commits and so on. So that's how I would answer that.

And in terms of I will state what I stated before, which was Alex's question is I feel very good about Atlas business, the durability of that business the innovation we are driving. Of course, we are not going to guide based on Mike's framework on how we are going to guide for Q3. And we are always going to be prudent about Q4, but we raised, that's why 27% guide for the year on Atlas. Mike?

Yes. So thank you, CJ. So Ryan, to that point, we've -- the guidance methodology and philosophy has stayed consistent all year, and we want to stay with that, which is when we guide for the current quarter, I will call it or the first quarter out, we always want to stay within that, "Hey, you should look at that 200 to 300 basis point range we provided." Again, hopefully, consumption comes in better, we finish at the upper end of that. And we will always Ryan be prudent on the out quarters. It is a consumption business. I know it only seems like not that long one more quarter out, but we want to be prudent. Hopefully, then we execute well in Q3, and we're able to increase that guide when we get to Q4.

Our next question comes from the line of Kirk Materne with Evercore ISI.

CJ, the question for you is really about sort of attach rates on voyage and vector. And I'm kind of curious, when you land a new customer with these products, are they coming in and experimenting first and then scaling quickly. I'm just kind of curious, obviously, landing a new customer does great, but getting them to scale and getting the ARR to be more meaningful from a total company perspective is where you want to go. I'm just kind of curious how fast those products can go from something that's maybe piloted in the department to being thought of as strategic or company-wide as something like Atlas or EA.

Yes, of course. So I'll touch on both. Atlas is completely consumption driven, as you are fully well aware. So when I see some of the large customers like these are a few Fortune 100 customers, the ask, they did all their testing. They're like, okay, we do search in this other siloed system or we are trying to do Vector Search from some -- our [indiscernible] start-up that provides a Vector functionality. This large bank told me that based on their testing they believe that Vector should be integrated fully in the operational data layer and MongoDB doing that was seen as a huge advantage. And the time to value there was few weeks, and then we would have a dedicated search node and so on, which drives the consumption.

Even a large media company, which became one of our biggest Vector Search customer, that was driven by an agent trying to do the cementer query and figuring it out, okay, if this is an operational data layer, then it just works. And we are seeing that even in AI-native cohort, that vector being part of the database is received really, really well. And one of the examples I shared last quarter, 11 labs, which continues to scale nicely with MongoDB, they see that as a huge advantage of vector being embedded. And we are doing the same thing now in EA.

Now on embedding, we are making it easier, like I shared in my remarks, to make sure that we have auto embeddings in Atlas how it works, how does it work in the cloud. And that time, what I am right now in all the customer conversations seeing is that still the awareness is low that Voyage is actually coming from MongoDB. And this customer told me, "Oh, we love Voyage. We are using Voyage." And I said, "That is a MongoDB product." And they're like, "Oh, we did not know that." Okay, then we should now look at Atlas because you have Atlas Auto embeddings. So it varies. But Search, Vector Search, I would say the time to value is not that long because the moving pieces as in the moving systems are fewer, and that's why it works.

Thank you. Ladies and gentlemen, due to the interest of time, we'll take 2 more questions. Our next question will come from the line of Tyler Radke with Citi.

CJ, you talked about some inference workloads at AI Labs. Can you just elaborate Were those new this quarter, how do you see sort of the sizing of those workloads compared to some other kind of large workloads across traditional companies? And then Mike, just on EA clearly, a big outperformance this quarter. I think you had some of the new capabilities released from a GA perspective in July. So I guess, what gives you the confidence that there's not even more upside in the second half given that most of the raise in the second half was Atlas for CA?

Yes. So Tyler, we were very specific on comments because we want to be extremely transparent with you. So with one of the labs, they started towards the later half of last calendar year with one of the workloads that was running in France on MongoDB and used us as a memory layer. With that lab, our team, what they saw on the Atlas performance for that specific inference, then they said, wow, Atlas is performing really well across reads and rights compared to post [indiscernible] that's what they were using originally. They started then moving just recently in Q2, a few other workloads for inference on Atlas.

So we had one inference workload that started last year in November, December time frame. And then they moved another couple of workloads for inference or there's some other products that they have created. And I want to say this is August or around June, July time frame. So we are seeing -- they told me like straight up. This is the technology team that Atlas has taken all the pain away from an uptime perspective, performance perspective, we don't even think about it. And we are now, as we create new products, we want to run inference on it. So that's what I would say that we started with one in France, some of the other workloads, and then we got some additional just in Q2.

And Tyler, this is Mike. On your question on EA, great question. Thank you for that. So the last 3 quarters, we've seen ARR growth, as CJ talked about, in double digit. We did increase the full year guide from mid-single digit to 11% for the full year. Just like us, the hard part here is estimating the multiyear deals. We will always be prudent for all of our sake, to make sure that we don't lean over until we see those deals land. If the last 4 quarters are any history, hopefully, some of those do come in as multiyear yields or larger than we expected. So certainly, we want to make sure that especially for EA we're prudent on the guide. But as CJ talked about, we feel really good about the progress there. We think it can be and now it can be a durable growth driver. Hopefully, we can do better than we guided.

Our last question comes from the line of Koji Ikeda with Bank of America.

I wanted to ask about EA and really around Atlas in the total business, too. And so clearly, in the prepared remarks and your answers to all these questions, AI is definitely sounds like it's becoming a driver for the total business. And EA sounds really, really good, too. And CJ, I think you mentioned that EA is not coming at the expense of Atlas, but how should we be thinking about just Atlas and EA? And any sort of change ultimately to how Atlas could become -- or the revenue mix from Atlas to total revenue over the next 3 to 5 years?

Koji, it's Mike. So let me take that. So we will talk more obviously as we guide next year, we'll have a financial session at Investor Day. If you take a look at this year's full year guidance with Atlas at 27% and EA now at 11%. If Atlas is around 74% now, that certainly should continue to increase as a percent but we do expect EA to be a more durable growth driver. So to that extent, it should continue to increase, probably not at the rate we thought before because as you talked about the AI push is both in Atlas and in EA, and we feel very good that it is an and, not an or. So we expect Atlas to continue as a percent of total revenue but certainly, EA also contributing much more than we thought when we started the year.

And Koji, I would say from a technical perspective, this run anywhere of resilience, hybrid multi-cloud, these are different terms that customers use with us. And what we are seeing is like specifically, there was a question on Neo Clouds and others, yes, what happens is when somebody wants to run in Neo Cloud because of the capacity issues in a public cloud, that they may have, they are saying, "Hey, can we run EA in that Neo Cloud, which goes to our run anywhere and driving demand." We offer database as service in some of the other Neo Clouds, which also goes to EA bucket line. And that's why we are very clear that EA doesn't come at expense of Atlas and just seeing that broad-based strength versus just one particular customer, all 3 or 4 customers is what very encouraging for these to be durable growth drivers.

Ladies and gentlemen, at this time, I would like to turn the call back to management for closing remarks.

Thank you very much, operator. So in summary, we delivered a strong second quarter with broad-based strength across Atlas, EA and AI workloads. What's notable is the breadth of the demand, Frontier labs, global banks, public sector, fast scaling start-ups, some expanding what they already run with us, others coming to us new. We are seeing AI workloads land on MongoDB across all of them. That's why we raised our outlook for the second half and why we are confident we can keep expanding operating margin while we invest. MongoDB is emerging as the real-time intelligent data platform of choice and I have never felt better about our position with our customers. Thank you very much.

Ladies and gentlemen, that concludes today's conference call. Thank you for your participation. You may now disconnect.

Okay. Great. Thanks, everyone, for joining us. For those of you who don't know me, I'm Rudy Kessinger. I cover security and infrastructure software at D.A. Davidson. With me today, CFO of MongoDB, Mike Berry. Mike, thank you for joining us.

Thanks for having us, Rudy.

Yes, for sure. We'll make best use of the time, jump right into it. Mike, I think the biggest debate and topic that I talk about with investors is just is MongoDB an AI winner? And if so, what does that look like? When do they start maybe to see material benefit? How are they positioned, et cetera? So let's start there. Mongo, what's it look like ahead for you guys with AI?

Yes. Thanks for the question. Look, we certainly view ourselves as an AI winner, and we've started to talk about it on the last call in terms of seeing some movement in the enterprise. So let's take a step back. Why we think we are? We think our product is perfectly suited for the AI world.

Back when the founders built Mongo, good or bad, the way they built it was, we think, exactly what we need today. So you think about it, document model, built-in JSON native JSON built in. You don't have to do plug-ins or anything. Scales horizontally, so it's much more economical, flexible schema, which is really good for unstructured data. Then you add the stuff we've done in the platform around vector search, around search. So that's all in the platform. You don't have to bolt on any other products.

And then I think a couple of big things that people -- we talk about it, we need to talk about it more is the multi-cloud capability, not only on-prem, but you can run Atlas not only in the individual clouds, you can run the same workload at the same time across all 3 clouds.

And now with all the economics as well as the geopolitical situation, multi-cloud is becoming a big piece. And we've talked more about -- it's also a great place as they deploy agents for the longer-term memory to start storing that data. So we feel really good about the product. We've talked about, hey, not only the frontier labs, AI natives and then enterprise, we think it's only a matter of time, and we're starting to see some movement in enterprise. That's going to take a while because they have reputational risk. They have governance, they have security. But as they start to deploy that, we think that we're in a really good spot.

Yes. So you guys have talked about probably being downstream from the likes of Snowflake and others. And some investors, they hear that. They ask why they don't really maybe understand the nuances and the differences of where they sit, what they're doing, where you sit, what you're doing and how it's going to take real enterprise consumer-facing workloads. So -- but just expand on that. I guess, why are you downstream? And how long might it take until we start to see more of this enterprise AI consumer-facing workloads?

Sure. So if you look at where enterprises have deployed, everybody has deployed almost everybody internally. And it's the use cases that they're using around running their business. Certainly, you've seen everything with Claude and code, but also internally in terms of marketing, finance, other areas, and that's very well suited to where they sit in terms of using a data warehouse for that.

If you have marketing people accessing data, great, finance people, you can control that. And what we've seen is a lot of deployment internally. Everyone is dealing with the cost issue, separate discussion. And so that's why we think that they've seen that bump. For us, this is really -- if you take that data warehouse, now you take where we are in terms of the database, real-time data, that's where we think they're going to deploy customer-facing enterprise workloads at scale, and that's where we fit in much better.

So again, we feel very good in terms of that movement. And why do we say that? We've seen a much bigger uptick of our platform, especially vector that we know is related to AI workloads. Voyage is starting to actually have a material impact in terms of customer adds around embeddings, and we're starting to see more POCs.

And again, it's tough for us to see the activity in the clusters because they may be putting agents on top of enterprise data. For us, it's just another workload, and that's what we see. So as it relates to where they are and where we sit, yes, there's some overlap, but that's why we feel good about, again, as they move for enterprise deployment that, that is where we're going to really see growth.

Yes. And CJ has talked about enterprise deployment, he thinks probably 12 to 18 months from now. What's the likelihood that could happen sooner in terms of you guys seeing more meaningful benefits? But on the flip side, what's the risk it could take even longer?

Yes. So pinning a time line is super tough. And that's why we started to talk this quarter about we're starting to see activity in the enterprises. A lot of that is, I don't want to say out of our control in terms of completely, but this is where enterprises have to get comfortable with the deployment of an agent to a customer in terms of reputational risk, governance, security. We're in there helping them do that. Is it 12 or 18? Is it 6 to 12? We'll see. We -- I think all this -- it's such a fast-moving market.

And now you've seen even some people talk about, hey, maybe we need to lower the prices of tokens. That right there, I think, is -- it's just what happened in cloud. How many times did the cloud providers take their fees down. So that may move it a lot faster. But -- so it's -- we certainly hope it's sooner than that. We'd like to see some inflection by the end of the year, but it's something we'll watch.

Yes. Okay. So if you put the enterprise AI workload deployments aside, there's kind of 2 other areas. You have AI natives or digital natives and then you have the frontier labs. And I want to ask on each of those. Firstly, with the AI natives, going back to what you said earlier, you feel like multi-cloud has become even more important today.

Do you feel like with these digital natives and AI natives that you are catching them and winning those workloads earlier in their life cycle than Mongo has historically won workloads in other customer segments? Because one of the things I think I hear and a lot of people here when they talk to customers is they started with an offering from the hyperscaler and then they scale and they upgrade to Mongo, if you will. But do you feel like with those AI and digital natives now because of the requirements of those workloads, you are able to catch them and penetrate them earlier?

So thank you. Great question. We want to make sure that we do both. And we spent a lot of time trying to get them before they even start their company in terms of using Mongo. This is around the reclaim the [ base ], all the stuff that we've done with our self-serve motion. This is where Voyage really helps because a lot of those folks will start with Voyage to do embeddings and they don't use Mongo and then we'll move them over. So we -- and that's really a marketing awareness.

But we feel very comfortable as they start to scale and run into scalability issues, run into performance, run into cost issues that we will get them there. That's been the majority of the AI natives. And in that group, we're super excited about that. There's a couple that have gotten, I would call it, breakout size, but they're largely still pretty small. At some point, they'll just move into enterprise because they'll become real companies. And for us, those use cases are great because they are building their company and running their business on MongoDB, which is super important. And those are great use cases.

Yes. And then on the Frontier labs, I know they've asked you not to share much, so I won't push too hard on this. But what can you tell us about the frontier labs and the opportunity there with them near term?

Yes. There are certainly huge companies growing very quickly. So we feel really good about that they are using us and that their customers, they've asked us to not talk about the details. They are important use cases. They vary by the individual labs. This is something where we'll continue to work with them. Again, we're super excited about having them as customers. Hopefully, there's more growth there. And there are a bunch of different use cases for us to go after, but it's going to take time, and we'll see where that goes.

Yes. Okay. And then lastly, just on this thread, I guess, if we think about Mongo for the balance of this year, next 12 months, which of those 3, I guess, verticals do you think represents the biggest near-term opportunity? But then also long term, it sounds like it's definitely the enterprise AI workloads. But near term, frontier labs, digital natives, enterprise AI workloads, I guess, what do you expect to drive the most consumption and new business near term?

So we haven't built in much inflection in either of those 3 in the guidance that we've given. Absolutely, the enterprise is the long-term growth. I also think that as they start to deploy, hopefully, if we see better results this year, and hopefully, we do, I think it will because we'll see enterprise start to deploy more. The AI natives are great, again, but they're still relatively small. And then the frontier labs, we'll see what that looks like. I think it still all revolves around the enterprise.

Got it. I want to shift gears. One of CJ's big initiatives when he joined was to drive more of a top-down selling motion at the C-suite level and get broader buy-in for the MongoDB platform for -- as opposed to workload by workload, 100 workloads, things like that. We have seen your RPOs inflect, obviously, that tells us you're signing longer-term deals with larger commitments. But just tell me how that initiative has been going? And I guess, what inning are we in, in terms of that motion starting to complement that workload-by-workload winning?

Yes. So this -- I'm going to go back about 18 months. So CJ, I would say, has accelerated it. This was a movement that Mongo did even before that with Dave and the team in terms of moving more resources upmarket. As self-serve became a better and better customer acquisition vehicle, and the ability to move upmarket, call it, into that small to mid-market, we were able to move more resources to the enterprise. So we had already started that movement.

I think we're -- and then last year, even in the second half of the year, we started to push more incentives for the sales team, hey, long-term incentives and long-term deals are good as long as it makes sense for both companies. So that's what you've seen in RPO.

And then CJ has come in to help accelerate that. And then with Ryan as well, he understands the consumption business, but he also understands enterprise. So it's a process that started several years ago. Hey, the workload-by-workload and marketing awareness to your first question, is still super important.

And we feel like we can do both in terms of continuing to drive net new adds, build marketing awareness, get them before they get started while we also build awareness at the enterprise level. And it's the same thing, developer awareness, either in an AI native or a large multinational bank, very much the same motion.

Yes. I just want to double-click on the RPOs because I think one thing maybe investors I've heard at least is they see RPO growth inflecting and then your Atlas growth is very strong. It's been 29% plus, I think, the last 4 or 5 quarters, but not a one-for-one kind of inflection there. And so just talk about how we should expect RPOs to look on a sequential basis from here throughout the year, but also how should they relate to your Atlas growth and your total revenue is growing going forward?

So RPO is both EA and Atlas. Atlas has become a bigger percentage of that as we went through fiscal '26. Keep in mind that, that is the -- those are the RPOs for multiyear deals, everything above 12 months. So at some point, it will find its way to revenue. It won't be a one-for-one growth, clearly. And also keep in mind that if a customer does a longer-term commitment, they still need to consume. It's not a seat-based model where you're going to recognize that revenue without the consumption. So we feel good about that. Will they move together? Sure, but not exactly.

Yes. Got it. You made a comment on this past earnings call that we should not expect significant variability to your next quarter Atlas guidance. And I know why you made that comment, but some investors have asked, was there a change in guidance philosophy? Is there more to that? What's the right level of upside to expect? Just expand on that comment. What did you mean by that?

And if you look at the last couple of quarters, basically 2 to 3, maybe 3.5 points of upside in your current quarter Atlas guide. Is that the right range that we should think of if consumption is in line to a bit better going forward? Or how should we think about upside to next quarter guidance?

Yes. So thanks for the question. And we've talked about this a lot. And in retrospect, I should have just done the math on the call because I think there were some questions. Let's back up for a second. The company historically did not give specific Atlas growth guidance. And I think there's been some questions about why wouldn't it be the 500 basis points we've seen in the past? Folks, that's total company, right?

And EA, historically, we've talked about this publicly, has been the biggest driver of outperformance historically. In the last 2 quarters, we've given specific Atlas growth. As you mentioned, in Q4, we came in above that by 220 basis points, and we said consumption was relatively in line. In Q1, it was 340 basis points and consumption came in a little bit better than expected.

So what we're -- and keep in mind that 2 years ago, Atlas was a $1 billion business. It's now run rate over $2 billion. It is getting bigger. The ability for one cohort to move it, it's just a fact, is less so in the past. So we feel really good about Atlas, 29% growth the last 4 quarters. Going forward, that's the kind of range you should expect given the guidance and consumption will drive whether we're a little bit better or a little bit higher.

Yes. Got it. Okay. So in Q1, -- you did say that consumption was actually a bit better than expected. So first, a 2-part question there. Firstly, what drove that? I guess, if there's anything in particular you'd call out or if it was broad-based? And then second question on that is your Q2 Atlas growth guidance of 26% was still similar to your Q1 guide. And so given that the consumption improved, I know your Q2 through Q4 consumption assumptions have not changed, but why didn't we see Q2 guidance be at least maybe 1 point higher given the better ending point in Q1?

So keep in mind that the compares get tougher as we go through the year. Last year in Q2 is really when we saw the inflection in growth up to the 29% to 30%. So that's one thing. What happened in Q1 is we saw better performance in the enterprise, and we talked about this across mostly the U.S. and Europe and in the larger accounts.

So because you then enter Q2 at a higher level, that's what we rolled through. We did, in the raise across the board for our modeling and everybody has their own, the very large majority of that is in Atlas for the rest of the year because you start with a higher base. To your point, we haven't changed the consumption patterns and seasonality we would expect through the year. So higher base rolls through the year, and then it is a little bit tougher compare in Q2, and that's how we guided.

Yes. And then I think as we look to the second half of the year, obviously, I think Q3, Q4, that leaves more variability for potential upside. How should we think about second half, potentially the year? And what would be the upside drivers to maybe sustain a similar Atlas growth rate in the high 20s all throughout the year?

I think the big variable there, Rudy, is just how does the economy perform. And that's the part that we don't have visibility to in terms of, hey, is inflation an issue, what happens with rates, all the geopolitical stuff, that's the part because it is a consumption business where we feel really good about driving Atlas growth.

Customers are also able to spool up or down as they go through the year. So that's the part that we don't have visibility to. As we get through each quarter, obviously, we get better visibility. So that's really the big driver for us. And then if we start to see some inflection in AI, hopefully, that's a positive as well.

Got it. Okay. I want to jump to EA because EA has been, one, a big source of upside actually the last number of quarters now, but also there's a renewed focus on it, bringing the feature parity with Atlas. Why the renewed focus, right? I think you're going to have some of the vector search and search capabilities available within just a few weeks. What does that mean for EA? Do you have pent-up demand for those capabilities on-premises to build AI workloads on-premise? Just talk about that.

Yes. So this one to me is -- it makes me scratch my head a little bit. So let's back up. The EA business, I think, in the past and even at Mongo, I don't think we've done a good enough job talking about the benefits of EA and the customers that are on it. It's 20-plus percent of the business. It's some of the largest companies in the world. They signed multiyear deals to commit to MongoDB, which is great, awesome.

