MongoDB Aktie

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.

Just going back to the EA and Atlas bundling. I guess I'm wondering, were these existing workloads that move from Atlas to EA? Or was this sort of plans for new workloads? Just higher bias on EA. And just curious like why do you think that customer, in particular, chose to do more on EA as opposed to Atlas?

So it's always going to be customer-specific, Tyler. And a lot of these transactions will have renewal as well as upsell also. So it's very specific to the customer. and it really depends on their internal plans as it relates to how they want to use MongoDB going forward. So there's no pattern there. It's very specific.

Yes. And Tyler, what I would say is that with this specific customer is that they have, in the past, moved some of their EA workloads to Atlas. Some of their Atlas workloads are growing incredibly well, and they want to continue to do that. And they are currently also getting ready for some of their workloads, AI-ready where they are using vector search and embedding in the future.

So it is a kind of classic case of truly hybrid infrastructure on how they are dealing with their core product strategy and some is built on A and some is on Atlas. And from my standpoint, when we look at the numbers and the transaction, which was meaningful, as Mike said, very meaningful, is that what we also saw was the expansion because this customer besides making a long-term commitment continues to grow their data estate with MongoDB.

Great. And CJ, a follow-up on the go-to-market changes. Clearly, your background at ServiceNow has one of the more successful partner ecosystems out there. I think on the on the database side, particularly for Mongo, the partner ecosystem, I think, has been tried, but certainly it's not nearly as robust. And given that being more of a focus on some of the new go-to-market leaders bringing in, can you just help us understand maybe some of the challenges with the prior approach that didn't lead out to as robust of a partner ecosystem and what makes you and gives you the confidence that this approach is going to be successful?

Yes. Tyler, absolutely, and I have been told what he just outlined. So I would put this in 3 buckets, Tyler. First bucket, which is super important is our hyperscaler relationship and how we work with them. And as you know, that we work with them very closely because when we win, they win, whether we are running on GCP or AWS or Azure or others, okay? So one bucket is just continue to still stay focused on hyperscaler.

And in today's world, the multi-cloud resiliency, whether it's on-prem and cloud or between multiple public cloud, which is an advantage we have it is proving out more and more important between the outages that happened last year with some of the hyperscalers and the geopolitical issues that we are seeing being played out. So that's number one.

Number two, system integrators, which is where we scale at my previous company, that is definitely -- when you think about the modernization and the real estate on modernization to move to MongoDB, we could definitely benefit by focusing on 2 or 3 of them to start with, and that is something that our teams are saying, we do need help as we think about this 2 or 3 system integrators.

And make no mistake, the third bucket is also equally important is this AI native ecosystem that are framework providers. There are other providers like LLM providers, and what can we do with them and truly create partnerships that really matter. Those are the 3 buckets. And that will allow us to scale for a long time.

So hyperscalers a few system integrators who wants to lean in on the modernization and the AI ecosystem where we really need to make strong technology trends is how I look about it, and I think it is extremely essential to do that on -- and this is the inflection point.

And our last question comes from the line of Sanjit Singh of Morgan Stanley.

Great. I have a last question. So CJ, I wanted to just get your latest thoughts on a couple of topics. Given that the business has been accelerating execution has been improving in the past several quarters. As we look forward, do we start to see like the kind of AI part of the story start to play a bigger role in terms of the growth equation you guys have a number of AI customers as sort of we discussed on this call. But in terms of contributing growth, does that become more important as we think about potential upside to this guidance that you laid out? -- kind of feel like over the last couple of weeks, we've seen a step-up in terms of agentive momentum, not necessarily in the enterprise, still feel kind of consumer personal productivity.

But just wanted to go check your thoughts on the importance of the AI app story coming to fruition maybe a little bit earlier than maybe you anticipated?

Yes. I would tell you it's not if but when, okay? So right now, we do consider, I mean, Sanjit, one of the advantages that I have in speaking to all these customers, I ask them that simple question, where are you on your Agentic workloads? And I'm talking about Fortune 500, okay, or big retail companies, health care companies pick one and ask them -- where are you on agentic workloads? And are they really scaling? And the answer is still not yet. Yes, they have done a few productive productivity types of apps internally, but nothing of scale that is customer-facing, even including with a large retailer on agent commerce and so on.