A lot of the work around EA has been customers asking us to add that functionality. And this is a question, not a statement. Is this part of the push for more AI on-prem? And that's certainly what we're starting to see is I want to run those AI workloads in my own data centers or the colos that I have. Hey, economics matter a lot. And I think they're saying, if I have the -- why would I move the data, data has gravity, I'll run it on-prem. So I need the same functionality you have in Atlas on-prem. So great.

Is there a pent-up demand? Yes, because everything we do in EA is coming from customer requests. And that's a big part. And I'll get on my soapbox a little bit. Some investors will say, all we care about is Atlas. As a CFO, I love Atlas. I also love EA. Biggest customers in the world, long-term commitments, very good profitability. And at some point, if they transition to the cloud, you want them on-prem. And multi-cloud is not only multi-cloud, it's also on-prem and in the cloud.

So for us, it's a super important driver of growth. Can it be better than the mid-single digits that we guided? We certainly hope so. And again, it's huge customers making commitment and a lot of those bigger ones will run both on-prem and Atlas. So there's a big piece of that as well. So we feel really good about the EA business, and we'll continue to look at what else we add.

For instance, we're going down the path to get FedRAMP High certified. That's all on Atlas. At some point, we should also bring that on-prem. So we'll look at every feature like Voyage and all that and decide do we bring it on EA or not. And a lot of that is customer requests to add functionality.

And the last thing I'd say is you talk about AI winner. That is where we believe a lot of AI workloads will be, and we'll talk more about that, not only the Atlas growth, but the EA growth being driven by customers running AI workloads on-prem.

If I listen all that back, it somewhat maybe it sounds like you actually expect some of the large enterprise AI workloads to maybe take place first on EA as opposed to Atlas. Is that a, fair, maybe readback? Or how should we think about that?

So that is a fair readback, and that's what we expect. And that's what customers are telling us, which is, I'm not going to move all my data to the public cloud. I'm going to run those on-prem and maybe they train it in the cloud and then they run inference or other -- or build agents on-prem. We'll see. We want to meet the customers where they are, and I think that's a big part of the adds to EA.

And how does that change? I'm just trying to think like if we actually see the enterprise AI workloads on-premise first as opposed to the cloud, how does that change maybe the competitive environment and the competitors that are available to do that? Is that materially different than relative to the cloud? How do you think about that?

So we think -- and 2 things. One is, for us, this isn't -- it's an and, not an or. We still fully expect to grow Atlas at healthy rates and EA. So for us, it is additional TAM we can go get. And to that point, from a competitive environment, we feel really good about, again, this is where multi-cloud comes in. This is where the platform scalability across those, and we feel really good about being able to do that both on-prem and in the cloud.

Okay. And then you also said hope to maybe exceed the mid-single-digit growth there on EA this year. I guess I know ARR growth in EA was 11% in Q1. I guess if we think longer term, I'm not asking you for guidance for the next year or anything like that. But if you get this AI capabilities and feature parity on-premise, there is pent-up demand. Is it within the realm of possibility that EA could be a durable double-digit growth business over the next few years for you guys?

My answer to that is we wouldn't make the investment if we didn't think we could drive better growth.

Good answer. I like that. Okay. The messaging around margins has been super clear since you joined, and I think very much appreciated by myself and investors. You've made a very strong commitment to, I think, 100 to 200 basis points of EBIT margin improvement each and every year.

Obviously, last year was a big year way ahead of that schedule. If you do see kind of an inflection in the enterprise AI workloads, whether it's 6 months from now or 12 months, 18 months, whatever, would you potentially pull back from that commitment a little if it really inflects and there's a massive opportunity to go win market share? Just how should we think about that if and when we see that AI inflection?

Yes, it's a great question. So I'll answer that 2 ways. One is we've been super clear. The driver of margin growth and expansion is going to be driven by revenue growth. When it comes through gross margin at 75%, there is a ton of investment to do. So we still believe that. We don't believe that the enterprise inflection is because we don't have enough resources.

Again, if you go back to the work that they have to do to be ready, if we thought, Rudy, that we could put more dollars in go-to-market or there were product things that we could add to accelerate that growth, we do that now. That's all based in our margin goals. So we fully expect we can continue to grow revenue, margins are going to go with it. If there are some trade-offs, we'll make those.

And then the third piece I'd add is keep in mind, and we've talked about this a lot, Mongo is a great company. We also have efficiencies we can drive internally. We have not pushed nearly enough offshore. We're going to do that. There's areas for us to drive more efficiency. We're going to do that. And we are a little bit late to the AI game internally. So there's a lot of areas for us to reallocate internally to go drive that growth, and we're doing that now. So we don't think that the enterprise inflection in AI is because we haven't allocated resources. If there's a clear return, we will certainly look at it.

Yes. Okay. I want to -- going back to the enterprise workloads because I agree. I hear it in our checks, these customers, they'll get a workload, they're excited about it, but there's a 0.01% risk that it does something it shouldn't and then it gets shut down by compliance, things like that.

Is there anything that you feel Mongo does have in its control to maybe help avoid that or to help limit the chance that this workload screws up 0.01% of the time. Is there anything -- like, again, I know I would agree, it mostly seems out of your control, but anything that Mongo can do to help alleviate that and therefore, pull forward maybe some of these enterprise AI workloads?

I think the big area of focus for us is, number one, making sure that the product is there so they feel comfortable. The ability to run it across multi-cloud, I think, is super important for their risk. From a security perspective, we think that we bring great reliability and security.

The other thing is to bring use cases that we see within our customer base to make them more comfortable. There is that part, which is, hey, what are you seeing across your customer base? What are people deploying? How are they doing it? That all helps. But at the end of the day, that company still needs to say I'm going to go.

Yes. Got it. Okay. I also want to ask, you touched on competition a little bit at you're in the multi-cloud stuff. Postgres, we still get asked about it all the time. What's the update there? How do you see them as a competitor? Is it still an overblown fear? Just what's your view?

So I don't want to say something is overblown. They're a competitor. I think that we see them mostly in the smaller companies where it's really not a decision and where they don't look at building out the infrastructure. They just want to deploy an app. But once they get to a certain scale, that's when we start to see them come to us. And I know they've added as a plug-in JSONB, and we look at that and say, interesting. But if you are an enterprise running a real enterprise workload around scalability, performance, again, multi-cloud, MongoDB is the place to be.

Got it. And then maybe one last one before we wrap up here. Another question I think I get a lot is, if you look at these customers who are running tons of internal AI agents, Copilot, Claude, et cetera, does that have the potential to meaningfully benefit Mongo? I mean what does that tech stack look like? Do those types of agents use Mongo? Do they use a different database? Do they use no database? I think some investors are just curious why doesn't -- I think they understand maybe it's going to take longer for the enterprise AI workloads. But with those agents, like why doesn't that meaningfully benefit you?

So it's a multifaceted answer. So again, it goes back to the use cases that the foundational models are using us for. And some of those will scale with their growth. Some of them will not.

The other thing is when you get, there's also all the vertical applications or specific that they may use us as a database. That's where the AI natives, I think, will drive growth, which is if they're actually building their product on Mongo, then that would scale more with their business in general. So there's a lot to that question. And again, this is why we go back to Atlas is a great business. It's $2 billion of run rate. It takes a lot to move that, thankfully. And again, we feel really good about driving durable growth there. It depends on the use case.

Got it. Super helpful. That's it. I think we'll go and wrap it up there. Mike. Appreciate the time.

Thank you, Rudy. Appreciate you having us.

Yes.

My name is Koji Ikeda. I'm one of the software analysts here at Bank of America. Thanks for coming to day 2 of our tech conference. I am absolutely thrilled to have MongoDB here with us today. We have CFO, Mike Berry; and Ben Cefalo, Chief Product Officer. Thanks so much for being here. Appreciate it.

Thanks for having us.

Thanks for having us.

So you guys reported results. Let's just kick it off, right off. You guys reported results last week, last Thursday. Maybe a question for you, Mike. Can you give us the key highlights. We're in -- I guess, most importantly, where are you getting the most questions? What topics around? And how are you answering those questions post results?

Sure. So thanks for the question. So as we talked about in a lot of the different calls, hey, we had a strong Q1, four straight quarter for Atlas growth, 29% plus. I think it was a fifth straight quarter where we added incrementally more Atlas revenue year-over-year, which is a key thing for us. We rolled the beat for Q1 into the full year, and we raised the back half. Importantly, you guys -- you folks all have your own models for us. Yes, we bumped up EA in Q2 because of what we see in the pipeline, but everything in the second half for us was a raise in Atlas. We are starting to see some movement in enterprises as it relates to AI. It's still early days, but a lot of the feedback we're getting from the sales team in terms of, hey, the work that's going on there, enterprises are all focused on it. There's still a lot of has to be done for them to actually roll it out in mass, but we're starting to see some movement, which is great. And we continue to feel good about driving durable growth as well as driving revenue and profitability growth. And a small thing, second straight quarter where we were GAAP profitable, which is great. Biggest questions we're getting is durability of Atlas growth. We do get a lot of questions just in terms of, hey, EA and Atlas, and we just want to be clear, folks and -- for us, this is an and, not an or. We do not expect the EA growth to have an impact on Atlas growth. There are different use cases. It's largely a different go-to-market motion. And then, of course, there's all the questions around AI.

Got it. Got it.

Fair, definitely.

Ben, since we have you, you're the product guy, I want to kind of go back to the core value proposition for MongoDB, especially in the fact that you guys are operating in such a dynamic and evolving world. And so for those investors in the room and those on the webcast that are just trying to get back to first principles on MongoDB. Can you talk a little bit about what the core value is that MongoDB delivers today. And maybe more importantly, as we go forward, why are customers increasingly standardizing on MongoDB, what is that reason?

Thank you. It's a great question. So I think first principle wise, from the very beginning of the birth of MongoDB, it was always about the document model in JSON, right, the JSON format. And that is either the founders, and I wasn't -- I've been here 9 years, but I wasn't part of the founding team, either they were really smart or really lucky that all of the AI world has really done -- has standardized on the JSON document model format. It provides great flexibility. It provides great performance. It allows you to really look at your data model in this unstructured world. And majority of data that exists today and all the new data that's being generated, the majority of it is completely unstructured. So from the day 1, built from the ground up, we natively support this. It's the only type of format we really do support, and we're continually investing in that. As you saw when we released MongoDB 8.0, fastest release of the database that we've ever had. So we're still as much as we're investing in all of these other areas, we're still very much focused on making the database better and more scalable and more performant.

The second thing from just a value side is, to answer your question more on the standardization, customers really agree with our strategy of if MongoDB is the system of record keeping. If You think about like a circle and your core data is in the middle, why do I want to have to copy that data for my search use case or a copy of that data for Vector Search use, copy that data out for an analytics use case, whatever it might be. And our strategy, especially when it comes to the Atlas platform, has always been, we have your system of record data and that's continually add more use cases, but not via bolting on plug-ins by actually building out new value-added services, but do all the wiring for you behind the scenes as well as keeping the querying and the developer experience exactly the same. So we've done that with Atlas Search. We did that with Vector Search, we acquired Voyage last year. We've done the same thing with that. And so it's really about keeping the developer user experience in mind at all times. So the developer doesn't have to query multiple different systems for a singular use case. So I would say those 2 things are really why people are standardizing on us and why they find a lot of value in Mongo.

Ben, you mentioned something about 8.0 being the fastest release ever. Can you talk about that a little bit? What drove that to be the fastest release ever, something you're doing on the R&D side, maybe customers pushing you hard for new features. I mean, why was the fastest release ever? And could we expect 9.0 to be even faster than that?

We always can go faster, and we're always going to be investing in the core technology. So that has not stopped. We have never taken our put off the gas there. I think what would cause it to be a big jump really was, one, around the same time we started development on 8.0, CTO came in, Jim looked at different engineering principles and what -- how we wanted to focus on and really got back to the first principles of how he came from a massive database background, and he just simply said, we can do better. We can make the bars better, and we just put high focus and investment in that area. And obviously, the results paid off.

When I think about Atlas specifically around the core offering, Vector, Voyage, all the other features that are around it. How important is this all-in-one platform for customers today? Is it one of their key topics or key focuses? And how does that keep being a key focus for customers in the future?

Yes. I think it shows up a couple of different ways. First of all, I mean, customers do like what was less vendors that they have to have to deal with. I think secondarily, what makes us really unique and something we don't talk about publicly a lot is that we're not locked into any one deployment model, whether you want to be in a hyperscaler whether you want to run it inside your own 4 walls, whether you want to run it in a Neocloud, whether do you want to run it in a Sovereign cloud in Germany, we have different form factors that meets the customers' demand, right? And so being able to give them that true portability is something that no other database vendor, I'd say, modern database vendor can do and certainly not something you're going to get from the hyperscalers. I think all of those things added up is one reason why our customers really love us and are choosing to standardize on us.

And if I could on that, Koji, we talked about it last week. Now about 45% of our customers over $100,000 in ARR use more than 2 products within Atlas. And for us, the way we monetize that is it's more Atlas consumption. It's revenue because you get it included in Atlas. So for us, that's a good thing, and we certainly see the customers adopting multiple features.

Mike, remind me on the [ 2 plus ] front, what are the most common add-on products that are driving that [ 2 plus ] for the Atlas?

Vector and Vector Search. Search and Vector Search, sorry.

Last week on the call, you guys called out a couple of big -- some AI customers that you won. And I remember when we were discussing on the call back, and even on the call, One of the reasons why these customers were looking at you as one just Atlas, period, but also the interoperability between hyperscalers. How good a differentiator is that for you guys? And how should we think about the capacity constraints at hyperscalers being a good driver for you guys for the next several years?

Do you want to do the multi-cloud? I'll do capacity?

Cool. So what we -- when we say we run anywhere, it goes back to what I was just saying a minute ago, customers really have their choice. And what we don't, I think, do the best job at marketing, this feature when I'm working on fixing that is we actually -- what we mean by run anywhere in multi-cloud is that it's not just you get to pick what cloud you want to run on, you can actually extend a singular cluster across multiple clouds at the exact same time. You just can't do that with DocDB, Cosmos, Dynamo because they're just native hyperscaler services. And that is -- so then back into whatever else is going on in a particular region, capacity could be something. This could be that this app is slated for this cloud provider, this app is slated for that cloud provider. It gives the customer a lot of flexibility to say, oh, cool, I'm just going to add a couple of nodes of this cluster to GCP now, even though it was running on AWS. So it provides a customer a lot of flexibility, but -- and then also depending on what's happening in that particular region, it gives them [ an out ] and to move into other cloud providers if they had to.

And capacity so far has not been an issue. There's obviously a lot going on with memory and everything else and all the hardware piece of the world. we have long-term agreements with all of them. So we feel comfortable there. And hey, it's a multifaceted relationship. Certainly, we buy from them. There's co-sell that goes on there. So it is much more than there -- and there are certainly competitors as well. So it's -- there's a lot that goes into that. We watch it every day. We have conversations with them. It has not been an issue yet, but we're certainly keeping our eye on it.

I know you guys get the Mongo versus competitor, A,B,C,D, what the architecture -- why Mongo versus all these competitors? And maybe a question for Ben. If we could dig down into the technology of Mongo, what are maybe the top 3 reasons from a technological aspect of why customers like to choose Mongo over the competition out there?

Yes. So first of all, it's what I was saying earlier with the JSON document model, we're built from the ground up, our storage engine, how we actually write the bits to disk or to memory is all native JSON from the very first line of MongoDB, right? So 17 years old, 18 years old now, again, first principle from the very beginning. Number two, the way we scale is vastly different from a performance and price perspective than how you would have to scale our relational database, and that goes for all of them. And then third is really around the portability and allows the customer to have all that different levers about how they need to deploy. They want to start out. We still have an open source database, right? We still invest in that as well. We still get customers that started out there and they scale to a point of now they need to have the different performance and they want us to manage it from inside Atlas. And so that portability gives a lot of people comfort in choosing us as a technology.

When you talk about scale, the performance and price, what's the one-liner on how you guys are able to do that, offer such good performance in price?

So we scale different. We don't just need to scale vertically, which is either more machines or bigger machines. We can scale different things independently. So you can have different storages you might need -- your workload might be perfectly fine on a subset of, I would say, like this 1 tier of compute, but you need more storage because your data size is good, bigger. We allow you to either add more storage or you can scale out horizontally where it's the same type of box we call it charting. And so we do our own data routing inside of MongoDB's distributed system. So it's just a different way of doing it, but we're able to do that at a lower cost because smaller boxes are cheaper than the bigger boxes or bare metal from the cloud providers. So you can have more -- most of the time scale of economies, you can have a lot more smaller boxes then you can have a couple of larger boxes.

Was that something that Mongo was founded on originally way back when, like this type of architecture? Or was that something that had to be built in later as you guys got bigger?

I've been here 9 years. It was the charting technology that we've built, existed when I got here, but I -- if I'm rewinding the whole clock, I don't think it existed in like the first release of Mongo, but we developed it over time.

Okay. Something that you had to do because of the success that you -- I got you. Okay. Okay. So Enterprise Advanced, something that has become much more topical for you guys. I guess first question for Mike. On the nice guide for the second quarter, can you just talk about that a little bit? What is the visibility that you have? Is it a big contract? What's going on with the second quarter guide in EA?

Sure. So let's back up for a second on EA. So as we all know, this is a standard license support model. And we said this last September. If you look at the $500-plus million, about 70% of that folks is support coming off the balance sheet. That's most of what's sitting in deferred revenue, and we're going to get it whether we come into work or not. So a lot of this is -- there's a big piece of that that's ratable, then the license piece is either a 1-year deal or a multiyear deal, where the hard part for us to forecast are the multiyear deals because we don't know -- customers really don't know because it's based on their budget. Are they going to do a 1-year deal, they're going to do a 3 or even a 5-year deal. So that's the part where we try to be candidly conservative/pragmatic on. We're not going to lean over our skis on those. So entering Q2, we know what's coming off the balance sheet. We expect to see what's going to renew. And then there's already been deals that have been booked as multiyear that we know we're going to recognize. So that's what's in the Q2 number. Keep in mind, too, that in the second half, it's a very tough compare versus Q4. Remember, EA went up quite a bit. So what we said is, hey, for the full year, we expect somewhere around 5% growth, understanding that, hey, if more multiyear deals come in at the back half of the year, great, we're not going to include them in guidance. And then again, the activity we've seen and the momentum there has not been at the expense of Atlas. These are customers saying, hey, I was going to do something else. Now I'm going to deploy. And then I know, hopefully, your next question to Ben's going to be, what are you putting in there as it relates to AI. This is a part where I scratch my head a little bit, which is, hey, today, yes, you can see the growth in AI through Atlas. The future is going to be -- you're going to see it in Atlas and EA because companies are going to deploy AI internally in their own data centers, and they're going to use EA to do that.

Mike, you got your question. I'm sorry. Ben, you got your questions from Mike.

Yes. And so as Mike said, this is an and, it's not an or. For various reasons, we're seeing customers continually invest their own deployments of EA for various reasons, could be data sovereignty, residency, manufacturing use cases where they need to be inside the facility, hospital use cases where they need to protect themselves against from a hurricane or anything like that. So there's all these reasons why EA is a great business. And again, we're still investing in the core technology. And then we've said that we're working on, from a road map perspective, bringing not full parity from Atlas into EA, but where it makes sense and also based on what our customers' demands are, we're going to be adding more functionality that exists in Atlas only today into EA for those use cases because customers want to be able to have true hybrid workloads, and hybrid multi-cloud is something that a good chunk of enterprise that I've talked to over the last 3 months is really top of mind for them. They want to have that flexibility, and they -- to have that flexibility, the same core functionality that they've built on inside of Atlas if they ever need to pull that back or they're going into another geographic region where there is no cloud provider region or something that they're going run Enterprise Advanced themselves that they want to make sure that they don't have to change their application.

And 2 big, if I could on this, 2 big pieces. Hey, it's over 20% of the business at high margins, it generates a bunch of profit and cash. I love that. That's a big piece of it. The other thing is, in the past, I think we've been a little bit of -- we've done this to ourselves. We call the business Atlas and non-Atlas. We've changed that folks. It's Atlas, and it's EA and other. It's a meaningful piece. Biggest customers in the world buy it. It's a big driver of the financials, and that's how we're going to talk about it going forward.

I'm going to ask you 1 more question, and then I want to open it up to the audience to see if you guys have any questions out there. And so I know you guys like to introduce new products around .local events. And so as much as you could without telling us everything that you -- which .local should we be really paying attention to? I pay attention to all of them, but is there one or another that we should kind of be honing in on when we see it on our calendar?