So my first thing is, 1 day, it is going to hit in a positive way. where you will have agents making a meaningful difference to the growth of our customers for either new product lines or existing product lines. We are not seeing that today in the large enterprises across pretty much most of the verticals that we speak to because as you know, MongoDB is across every vertical. So my simple answer is it will be someday, not seeing that yet and don't want to predict it because it was supposed to be the 2025 was supposed to be that year. And what we saw in 2025, it was only mainly around coding and some vertical-specific AI, but nothing meaningful in the enterprises. Mike, would you..

And just as a follow-up, and maybe Mike, you can hit on this. It sounds like Atlas consumption more or less came in line with your expectations, controlling for this large deal -- you mentioned this potentially lower visibility in the second half. And I wanted to assess that comment in context of how the sort of calendar year '25, fiscal year '26 applications and workloads how are they ramping relative to your expectations? Maybe the -- if you look at the first half of last year and those applications are at the end of this year, are you satisfied with the quality of that growth in that cohort of application?

Yes. Thanks for the question. So I think a couple of questions in there. One is, yes, Q4 largely came in as we expected, except for that small thing that we talked about. It was -- there were really no abnormal things in Q4, which is great. On the comment about the second half, that's just more of a general macro comment, indeed, in that it is a consumption business -- while we -- visibility is always a little bit better earlier. We're also cognizant of pay, it's harder to forecast the back half of the year. That does not tie directly to any concern around the workloads that we've signed in the last couple of years.

And yes, those continue to perform as expected. As we've talked about strength that we've seen is really in the larger customers, especially in the U.S. and Europe. So all that is going as expected. That second half was more of a general comment, not specific to any set of workloads that were signed in the past.

This concludes the question-and-answer session. I'd like to turn it back to management for closing remarks.

Thank you, operator. In summary, we delivered an exceptional fourth quarter, highlighted by strong Atlas and non-Atlas growth robust customer additions and operating margin outperformance. We are issuing strong guidance for Q1 and full year fiscal '27 across Atlas and non-Atlas revenue, and we expect to continue expanding profitability while investing for growth all in line with our long-term financial model. Our results demonstrate 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. Thank you very much for everyone joining, and we'll see you soon.

Thank you for your participation in today's conference. This does conclude the program. You may now disconnect.

Welcome to our next session. Really happy to have CJ here with us.

Thank you.

Thanks for kind of making a travel. I just talked and you just came straight from London. So that's really great commitment here. Maybe like since you just joined recently, maybe just talk a little bit about your due diligence that you did when you joined MongoDB and like what excited you, but also like what you could bring to the company, given your experience and kind of a slightly larger organization as well?

Absolutely. So I did do quite a bit of diligence on MongoDB before joining MongoDB, mainly spoke to customers who use MongoDB or if they were not using MongoDB, asked them, why not they were using MongoDB.

Second is also talk to some hardness on how do they think about it? Everybody from technology partners to system integrators and others on how do they think about MongoDB. And Raimo, I mean, at the end, you need to participate in a large market. That has always been the hallmark of my career. My first job out of college was Oracle because I felt Oracle was a large market. That time, Oracle was not that big. Oracle was actually as big as MongoDB when I joined Oracle.

And then when I joined ServiceNow, same thing, I thought it was a large market. And then Cloudflare was the same. So first, you need to participate in a large market. Do you have conviction that this could be truly a data platform. In Cloudflare, I thought it was a great platform on which you can build many products. The network was the platform. ServiceNow, you and I work together. ServiceNow also, I made the pivot from product company or applications company when I joined to a truly platform company and MongoDB has that. So that's why I joined MongoDB, large market, platform opportunity. And the company has been only around 18 years. Database market, my first employer, Oracle in the next couple of years will celebrate their 50th anniversary. So -- and we are just 18 years old. So we have a lot of room to grow.

Yes. And then the -- if you think about the -- what you can bring to the table, like obviously you said, like you've been in -- on these journeys before. Like if you look at Mongo and kind of now that you spend a little bit of time on the organization, what's your thinking there?

Yes. So I'll give you an example. I was talking to a large French customer last Thursday. And they have built mission-critical, which was an important point for me, mission-critical 40-plus applications on MongoDB okay? So their business runs on MongoDB. When somebody tells you that, that means MongoDB has already arrived that you are getting mission-critical applications to be built on top of MongoDB, which is their business, right, which is their customer-facing business. And then he said, CJ, as I'm creating a few AI workloads, I'm going to add MongoDB for those AI workloads as well, but we are still early for that AI workload.