So you should pay attention to all of them. We've reduced the number of .locals to a smaller number. Look, the big ones are -- San Francisco is always a big one just because of what's going on there with AI. Our biggest event is .local New York and CJ talked about it. We'll have our next Investor Day right around that as well. Those are probably the biggest ones. Hey, London was a good one as well. I think India, I think there was a lot of good stuff. So really New York and San Francisco are probably the biggest ones. But we've also shrunk that population to make them all more meaningful.

Got it. Any questions from the audience? I got a lot.

Okay. You're going to fill in.

I got it. So one thing that has been coming up more with Mongo is how to attach Mongo to kind of the Codegen, Vibe Coding tools out there to become that either preferred, default database of choice or one of the top 3 or whatever it may be. What exactly is going on there with that? And is there something that needs to happen from a technological aspect to get that initial default database? Or is it just kind of like a partnership agreement? Help me understand that a little bit.

Yes. It's actually either, to be honest. It's not a partnership, it's not technology. We're not missing technology. We actually have more functionality. What -- there's a big thing in the industry right now around AEO for agent optimization. So there's a lot of things that we're doing just from a content generation perspective, making our -- even little things, making our website even just easier for agents to read, which is a whole new skill set on its own new profile of worker. But the reality is MongoDB came out in, what, 2010-ish, right, Postgres has been around since '96, SQL came out a lot longer before that. There's 50 years of content related to relational and SQL Server, and we're playing catch-up there. And so the prompts, the LLM, regardless of the Vibe Coding platform, they're all using something else behind the scenes as their model. They're trained on public Internet data. And so we're just in a content game right now with how much content is out there versus how much content is out there about MongoDB. So we're obviously working on that.

Number two, we are -- we have done things to make agents like us better. We have MCP servers. We're publishing all of our own [ skills ]. We've released an architectural center last year on the documentation page. And so we're hyper-focused on all of these different avenues, but to drive that awareness. So it's a work in progress. And I think it's going to be work in progress for a while, but I think that's just the macro of how fast things are moving. It's not just a Mongo problem.

For an application that was built on something else other than Mongo from an AI native startup, whatever it may be, how easy is it for them to transfer what they had over to Mongo for the future?

Sure. So there's 2 different flavors of this, right? Even with what I was just saying, the funny thing that the LLM are doing is that they're modeling the data properly, meaning that the data model is JSON or in the document model, they're just picking some Postgres flavor with JSON to say the least. That's the most common one that we've seen. But when they do that, that migration is really simple. And I'm not even talking about from the data side. We're really good at moving data. We've always had a migration practice inside of Mongo ever since the beginning. We're very good at moving data from Postgres to MongoDB, we do that all the time. What makes it easy is if their application is already thinking in the document model perspective, it's on the easier side to then tweak that application, use our driver and then start using Mongo. So it's both flavors of it is if they start out with something else, but still on the JSON model that's on the easiest side. But even if they didn't model their application using JSON and it's more relational or they're using key value, whatever the case may be, that is something that we've done all the time too. Nothing about this AI generational depth has made it harder or easier to migrate into Mongo. But we have a really good migration practice that focuses on that.

Got it. Got a question for you on Voyage. So 2 weeks ago, I spent a lot of time trying to understand what Voyage is and why maybe you guys bought it? And then I realized I had you on the stage in 2 weeks, I said, forget it, I'm just going to ask you. What is it specifically about Voyage that makes it so important for the Mongo strategy?

Yes. I'll -- I have an answer. I will admit, I am not the expert on Voyage, but I know enough to get myself into trouble. So a couple of reasons why this made a lot of sense. Number one, as we were talking about earlier about really focusing on the Bay, we've announced a lot of -- with .local San Francisco and the focus of what we've been calling Reclaim the Bay. We -- somewhere between -- before COVID into now, I think we've lost a little of the coolness factor in the Bay Area, right? And so one, Voyage had an amazing brand, not just -- the technology is amazing, too. But we also were thinking of it from a brand perspective. And we wanted a way to make sure we had credibility and a coolness factor in the Bay, that had something to do with it. But the technology is stellar. We're investing heavily in there. When we bought them, they were the #1 embedding model on Hugging Face. They're still a #1 embedding model on Hugging Phase. The Frontier Labs actually suggest customers use Voyage embedding models. So that was obviously really important to us. But then third, if you look at what the value prop of Atlas has always been and what we've been building as a platform is we don't want customers to have to stitch together all of these different things. And so that -- even though we had Vector Search for a really long time, the customer had to bring their own embedding models, and we still support that today, right? You can use Bedrock or whatever else you want to do to embed your data. But now having it inside of our ecosystem, directly inside of our platform, it's something else a customer doesn't have to worry about. They can just start using it, right? And having the re-ranking models there at the same time is also super important because then we can continually keep those embeddings up to date.

And then the last piece that customers are really gravitating towards is, they like that it's all what's inside of the MongoDB ecosystem. And as Mike said earlier about security and governance and reasons why the enterprises are typically slower to adopt any new technology, they already trust us. They don't need to go get another contractor to use some other random embedding model, that's is all part of our ecosystem. So all of those things combined were why Voyage was an important acquisition for us.

Maybe a follow-up question for you or Mike. Clearly, embedding and reranking models are important. That's why you guys bought it, good technology. I mean, when I pull up -- when I did my work, you guys always were top-top-notch with Voyage. And so if it's so important, why doesn't the embedding and reranking side get more competitive? Is this something we need to watch out for from a competition front?

So I think it's something that, hey, it always will, to the extent that -- and we've seen that. We've seen other folks join. So -- and we welcome all that because it makes us better as well. So it is something that we watch. I think what Ben talked about is it makes -- it's more imperative for us to make sure that we are continuing to evolve the product, and that's a big piece of it. We also talked on last earnings call, the number of Voyage customers doubled quarter-over-quarter. Last quarter -- not year-over-year, quarter-over-quarter. So it's still a small revenue contribution, but that's a great driver. And we also talked about in the 2,500 net new customers. We actually saw Voyage become a bigger piece of that as well. So yes, it certainly will become more competitive. It's incumbent upon us to keep adding functionality, but we're seeing great traction.

I'm going to guys -- I'm going to ask you guys the when AI tailwind question here, but maybe in a different flavor, not looking specifically at Mongo financials as the indicator or the trigger. What -- as -- for those in the room and those on the webcast, what should everybody be looking for as signals broadly that the enterprise AI tailwind that should benefit Mongo is really inflecting higher? I mean, is there something to look at something, some sort of demand signal that we all should be focusing on?

So I guess the way I look at it is, hey, all of us are -- have -- the enterprises are our customer, we're their customers as well. I think you started to see them roll it out in things like chatbots, right? You can't talk to a human today if you tried. There's -- you're going to see some of that incrementally move. I think for us, it is when you start to see actually things like your bank is telling you, here's the stocks you should buy. When your insurance company is -- now it's a virtual agent, not a physical agent. When you're starting to see AI actually impact what you do every day outside of doing, hey, what's a new recipe on ChatGPT, I think that's when you're going to start to see the inflection point. When enterprises are actually going to their end customers using AI to drive their business.

Think about Atlas and EA, 2 good products, 2 different line items. How do we think about the durability of growth or maybe even the mix of growth of these 2 products as a composition of total revenue over the next 5 years. Is that something we could even think about right now? Or do we just have to wait to see how it plays out.

Yes. So great question. So we'll talk about this again in September. Last September, what we talked about was, hey, expectation for, again, 3 to 5 years, not next quarter, atlas growth continuing to be north of 20%, and then at that point, we said total revenue growth in the upper teens. And that basically embeds, you can do the math says that, hey, EA is, call it, a low single-digit grower. I think what we've seen is, we continue to be very excited about Atlas. But to the extent that all the investment and what we've seen from a customer perspective can bump EA up, maybe that moves it up. I mean, we'd love to talk, and we guided this year for 20% total company growth. But no matter what, Atlas growing where it is, 75% of the business will continue to be the biggest piece and the biggest driver of growth. I don't think it ever goes to 100%, there's -- at some point, it's going to asymptotically stop. And where is at 85-15, is it 90-10, we'll have to see. I think a lot of that depends on how do enterprises want to deploy AI between the public cloud and on-prem.

Coming up on time here, so I'm going to ask you the growth and profitability question. And I think it's become more interesting after the second quarter earnings cycle where we've seen a bunch of infrastructure software companies prove out the growth and prove out the strategy. And so my -- last question for you. How do you think about that balance between growth because clearly, the growth is there, but profitability matters. And so how do you think about it?

Yes, it's a great question. And this is one of the things when I started almost a year ago now. I didn't realize the efficiency of the model. And what we talked about last year was we took a little bit of a step back on OpEx. We said, hey, let's make sure that we see a return on all those dollars. We've started now to increase the investment in fiscal '27, and you see it in our guide. OpEx is still growing upper teens. The focus for us is where can we put investment in that's going to drive revenue growth that's going to go to the bottom line. It's a great model, and that's how we're going to drive margin expansion by growing the revenue line. We're not in the game of cutting heads at this point. We're in the game of where can we get efficiencies, invest more in product and in [ quota-carrying ] reps, most importantly, that then we can touch and feel and say, there's a return there. There's incremental revenue and boom that's going to go down to the operating line. And it's a great model. It's very efficient, and that's the goal now. And we look very hard at incremental investment needs to drive incremental growth, but where we see that, we will absolutely make it.

Got it. We're out of time. Ben, Mike, thanks so much for doing this. Appreciate it.

Thanks for having us.

Thanks for having us.

Thank you.

Welcome, everybody. Thanks for joining us. I'm Jason Ader from William Blair. I'm pleased to introduce Mike Berry, CFO of MongoDB; and Ben Cefalo, Chief Product Officer.

Before we begin, I'm required to inform you that a complete list of research disclosures for potential conflicts of interest is available on our website at williamblair.com. We're going to go through some slides, and then we'll have some time for Q&A.

Take it away, Mike.

Great. Thank you, Jason. Good morning, everybody. Good afternoon to some of you on the call. So my name is Mike Berry, and this is Ben Cefalo. We're going to ham and egg the presentation. This goes way back. We don't typically do these anymore. So give us a break. We just normally do the fireside chat. So I'll talk a little bit about MongoDB. We'll try to go through these pretty quickly, and then we'll jump into Q&A. So, safe harbor, keep it up there long enough.

Okay. So MongoDB is an infrastructure software company. We market databases to companies across the world. We have over 65,000 customers. This year, we guided to just short of $3 billion in revenue. You'll hear us talk about 2 major product lines: Atlas, which is our cloud-based service offered in all 3 of the public clouds. And then you'll hear EA, Enterprise Advanced, that's the on-prem version. Atlas is about 75%. EA is about the rest. We play in a market that's huge. It's $100 billion, both data warehouse as well as online transaction processing, which is where we play as well, and it's growing very quickly. So it is a huge market. While we have a lot of customers, we have a huge TAM to go after across all of those customers.

Right. So the document model is the foundation of our growth, right? So there's a lot of talk lately about AI workloads, unstructured data and what -- all the new data that's being generated is largely unstructured. And so we like to think of JSON as the lingua franca of the AI generation. And the main reason why is all of this data is being modeled and best suited for a JSON document model, which we're the only database that ever built this from the ground up from the very, very beginning.

Every question I have is always about Postgres. What do you think about Postgres? Does it scare you? Does it keep you up at night? Well, I just want to walk through a couple of different things here, right? So first of all, we like to consider ourselves a flexible data model, unlike a relational data model that has a very rigid schema. We have a very flexible data model, right?

Number two, we have a lot of native querying capabilities around security and governance that's even more important in the AI world. One of the reasons why enterprises are very slow to adopt AI is around security and governance, especially in regulated industries.

The third is about being able to run anywhere. And I think we misuse this term sometimes. And what run anywhere, I think, means to some people is, okay, cool, you can run it anywhere you want to, on-premise data centers inside of different clouds, et cetera. But what we actually mean here is about multi-cloud and something that no one else can provide, especially the hyperscalers is the ability to extend a singular data cluster across multiple clouds or multiple regions at the exact same time, serving simultaneous workloads. And that's extremely powerful depending on where you are in the world, different regulatory regimes, data sovereignty, data residency requirements, we can spread that data and that availability of that data across multiple geographies.

And then lastly, we -- one of the things where Postgres falls down a lot is on the cost of scaling. And one thing about using the hack of JSONB is that it requires you to have to scale Postgres a lot faster than you normally would have to, and that gets very, very expensive. So natively, MongoDB offers the ability to horizontally scale at a much cheaper cost as well as vertically scale with how we separate out the data into different charts. And that's all built into the database.

The other thing to think about is what we're doing at the Atlas level when it comes to our data platform. And a lot of other providers and competitors are using multiple different technologies and making either the customers stitch them all together or understand and manage each one of those technologies or the company that's posting this is stitching them all together and providing different interfaces to them.

Our effort since the very beginning of MongoDB, we wanted to focus on the developer and making the ease of the development cycle as simplistic as possible. And so we keep adding capabilities to our platform, but we're keeping the same developer experience. So when we added Atlas Search, when we added Vector, we're not making the developer or the application owner query all of these different things separately and bring them all back to complete their use case. It's all a single query language, and we do all the stuff behind the scenes to keep the data in sync. There's no more ETLs. You're not doubling up on your data size. We do it in a very efficient way. And at the end of the day, it's all about JSON being built from the ground up as far as the document model is concerned.

Handing it back to Mike.

Okay. Thank you, Ben. So Atlas, which again is about 75% of the business has grown very quickly. And we talked about it on the earnings call. The last 4 quarters, it has grown by 29% plus consistently from an Atlas growth perspective. And again, it started out -- it was $800 million 4 or 5 years ago. It's now over $2 billion. Keep in mind, this is a consumption business, and it's our customers that deploy that data. So the good part is that consumption in a given quarter, you typically won't see that growth until the next quarter. It's hard to move the needle in a quarter, which is nice. But people say, hey, if you see consumption in a quarter, when will you see it? Typically in the following quarters. And again, this is across all 3 of the hyperscalers.

So Q1, we reported strong results. There is the Atlas growth. Again, that's 75% of the business. EA and other, that is the Enterprise Advanced, that's the on-prem version. That's both license and support. And we've talked about this, if it's 20-plus percent of the business, almost 70% of that revenue stream is support. It's what's sitting in deferred revenue coming off the balance sheet. So there is a good bit of ratable recognition there. The nuance there is that we have to recognize the license as we deploy it. And if somebody does a multiyear deal, we have to take all 3 years upfront because we've already deployed the software and they own it. That causes some volatility within that number.

Again, net new customer adds, we have a great enterprise motion, but what really Mongo started at the beginning was what we call self-service. This is product led. And we've added call it, 2,500 up to 2,700 net new customers. Those come almost entirely from self-serve, where they'll use a credit card, they'll start using our product. We talked about it last September at our Analyst Day, our Investor Day, that a good number of the customers over $100,000 in ARR started out as self-serve. And then what they do is they get bigger, they get bigger, they call us because at that point, they're using self-serve, they don't get support and then they want it. Once it becomes an enterprise workload, then that's when the sales team steps in.

Operating margins, we've taken up from the low teens now up to 18% this quarter, and we've guided the full year to about 20% and I'll talk about guidance in a second. And then cash conversion, this has been a concern I know with investors and me as a CFO is, hey, we're generating operating profit, but where is the operating cash flow? So last year, we actually -- cash conversion was over 100%. We expect that to be between 80% and 100%. If you're going to earn profits, you need to bring the cash with it as well. And kudos to the whole team for really driving that.

Okay. We raised guidance across the board after Q1, and I'll just hit the big numbers. So total revenue now $2.9 billion to $2.96 billion -- or the prior guide was $2.9 billion at the high end. Now it's $2.96 billion. That we took the Q1 beat, rolled it. We raised Q2. The back half for us, the entire guidance range raise was in Atlas. We think EA will do better in Q2, but we didn't bump EA for the second half. Again, that's -- while we're trying to be prudent in the second half because, again, folks, it is a consumption business, we do feel very good and confident about the Atlas business.

We also took operating margins up. It has a very good operating model. When you bring in new revenue, it flows through operating -- or gross margins at about 77%, 76%, and then it cascades down. We're doing a much better job of driving efficiency in the operating margins, and then that also flows through to EPS. So we increased -- we rolled the beat and we increased across the board.

These are the long-term targets that we talked about last September. We have announced we'll do an Investor Day again in September, and we'll -- at the end of September in conjunction with our dot local in New York. And we have 3 pieces of that. Total revenue growth in the high teens, Atlas growth above 20%, operating margin of 20% plus, and I want to underline the plus, it's not a cap, folks. As we continue to grow, we expect to grow operating margins as well. And then free cash flow conversion greater than 80%. The guide we gave for fiscal '27 hits both high revenue growth at 20% and operating margins at 20%. So we are guiding for a Rule of 40 this year ahead of our targets.

So before we hand it over to Jason to ask Q&A, and I told you we'd finish in more than 15 minutes, hey, 4 things coming out of the earnings call, I just want to reiterate, and we just talked about them. Hey, we had a strong Q1, fourth consecutive quarter of Atlas growth above 20% and another strong quarter of momentum in EA. The importance of EA is that's some of our largest customers, regulated industries, financial institutions that still want to run their database and workloads on-prem. And with AI, that's starting to become a bigger thing. Hybrid cloud is a real thing in our mind.

We issued strong Q2 guidance, and now we're talking about a Rule of 40 for the full year ahead of our targets. We are starting to see contributions from AI, but it's still early days. And we'll -- I'm sure we'll talk about that. And we are still confident and remain very confident in our ability to drive durable growth and be a Rule of 40 company. So those are 4 things I just want you to take away from the earnings call.

So with that, Jason, we'll hand it to you.

All right. Thanks, Mike. Let me start out -- I am actually going to ask every single one of my companies at the event here, can you frame the case for investors for why MongoDB is a winner in AI?

So a couple of reasons, in my opinion. So the first thing is we have 65,000 customers globally, and we're the system of record for a lot of those critical applications that are already in use. And so what's happening and what we're seeing in these enterprises is that they're starting to prototype and develop different agentic workloads, whether it's agents, whether it's simple chatbots, different experiences for both internal and external. And where is the data coming from? It's coming from the data that already is stored inside of MongoDB.

Number two, as more agents are deployed, and I'm sure there's been a lot of research about this, is agents need memory to be viable, right? We just announced a partnership a couple of weeks ago with LangChain with their memory frameworks, et cetera. And if an agent and a chatbot and an MCP server and everything is already communicating with itself and with other agents via JSON, the memory is going to be stored in JSON. So what better way to store that memory is inside of MongoDB.

And then third, on the longer tail of the funnel with the, I would say, new AI native companies, as I said earlier, the VHS BetaMax war of the best way to model data in this world is over. It's all about JSON. Even the workloads that we see that end up going to Postgres or Supabase or Neon, you pick your poison of flavor, they're not being modeled in relational schemas. They're being modeled in JSON, right? And so we eventually will get that workload if it doesn't weigh on us from the beginning, but we're obviously focused very hard on making sure we're there from the very beginning. But the data is being modeled in JSON.

Okay. Great. I'm going to just like poll the audience here because I know there's probably some people that are not that technical, but how many people know what JSON is? And I'm not talking about me, JASON. JSON, the technology. How many people know what JSON, the technology is? Just raise your hand and you know what JSON is.

More people than I thought.

Maybe just spend a couple of minutes talking about what JSON is, why it's differentiated. You guys sort of created your product around that and then like why it's so tied so closely with AI.

Yes, absolutely. So everyone has used an Excel spreadsheet before, right? Good. A lot of heads. That is the best analogy for what a SQL relational post-stress schema looks like. It's a bunch of rows and it's a bunch of columns. You only can fit so much data into a cell and think of every cell is like a record. Well, that creates -- that's better for certain types of workloads that, I would say, is very structured data, data models that don't change.

JSON is a different way to model data that's not trying to fit all the data into a singular cell and allows you to separate out the data into a document. The flexibility is important because we don't have to abide by a schema is because data changes all the time. And I'll give you a perfect example of this is if you're building a, let's just call it a chatbot, you're talking to one of your cable provider about getting higher internet speed or adding HBO. You couldn't -- an application developer could never model what the consumer would be typing into that chatbot, right? But it needs to be able to handle it. So that flexibility, especially in the AI world is hypercritical. So you need a data model that is able to handle that uncertainty when it comes to the data input or the data output that it might be generating. So being able to model everything in a document that has organization but doesn't have a very strict structure is what makes it flexible.

And that takes a special -- this is more technical, but how we actually store it in bits on a hard drive or a piece of memory. And we built the database from the ground up being able to do that from the very beginning.

So the Postgres and other relational database models have the ability to support JSON, but your point is like they don't -- it's not their kind of grounding. It's something that -- it's like an add-on for them?

Correct. It's what's called a plug-in on top of Postgres. It's called JSONB. And so it allows you to model data in that JSON format that I was talking about, but it needs to store it somewhere. It's on a different storage engine. It actually just puts it all into a singular cell. Well, that cell is only so big, 2 kilobytes, which is not a ton of data. MongoDB document sizes are 16 megabytes. And I know that doesn't sound that big -- as much as how big data actually is, but that's a lot of text. And so we have a much bigger document size that we can support. So what happens in Postgres is if you have a bigger document size, it has to spill into a second cell. And that you sacrifice performance on query writes and reads when you're dealing with multiple cells for the same document.

Got you. So it just becomes sort of -- you hit some kind of performance ceiling.

Very quickly. As the app scales, that's when the performance would happen.

What does CJ mean when he says you guys are kind of downstream of where Snowflake and Databricks play?

Yes. So a couple of different things with that, right? So I think, number one, OLAP has its place. I'm not saying it doesn't. But that data by nature of the use case of OLAP is old. And so is that good for a data analyst or internal use cases where there's nothing critical about that? Sure, makes sense. But we firmly believe, and actually Databricks and Snowflake have both validated our belief by purchasing 2 OLTP companies is that OLTP is actually the higher ground for AI because to do any type of real-world data transactions, whether it's some bank is recommending a stock trade or you want to go update your insurance provider or you have to file a claim, that's all OLTP. You can't do that against historical archival data.

And so MongoDB being an OLTP database from -- also from the very beginning and also the fact that all of those customers are also our customers as well, why we say downstream is and why we think they've seen the uptick is it's been a lot of internal use cases, some playing around data analysts crunching data, doing research and everything like that. But the keys to the kingdom that are actually running all of their mission-critical workloads that are generating all of the revenue for these companies is actually in MongoDB.

So that means that you will kind of see more of the benefits later than they've seen them so far? Is that the right...

It's not a question of if, it's a question of when.

All right. And let's just talk about the opportunity across the 3 different AI customer cohorts that you guys have defined, the Frontier Labs, the AI natives and enterprises.

Yes. So let's break that down. So we talked about this on the last call. So you have the Frontier Labs. And quite frankly, we're thrilled to have them as customers. They've asked us to not talk about what -- how we're used with them, so we won't. But -- so that's one piece of it.

The second piece is AI natives. And this is where companies are making their own product that is selling an AI product that is run on Mongo, powered by MongoDB. It's a bunch of smaller companies, but nobody has really hit exit velocity. They will at some point. And that's a great use case because -- and this is -- as Ben was talking about it, sometimes it's a lot of unstructured data. If that database doesn't scale with the product as they scale, they have a problem, which is why they may start out on Postgres and then they come to MongoDB.

The third piece, which is where all the money is, is in enterprises. So as we've talked about, we started to see some activity. There's a lot of POCs. There's a lot of betas. And we know customers are big banks, insurance companies, consumer products, retail, starting to build agents, but they're not deploying them at scale externally because of security, governance, compliance. Nobody wants to be on the Wall Street Journal because they had an agent do stock trades that went the wrong way. Their -- and that's going to take time, and that's the third piece of it.

So we feel very good about the -- what we have in that enterprise market. And we know that customers are deploying agents, but they have not deployed them at scale nor have they come out of beta or POC. So those are the 3 layers we've talked about, Jason, for AI.

Got you. And I think CJ has said for at least enterprise, like the kind of 12 to 18 month time frame is a realistic kind of timing of when it starts to become more meaningful for you?

So we'd like to see it faster. I mean, look, we can try to help them, and I know we're going to talk about this. And we have customer success folks in their sales teams. But at the end of the day, they have to move at their own speed. So even, for instance, MongoDB, we have a lot of internal use cases for us to then deploy it to our customers. There's a lot of things we have to go through. So we hope it moves faster. We're starting to see some pickup, which is why we talked about it. And again, like Ben said, for us, it's not a matter of if it is.

Okay. Great. And then for Ben, I just want to ask you, so a couple of things on the kind of start-up side. How do you guys get in the door with the start-ups right away versus like having them fail on Postgres and then kind of come to you, number one? And then number two, is related to that is how do you make the product kind of more agent-friendly?

Good question. And actually, they're really related. So if we rewind the clock like 5 years ago, if you're an application developer, the first thing you did was saying like, okay, what's my stack going to be, right? Whether you pick MERN or Node.js or some other, you picked your technology stack, you picked your hyperscaler, you pick where you're going to run it, you knew what the security model was, you wire up the network and you'd like, okay, great. Now I'm going to go start developing my application, right?

So today, what happens? You go to whatever pick your poison code platform and say, I have an idea for an app. The LLM writes back and says, cool, where do you -- how fast do you want to deploy? And they're like, I don't care. And they just start asking you questions about what you want the app to do and it lands on a piece of technology and something.

So why is that? The way the LLMs get trained is on publicly available internet data. And SQL has been around for 50-plus years. Postgres is 20 years older than us, right? So we have a lot of content catch-up to just plainly do to just get the training in the right place to where the recommendations happen more. But what's actually happening is fascinating is all that data is actually being modeled in, as I said earlier, JSON. They're not trying to model the modern application in relational. It's actually being modeled properly because the LLMs themselves are utilizing JSON for how it stores its own training data. So it picks the better data model, picks the wrong technology.

And we have a lot of focus on building up that awareness, content, varying other things. There's all this new segment of AEO, making sure it build up agent awareness. There's a lot of different focus areas there to help with the awareness. And then secondly, more specific on the agent friendliness. We do a lot today with MCP servers, the way our API is written, we'll continue to obviously invest in that area. But once you have decided on MongoDB, we're very agent-friendly, it's about that first step of having the agent select us without the need to prompt. If you're creative with the prompt or you have a good idea that you want to go with Mongo, it doesn't -- it's not very hard to get the LLM to select Mongo. But if you're being generic and you're at 80,000 feet, it just default to what.

It's an -- for a lot of people, it's an afterthought right now.

Yes, it's an afterthought.

How you get it to be not an afterthought.

Exactly. And -- but we know that to be the case because as soon as it becomes a real thought, whether it's applications are generating revenue, they have a security concern, they run into a performance problem, they all come. They all eventually end up on Atlas, which is great, and we're happy about that. But I want to save them the pain upfront.

Got you. And is it fair to say that I think CJ kind of alluded to this on the call that there's sort of stay tuned on the product innovation side this year that you guys are going to be releasing a bunch of new technology that will sort of maybe make that...

We're always innovating.

More easy.

We have a lot of ideas.

All right. A couple more things I want to hit before we wrap up. And then the breakout is going to be upstairs in [ Mayer or Maher ]. I don't know how to say that. The first question is just on the new sales leadership, Mike. You had 2 guys that were there a long time and really established and really respected. How should investors think about the sales leadership change?

Sure. So Cedric and Cap, as we call him, Paul, were there for many years, I think 8 or 9 years, both of them. They did a great job building Mongo into what we are today. They both raised their hand and said, hey, it's either time for me to go do something else or bring in new leadership. So both of those 2 exited Mongo at the end of last quarter. We brought in -- and we made -- what was that? Sorry.

So we broke the team up into, call it, presales and postsales. So we brought in a new leader, Ryan, who's going to run -- he's the CRO, and he is running the sales team. He came from Confluent. He's all of 5 weeks into the job, folks. So he's still trying to figure out where the restrooms are to use that analogy. And then Erica came in as the Chief Customer Officer, and she has not only technical support, PS as well as the customer success team and also partners, which is what she did very well at ServiceNow.

Very importantly, before we made the announcement, and we were clear going into the year, folks, all the territories, quotas, comp plans were all done for fiscal '27. So there's going to be no changes to that. Ryan knows very well. The last thing you want to do is come in and change those. So we don't expect any changes for fiscal '27 as it relates to that, where all the execution is set.

The great part about him is he understands the consumption business. He's done it at scale, and he also is very focused on enterprise. So that's great. And then Erica, not only will continue to focus on the customer success, but we've also said that, hey, we can do better in the partner community in terms of SIs or other folks that we sell through and with. So that will be a big focus for her. We don't expect any issues, and we haven't baked any into the guidance because things are going well, the next level down. Certainly, there's always changes at that point, but we feel very good about the leadership.

Okay. Great. And then when we think about the FY '27 guidance framework, it looks like it bakes in a fair amount of conservatism as sort of your MO, as I've worked with you for many years, not just at Mongo, also at NetApp. Can you walk us through kind of the key assumptions with the FY '27 guidance? And if you end up doing much better, like where would the upside come from?

Sure. So let's back up for a second. So understanding that the company historically has not given specific Atlas guidance. Two quarters ago, we started to give that view. When I started a year ago, that was the #1 ask for investors. Give us a little view of what you think Atlas is going to grow. So when we set the guidance for fiscal '27, we talked about Q1, we thought Atlas would grow, call it, around 26%. And then for the full year, total Atlas growth was 21% to 23% and then EA was low single digits. And that was the framework that we set. Again, we feel good about the business. It is a consumption business. The thing that we are -- have less control over is what happens in the economy. The great part about consumption is you can spool up and you can spool down. So we always want to bake that into the guidance.

After Q1, we grew Atlas by 29.4%. We bumped Q2 up to 26%, and then we raised the second half as well. In our models, the entire second half raise was in Atlas, which goes to your other question, we feel good about the business. If we're going to do -- if we're going to beat those numbers, it will almost assuredly come in Atlas. Is there room in EA? Sure, because we don't bet on the duration. We may get a multiyear deal. And if it comes in at 3 years, it boosts the license revenue in that quarter. Folks, that's tough to forecast because that's not our decision, that's our customers. And quite candidly, they don't even know whether it's going to be single or multiyear because their budget is the driver there. So -- and then we took up operating margins across the board as well. So we feel good about setting ourselves up for success.

Very importantly, on Atlas, folks, we talked about, hey, when you look at the range in Q4, we guided Atlas to grow 27%, and it grew 29.2%. Consumption was largely in line with what we expected. In Q1, 26%, and we grew 29.4%. Consumption came in better than we expected. So that kind of gives you the range. And we're guiding Atlas because, hey, the consumption business is new to a lot of you folks, and that's why we've started to give that incremental disclosure to show, hey, the consumption business is a little bit different, and we said it now 2 quarters in a row, it's a big business, less so 4 years ago, where you had customer cohorts or groups of customers that can move the needle. It's less so now, which is great. So consumption in the quarter, while it's great in a quarter, you'll largely see that hit revenue in the following.

Okay. We'll have to end it there. Thanks, everybody, for joining, and thanks to Mike and Ben.

Thank you.

Thank you.

Thanks.

Hello, and welcome to MongoDB's First Quarter Fiscal Year '27 Earnings Conference Call. [Operator Instructions] I would now like to hand the conference over to Jess Lubert, Mongo's Vice President of Investor Relations. You may begin.

Thank you, operator. Good afternoon, and thank you for joining us today to review MongoDB's first quarter fiscal 2027 financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB; and Mike Berry, CFO of MongoDB.

During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, our opportunity to win new business, our expectations regarding Atlas consumption growth, the impact of EA and other business and multiyear license revenue, the long-term opportunity of AI, our financial guidance and underlying assumptions in our investments in growth opportunities in AI.

These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks described in our annual report on Form 10-K for the year ended January 31, 2026, filed with the SEC on March 11, 2026.

Any forward-looking statements made on this call reflect our views only as of today, and we undertake no obligation to update them, except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures.

With that, I'd like to turn the call over to CJ.

Thank you, Jeff, and thank you all for joining us today. I continue to spend a lot of time working with a wide range of customers from AI natives and digital natives to large enterprises and public sector organizations. This customer-driven focus is to deliver meaningful outcomes for MongoDB.

The process I follow is tightly linked. So each part strengthens the others. Number one, engage directly with C-suite leaders to elevate MongoDB from a technical decision to a strategic platform commitment. Number two, surface new pipeline by helping customers connect their most pressing modernization and AI opportunities to what MongoDB can uniquely solve. Number three, feed what I learned directly into our product and technology teams to accelerate our customer-driven innovation road map.

These conversations reinforce my conviction in both what we have built and the scale of the opportunity ahead. That opportunity has 2 dimensions. The first is core workloads where large customers run their most demanding, mission-critical workloads on MongoDB across on-prem, public clouds and hybrid environments. The second is AI, where enterprises, digital natives, frontier labs and AI natives alike are moving agentic applications into production and choosing MongoDB as the data platform to power them.

As you heard from other software companies, these 2 opportunities are not distinct and, in fact, reinforce each other. Enterprises are starting to build agentic application on top of the very data already running on MongoDB. This dual opportunity compounding together is what gives us so much optimism about the road ahead. Today, I'm proud to share with you our Q1 results.

We generated total revenue of $688 million, up 25% year-over-year, beating the high end of guidance and accelerating from the 22% growth we reported in fiscal Q1 of the prior 2 years. Top line strength was driven by Atlas, which grew 29.4% year-over-year including a record $117 million year-over-year dollar growth.

Now at a $2 billion run rate, this is the fourth quarter in a row Atlas delivered year-over-year growth of at least 29%. EA & other previously referred to as non-Atlas grew 13% year-over-year. We delivered a non-GAAP operating margin of 18%, above the high end of the guidance. We ended the quarter with over 67,700 customers, adding 2,500 customers in Q1, growing year-over-year and quarter-over-quarter.

AI adoption of MongoDB technologies across our customer base continues to accelerate. MCP server usage is growing significantly. Voyage customers have more than doubled quarter-over-quarter and vector search adoption is far outpacing overall company growth. Let me walk through each dimension of our opportunity.

Across my conversations with customers, 1 shift stands out. MongoDB is starting to become a strategic platform decision in addition to a workload-by-workload evaluation. This is driven by a powerful combination of our platform technology fundamentals, high performance at scale, the ability to run anywhere and AI capabilities that are fully integrated in a single data platform. Zoom is a clear example of that.

Zoom, a global leader in AI-powered workplace collaboration runs MongoDB Enterprise Advance as a unified data platform for Zoom Meetings, Zoom Phone, Zoom Contact Center and Zoom Virtual Agent deployed across dozens of clusters globally to deliver low latency, highly available communications at scale. By standardizing these workloads on MongoDB, Zoom gains are cloud-agnostic, hybrid deployment model that runs anywhere their business requires.

This simplifies the previously polyglot data estate, improves our resilience and reduces total cost of ownership across mission-critical services. We look forward to continuing to support Zoom as they deliver the next generation of workplace experiences. Turning to AI. This opportunity spans 3 distinct segments. First is the frontier labs. Several of this have selected MongoDB for use cases that are mission-critical to the deployment of their products among the most demanding data workloads in the industry.

The depth of engagement varies by lab and by workload, and it is still early. But we feel great about the use cases we are winning and the ability to expand within these customers over time. Second is AI-native companies. These customers are choosing MongoDB as the foundation for their AI products from day 1 because the data layer determines if you can scale to support rapid growth. For example, Endor Labs is an AI-native application security platform, protecting over 7 million applications across both human written and AI-generated code. Endor selected Atlas as its default database to support 225% year-over-year revenue growth. Endor uses Atlas and Atlas Search to power its mission-critical security workflows, including AURI, its new security intelligence layer for AI coding agents, allowing the company to reduce operational friction and accelerate delivery of its differentiated offerings.

Third is enterprise deploying AI. It is still early here, but we are beginning to see customers move from experimentation into production, building AI application on top of the operational data layer already running their business. Zomato is a great example. The world's second largest food delivery company with 25 million monthly active users build Nugget, an AI-native customer support platform, they are now selling to other enterprises on Atlas.

After evaluating DynamoDB and DocumentDB, they chose Atlas for its aggregation pipeline, right consistency and flexible schema. Nugget now orchestrates 15 million conversations per month on MongoDB's platform, reducing support cost by 55% and improving human agent productivity by 40%. Another exciting pattern is also emerging across these segments. Something I'm really excited about.

Customers choosing MongoDB as the memory layer for AI agents themselves. agent Workloads need memory, that's transactional, high velocity and able to retrieve the right context at the right time. Adobe's Journey agent is a clear example. A composite multimodal AI agent that unifies Adobe's marketing suite and orchestrates end-to-end customer journeys for their global B2C user base with MongoDB as the agent's long-term memory and reasoning layer.

Adobe leverages the MongoDB platform, Atlas Search and Atlas Vector Search together to power the sub-100 millisecond hybrid search the agent needs to act in real time. To be clear, our results today are driven primarily by core workloads, but we are seeing real and growing momentum from AI and agenetic workloads and believe MongoDB is purpose-built to be generational data platform for the agentic era.

Built natively into the platform, MongoDB's innovations in the core database, embeddings and vector capabilities are moving us beyond a system of record to becoming the real-time system of intelligence. That just comes down to 5 core strengths. Number one, MongoDB is architecturally built for AI in 2 key ways. First, our flexible schema is uniquely suited to how applications get built in the agentic era. A growing share of software is now created through pro-driven development, natural language iteration rather than line-by-line authorship.

Whether the prompt comes from a developer or an agent, the shape of the application shifts with prompt and a rigid relational schema becomes [ a tax ] on every iteration compromising agility. In addition, LLMs are the lingua franca for AI, and they speak in unstructured documented shape data, the exact form MongoDB was built around. We have been compounding both advantages for 15 years, well before the current AI wave gave them a tailwind. Second, MongoDB is a transactional, high-performance data platform built for how agents actually work.

Agents don't behave like traditional applications. They read, write and act continuously across multiple simultaneous threats with a single agent responding subagents that each make independent reads and writes in real time. Analytical systems built for off-line processing weren't designed for this, and it shows in the performance when you run agents on top of them. MongoDB 8.3 released this month takes that step one further, delivering up to 45% more reads, 35% more writes and 15% more [indiscernible] transactions over 8.0 without changing a line of application code.

Third, MongoDB is a data platform that delivers the retrieval accuracy agents need to be trusted while optimizing tokens and cost in production. For internal tools, occasional errors may be tolerable. But for customer-facing application such as clinical decision support, fraud detection, financial transaction, insurance transaction, accuracy is nonnegotiable. MongoDB delivers best-in-class retrieval through integrated Vector Search and Voyage embeddings and reranked models, purpose built to surface the most relevant context when agent needs it.

This quarter, automated Voyage AI embeddings entered public preview, removing weeks of infrastructure work and enabling developers to deliver semantic search in minutes. Fourth, MongoDB runs wherever the agent needs to run across all 3 major clouds, on-prem and in hybrid environments. The assumption that every workload eventually migrates to the public cloud is being challenged by real factors: cost at scale, capacity challenges, latency requirements and regulatory mandates on data residency.

Many customers run Atlas and EA simultaneously, and they need a platform that doesn't force a choice. Fifth, MongoDB is embedded in the tools, developers and agents actually use to build agentic applications. LangChain is the world's most widely adopted agent framework with over 1 billion downloads. We delivered 10-plus native integrations with LangChain for vector search, hybrid retrieval, semantic cashing and agent memory. We recently announced that MongoDB Checkpointer for LangChain deployment, which collapses what used to be a dedicated [ postgres ] instance per agent into a single, shared Atlas cluster, state memory and operational data unified in one place.

Last month, we also launched the MongoDB plug-in and agent skills on the Claude core marketplace, where we are already seeing strong early traction with developers. Whenever agents are built, MongoDB is already there. Executing on this opportunity requires a world-class team. On the product side, we recently announced 2 CPO appointments. Ben Cefalo, a long-time MongoDB leader, is now Chief Product Officer for core products overseeing Atlas and Enterprise Advanced.

Pablo Stern-Plaza, who is based in San Francisco joined as Chief Product Officer for AI and Emerging Products with responsibility for our AI product portfolio and our strategic relationships with top AI native and frontier customers. Over the years, Pablo has worked for many software companies in technical roles, helping scale their product lines into meaningful thriving businesses. Anchoring our technology organization is Jim Scharf, our Chief Technology Officer, who continues to focus on the enterprise requirements that matter most: security, durability, availability and performance.

On the go-to-market side, Erica Volini joined as Chief Customer Officer earlier in Q1, bringing 2 decades of enterprise growth experience, most recently architecting the partner-led motion that drove ServiceNow from $5 billion in revenues to more than $10 billion. Ryan Mac Ban joined us as Chief Revenue Officer, bringing 20-plus years scaling global go-to-market organization, most recently as CRO of Confluent, where he led a cloud-native consumption-oriented platform business with strong parallels to our own and previously in senior roles serving large enterprise customers at VMware and Cisco.