Raimo, my personal value add is the massive connections I have in Fortune 500, not in just United States, but in Global 2000, specifically Europe and United States, including Middle East and so on. I have lots and lots of connections top down. And that will just help for us to become a strategic platform. So MongoDB nailed the developer motion. Otherwise, we won't be here today, right, to where our database developer says, I'm going to build on MongoDB and that community that exists, you go on Reddit threads, you go on social media. And there is a lot of energy about MongoDB.

If you go to YouTube and when I was doing diligence, there are like videos created in multiple Indian languages on how to get started with MongoDB. Look at India has how many developers, right? That's nontrivial, right? That didn't happen at my previous company. So that, like viral moment is there. And now with me joining, how can I also help our sales team at a C-suite level, so we become truly the strategic platform.

Yes. Okay. Yes, makes sense. And then if you think about it and you talked a little bit about it, Mongo is operational database. So their operational databases from my experience as well, like running long cycles. How do you see that market evolving? We still have -- Oracle still there, apparently doing better. The Microsoft, Postgres, Yuga, it's like how do you think about that?

I think -- I'm a very simple person, okay? And if somebody tries to make this too complex, I say, please time out. Let's just get to simplicity. Database market, like we discussed, has existed for 50-plus years. If you believe somebody, they will say 60-plus years. The reason it has existed this long, it is a very sticky market because data is core part of your infrastructure. So let's start there.

Now number two, 80%, depending on whose numbers you believe or maybe 85% or maybe it's 75% of the enterprises, their data is unstructured or semi-structured data. Relational databases are suited for certain workloads really well when it is very textual and I can put rows and columns, text, numbers, things like that, very simple. But when you look at e-mails, PDF files, my insurance policy, my credit card transaction where I had a dispute, my voice call, and I can go on and on. And in the AI world, the unstructured data, you go to your favorite AI tool today. They're like, "Hey, do you want to create an image here? Do you want to upload an image and modify it?"

Today, you see the announcement between Disney Corporation and OpenAI, which is around videos, right? So you look at this AI world is forcing how do I leverage my unstructured data documents that I have on my Google Drive or OneDrive or whatever. And MongoDB, which was just created in 2007, is the best database for unstructured data. It has a scale-out architecture, so you don't really need to worry about the scale up and it is portable across multiple clouds.

And by the way, if you are a regulated industry, you are just going to use our on-prem version because which is necessary. Certain banks, as you know, do not want to move to cloud. Yes, I talked to Craig in the past about transformation you guys are driving. So it's ideally suited. And the reason I bring up the 18 years point, you look at all the other -- even Postgres has been now around 40 years. So that's how I think about it, that we are still fairly early, and we have a long ways to go, and we will celebrate our 50th anniversary someday, too.

Yes, yes, yes. Okay. No, I'm looking forward to that. And -- the one thing that drives database adoption or that was always kind of mentioned a lot with developers. Do you remember Steve Ballmer, developer?

Yes, yes I remember that in 90s, yes.

How do you think about that -- like a few years ago, Mongo was like the next one. Now I think there's kind of more work that you could be doing there to kind of get that kind of move towards a little bit back...

I think, totally fair. And I shared briefly and you were there on the earnings Q&A last week is we lost specifically in San Francisco Bay Area or maybe Seattle or New York or as the company grew, in the Bay Area, we lost that mind share, and we were not top of mind for some of the new developers here. We were doing really well about 10 years ago.

10 years ago, MongoDB was cool here. People wanted to base some of the tech companies at the time, built on MongoDB. But then we kind of lost our way because we were doing really well in other parts and are growing like crazy. 2017, Atlas was only 2% of the revenue. Now it's 75% of the revenue. So the company was doing a great job. And Dave will personally tell you yes, we could have done a better job.

So we started to reclaim the Bay as in the San Francisco Bay initiative where we meet with the founders, do founder dinners and say, here is what MongoDB can do for you. And we are relaunching MongoDB on January 15 after a few years to -- and I'm personally kicking that off to be with the developers here.