Erica and Ryan are partnering as a unified go-to-market team jointly responsible for the full customer life cycle. With this team in place, I'm confident in our ability to capture the opportunity ahead. I also want to extend my deepest thanks to the entire MongoDB team and especially our go-to-market organization whose hard work and sharp execution delivered a stellar Q1.

One last note before I hand it over to Mike, I would like to personally invite you to our Investor Day, which will be in New York City on September 29. Please e-mail [email protected], if you would like to attend. We hope to see many of you there.

With that, Mike, please take it away.

Great. Thank you, CJ, and good afternoon to everyone on the call. I will start by reviewing our first quarter fiscal '27 financial performance before moving on to our outlook for the second quarter and the remainder of the fiscal year. I will be discussing both GAAP and non-GAAP results. As CJ highlighted, we delivered a strong quarter that exceeded all of our guidance ranges, and we are raising our outlook across the board for fiscal '27.

Before diving into details, I want to highlight 3 key takeaways from the quarter. First, Atlas growth remained strong, with the fourth straight quarter of year-over-year growth above 29%. Second, EA growth remains durable as we continue to grow both Atlas and EA. And third, our business model continues to deliver operating margin and cash flow expansion. Looking at the top line in more detail. Total revenue in the first quarter reached $688 million, representing 25% year-over-year growth compared to 22% growth in the year ago quarter.

Turning to our product breakdown. Atlas consumption was stronger than expected in the quarter, and revenue grew by more than 29% year-over-year and exceeded our guidance. This is the fifth straight quarter of year-over-year dollar growth in Atlas, adding a record $117 million in the quarter. Atlas now accounts for approximately 75% of total Q1 revenue, up from 72% in the year-ago quarter.

Our main growth driver continued to be the strength in use cases at established enterprise customers with momentum across the financial services, technology and media industries in Q1. Smaller but accelerating growth drivers included early AI deployments with many of these same enterprise customers and momentum with Frontier Labs and AI native companies. We experienced particular strength in North America that was driven by our larger customers, although our self-serve business also performed well in the period.

This ongoing momentum across our customer base is reflected in our total company net ARR expansion rate, which was 121% for the quarter compared to 119% a year ago. Turning to EA & Other revenue, which encompasses the metrics we previously referred to as non-Atlas, we saw solid results with revenue growing 13% year-over-year. This strength was driven by existing customers across all types of industries, particularly in the finance and technology verticals, where customers continue to expand their on-prem footprint to support both traditional and AI applications.

EA & Other ARR, which normalizes for duration impacts grew approximately 11% year-over-year. Moving down to P&L. Total non-GAAP gross margins of 74.5% expanded by approximately 40 basis points year-over-year and were approximately 100 basis points below the fourth quarter. Subscription gross margin finished at 77.1%, approximately 60 basis points below the first quarter fiscal '26 and 170 basis points lower than the fourth quarter.

The quarter-over-quarter variances were driven mainly by product mix between Atlas and EA as well as the normal seasonality impact to margins in the first quarter of the fiscal year. Moving to profitability. I'd like to start by noting that we had our second quarter in a row of GAAP profitability, which is a great trend. Non-GAAP income from operations came in at $123 million, yielding an operating margin of 18% compared to 16% in the year ago period. We are very pleased with our operating margin results which benefited primarily from strength in revenue, driven mainly by Atlas.

First quarter non-GAAP net income was $112 million, which translates to $1.32 per share based on 85.3 million diluted shares outstanding. This compares to net income of $86 million or $1 per share on 86.3 million diluted shares outstanding in the year ago period. Our remaining performance obligations, which we define specifically as obligations for contracts with a duration greater than 12 months stayed relatively consistent quarter-over-quarter and ended the period at $1.46 billion.

This represents year-over-year growth of 88% with the current portion growing at 69%. Customer adds grew by 2,500 sequentially, bringing the total customer count to 67,700, which is up from 57,100 in the year ago period. The growth in our total customer count is being driven primarily by Atlas, which had 66,400 customers at the end of the first quarter compared to 55,800 in the year ago period.

Within Atlas, we saw a strong quarter of Voyage customer additions, reflecting early but encouraging demand for our AI embedding capabilities. We feel good about the momentum we are seeing with new customers and please keep in mind, this metric will fluctuate from quarter to quarter. We closed out Q1 with 2,895 customers with at least $100,000 in ARR, representing 16% year-over-year growth. Revenue growth from this cohort was strong and outpaced total company revenue growth, consistent with our move upmarket.

Furthermore, we continue to see strong Atlas platform adoption. Of our Atlas customers generating at least $100,000 in ARR, 45% are leveraging 2-or-more features of our platform, which is up from 37% in the year-ago quarter driven largely by Vector and tech search adoption.

Moving on to the balance sheet and cash flow. We ended the first quarter with $2.4 billion in cash, cash equivalents and short-term investments. During Q1, we allocated $100 million towards share repurchases and $58 million to settle taxes on employee RSUs. Operating cash flow for the quarter was $202 million versus $110 million last year, and free cash flow was $198 million versus $106 million last year. Our cash flow results were driven primarily by strong operating profit and seasonally higher cash collections.

Before moving on to guidance, I am pleased to share that we have acquired Clarity Business Solutions. As we have discussed previously, we are strategically increasing our investment in the U.S. federal vertical and this acquisition is a key component of that strategy. Clarity has been a trusted partner of ours since 2021, providing specialized support and professional services for highly classified workloads within the U.S. government.

We have held a small equity stake in Clarity for some time. And this acquisition brings in MongoDB, the deep domain expertise and high-level security clearances required to further accelerate our U.S. federal vertical. Financially, this transaction represents approximately $10 million in services revenue annually at roughly breakeven profitability, and these impacts are already reflected in our updated guidance.

Now I'd like to share some of the assumptions driving our Q2 outlook and provide some additional detail into how we're thinking about the rest of fiscal '27. To begin, as I mentioned earlier, we continue to see strong and consistent Atlas growth. This performance is driven primarily by strength in core workloads as well as early AI tailwinds from both enterprise and AI native customers. We are encouraged by the continued strength in Atlas and feel good about the business entering the second quarter where we expect Atlas revenue growth of approximately 26%.

This strength is not only driving our second quarter fiscal '27 outlook, but is also giving us confidence to raise our full year growth expectation to a range of 23% to 25%, an increase of 200 basis points. As we said last quarter, we would like to remind you that as Atlas has gotten larger, it has become more predictable and less sensitive to revenue movements with any individual customer or cohort. With this in mind, we would encourage you to not expect large swings versus guidance for the current quarter as changes in consumption in inter-quarter only have a modest impact on revenue within the period.

Given Atlas as a consumption-based product, there is more room for variability as we go further out in the year. For EA & Other, we have line of sight into a very strong Q2 and expect to see revenue growth of approximately 20%. This reflects our expectations for continued ARR momentum as well as the timing of several large multiyear deals with existing customers. The continued momentum highlights the strategic importance of EA to some of our largest customers.

Given our current momentum, balanced against the timing of certain deals and a more difficult Q4 compare, we are raising our full year expectations for EA & Other revenue to mid-single-digit growth in fiscal '27. This implies that EA & Other revenue will be approximately flat during the second half of the year, again, due to the tougher compares from the second half of fiscal '26. While we remain optimistic regarding our ability to grow our EA & Other revenue over the long term, it remains difficult to predict the duration of our EA deals. So we only include deals in our forecast that have either closed or have a high probability of closing to limit the risk of a negative surprise.

Turning to profitability. We remain committed to driving both revenue growth and operating margin expansion, and we now expect to expand operating margin by 100 to 150 basis points in fiscal '27. We will achieve this expansion while investing in key growth initiatives across both products and go-to-market. Our product investment is focused around enhancing our AI capabilities, which includes Vector Search and Voyage and expanding EA's product value with new and advanced features, including native AI functionality.

Our go-to-market investments include building out our presence in Japan as well as strengthening our U.S. federal vertical, highlighted by our acquisition of Clarity Business Solutions. We will also continue to invest in quota-carrying headcount, marketing programs and developer awareness. Now let's shift to how that translates to guidance for Q2 and fiscal '27.

For Q2, we expect revenue of $729 million to $734 million, which equates to 23% to 24% year-over-year growth. We expect non-GAAP income from operations to be in the range of $152 million to $156 million for an operating margin of approximately 21% at the high end of guidance. We expect non-GAAP net income per share to be in the range of $1.58 to $1.61 based on 86.3 million diluted shares outstanding. For fiscal '27, we expect revenue to be in the range of $2.92 billion to $2.96 billion, representing full year revenue growth of 19% to 20%.

We expect non-GAAP income from operations of $571 million to $591 million for an operating margin of approximately 20% at the high end of guidance. With a combination of 20% revenue growth, the 20% operating margin, we are targeting a Rule of 40 performance at the high end of our outlook. We expect non-GAAP net income per share to be in the range of $5.95 to $6.14 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the second quarter and fiscal '27 assumes a non-GAAP tax provision of 20%.

In closing, I also want to thank all of the MongoDB employees for staying focused and executing very well in Q1. We are very pleased with our Q1 results and remain highly confident in the long-term opportunity ahead for MongoDB. We are optimistic regarding our growth prospects, and we'll continue to invest responsibly to drive long-term shareholder value.

With that, operator, we're now ready to take questions.

[Operator Instructions] Our first question comes from the line of Matt Martino with Goldman Sachs.

CJ, maybe to start with you. The agentic conversation seems to have really shifted even over the past 3 months from proof of concept into real production deployments. And Mongo has put a lot of work into the platform to meet that moment with the LangChain partnership and the performance upgrades to the core database. I think as those pieces come together, do you feel like we're approaching the point where agentic workloads start to genuinely move the needle on consumption? Or is the bigger inflection still ahead of us? I'd love to get your thoughts there.

Thank you, Matt. We wanted to make sure on behalf of our products and technology organization that we are ready to scale when somebody wants to create an agentic workload in production that is customer-facing, which is typically where the scale is much higher and have all the capabilities in a single platform, so you are not doing search somewhere else. You are not doing vectorization somewhere else and embeddings which I was still trying to understand the power of embedding and what would that do for agentic workloads. .

But now seeing that with some of the large financial services and health care companies gives me a lot of confidence that our data platform can truly act as a real-time system of intelligence. So the answer is, I'm seeing it's still early math, just to be clear, because the security governance, observability, there are many, many aspects to the agents and what kind of outcomes they deliver if it is agents at scale. But we feel that we are ready and just yesterday, Matt, I was with a Fortune 25 firm. And when we outlined what we already have, where MongoDB can not only act as an operational data layer, but can also act as a long-term memory and some of the things that we are building right now they got really, really excited as they think about rolling out production agents at scale. So early but I'm seeing very encouraging signs, and we are ready.

And then, Mike, for you, you made a comment, I think, not to expect huge swings on Atlas revenue for the quarter ahead. Can you unpack that comment a bit? Should we take that as [indiscernible] magnitude similar to what we saw this quarter or something different?

Yes. Thank you for the question, Matt. So as it relates to guidance, we think it's important that our guidance reflects the true strength of the underlying business and feel there's room to do that while still being imprudent. As Atlas has gotten bigger, it has become more predictable and has become less sensitive to movements from individual customers or cohorts.

Coming off a strong Q1 where consumption came in better than expected, we're guiding Q2 consistent with the framework of how we've guided the past 2 quarters. To put that in context, in Q4, consumption came largely in line with our expectations. And in Q1, it came in a little better, which you can see reflected in our results versus guidance. The strength in Atlas this quarter allowed us to roll the beat and raise guidance for the full year.

And then, of course, that revenue drove higher profitability and EPS. For the full year, given Atlas is a consumption-based product, there's a little more room for variability as we go further out from the year -- in the year. So we've not changed our philosophy on EA, where we'll always guide conservatively due to the uncertainty around the timing of the deals. So hopefully, that gives you the context of the framework in terms of how we guided Q2.

Our next question comes from the line of Ryan MacWilliams with Wells Fargo.

Mike, you're guiding to another strong 2Q for Atlas against the strong performance you had last year. Is this how we should think about the seasonality for the Atlas is going forward? Or is this Atlas guide being impacted by other factors we should keep in mind?

Yes. Thanks for the question, Ryan. So as we guided Q2, a lot of that was coming off of a strong Q1 in terms of consumption. And as we've talked about, Ryan, as the business gets a little bit bigger, there's always some small seasonal changes, but on a year-over-year basis, I wouldn't expect significant changes. Now quarter-on-quarter, certainly, it does change a little bit.

But year-over-year, I wouldn't expect much change in the seasonality.

Excellent. And then for CJ, I'd like to hear about the opportunity for AI native with Mongo as those customers really start to scale their own businesses are there use cases for large AI native that they may make more sense for Mongo? And I guess for the quarter itself, like how can we think about the contribution from AI natives to Atlas?

So Brian, first is that AI natives what we are finding, and I shared the example of somebody like ElevenLabs and .local London a few weeks ago, they were using first-party database for operational data. They were using another software for search. And basically, most of those product lines were really choking as ElevenLabs was growing significantly, right? They are now at a $500 million ARR.

So when I asked the team, technically the engineer who made that decision saw that the growth of the company as in that AI native company, ElevenLabs was being held up by the data layer. And us having search, vector search and operational data in a single platform, they are -- they made the decision to move to MongoDB not too long ago. And two things they said that really resonated with me, Ryan. Number one, they are like, gee, we should have done this a lot sooner. Otherwise, we would have not to deal with all these outages and other things they dealt with the previous platform.

And number two, now choosing MongoDB even though they have scaled significantly on their ARR as an AI native company gives them peace of mind. I'm hearing them from other AI native companies who also chose maybe a [ Postgres ] or something and Postgres completely choked on the performance. So that just gives me a lot of confidence that if AI native company where AI is the business or agentic layer is the business and they feel that they can scale with MongoDB. When that moves over to the enterprises, whether banks, health care and other firms, they will also realize the same thing a little bit later. And as Mike shared and I shared earlier, the contribution is there. We are seeing very encouraging signs right now. But a lot of growth was still driven by core enterprise workloads, which I would argue are also getting ready for AI world.

Our next question comes from the line of Raimo Lenschow with Barclays.

Congrats from me as well. CJ, on that note, you're meeting a lot of customers at the moment. The one theme that comes up in the industry around data is that people realize with AI, should data needs to be consolidated and cleaner. So what are you seeing there in terms of that kind of consolidation move towards Mongo.

And maybe just talk to how that's kind of impacting Atlas and EA. And then I had 1 follow-up for Mike.

Raimo, great question. So we definitely see I would say, and Raimo, thanks for acknowledging. But in Q1, just in Q1, I individually met 200 customers, okay? So I have lots of data points. And what we actually see is that a lot more modernization acceleration where somebody is moving to Atlas so that they are ready on scaling out for AI workloads rather than a consolidation play. What I see.

Yes, there are some examples where they are [indiscernible], now you have Search and Vector Search in the database that improves our data pipelines. We don't need to ETL now to some other search provider. We try to use open source that didn't work. So we are seeing some movement of data. And we are also seeing some migration from Postgres and others into MongoDB given that we do unstructured data really, really well.

And LLM speak the language of Json or low Jason. So that's how I would describe it more than data consolidation, modernization and also getting ready where you're not ETLing out data and just use MongoDB as the layer for AI.

Okay. Perfect. Make sense. Sounds exciting. And then, Mike, one for you, like with the 2 new hires on the go-to-market side, I know it's now we are now in Q2, but any changes we need to be aware of there? Or what are you thinking there in terms of impact on the organization this year? .

Yes. So thanks for the question. As we talked about going into Q1, we felt very confident in terms of making sure that there was not going to be any disruption. So from a territory plan in quota, all of that stuff, those are all out. We don't expect there to be any changes in the year. As you know, making changes to comp plans during the year is always fraught with issues. Ryan has done a great job so far. He'll get his arms around the organization, maybe some tweaks next year. We'll see what he wants to do. But I wouldn't expect any significant changes for the remainder of fiscal '27.

Our next question comes from the line of Ittai Kidron with Oppenheimer & Co.

Guys, congrats on a good quarter. CJ, I wanted to get your perspective on the AI natives. In what way do you think your go-to-market needs to evolve to address them differently? Is there a need to address them differently in the go-to-market effort?

Yes. Ittai, I'll give you a straightforward answer. This is work in progress. So what we find is that some of these AI-native companies come through our self-serve motion. We constantly watch -- we had so many customers through our self-serve motion, and that motion has been working really, really well as a lot of venture investments have gone into AI native companies. So post 2023, first, I want to acknowledge through our self-serve motion, we are getting some of these iconic logos that have now become a truly company with $100 million ARR plus.

With Ryan now in place, we are figuring it out what is the right point to intervene and that is a work in progress, okay, what are the characteristics? It's a Tier 1 VC company? Maybe it's not. Like, for example, a customer that grew in Q1, we found out that there was a AI/robotics company and they were growing a lot on Atlas, and then our team reached out to them right away.

So this -- we see that some of these companies are coming via our self-serve motion. And then one, when do we intercept and put a field wrap on it. And number 2 is that how do we scale and focus on that motion because we have a great database for those kind of companies. So work in progress, but we are making definitely improvements as we learn.

Fantastic. And then for you, Mike, great numbers again. Two small things. First on the EA comments on your second half when you talked about flat year-over-year in the second half. I'm just wondering, is there -- were there any large deals? I talked about large multiyear deals in the quarter. Was there any movement from future quarters into 2Q that have made that, that could also explain the flat second half or kind of things kind of fall where they would you expect them to fall? .

Yes. Thanks, Ittai. They largely felt where we expected. The biggest impact in the second half is really not this year, fiscal '27, it's '2%. As you remember, we had a very strong Q4, especially in '26. So that's really what's driving that guidance. I would say, and I've said it the whole time, hey, this is an area where we're going to be prudent. We're not going to go over our skiis in terms of multiyear deals. Hopefully, those build as we go through the year. You saw that last year. But we need to guide what we see today. .

Our next question comes from the line of Jason Ader with William Blair.

I wanted to ask CJ about the federal business. I think it's interesting what you're doing there. And historically, has that not been a big part of the business and that's what drove this. Maybe just talk about the catalyst for the acquisition of Clarity.

Yes, I'll touch on it, and then Mike will add. First is we see tremendous opportunity in federal business, not only just United States, but in Europe and other places as well. Federal business, when you think about whether it's tax agencies, whether you think about other types of agency, for example, administrations of various kinds, there is a lot of unstructured data. And there is a lot of unstructured data that needs to be stored properly or documents for a lack of better term, and that needs to be retrieved, performance has to be high and the cost has to be lower.

So I am 100% believer that this is a large TAM for us. We have not invested significantly both from a go-to-market perspective as well as product perspective in the past. But the good news is we will have FedRamp high certification for U.S. federal this year. That comes with other set of requirements on how we support these federal customers. And one of the things that I have observed after being here is that a lot of these customers are still using our community version, and they would love to understand as we get FedRAMP High certification can we sell to them properly and serve them properly and have enough coverage. So massive potential, and that's why the acquisition, and I'll ask Mike to add.

Yes. So great answer. Thank you, CJ. Just to add on to that, Jason. One of the things that when we looked at the business, it has grown nicely, but it is a pretty small piece of our business today. We would like to make sure that we can play in all areas of the federal government, civilian intel, defense, all those areas. And we've partnered with Clarity, they've been a wonderful partner for several years. But when we have services and other engagements, we've typically had to use them. We would like that to be a MongoDB capability going forward. And then you marry that with getting FedRAMP High later in the year, we feel really good about our momentum going into next year.

Then a quick follow-up for you, Mike. NRR up by a point sequentially. What's the right way to think about the drivers there? Is it the 45% of customers that are adding additional capabilities on the platform? Or is there something else going on? .

Yes. So thanks for the question. I would say it's all of the above. Keep in mind that, that's a total company number. Atlas is higher than the company average EA is a little bit lower, and it's really Atlas that can drive that growth. And a lot of that is due to the platform adoption as well as really the big driver there with the adoption to is the move-up market and our focus on the large enterprises.

[Operator Instructions] Our next question comes from the line of Patrick Colville with Scotiabank.

congrats on a healthy print. I guess, CJ, I want to ask you this question, please. In your prepared remarks, you mentioned Frontier labs and it sounded like it was labs plural. I know you choose the words very carefully in the prepared remarks. I guess, did I pick that up correctly, that Mongo might not be working with multiple Frontier Labs? And then of course can you just unpack the statement around kind of mission-critical workloads and use cases because that sounded really interesting.

So short answer to your first question, yes, it is plural, and it was chosen carefully. Thank you for noticing, Patrick. Number two, as we work with them, and as they have tried, whether it's Postgres alternative or others, they have come to realize that. And these are truly at the forefront of innovation in AI space or driving innovation that MongoDB is just a great data platform for some of the workloads. And the point around -- of course, we cannot go into specific details with our agreements with them on type of use cases, but they vary and there are multiple use cases depending on the lab, that we're working with them, and it's certainly, but we will continue to expand.

Our next question comes from the line of Siti with Mizuho.

CJ, you talk about AI opportunity early at this point, but some of the moves like your partnership with LangChan, now you extended that to more strategic there. So can you talk about how that's going to help? And specifically, you talked about expanding platform now that you have 2 CPOs there. Can you help us on your road map? How should we think about the expansion of platform to further capture this AI opportunity?

Absolutely. So I'll answer your first question. LangChan, great partner. I'm really proud of what Harrison and the team are doing. And the simplicity when we talk to customers is three Legs of the stool for any agentic workload is harness, LLM and data layer. And if they are being used as in LangChain, they have significant traction. Even when I talk to some of the large banks, whether it's on-prem or in the cloud, there's significant traction, the harness layer.

And then they say, okay, what about the data layer and data layer, MongoDB being a choice for the data layer just makes sense. So we have done many integrations with them, and we are seeing this being played out at some of the large enterprise customers who say, I'm glad that the data layer as in MongoDB really works with the harness layer. And of course, we can choose whichever LLM we want. So that is actually being played out right now in some large customers who are trying to create agentic applications at scale.

Number two, in terms of the CPOs. I really, really proud of Ben and his long tenure here and focus on somebody wakes up every day focused on our foundational layer, whether it's Atlas and EA. And he will continue to do that. And with Pablo, who is based in San Francisco, he will look at emerging products and because the AI ecosystem right now is very concentrated actually in San Francisco City working not only with just the Frontier labs, but also with a lot of our AI native customers who tend to be in Silicon Valley, he wakes up every day to make sure how we are relevant in that ecosystem, and he is a product and technology guy who has scaled many, many product lines over time. So that really gives me one person focused on foundation, second person focused on emerging products as well as AI workloads. And what I just wanted to share with you briefly, I am really fired up about our innovation road map that is accelerating, and you will continue to hear new potential products as we move through this year at various .local conferences.

Our Next question comes from the Karl Keirstead with UBS.

Okay. Great. CJ, 3 months ago on the call, you announced pretty blockbuster deals. I think one was a $90 million tech deal. The other was a $100 million financial deal. Did the incremental portion of those deals ramp during the April quarter? Or is that still really sitting in front of us?

I would have Mike answer that on how that plays out, given those were long-term deals and how we think about it.

Yes. So thanks for the question, Karl. So those are multiyear deals. We talked about -- some of those were a combination of Atlas and EA. So there is almost always future growth in Atlas as we grow. They were not part of the original transaction. But that's certainly part of our go-to-market motion is to expand those relationships. So what we booked in the last quarter is largely what you saw in this quarter.

Yes. And I would say a, that you also see some of that as we continue to move forward from Q4 to Q1, more than the CRPO number that Mike outlined and how -- whether it's long-term commitments across EA or Atlas is really, really encouraging for us.

Our next question comes from the line of Sanjit Singh with Morgan Stanley.

CJ, in terms of the opportunity around AI and agents, which sort of part of the stack. Do you think is going to create the most value or the value capture opportunity? Was it sort of being at the embedding model layer? Is it being that long-term memory that you referenced multiple times in your script? Is it that core operational database? And maybe you can sort of stack rank if there's a sequence of that opportunity that should unfold over time?

And then for Mike, just a quick follow-up on the RPO CRPO performance the second quarter of really phenomenal bookings performance. My question is to what extent that represents sort of new business expansions, landing new logos versus maybe catching up to the existing consumption rate of your existing customers? If you can give us some color there.

Sanjit, I can't believe you asked me to stack rank, but here is how I would say. What I'm seeing today is that our ability to be that because AI workloads fundamentally will the requirements keep on changing the tech stack that these large enterprises are building AI workloads on, whether it's LLMs, they want to use multiple LLMs or SLMs, they want to use continues to change. And as people are building these agents as in developers are building these agents, us being super flexible with a no schema rather than rigidity of relational that you understand well, definitely helps us.

So I would say that architecture of MongoDB on native JSON even the chat conversations that you want to store could become a long-term memory. So next time you come in and ask a question, it knows the context. But I would say that architecture, it is almost our founder calls it really well that. We would rather be lucky than smart. And when we created MongoDB, this is from [indiscernible]. We didn't have AI workloads in mind, but this architecture is perfectly suited for AI workloads.

I would argue that, that's the first part of the stack rank. And then the second part is our ability to do real-time and provide real-time intelligence on operational data and having embeddings so that your token costs are lower and you have right retrieval that is accurate would be the second in the stack rank.

And then, Sanjit, it's Mike. So on your question, I would say it's more the latter, the second piece, but I do want to qualify that while we do certainly bring in net new logos, the majority of the RPO is going to be the existing enterprise customers, but with a big caveat, please don't read that to be. It's just the base business we get today. We certainly always want to drive incremental ARR in those relationships. That's going to be through net new workloads, new applications, expansion. While it's focused on the existing customer base, we always want to drive incremental revenue with those bookings.

Yes. And Sanjit, what I'll just add is that what Mike outlined we were really, really pleased that our go-to-market teams globally executed on what we asked them to execute on Q1, which definitely helps that met .

Ladies and gentlemen, I would like to turn the call back over to management for closing remarks.

So thank you, everyone. We delivered a strong first quarter with broad based momentum across Atlas Enterprise Advanced and our AI workloads. We are issuing strong guidance for Q2 and full year fiscal '27 and we remain committed to expanding profitability while investing for growth in line with our long-term financial model. Our results, our customer engagements and the leadership team we have assembled all point to the same conclusion. MongoDB is on its way to becoming the generational data platform of choice for the AI era. Thank you very much for dialing in today.

Ladies and gentlemen, this concludes today's conference call. Thank you for your participation. You may now disconnect.

All right. Good morning. I'm Sanjit Singh. I cover the infrastructure software space on the Morgan Stanley research team. We are super thrilled to have the management team from MongoDB, CEO, CJ Desai; and Chief Financial Officer, Mike Berry. CJ, Mike, thank you for joining us. You guys reported earnings this week. So thank you for coming down and joining us at the TMT conference.

Thanks for having us.

Thank you.

So before we get into the conversation, for important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. Today is like a special day, like a great lineup, and I think you guys have a great story. And so I want to dive in and tackle the debates on the story around AI. We'll talk about the quarter. So a lot to get through. But maybe to start off, CJ, investors are sort of getting back to basics in terms of thinking about these software companies, what's the value they create for their customers. So maybe you can walk us through that. What problems does MongoDB solve for customers today? And how will MongoDB create value for customers going forward?

Sanjit, I have been in the software industry for a long time. And one of the things that I would say is I constantly speak to customers on this question, why are you using us? If you're not using us, why not? And what value do you get out of it, okay? Very simple 3 questions, and you'll be amazed on the great answers you get from customers as they think it through. So we are software, but we are infrastructure software. And I know you know that, but we are infrastructure software and infrastructure software is extremely hard to create, as in to create and then innovate on. And as you add R&D to it over a number of years, it becomes a massive moat that you cannot just cloud code your way to it, okay?

So when I'm having conversations with customers specific to MongoDB, in my first 100 days, what is super encouraging is mission-critical applications at financial services, health care companies, insurance agencies or government, whether it's AI native company or a SaaS company that is now pivoting with AI agents and using MongoDB. So MongoDB becomes this layer of infrastructure software where the data needs to reside then they need to be able to search on it. And all of that in a single platform with even vector search because there is a lot of unstructured data in the world, that is what customers want. And when they say value, when they look at other potential choices because this market has existed for 60-plus years, they look at potential choices.

We are fairly unique compared to like a first-party service from hyperscalers, which only works in that particular hyperscaler. And you saw recently another hyperscaler outage in the Middle East a day before yesterday. So resiliency, scale-out architecture and having that peace of mind that my mission-critical data resides in it. And last thing I'll say is one of the banks I spoke to in Europe, they told me 1/3 of payments for my 45 million citizens that happens every week comes out of MongoDB. So we are the data layer that is super important to store, rely on, secure, perform as you run your business.

Yes. I think that point around the global availability and the resiliency is an important one, because in some sense, the superset of all of the data centers across the major hyperscalers. So if you're running -- want to run a distributed application anywhere in the world, Atlas is a great place to do it. I want to...

Let me touch one more thing. I was speaking to one of the AI native companies that is crushing it durably, that is crushing it durably. And they said, CJ, we want to expand even more workloads on MongoDB. And I said, okay. Everybody -- every hyperscaler is trying to sell you, everybody is trying to sell you, you are being very successful. Why? And the answer from the CTO is very simple is that you are the only database that I can run in -- he picked out 2 clouds, GCP and AWS side by side without me having to worry about it. And that's the biggest advantage you have that I can have a logical MongoDB across 2 physical cloud, and that is the reason we'll expand even more.

Awesome. Let's talk about Q4 results, fiscal year '27 guidance, especially given the stock reaction in the past couple of days. And so when you look at the numbers, a lot of great things to like here, 27% revenue growth. Atlas grew 29%. That was 1 point down from 30% in Q3. Meanwhile, the non-Atlas business grew 20% with the ARR from that side of the equation accelerating to 13% from 8%. Mike, one of the factors that you laid out that weighed on Atlas growth in Q4 was a large deal in which the customer bundled both Atlas and Enterprise Advanced. And so given that you called out that bundled deals are not a new phenomenon, can you speak to how you get to that normalized growth rate of 30% on Atlas?

Sure. And thanks again for having us. So this is a little bit of housekeeping. So I apologize if I didn't do a good enough job explaining this. So what we wanted to do is we typically don't call out onetime items, but we wanted to help bridge you from Q3 to Q4 and also make sure and connect our comments, which is the business performed largely as expected, okay? So -- and we got a lot of feedback from several investors saying, "Hey, thanks for bridging it." I think there are some folks that are not following it. So at a very high level, we have a very large customer that's great. They use both Atlas and EA, but they bought them separately over their journey with Mongo, Great.

In Q4, we did a new transaction with them, a multiyear transaction, and we had to combine them. It's one transaction. And the accounting rules say, when you do that, now you need to go through and use all the 606 rules and allocate the revenue. When we did that, no change to the underlying terms or consumption Atlas got less of an allocation in Q4 than it did in Q3. If you normalize for that, Q4 growth rounds up to 30%, okay? Full stop. That's that transaction. 3 important things. It's discrete to Q4. We don't expect this to recur again. All this is fully baked into the '27 number.

And this is not a trend folks, so don't make more of it than it is. We wanted to bridge you from Q3 to Q4. We have a lot of great customers. We love them all. They're wonderful. This was a big enough change Q3 to Q4, and it didn't connect with our comments, hence, the additional disclosure. Thanks for asking the question.

Just a couple of follow-ups on that. So you mentioned that outside this large deal, basically, what you saw basically came in line with expectations. When we go back to Q2 and Q3 when we saw like a bigger beat to beat, [ just to beat that ] was there anything in those quarters that drove that higher magnitude of outperformance from a onetime nature perspective? Was there anything unusual about what we saw in Q2 and Q3?

So thank you. So important note there. So Q2 and Q3 came in better than expected. Atlas, a little bit better than we thought. But the bigger beats in Q2 and Q3, as we talked about, were really related to EA. And this is the multiyear deal impact and dynamic that we have. No other big onetime events. I mean we have 60,000 customers. It's a $500 million business in Atlas. There's always puts and takes, but nothing to call out. And the bigger beats that we saw in Q2 and Q3 were directly related to EA.

One key strength in Q4 was RPO. What drove the strength in RPO? And how does this -- how does the growth in RPO compared to the growth in current RPO?

Yes. So thank you for that. So this is a number that we put in our Qs and Ks. Going forward, we will put it in the press release table so you get it when we do the call. At the end of Q4 last year, RPO was $748 million. These are commitments across all of our products that was mostly EA and about 59% of that we expected to recognize in the next year. At the end of this year, that number went to $1.47 billion, and we expect about 52% of that to be recognized in the next 12 months. So current RPO grew by about 74%.

Very importantly, folks, commitments on consumption businesses, that's our estimate. That number will vary because they still have to consume the commits. But for us, it's a wonderful sign because it's big customers committing long term to Mongo. And so that's a great thing. Don't assume that's all incremental. We always try to get incremental business, but it really gives us more visibility into this year.

So just on a current RPO basis, we saw about 70% growth in current RPO.

74% growth, and it grew by $325 million. So that was great.

That's great to see. Let's talk a little bit about guidance, and then we'll talk -- CJ and I will dive into the growth story at MongoDB. For next year, for fiscal year '27, you're targeting 17% growth at the midpoint. Atlas growth starting at 26%, ending the year at 21% to 23%. The question for you, Mike, is what are the swing factors that could drive upside to the outlook? And here's the unfair part of the question because you weren't the CFO last year. But how would you compare the degree of conservatism for the current fiscal year '27 guide versus the same time last year when the company guided for fiscal year '26?

So this is more about the year. I just want to make sure the question is for the year, correct?

That's right.

Okay.

Thank you for that. So I wasn't here last year. So I'm implying a little bit. As to your great question earlier, the performance in '26, we're super happy with. It was a very strong year. While we did a little bit better in Atlas, the majority of the beats were in EA. So as we look forward to this year, we -- and we said it multiple times on the call, folks, we feel really good about the business. As we go into '27, we think there's multiple growth drivers. We always want to set up the guidance to make sure that we feel really good about achieving it. And we will bake in -- we will always make sure there's more upside than downside, especially related to the EA business because that's tough to forecast. But we feel very good about Atlas going into '27.

And to your question, multiple growth drivers. We have not baked in a lot of growth in AI, either through AI natives, but most importantly, through enterprises deploying AI at a wider scale. We believe it is when, not if. Tough to forecast when. We've had a great move up market, and that's driven a lot of growth in Atlas. We continue to put more resources against that. We have some of that baked in. But if that does better than expected, that should drive more growth as well.

We're seeing a lot of migrations now as companies try to prepare for AI. That's another growth driver as well. And then, hey, from an economic perspective, we're assuming it's largely what it is. If rates come down, things get a little bit better, that's an upside as well. So multiple growth drivers on top of the guide.

Yes. So can I just add one thing? And I think this is a very important point in the previous 2 companies I worked for, our customer base was not growing as much as it is growing in MongoDB. So right now, when you look at the new customer adds that happened in fiscal '26, the new customer adds grew by 60%, okay? And now customer base is at 65,200. So how much would that customers grow? And how does that cohort play out? But overall, it's a positive sign because to get a new customer that I've learned over my career is so hard.

And for us to continue to add across the spectrum from small digital AI natives all the way to the top line, some of the AI native companies we didn't even know and we find out, wow, they got multibillion-dollar valuation and they think we are a product-led growth motion. So that specific base of 65,200 and 60% new customer growth that just happened, it will take time, but it is also how that cohort plays out is very important.

I mean it's one of my favorite assets of the story is the new logo growth in MongoDB because it not only implies like how you guys are sort of executing on the field, but it speaks to the durability of the opportunity 2, 3, 4, 5 years out. And it's something that we pay attention to. You guys are in the top tier in terms of customer base growth. And so that's definitely fantastic to see. The only other context that I was going to add is when the company guided to Atlas last year, I think it was in the low 20s. We ended up at 29% when fiscal year '26 was all said and done.

So let's zoom back out and talk about the growth opportunity. Past the $5 billion in revenue, right? So CJ, you've been -- you bring over 25 years of experience and mostly helped scale ServiceNow from $1.5 billion to a $10 billion annualized revenue business. When you think about your $5 billion revenue aspiration that roughly implies doubling the business. Can you outline the strategy for doubling the business over the coming years?

Absolutely. So on first principles basis, do I see a path to $5 billion plus? The answer is 100% yes. That's why I joined the company. I did my own diligence to really understand the TAM, where we play and why should we win? Because TAM is a TAM, but you have to earn your right to say this is the right architecture. And in this world, in 2026, why should somebody put their workload on MongoDB. So one is, absolutely, I see the opportunity by speaking to hundreds of customers before joining to see that we can see a path to $5 billion plus. So that's at the highest level, I absolutely believe that we can get there, $5 billion plus.

Number two, I see that in 2 buckets. Bucket #1, of course, is Atlas, super important. I mean now it's at a $2 billion run rate, growing 29% when I look at the year behind us. So if you can do that math and if we can sustain and commitment to the long-range model that Mike gave in September is that 20% -- he said 20% plus. He said 20% is the floor, 20% plus growing. You can do that math and you can see a path in a few years to get there from that $2 billion base to 4 plus. And then the question comes about EA, right? And that's why in my prepared remarks, in speaking to certain customers, this trend that I'm speaking with some customers, not all, but regulated industries, definitely public sector, where there is this almost with what's going on in AI and others, we -- they do want to use on-prem, okay?

And on-prem, on-prem cloud, it's not just banks, but there are like other companies who are saying, "Hey, we need to hedge and we need the hybrid option." So EA, which is still 20% plus of our business, what should be the growth rate? How should the product strategy evolve? So we are investing in the product strategy this year as in specific innovations like vector search and others in EA. And then how do I consider that as a business that still has growth -- durable growth assumption that adds to the Atlas 20%-plus growth that Mike laid out. That's pretty much it.

Now in addition to these 2, because we have -- people have always seen us as a single product company that builds a database, whether it's EA or whether it's Atlas. Make no mistake, we are going to add products. We are going to create a platform. And coming back to my previous employers, last 2, that is what we did systematically that expand the TAM, go to the adjacent buyer or maybe to the same buyer and have that platform play. So that is an additional thing. So we are currently investing in R&D, as Mike called out on the call, besides other investments that we are making. So that's how I think about on a first principles basis, you Atlas, Mike gave very clear guidance.

How do I think about EA, which is still a big number on that path to $5 billion. We are going to add additional products to lock that path to $5 billion. And last but not the least, which Mike called out, we are not in Japan. We have to be in Japan. So this will be the investment year to be in Japan, investment year, so expanding the team and so on. And public sector, which has very specific custom workloads, we have to be in public sector in a meaningful way over time. So that's an investment year as well. So those are the 2 additional things. So you combine these together, and I can see a path. Of course, we have to execute. A lot of things have to happen correctly. But if I didn't have that opportunity, I would be coming and telling you, hey, I just don't see that, right? So that's what I think.

It's a perfect segue to my next question is a question kind of focused on TAM. And if you sort of zoom out and look at what's happening in the broader ecosystem, you see some of your peers coming from the analytical data stack, really trying to build, I would call, an agent stack. So they're trying to get build -- acquire OLTP databases. They're getting into observability. They want to get into orchestration, right, to sell customers an application stack, right, where -- and that's kind of where MongoDB has played.

When I look at what you guys have been -- I feel like you guys have been more focused on just being that generational data platform. And so why does MongoDB seem to be following suit in terms of building your own agents, expanding to other layers of stacks? And why is the focus on being a foundational data platform, the right strategy for MongoDB?

One, fundamentally, I would say we always want to keep main thing the main thing. This is architecture, even though it was created in 2007 by the founders, it is the right architecture for modern workloads and AI being a platform shift. You do not want to take focus away from it, okay? So we have to innovate on the core is how I would simplify it. And then make no mistake, as I work with our CTO and the product teams, we are figuring it out where else do we have a right to play?

And what else should we be building, which we are building, leveraging AI, so that we can come at the right points throughout this year at our .local conferences and say, this is an additional thing that you should expect outside of the core database layer X, Y and Z, where MongoDB is going to play because customers want us to play there. So those are the announcements you will see real concrete product announcement that we are playing in addition to the core database.

Yes. So it looks like we've got lots to look forward on the product innovation side. CJ, you've been a pioneer when it comes to building and delivering AI products to market. And so when we think about embedding models, reranking models, vector search, hybrid search, to be able to do transactional processing, where do you see the most enduring value being created when it comes to AI apps and applications?

So on AI apps, what we are already seeing with some of the AI native companies. So I spoke to the founder of this vibe coding platform. And people are coming in there. I mean, literally, I said, can you give me the top 5 prompts or top 10 prompts, what kind of apps people are building on your vibe coding platform? And it varied from simple things like I'm trying to create an observability dashboard based on these data sources, something very simple that an internal team using. Two, people are putting things like, I don't like my CRM vendor give me a lead management software that I can build on my vibe coding platform. So you have like -- he showed me like the actual prompts that get put in this vibe coding platform.