And that's one of the reasons when I got hired, many customers are excited because I have dual Coast, I do New York and San Francisco, and I've been here for 25 years. I have deep relationship in venture ecosystem as well as tech companies here. And leverage that to be top of mind.

Yes, yes, yes. And the -- like I guess part of that problem was that we were somewhat stable and then Gen AI came in and then all of a sudden, developer world got crazy again. Talk a little bit about how you see that kind of Gen AI world playing out from a database perspective?

Right. So the way -- if you look at late '90s and what happened here as in Silicon Valley specifically, late '90s, early 2000s, you had a lot of digital natives, what I would call it. They were figuring it out what infrastructure they use and software as well as hardware. And then around 2,000 -- because I was here, 2008, '09, you started seeing AWS being really aggressive here in San Francisco Bay Area and the entire company like Netflix, got built on AWS, right? That's a public information. You think about that.

Same thing will happen, right? So you look at Netflix growth from 2008, '09 when they switched from CDs to online streaming. And then that journey is still going on, and Netflix is still doing very, very well, now 17, 18 years later. I would say the same thing on AI natives. What really encouraged me -- we used the example of Mercor last week in the earnings remarks. Mercor is fully built on MongoDB, and it's an AI-native company that is doing really well. Most of the Raimo AI investments happened between '23, '24, '25. It's still happening, like a lot of new companies are getting created every day here.

And what really, really encourages me besides the Mercor example, 2 prominent AI companies are fully built on MongoDB. So if they someday become like Netflix, imagine what that would look like, right? And they are doing very well right now, but been around for 2 years. And I just -- I'm really proud of the team. This one AI-native company that used to give us a few thousand dollars a year, then it became somewhere around 2 years ago, $130,000 a year, 2 years ago, okay? $130,000 a year. And today, they are giving us $9 million a year.

So that's an order of magnitude growth, but they're built on MongoDB. I mean -- so when I talk to the enterprises, and I say because this is a prominent company, we don't have permission to share their name. But when I talk to other enterprises, I say, if these guys are building their entire AI business on MongoDB, why are you not? And they're like, oh, CJ, we didn't know that. This is early still and so on.

So that's what -- coming back to your question on AI-native companies, us now being focused here in San Francisco Bay Area, but I've asked our CMO, Seattle is where a lot of companies are still getting created. New York City also has some AI companies that are being built. Of course, Tel Aviv is another area. And then even in Bangalore, India and in China, and we need to...

Yes, yes. And then more a conceptual question on AI because like -- even like the debate feels very much like black or white, like it's one guy or the other. But if you think about it, the market seems to be evolving so fast. There's so much going on there. Like should that all be like just in terms of need for databases to be the persistent layer around all these new AI apps, like you don't need to be -- like everyone is going to benefit like depending on -- independent of market share. Is that kind of the me dreaming too much? Or...

It's a totally fair question. I'm going to touch on one important thing, right? Whether you pick 2007 as the year where AWS really got serious or 2006 or 2008, cloud migration is still going, right? You speak to even Craig, he will tell you that it's still going on, right? Banks, health care, we are in 2025. So now 18, 19 years in, and you look at next 5 years, and if you speak to leaders at Azure, AWS GCP, they feel that it will be still double-digit growth for foreseeable future.

So you look at the cloud migration, we are in the -- we are going to approach 20th year of cloud migration. So at the highest level, AI will be the same. It will take 10, 15, 20 years, and we are in the year 2 of that. So that's why I don't get like too worked up on, "Oh, my God, CJ, are you guys present here, not present here and so on."

And to answer persistent layer, which is a very important point you made, you cannot create an agent on top of a data warehouse. You want -- if you are going to say this agent is going to replace human and either do a domain-specific tasks, say, a legal task or a finance task or do multiple things. The agent is going to have like a human personality that I can do this and I context switch to that and all of that, you cannot do that without a real-time database.

And that's why these AI native companies who are building on MongoDB, of course, they are smart. They know the real-time database. And AI also has a lot of unstructured and semistructured data, images, PDF files, this, that, whatever needed. So that's why persistent layer is a must-have and a modern data basis a must-have.