This vibe coding platform, he told me, CJ, that I have very simple tech stack. One is fast APIs. He still doesn't believe in MCPs, right? So outside of LLM, fast APIs and he thinks MCPs are not scaling for his business. Number two is React on the front end. And then M was MongoDB. So FRM is basically he said. And I said, why MongoDB, you built in. He came over our website, picked it, the company is like doing really, really well globally. And he said, because this can scale, whether it's images, PDFs, documents, search, vector search and so on.

These kind of examples or a frontier model company using us for some of the use cases just show the power of our platform as in our database. And that's how I look at it when you think about it that if these kind of leading-edge companies that were created like a year ago or 2 years ago, right, they have not been around for that long. And if they choose us as an architecture because I asked them the same question, Sanjit, you had a year ago, did they look at Postgres? Yes, they did, but Postgres didn't have multi-cloud resiliency, if you're a vibe coding platform, that is your business, you want multi-cloud resiliency.

Then number two, can it scale for unstructured data and JSON native. So -- and then he uses multi-tenant and things like that. So these are the kind of examples that gives me a lot of optimism that this is a great platform in the age of AI and multi-cloud because multi-cloud is a tailwind for us. And every year, all hyperscalers had outages in 2025, and it's already '26. And you saw that what is currently happening in the Middle East with one of the hyperscalers and how it is impacting so many customers.

I had prepared a question on Postgres a little bit later, but I might as well hit it now. You mentioned the global availability on the resiliency of MongoDB as an advantage. When it comes to other companies on Postgres, I mean you guys have been clear that MongoDB doesn't have to lose for us to win, like there's plenty of opportunity. But what are the things that you would call out? Is it the rigidity of the data schema? Is it scale-out versus scale-up architectures? What do you think that developers are going to look to MongoDB to be the destination for next-generation apps?

So I'll tell you the positive feedback, and I'll tell you what they expect from us. So on the positive side, and you have done -- Sanjit covered infrastructure software for a long time, truly scale-out architecture that was built when Eliot and the team created this database, scale out 100% matters because they do not know what -- how much they are going to grow, where they are going to grow, which region they are going to grow and our availability in multiple regions and clouds and so on, definitely helps.

So scale out from an architecture perspective, native JSON and how friendly. This one AI company I met and I said -- and they're a very successful AI company, why did you choose MongoDB? He said, my last 2 start-ups, I was very successful. I sold them. Now this one, I have scaled already. I'm not going to sell it. All 3 I have built on MongoDB. So there is also this developer-friendly thing that you hear about MongoDB, how easy it is, flexible it is that as the business or use cases changes, relational, as you know, is very rigid is the term.

And then the third thing I would say, vector search, search, all in a single data plane, including now embeddings, that is a huge advantage because otherwise, you need either bolt-on or you ETL for search performance when it comes to queries. And then the last thing I'll touch on, write performance because we have so many writes happening, in addition to read performance, we are world-class.

Awesome. When you look -- you guys have disclosed approximately 30% of ARR now comes from customers with at least one AI use case, and there's thousands of AI apps running on Atlas today. As those trend lines continue to improve, what needs to happen for MongoDB's growth to start to benefit from AI applications in a more material way?

AI natives are helping us grow. AI natives, you're fully aware that we did .Local in San Francisco less than 6 weeks ago on January 15, and we are going to do it again in August just to keep that drumbeat on to do that. So we are top of mind for this company. So that cohort, Sanjit, that cohort is important to us because that is contributing to our growth. But like Mike has said, not in a meaningful way when you look at the big base of $2 billion plus. Now you look at enterprises, and I speak to them all the time, including large banks or health care companies. And they have started creating agents, but these agents are not like at the scale of active users that you have in the consumer side of AI frontier model companies or on the AI native companies. Their monthly active users are not that high or a weekly active user.

When that actually happens, we will benefit. When does that happen? I do not know. And that's why Mike said that, hey, that will be a tailwind, but we are not baking on it right now because speaking to customers, it is still early. Like they tell me, yes, we have this productivity agent here. Of course, we are using the coding agents here, but that's pretty much it. Nobody -- like every retailer I speak to, including the large they talk about agentic commerce. And I tell them, show me. And they're like, yes, we have ways to go, but that is our strategy.

Awesome. Let's bring Mike back into the conversation. Mike, at the Investor Day last year, you spoke to the ability to keep investing while driving 100 to 200 basis points of annual margin expansion going forward. How are you driving efficiency across business operations? And what role AI is playing in driving those efficiencies?

Sure. So thanks for the question. As we look at '27, and we talked about it, Mongo has such a great business model that we are able to invest in the things we've talked about, largely driven by the growth in the business. So revenue will drive profit, but it allows us then to drive the innovation and also expand into new markets. One of the nice things about Mongo is that, hey, we have a great customer base, but we have also not done as well as good a job, I'll say, expanding internationally. So a lot of the new headcount that you'll see at Mongo will be in lower-cost locations, especially across R&D and G&A.

We will continue to invest in selected headcount for sales. On the AI side, that is another opportunity for us. While we've looked at it, we're like most enterprises, we've not deployed it at scale. So a little bit of coding. Certainly in marketing, there are some good areas, in G&A as well, but that's also an opportunity. So while we will add headcount in fiscal '27, the goal is, hey, going into fiscal '28, that headcount should be relatively consistent, and then we'll see if we can drive further efficiencies after that.

You touched on an important point that I think the market is sort of debating given some of the announcements in the recent weeks. To what extent is MongoDB's ability to grow in the future at an attractive rate? How much of that is tied to headcount growth? And do you see these 2 forces decoupling as we progress into the AI era?

Yes, it's a great question, and you read about all the other reductions, and that's certainly not what we're doing. So for us, there is certainly reliance on sales headcount. Quota matters, sales capacity matters. And then in R&D, probably more importantly, all the innovation that we need, we do need headcount to do that. Now over time, that's going to get more and more efficient. And obviously, with the coding tools that folks have. So you'll see that increase in '27 because we have a little bit of catch-up to do.

But going into '28, all those things should allow us to be a lot more productive. I'll give just a huge shout out to the sales team. They had a really good year, and they drove their productivity up in '26. The rest of us need to do the same thing as well. AI tools will help that. So we don't need to continue to hire at the rate we are at this point, and we will become more efficient as we move forward.

My next question for you, CJ was around partnering with the ecosystem, just you're partnering with Anthropic. The company announced that partnership with Anthropic and you also announced partnerships with other ecosystem partners, including LangChain and Vercel. What will the Anthropic partnership mean for MongoDB's customers? And how will that accelerate your traction with AI workloads?

I think of this in 2 buckets. So one, when frontier model companies use us, it is a validation of our architecture because they could have said totally themselves because they are in that business, we should have just coded this ourselves, but they said, no, we are going to use MongoDB. So on the first principles, that is a huge validation that when they are building version 4.x or whatever it is, and they're doing that on MongoDB, it's a huge validation of our theory because they could have easily said, we don't need a data layer and we are going to code it ourselves, okay? So that's one.

Second is, so them being our great customers, frontier model companies is always a good thing. And as they grow, we want to grow with them based on the use cases that they used us for. Second thing is I had a conversation with one of the frontier model companies, and I said just recently that now the need for modernization, I would argue, is even higher in a Fortune 500 Global 2000 because of AI. So I said, hey, we want them modernization. We have the most modern database. Is there something we can partner with that if we provide you all the things we have learned as customers have modernized on MongoDB new workloads, can we work together so that you, just in the model, help people get to MongoDB as a destination that we can go to the bank and so on.

So we are in very -- Sanjit, early stages for those kind of things. But that is the second area of partnership that I think that where can they help us if the destination is MongoDB, which will be Atlas and will drive Atlas consumption over time. All this technical debt that large companies have, what role they can play to partner with us because we, from a future perspective, want those workloads on Atlas or EA, because depending on the company and where they want to run it. And so if they are going to help us and partner with us, what can we tell them about what we have learned so that they can do that while we add our own IP, which we are in the process of to get to that destination of something running with the right architecture and right performance on MongoDB.

So these are the kind of extremely strategic conversation. And third thing that I underscored in the earnings call, which is really, really important. When I understand, even after speaking to the founders and speaking to customers who love MongoDB, right, there's a huge fan base that I'm really proud of is the reason the leaf exists is that, hey, this is a very natural way to interact with the database to create a database for that human developer. And I want machines as an agents who are creating apps, whether it's a vibe coding app, your question around some of the partners that you talked about.

I want that the same thing that how easy for the machine to say, "Oh, this use case, it is perfect for MongoDB, and I'm going to spin up an Atlas and we have scale out." So don't worry, we are not going to overprovision it, and it will autonomously scale for you or chart for you or perform for you. That's the hallmark in the next few years from an innovation perspective. Human developers love MongoDB. Machine developers should also love MongoDB, which requires a bunch of work to be done on the platform side for us and then right partnerships to be had. That's why I hired Erica.

Awesome. That's a very compelling vision. CJ, Mike, thank you for giving us an update on the MongoDB story and best of luck for fiscal year '27.

Thank you.

Thank you.

Good day, and thank you for standing by. Welcome to MongoDB's Fourth Quarter Fiscal Year 2026 Earnings Conference Call. [Operator Instructions]. Please be advised that today's conference is being recorded.

I would like to hand the conference over to your first speaker today, Jess Lubert, VP of Investor Relations. Please go ahead.

Thank you, operator. Good afternoon, and thank you for joining us today to review MongoDB's fourth quarter and full year fiscal 2026 financial results which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB; and Mike Berry, CFO of MongoDB.

During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, our opportunity to win new business, our expectations regarding Atlas consumption growth. The impact of non-Atlas business and multiyear license revenue, the long-term opportunity of AI, our financial guidance and underlying assumptions in our investments and growth opportunities in AI. These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations.

For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended October 31, 2025, filed with the SEC on December 2, 2025. Any forward-looking statements made on this call reflect our views only as of today, and we undertake no obligation to update them except as required by law.

Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures.

With that, I'll turn the call over to CJ.

Thank you, Jess, and thank you, everyone, for joining us today. To begin, I would like to provide some observations from my first full quarter at MongoDB. Over the last 100 days, I have spoken to more than 200 customers globally, spanning from AI natives to Fortune 500 enterprise customers that are leveraging the MongoDB platform to drive innovation that is critical to their business, whether it's an AI or digital native looking for a highly performing solution that dynamically scales, a large enterprise looking for multi-cloud resiliency for their modern mission-critical applications or a customer seeking an integrated offering for AI agents with features such as search, vector search and embeddings in a single intelligent data layer.

Customers are excited about the strength of the MongoDB platform. My key takeaway is that MongoDB's foundation is in great shape, and the company is well on its way to become the generational data platform of choice in the AI and multi-cloud era.

Now onto this quarter's results. We generated total revenue of $695 million up 27% year-over-year, beating the high end of the guidance by 4%. Top line strength was driven by Atlas, which grew 29% year-over-year crossing the $2 billion run rate mark for the first time and generating a record $114 million in net new revenue in the quarter. non-Atlas grew 20% year-over-year, our best growth quarter in the last 2 years.

We signed several large deals in the quarter, including an approximately $90 million transaction with a large tech company that plans to expand both core and AI workloads on Atlas and a greater than $100 million transaction with a large financial institution for enterprise advance referred as EA, representing the largest TCV deal in the history of MongoDB.

We delivered a non-GAAP operating margin of 23%, more than 100 basis points above the high end of guidance. We ended the quarter with over 65,200 customers adding 2,700 customers in Q4, growing both year-over-year and quarter-over-quarter. This brings our full year customer additions to 60% year-over-year increase. While AI is not yet a material driver to our results, we are encouraged by the growth we are seeing with customers leveraging our AI capabilities.

The number of customers leveraging Vector Search has nearly doubled year-over-year, and the number of customers using voyage embedding models has also doubled since the acquisition last February. This growth is across a diverse range of customers, AI natives, digital natives and large enterprises.

We finished fiscal 2026 on a high note, with strength in Q4, driven by our continued go-to-market execution and the broad base demand we have seen across the business. Our teams generated record new ARR in Q4, an acceleration of that metric in fiscal '26, highlighting the strength of both our upmarket and self-service motions.

Our EMEA team had an especially strong Q4, generating record new ARR driven by wins at major financial institutions, large retailers and leading tech companies. At the same time, we outperformed on operating margin, achieving above a Rule of 40 performance and demonstrating that we can drive durable revenue growth while simultaneously expanding margin.

Through my conversations with customers, a clear team emerged. Large enterprises are increasingly standardizing on MongoDB to power a wide spectrum of portals, including both core mission-critical applications and emerging agentic AI applications. Rather than treating AI as a stand-alone initiative, many are expanding their use of us as a strategic data platform that supports both foundational workloads and their next generation of intelligent applications.

For example, MongoDB continues to cover a wide range of workloads, including high-volume transactional systems, real-time applications and emerging AI workloads across multiple lines of business at JP Morgan Chase, the world's largest financial institution. The scale and breadth of our partnership with them reinforces our ability to serve as a strategic data platform for the most demanding enterprises.

We see tremendous opportunity to expand within our existing Fortune 500, Global 2000 and AI native customer base, where I'm actively leveraging my relationships to open new doors, engage the C-suite and drive strategic expansion conversations top down.

MongoDB is increasingly recognized as the architectural foundation powering innovation for frontier model companies, leading digital natives expanding into AI and AI native organization scaling globally. The database layer has endured through multiple technology shifts over the past 60 years, and it is even more critical in this AI shift. AI and agentic applications require memory, state and high-quality retrieval capabilities native to our modern OLTP platform, which powers real-time applications, with our ETL odd bolt-on systems through integrated search, vector search and embeddings.

In this platform shift, OLTP is the high ground and MongoDB's purpose built to win. Notably, Emergent Labs, a leading AI white coding platform in India that just crossed $100 million run rate, selected Atlas over post-CECL to power AI agents that build production-ready applications from natural language problems. They power nearly 6 million applications built across 190 countries and handle applications that averaged 35,000 lines of core with some reaching 300,000, all made possible with Atlas' flexible document architecture and reliable scale.

We are also fueling innovation at AI-native customer 11 labs, which is redefining conversational AI with its new enterprise agentic platform. 11 labs selected Atlas to power the critical long-term and knowledge base for their autonomous agents. By leveraging Atlas Search and Vector search, they enable their agents to retain complex context and deliver highly personalized interactions in real time and at global scale. Supporting the rapid expansion to $330 million of ARR and $11 billion valuation.

Another tailwind is the renewed importance of on-premises deployment in enterprise architectures. Many large customers, particularly in regulated industries such as financial services, telecommunications and government, view EA as mission-critical and are making long-term commitments that reflect the need for operational resilience and support for data that will not move to the public cloud.

Consequently, I'm confident in the durability of our EA business, pursuing feature parity to Atlas and continued go-to-market momentum are key priorities as we move forward. For example, Axon Networks, a global leader in telecom network management, serving 32 telcos and over 90 million homes and enterprises selected EA as the foundation for its operator as a Service platform.

This platform delivers a real-time digital twin and API-first architecture designed to handle massive data peaks and high-volume time series workloads. EA provides the flexibility to run across mission-critical environments including hyperscalers and bare metal, along with the enterprise-grade security and operational tooling required to support Axon's AI-first autonomous networking platform at scale.

What is truly compelling about our platform is that these tailwinds serve as a powerful force multiplier for one another. The combined power of these capabilities, the flexibility of the document model the performance and scale of Atlas, the ability to run anywhere, and our integrated AI functionality is what really resonates with our customers.

A marquee example of the platform in action is Adobe, which expanded its strategic partnership and long-term commitment with us to accelerate AI-driven innovation. MongoDB now underpins a range of Adobe's key initiatives, including Agentic experiences powered by Atlas Vector Search and soon voyage and embeddings.

Adobe leverages Atlas to manage large fleets and always on database deployments at global scale, while also continuing to partner with us for support of self-managed business-critical workloads on EA, highlighting our ability to operate seamlessly across both cloud and on-prem environments. After spending time with 200 customers partners and our go-to-market teams globally, it has become increasingly clear that we have a massive opportunity ahead of us.

The strength of our platform and the depth of our customer relationships is a direct reflection of our exceptional global team, and I'm proud to say we have world-class talent across engineering, go-to-market and G&A functions. During the upcoming year, my focus will be to build upon what's already working by.

First, remain relentlessly customer-focused to deepen strategic partnerships and accelerate growth, particularly across large enterprises and AI native customers here in Silicon Valley.

Second, accelerate our innovation agenda by empowering product and engineering teams to build a generational multi-cloud data platform for the AI era.

Third, thoughtfully scale ourselves or motion to expand adoption across the long tail with a disproportionate focus native companies.

Fourth, drive operational excellence across go-to-market, product and G&A to enable our teams to perform at their best, while sustaining durable, profitable growth.

Finally, I wanted to provide an update on our go-to-market leadership. Effective tomorrow, March 3, 2026, Erica Volini joins MongoDB as our Chief Customer Officer, reporting directly to me to accelerate our next phase of growth. Erica brings a rare blend of experience serving large enterprise customers at Deloitte and scaling go-to-market growth at ServiceNow.

At MongoDB, she will focus on accelerating our partner growth engine, deepening our enterprise footprint and ensuring a seamless world-class experience across the entire customer life cycle. As noted in our earnings press release, Cedric Pech, President of Field Operations; and Paul Capombassis, Chief Revenue Officer are leaving MongoDB. We have been thoughtfully planning this transition for some time. and we believe now is the right moment for this change. I want to extend my sincere gratitude to both Cedric and Paul for their contributions over the last decade. They were truly instrumental in building our go-to-market foundation.

Looking ahead, we have a deep bench of go-to-market talent and the team is well positioned to execute against our objectives without disruption. We are in the latter stages of a search for a new CRO. The caliber of these candidates is a testament to our momentum and the significant opportunity ahead. Paul will remain CRO through Q1 and serve as an adviser through Q2 to ensure a seamless transition to the new CRO.

With that, I'll now hand the call over to Mike Berry to discuss the financial results in greater detail.

Thank you, CJ, and good afternoon to everyone on the call. I will begin with a review of our fourth quarter fiscal 2016 results and then finish with the outlook for the first quarter and full year fiscal '27.

In order to spend more time on the fiscal '27 outlook, I'll be a little more concise on my fourth quarter comments. I will be discussing both GAAP and non-GAAP results. As CJ mentioned, we had another strong quarter as we exceeded all of our guidance ranges and finished our fiscal year on a high note. In the fourth quarter, total revenue was $695 million up 27% year-over-year and above the high end of our guidance.

Our income from operations was $159 million for a 23% operating margin compared to 21% in the year-ago period. We achieved positive GAAP operating income in the fourth quarter. We are very pleased with our stronger-than-expected operating margin results, which benefited entirely from our revenue outperformance.

Net income in the fourth quarter was $143 million or $1.65 per share based on 86.5 diluted shares outstanding. This compares to net income of $108 million or $1.28 per share on 84.6 million diluted shares outstanding in the year ago period. Shifting to our product mix, Atlas revenue momentum remained strong, with year-over-year growth of 29% in the fourth quarter, which accounted for 72% of total revenue up from 71% in the year ago period.

Atlas growth was driven by continued strength with our largest customers in North America and Europe, where we saw strong momentum with growth of new and existing applications. We believe this strength reflects the growing strategic importance of Atlas to many existing customers and is a positive indicator of future growth. You can see the success with existing customers in our total company net ARR expansion rate, which increased to 121% in the fourth quarter. up from 120% last quarter and 119% a year ago.

Turning to non-Atlas. We experienced strong momentum during the fourth quarter, driven by strength with financial services, public sector and technology customers that are choosing to build with MongoDB long term for their most mission-critical applications. This resulted in strong multiyear revenue and non-Atlas ARR, which reflects the underlying revenue growth of this product without the impact of changes in duration.

Non-Atlas ARR grew 13% year-over-year, reflecting the momentum we are seeing in the business. The strength in non-Atlas also resulted in a higher-than-expected number of larger deals with bundled Atlas and EA products. This resulted in a greater-than-expected attribution of revenue to EA versus Atlas in the fourth quarter. Adding back this impact, Atlas growth would have been approximately 30%.

The -- we are encouraged to see more of our customers growing on both Atlas and EA and believe these deals illustrate the strategic importance of having both cloud and on-prem solutions for many of our largest customers. You can see the strength in the growth of deferred revenue as well as the growth in RPO, which grew from $748 million at the end of fiscal '25 to $1.47 billion at the end of fiscal '26, a year-over-year growth of 97%.