Yes. Okay. Makes total sense. Yes. And then -- going a little bit more current on the questions in terms of like you guys had a like out of the gate as a CEO, a very good first quarter, share price reacted, nicely. Well done. Like -- but if you look at -- so if you look at Atlas, which is on the cloud side, there's 2 components. There's the direct Atlas, which is kind of, in a way, you came from Enterprise world, enterprise sales guys doing stuff, NNS self-service, both of them look kind of started to come through better, like can -- from your first look into that, like what drove that?

Yes. So I just want to acknowledge that it was Dave and the entire team of MongoDB.

Yes, yes, I know, yes.

They deserve all the credit for what happened in Q1, Q2, Q3 and how that continues to grow, right? And when I was during my diligence phase, I was seeing the signs that the business is on the right trajectory, and that was just fantastic to see even after I got into the details and how we think about Q4 guidance and so on.

So it is a tale of 2 cities. You want your product-led growth motion to work really well. Even this AI example I shared where we grew orders of magnitude with that company, that particular company, we got through our product-led motion, right? And that product-led growth motion, both in Q1, Q2, Q3 overall has done really, really well. So our CMO has focused on making it easy, removing the friction and how we make sure that Atlas is so easy to use from a product-led growth motion perspective. And that's why you look at -- we added 8,000 customers -- you how at ServiceNow, I used to talk about new logos and not every logo is created equal.

So that, for me, over time, becomes top of the funnel, right? Today, they may be paying a small amount, but over time, they will pay a big amount, some of them, not all of them, right? So that motion is working well and signs are very encouraging on the product. Product-led growth motion is very, very hard to do, very hard to execute on.

You need developer mind share for them to come and then do the credit card transaction. And then where I gave team also a lot of credit, our go-to-market team, we saw broad-based strength in Europe, Middle East, Africa across -- and I was there 2 weeks ago with our sales teams, northern Europe, Southern Europe, pick whatever, we go to market, we have 4 regions: Northern, Southern Europe, Central Europe and Israel. And that -- the team is doing very well.

We have a great leader and continue to execute. And in Americas, where we saw the consumption growth was on the high end of the enterprise, where Dave and the team with the go-to-market leaders decided to focus on the strategic accounts. And when they're focused on it, it expanded. So those were the 2 main reasons that the growth has reaccelerated. You look at 26% Atlas growth in Q1, 29% in Q2 and 30% in Q3. So -- but the self-service motion, Raimo, all goes to Atlas.

Okay. Yes. And then how do you think about -- like that's a task for you as a new CEO. How do you think about EA? Like there's -- from our investor side, it's like, oh, you need to push the clients into Atlas? And then like, well, I'm Barclays, it's not going to work. How do you think about like dealing with EA in the future? .

So Raimo, it's a very insightful question. So I was with a very large bank, one of the largest banks in Europe in London on Monday with a head of their infrastructure, who has the whole technology, okay? First meeting in Canary Wharf, 8 a.m. And he told me, CJ, we use EA. We love it. we just renewed the contract for EA because that class of application, which are customer-facing, banking application that they have created for commercial banking will always be on-prem.

So if you're going to come and pitch to me Atlas, this is going to be a very short conversation. However -- and then we talked about EA and making sure that the road map we continue to nurture on EA. He said, however, he is focused on GCP and AWS are the 2 cloud providers he's using.

And for some net new workloads, he is going to think about Atlas. And he said, "My team is currently -- my Head of Database platforms is working with MongoDB's team to figure out which workloads we can move, create in Atlas in 2026 calendar." But he said this property that they have built on EA, which customer-facing commercial banking system that is high online transaction processing...

That's always...

Yes. We'll still stay on MongoDB. So EA is 25% of our business. And when I think about regulated industry like financial services, when I think about public sector, where they want to create an air gap network and they want to use EA, you may have a health care company that may still want to use EA. That will be for foreseeable part. I'm not going to come and tell you, we are constantly asking people to move from EA to Atlas. Some will because they are migrating to cloud and whatever, but some will not.

The other thing that will be on your agenda, I would assume, is go-to-market. And you can see because Mongo is smaller, they still have to adopt. The company is growing and they need to do more work there or change things. A few years ago, like we move towards more consumption kind of model in -- on the go-to-market side. Like you've been in the industry for a long time, like walking in there, like how do you think about that?