We ended the quarter with 2,799 customers with at least $100,000 in ARR and 402 customers with at least $1 million in ARR representing 17% and 26% year-over-year growth, respectively. For each of these cohorts, ARR is growing even faster, reinforcing the benefit of our market focus. Of our Atlas customers generating at least $100,000 in ARR, 44% are leveraging 2 or more features of our platform, which is up from 36% in the year ago quarter. Average revenue from these platform customers is meaningfully higher on average as compared to the rest of the Atlas base, illustrating the benefit of our platform capabilities.

Turning to the balance sheet and cash flow. We ended the fourth quarter with nearly $2.4 billion in cash, cash equivalents, short-term investments and restricted cash. We spent $55 million to repurchase approximately 133,000 shares and used $60 million for the cash settlement of taxes on employee RSUs.

Operating cash flow remained strong at $180 million, and free cash flow was $177 million, which compares to $51 million and $23 million, respectively, in the year ago period. Our cash flow results were driven primarily by strong operating profit and improving working capital dynamics particularly related to higher cash collections mainly driven by the higher-than-expected multiyear EA deals.

Now I'd like to share a few guiding principles and some of the assumptions underlying our outlook for Q1 and fiscal '27. To begin, we continue to believe in the long-term model presented at Investor Day last September and remain committed to grow an Atlas by greater than 20% and being a Rule of 40 company. We will achieve this goal through a combination of revenue growth and margin expansion. But to be clear, revenue growth will be the main driver of improved profitability.

Our outlook assumes the business environment remains relatively stable, and we operate under similar conditions to what we experienced over the course of the past fiscal year. As I mentioned at our Investor Day in September, we have not changed our guidance philosophy as we will provide an outlook with more upsides and downsides specifically related to the EA business. We are early in the year, and we want to be mindful there could be risk that we do not have line of sight to at this time.

Now let's get into the details. Starting with Atlas. We have continued to see strong momentum and experienced relatively consistent consumption growth through the course of the past year. We expect these trends to continue through fiscal '27 and I would also note that as Atlas has grown larger, this has helped limit the volatility from specific customer cohorts. Based on our continued confidence in our market positioning, customer feedback and product advantages, we currently expect to see Atlas revenue growth of approximately 26% in Q1 and 21% to 23% in fiscal '27. This outlook reflects our continued confidence in Atlas while taking into account we are a consumption business and visibility is more limited in the back half of the fiscal year.

For non-Atlas, we have continued to see healthy ARR trends and we have been positively surprised by the momentum we experienced with large multiyear deals in fiscal '26. While we remain optimistic regarding our ability to grow this business over the long term, it remains difficult to predict, and we will only include deals in our forecast that have either closed or have a high probability of closing to limit the risk of a negative surprise.

At this point, we expect our non-Atlas business to see mid- to upper single-digit growth in Q1 and low to mid-single-digit growth in fiscal '17 and which reflects our belief that the impact of duration will neither be a significant headwind or tailwind to growth for the year.

In terms of AI, we remain optimistic regarding our opportunity and are seeing encouraging trends with a number of AI native. While this subset of customers has significant potential -- many of them remain early in their MongoDB journey and are not yet meaningful drivers of revenue.

Turning to profitability. We remain committed to driving revenue growth and expect to expand operating margin by 100 basis points in fiscal '27. I -- we will achieve this expansion while investing for growth. Some of these investments include enhancing our AI capabilities, further integrating voyage bringing feature parity to EA relative to Atlas, building out our presence in Japan as well as strengthening our U.S. federal business.

We will also continue to invest in marketing programs, developer awareness and select quota carrying headcount. With respect to cash flow, we made meaningful progress in cash management during fiscal '26 with our operating cash conversion exceeding 100% and up significantly from the approximately 50% experience in fiscal '24 and '25. This remains a key area of focus, and we would expect cash flow to remain healthy in fiscal '27. We currently expect cash conversion in the 80% to 100% range during the upcoming year and on a longer-term basis, which is in line with our long-term model.

Finally, we will continue to execute our share buyback program to partially offset dilution from employee equity awards and settle the taxes due on the vesting of employee RSUs with cash instead of issuing new shares. In fiscal '27, we currently plan to commit 100% of our free cash flow to these 2 actions and will also benefit from the settlement of over 1 million shares of stock for the cap calls associated with our 2026 notes that matured in January 26.

We will continue to manage share count prudently for the long term and demonstrate our commitment to being good stewards of your capital. Now let's shift to guidance for the first quarter in fiscal '27. For the first quarter, we expect revenue of $659 million to $664 million, which equates to 20% to 21% year-over-year growth. We expect non-GAAP income from operations to be in the range of $105 million to $109 million for an operating margin of approximately 16.5% at the high end.

We expect non-GAAP net income per share to be in the range of $1.15 to $1.19 based on 86.2 million diluted shares outstanding. For fiscal '27, we expect revenue to be in the range of $2.86 billion to $2.9 billion, representing full year revenue growth of 16% to 18%. We expect non-GAAP income from operations of $545 million to $565 million for an operating margin of approximately 19.5% at the high end of guidance.

We expect non-GAAP net income per share to be in the range of $5.75 to $5.93 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the first quarter and fiscal '27 assumes a non-GAAP tax provision of 20%.

To summarize, we had another strong quarter and feel very good about the business heading into fiscal '27. We are pleased with our ability to drive both revenue growth across the business while expanding operating profit and driving meaningful free cash flow. We remain incredibly excited about the opportunity ahead, and we will continue to invest responsibly to drive long-term shareholder value.

With that, operator, we would like to open it up for questions.

[Operator Instructions]. And our first question comes from the line of Raimo Lenschow of Barclays.

Congrats great fourth quarter. Two quick questions. One for you, CJ. At your big event in January in San Francisco, what were your impressions about developer buy-in Part of the reason for the doing is like to increase mind share again, like share a little bit your experiences there?

And then one for Mike. On EA, next year had a bigger cohort than this year, I'm just wondering if the strength in the second half of this year, was that earlier renewals for next year? Or is the core still in place?

Thank you, Raimo. Appreciate it. So January 15 event, our local San Francisco, I would consider a great success and I would put that in 2 buckets. Number one, we exceeded even though it was a week day, many, many founders, builders who came to the event, and there was a long line outside for people to get into the conference that give me really, really good feeling that we invested in the right area.

Number two, when we looked at the attendees, Raimo, I would say, compared to other typical do local events where people who are already customers or builders of MongoDB. Here, around 70% had not used MongoDB. And that's what gave me a lot of conviction that it was a successful event where we are increasing the mindshare of the builders in the San Francisco Bay area where a lot of AI native companies are being built.

The last thing that I'll touch on is that because of the success of that event, and continue to make sure that we are on top of mind for all these AI native companies, whether they are in security, whether they are in fintech, whether they are in domain-specific AI, we are going to repeat dark local in San Francisco, again, in August this year, which we have not done before based on the success. Mike?

Thank you, CJ. Raimo. Thanks for the question. So on EA, a couple of things. So we're super excited about the year we had fiscal 2016 was a very strong year and especially not only in the run rate business, but obviously, all the multiyear deals. So it's a big business, thankfully. There's always some puts and takes in terms of renewals. I would say there's no material change to the cadence of early renewals. And keep in mind that even if there is one, you won't see it in revenue until that deal comes up. So you shouldn't see any major impact in cohorts next year.

Our next question comes from the line of Matt Martino of Goldman Sachs.

CJ, maybe to start with you. You noted the transition for Cedric and Paul has been in the works for some time. Given that visibility, can you provide more color on the current status of the CRO search? And specifically, what are the primary attributes you're looking for in a successor that led you to announce Erica's appointment today while the search for a new revenue lead remains ongoing.

Absolutely. So Matt, here is how I would describe it. Personally, being here, as you have seen that I've spent a lot of time with not only our customers, but with our go-to-market team. So we are in the final stages, but we want to make sure that we get an excellent candidate for our Chief Revenue Officer. Erica is focused will be as a Chief Customer Officer to ensure that customers who purchase or decide to use MongoDB platform, they get to value by providing all the post sales support functions, whether it's technical success, technical support, many other things like professional services.

So one Erica is going to focus on customers who have already bought MongoDB or expanding with MongoDB, how do they get to value and how do they get to success. In terms of the CRO search, Paul is staying fully through Q1 and help us transition through Q2. And from the attributes perspective, I want somebody who is very focused on high end of the enterprise, understands how things work at MongoDB from a Main Street perspective, but also working with the management team as we expand into both the AI natives as well as enterprises who are building more mission-critical workloads on MongoDB, including AI. So that's the mix, I would say. -- is somebody who is strategic, who understands consumption-based models on how MongoDB really operates, of course, our Enterprise Advanced business and has relationships into the high end of the market where we are getting significant traction besides the native companies, which is hardly.

Okay. Very clear. And then Mike, maybe for you, just a couple of major EA deals were announced this CJ talked about the renewed importance of on-prem. I guess under that backdrop, should investors be recalibrating expectations around growth for the EA business as we look out over the next couple of years?

Yes. Thanks for the question, Matt. So as we talked about -- so 2 things, I think, of importance in the prepared remarks. One was CJ walked through some very large deals. As you look especially at regulated industries, governments, it is a very important product that we have, and those are some of the largest customers at MongoDB.

In addition, we're starting to see more of the bundled deals. The on-prem piece is a huge part of it. So what I would say is yes, it will continue to be of importance. We are actually investing in EA to bring it to parity to Atlas. So certainly, our expectation and hope is that we continue to grow that and can even accelerate it in the future.

And Matt, I would say in speaking to customers, because this conviction is over a large set of very important customers that is definitely the trend that I'm speaking from our customers is, number one, that because of a variety of issues related to also AI that for mission-critical application, there is this trend I'm seeing where they do want to keep their critical data estates on-prem.

And this is not just only in financial services, we are seeing that in health care and other verticals like government. But when I was in Europe and even in Asia, I'm also seeing there that there is a preference for those industries to also use MongoDB potentially with EA and only certain workloads in the cloud. So this will play out and all we wanted to outline in today's call is to say -- this is strategically very important as in the product line for our customers, and we need to invest in it because it is strategically very important.

Our next question comes from the line of Jason Ader of William Blair.

For TJ, my main question is, how is your product and go-to-market strategy changing, if at all, ahead of the growing reality that agents are going to be the things that are spinning up most databases and not humans in the future.

I would say, Jason, I have a very simple philosophy here. And the philosophy also was validated by 1 of the AI native companies that has completely built on MongoDB. They had many choices in many clouds and they chose MongoDB. And my initial intuition was the same as you outlined, is that MongoDB's success over the last many years since the company was founded in 2007 was that builders or developers love MongoDB. And if that's the premise, there was a lot of work done in the product to ensure that it's a very natural way, flexible way while keeping the business agile as in the database agile so that it can move with the business.

We want to do the exactly same thing for agents Agents also need to love longer TV. That requires to ensure that we have all the right integration with the right places, whether it's CP or whether we are looking at making sure that our APIs in how you manage how we auto scale, how we ought to perform during the peaks and valleys. All of that truly needs to be autonomous and driven by machines.

And that requires absolutely the focus from the engineering team that how would machines look at this if they want to provision an additional node or if they want to manage cluster because of resiliency across multiple clouds. So that will be the North Star for us that our agents will love MongoDB as much as today, human developers love MongoDB.

Okay. Great. And then just 1 quick follow-up on that. Just is that -- is that going to come in a future release of the database? Or how should we be thinking about sort of the deliverables on that vision, CJ?

Jason, we do have ambitious road map, of course. Today, we are already leveraged by some of the AI-native companies and some of them I outlined this time and also last time. And we are learning a lot from them. So we have ambitious road map in terms of truly machine friendly APIs or making sure that our protocol integration across a variety of protocols that machines demand and how do we Auto Scale, Auto chart. All of that will be throughout this coming year.

And what we are going to do is that our DotLocal conferences throughout this year, we will use that as an opportunity to announce new innovations that will show you that machines should also love MongoDB. So it will be throughout this year.

Our next question comes from the line of Ryan MacWilliams of Wells Fargo.

CJ, great to hear about Anthrapic as a customer at the MDB local event I'd love to hear how you think about the opportunity for Mongo to grow within large AI natives from here. And there's also mention at the event that Agentic workflows require heavier storage and memory requirements. Love to hear why you think MDP architecturally is that suited for these growing types of AI use cases.

Absolutely. Ryan, one of the things I would say is Mike and I look at the entire cohort, AI natives, frontier model companies, others, many of them choose MongoDB for performance, scale, security and other things. And I would say that the good news here from my standpoint is that we are not concentrated in any one customer when it comes to AI native cohort. So that's number one. And as they scale, we will scale with them, but we are not concentrated. Even when I look at the growth as a percent of total, we were not concentrated.

The thing that I'm seeing, Ryan, very specifically is that People are making initially database decisions in this AI native companies without realizing that they will run into scale issues or potentially there was one of the choices that people could have gone with as an AI 2 companies' founders had a massive security concern over the weekend where a couple of governments block them from being used. So what I find is that truly enterprise-class database that can scale.

And when I say scale specifically, as for these AI native companies as their weekly active users or monthly active users continue to grow, like the example we had with Emergent. 11 labs and so on, they find that MongoDB scales better with them, right performance as well as query performance really matters, and us being a native JSON with search vector search and embedding in one rather than multiple moving pieces -- if I have to just simplify that, that is the strength because it's an integrated platform that scales both for read and rights that as you scale your AI native company, they can rely that MongoDB will scale with them.

Excellent. And then a follow-up for Mike. The Atlas seasonality in the fourth quarter seemed a bit lighter than typical. Were there any holiday impacts to the fourth quarter for Atlas revenue or any other onetime items in the quarter besides the Atlas and EA bundling?

Yes. Thanks, Ryan. So looking back at Q4, the holiday season so played out largely as we expected. There were really no surprises or deviation from the historical trends. So it largely played out as we expected.

Our next question comes from the line of Karl Keirstead of UBS.

Mike, let's stick to Atlas in the fourth quarter. A couple of questions. One was the 2-point beat roughly the framework you would advise -- the Street to think about going forward? And then secondly, if you could just perhaps describe the bundling impact that, as you said, Nick to point off of Atlas. Just maybe you could explain why that happened and were you anticipating that?

Sure. So all right, let's take a step back, Carl, on Atlas. So Q4 played out largely as we expected, as Ryan's question was, there were really no big surprises during the holiday season. We feel good about Q4 with 29% growth again with the bundled thing. I'll talk about that in a second, would have been a little bit higher. As Atlas has gotten bigger, we are seeing less variability in the business.

And in addition, we're getting better every day at forecasting the Atlas business. So from that perspective, the size as well as customer cohorts don't make as much of a difference in variability has helped. So on the bundling things. So entering Q4, we certainly have our forecast as it relates to how we think Atlas will do. There's -- we always do bundled deals in a quarter, absolutely.

This was a little unique in that we had 1 large transaction that once it closed, and thank us again, it's a really good thing that it did. -- we had to attribute more of that revenue to EA versus Atlas, and that took a little bit off the growth rate. We did not expect that entering the quarter. So we typically won't walk through those kind of details because we always do bundled deals -- this was an exceptionally large transaction, Karl, that did move the needle.

Okay. That's helpful. Yes. And then maybe, Mike, as a quick follow-up. You reiterated the medium-term guidance that you gave at the Investor Day. Maybe I missed it. I didn't hear the reiteration of the high teens total revenue growth. Is that still on the table, just to be crystal clear.

Thank you for asking the question. Yes, we have not backed off on that total revenue growth from September. Sorry, we missed it.

Our next question comes from the line of Ittai Kidron.

Michael, I want to follow up on the last questions here mainly aronia. Clearly, you had a very strong fourth quarter here with 2 very large deals and also the bundle that you mentioned that weighed a little bit more towards EA rather than at. I guess I'm trying to think about your guidance for fiscal '27. It seems like you have a lot of momentum there. You're closing some feature gaps. I'm kind of wondering why low mid is still the target for 27, why we hold this momentum in the fourth quarter and in the bundling and the feature parity you hope to achieve, that number is not higher.

So thank you for the question. So we did have a very strong year in EA and Q4 especially. As we look out to the rest of the year, keep in mind that the product enhancements and bringing EA to parity with Atlas will occur throughout fiscal '27. So we are excited about that. And there was an earlier question about the cohorts.

Keep in mind, it is a large business. There's a lot of moving parts here. The biggest variability to the business is not the cohorts, it's what ends up closing as a multiyear deal versus a 1-year deal. That still is difficult to forecast. And as we have said repeatedly, and we'll continue to say it. We will always bake in more upsides than downsides in that number.

We sure hope to do better than that, but we don't want a negative surprise because a deal does not close on a multiyear basis, and that has such a big swing factor. So we feel great about the business. We're going to continue -- as CJ talked about, a lot of big customers are asking about it. It's a key part of our portfolio, and we certainly hope to do better.

Fair enough. And then maybe as a follow-up, just for both of you with the changes in the leadership on the go-to-market side and the CRO and the field, I guess to you, Mike, A, is there any more level of conservatism built in your gut because of this transition? And B to you CJ year-end? Any changes to count structure that you're thinking about also in light of what you're looking for as far as the CRO is concerned?

Yes. So I'll answer it first. So when we do guidance, we obviously take into account a lot of things. The economy all kinds of different things. So we have tried to bake everything in. It's certainly -- while it adds a level of uncertainty, I want to underline what CJ said in his prepared remarks. We've been working on this for a while. We feel very good about the team that's in place, and we don't expect any material disruptions. But certainly, that is a factor that we took into account when we did guidance.

And Ittai, what I would tell you is that personally, after joining MongoDB, I have spent a disproportionate amount of my time with our go-to-market teams to really understand what is working really well and of course, where we can improve.

And I would say that the bench we have -- so our leaders for Americas, our leaders for Europe, Middle East and Africa as well as our leader for now APJ, I have very high confidence in them. as we go through this transition. And these are the folks that really, really executed very well in fiscal '26 when you look at the regional performance, and I am really optimistic about their ability to execute as we move forward.

In terms of overall go-to-market, how sellers are motivated what we are looking for in the candidate to work on the main street with all these sellers and serve our customers. What I said to Matt, is just remains the same that no changes. We want disruption to be minimum. And with these 3 theater leads who exceeded even that number in Q4 greatly from a net new business perspective, I have confidence in them.

Our next question comes from the line of Alex Zukin of Wolfe Search.

CJ, maybe first for you, given some of the increasing inflection points that we're seeing in kind of the a genetic coating space and autonomous coding that's happening. Has that, in any way, changed the dynamic of how fast or how quickly you think that the enterprise modernization could start occurring? And then maybe just a quick follow-up. -- for Mike. To the point about the increase, maybe some of the surprising bundling, particularly with a large deal in the quarter, is there maybe a little bit less visibility on specifically the Atlas guide for both Q1 and the full year, given that increased potential for variability around bundling.

Yes. So Alex, I'll touch on the first one. I -- what I would like to say is, I was talking to a large financial institution in the U.K. And the Head of Transformation, she told me that, Hey, CJ, I have 50% of real estate that I want to modernize, I know that some of the AI tools can get me to some level, but I really, really need your health and your team's help to make sure that for this mission-critical applications, we take help from MongoDB to help us land once you prove this out for the first workload, a very critical workload that is moving to MongoDB.

The same thing happened, Alex, with a large customer in Spain when I was there a couple of weeks ago, this individuals said, "Hey, we are relying on MongoDBs, as we are modernizing. This is extremely critical workload, once you do that, we are going to open up the aperture and I know that AI will help us modernize, but we still need your help because the destination we want is absolutely MongoDB. So what I'm seeing is the feedback is the modernization and the need for modernization is still very much relevant in the high end of the enterprise, whether it's a health care company, financial services or even government for that matter or health care.

Number two, they know that AI tools can help you to some extent, but they definitely want to get there on a modern database to get AI ready where they won't help from MongoDB to be on MongoDB. And then the last thing I would say is that even with some of the use cases, they try it and they're like, hey, sometimes this is too hard to assure the reliability, security and all of those things for the application we build.

So I consider this as an opportunity in early stages, and this is definitely a top-down work that we have to do as MongoDB with the CTO and Head of Transformation, but the opportunity still exists and is massive.

Alex, thanks for the question. It's something that we will certainly watch. What I'll reiterate is we always do bundled deals. It's part of what we do was unique given the size of that, I'd love to sit here and tell you that there's a whole bunch of those that we'll do every year. I do think right now it's unique, we'll watch it. we get better and better at forecasting the Atlas number every quarter. So at this point, we don't think it adds variability, but it's something we'll watch going forward.

Operator, we'll take 2 more questions.

And our next question comes from the line of Tyler Radke.