So Mongo, when you look back at 2017, 8 years ago, while they were going public, Atlas was 2% of the revenue, and they nailed the cloud transition well because you know so many companies, software companies even could not nail the cloud transition well. They nailed it, and now it's 75% of the business.

So Dave and the go-to-market leadership team did a phenomenal job. When they switched to consumption, which was the right call, they were one of the early ones to do that, right? Snowflake, course, did it with Frank and Mike and what they did there. It was, as you know, a little bumpy, but now that's behind us, okay? It was a little bumpy on how we did that consumption migration from a sales compensation perspective, how do we capitalize the cost and so on.

But now that is behind us. Mike Berry has done a great job even in the short tenure, he has been here. The teams now understand it. When we hire our go-to-market sellers, they understand it. I've been spending a lot of time with them. They're like we get it now, that if we drive the consumption, we get paid. And what do we need to do to really drive that consumption for Atlas specifically. And so I think that's behind us. Now we just need to scale.

Yes. Okay. Yes. Okay. That's good. I apologize, there was a question for Mike, but he couldn't be here. So I'll try it, but -- and Jess is getting all nervous here. No it's not that bad. If you think about it, like one question you will get asked from us is obviously around profitability. And you are in that peer group of like high-quality, high-growth software. So that's like Datadog, Mongo, Snowflake Profitability-wise, Mongo was always a little bit behind versus that peer group. How do you think about like structural reasons for that? Operational database is longer cycles. You have a longer customer lifetime value that could explain it maybe a little bit. But how did you think about it?

Even I can just go back in time, even with ServiceNow, when I started, ServiceNow was growing 40-plus percent year-over-year, and we have $1.3 billion in ARR between Frank, Mike and me and our Head of go-to-market at the time, Dave Schneider, we were always aligned on durable, profitable growth. And Mike will always say that, hey, while we are trying to hit for $4 billion in ARR by 2020, Mike will say that in 2015 and '16, we will still see the op margin improvement by 100 basis points, he would say, at that point in time.

And then Cloudflare, Thomas, very clear that we want to be Rule of 40, and that's what we are solving for. Same thing is here, Raimo. When I came in, Mike had already figured out with Dave that we want to be durable, profitable growth company. And that's why on September 17 at our Investor Day, we provided the long-range model that we want Atlas to grow at least 20% plus and we will, on an average, improve the op margin by 100 to 200 basis points.

So if you look at today, I think we said that we will exit this fiscal year at 18% op margin, which is a significant improvement compared to when we guided for the year, right? And number two, we said our growth -- top line growth is somewhere going to be 21% to 22% when you combine the Atlas with EA. You're almost approaching Rule of 40.

And then how do we continue to just improve that op margin. And we are looking for efficiency. So just to be very clear, how do we leverage low-cost location for engineering? How do we make sure a lot of our back office is driven through our low-cost locations we have. We've already made some progress. We can do that. We were so focused on growth in the past that those things were not taken into account, but now there is a very conscious efforts to do that.

Then the last question for me is like I need to start with the funny anecdote. So when you left ServiceNow, all the bankers in Wall Street were like, oh, finally, because CJ was the one that stopped all the M&A because he always thought and believed in internal software development and didn't believe in buying stuff.

I'm glad you guys knew that because that's true, that's 100% true.

How is the situation for you at Mongo now? Like I mean, you always were a believer in development.

Yes. And even Cloudflare, when I was there, even though it was a 1-year tenure, I still believed in organic growth, and we expanded product lines there. That is 100% true here. The TAM is large. We already have the right elements between core database, vector, search and others that the team introduced a couple of years ago, which is still growing, but small percent of our customers use it.

And then now with Voyage acquisition, which was a very people acquisition, a fantastic team and great IP that they have built on embedding. Those in itself have all the makings of the data platform that I want. And I'm a big believer in organic growth, and that's also why I joined MongoDB because there is a huge runway. Will we acquire -- yes, we'll acquire some teams or some small tech like we did at ServiceNow, but never like do something massive or whatever, that's not the plan right now.

Yes, yes, yes. And then last 50 seconds, like your first 100 days, is that like just traveling the world?

Raimo, one of the things I think you know about me that I am very customer-focused. So one, just deepening the relationship with our customers, the large or medium, including the AI natives right here in the city. That is focus number one, that I've already started doing that.

Number two, as I listen to these customers, is the product really headed in the right direction? And is our innovation velocity high, it's the second. And third, we have all the elements, but how do we truly create a data platform, the generational data platform? I meant it when I put this in the remarks last week rather than us being part of just a database company, truly a data platform company. Those are the 3 things that are my 150% focus on it.

Yes. Perfect. And it's -- our time is up, but it's a great closing statement as well. Thank you. Really enjoyed our conversation. Thank you.

Thank you. Appreciate it. Thank you.

Okay. Hi, everybody. Let's get started. Let's have a fun day 3 today. I'm actually excited with at least the lineup that I've got. I was only half joking with CJ when I met him this morning that having hosted him at previous conferences in addition to him being a very, very capable tech sector executive, he's also one of the more fashionable executives that I know. So when I was pulling my wardrobe together this morning, I knew I had to totally raise my game. So this is the best I could do. I think CJ wins out.

Thank you, Karl.

CJ, Mike and Ben, thanks so much for coming to our conference. Your presence always makes it much better. You're also coming off of a wonderful print, which evidently most investors appreciated. So Mike will talk a little bit about the financials in a moment.

But CJ, welcome to MongoDB. Welcome back to Scottsdale. And maybe where I wanted to start is a little bit about your early vision for Mongo. I know you've only been here probably 30 days or something.

But I guess, I was struck by your comment on the call that you envisioned MongoDB as being a modern data platform. When I hear that, it sounds like you want Mongo to be more than amazing document store database. And so maybe you could lay out that vision even though it's quite early.

Absolutely. So at the highest levels, Mongo participates in a large market which is always a good thing that it participates in a large market. And this market, my career started at Oracle Corporation.

You know a few things about database.

I do know a few things about databases. And I stayed with Oracle for 7-plus years when Oracle was a high organic growth company in late '90s. And so database as a technology has been around for a long time. And I would say 70s, 80s and 90s, 70s is when Oracle started was very structured data, rows and columns, and that's what we obsessed about in terms of performance, and it was always scale-up architecture.

And looking at MongoDB and I've been a customer of MongoDB at ServiceNow, we did a lot of technical integrations with MongoDB discovering MongoDB in cloud or on-prem and so on. And so I have a lot of respect, first of all, on what the founders created in the first decade of this century.

And enterprises, Karl, when I speak to customers, enterprises everywhere they have so much semi-structured and unstructured data. There are so many workloads nowadays with as enterprises are looking at AI, and everybody is looking at it, I would say, they're piloting. I would say with a large financial services company last week, and they said, we have 45 to 50 agents that we are piloting, which is an important term whether it's about maybe 3 or 4 are in production employee-facing, not really in a meaningful way customer-facing.

And when I see all that, the opportunity for MongoDB to go from a great database, document database with native JSON support and others to become a truly data platform. That opportunity excited me and that's why I joined.

And one last thing I would say is that product to platform transition that I've done at previous companies, MongoDB has all those ingredients on when customers, and I've already spoken to 30-plus customers in 30 days, when they look at -- when I speak to them, 3 things they tell me. One, the cloud migration to a GCP or any hyperscaler you pick, that's still going on, and it will still go on for 4 to 5 years. As part of that, there is some, of course, lift and shift, but there is also, hey, we are going to modernize these workloads and given how much unstructured data they have, MongoDB has the right position to it.

Second thing is, AI workloads, how do we clean up the data. Of course, everybody is going to these customers saying, we have agentic platform built on us. We have this, we have that. But customers are like, if I'm going to fundamentally change my business or make a truly efficient business, I want something at scale. And to have real-time data store with the right platform is the high ground where MongoDB is positioned.

And the third last thing that I would say is that the data estate still continues to grow. So you have a confluence of this cloud factor, AI factor and just core data. And that's what excited me and that's why I joined.

Yes. And you've also got an amazing opportunity to leverage the developer mind share that Mongo has built over the years. I followed your story from way before when Dave became CEO and one of the tenets of Mongo has always been that developers love the product. So that's an amazing base to work off of here.

CJ, let's talk a little bit about some of the broad themes out there. I think I'm certainly not the only one noticing that across the software space, there are pockets where we're not really seeing any growth acceleration. Frankly, it's flat to down. The one area where there's consistency and it feels like the whole category is inflecting is interestingly in the data space, where Mongo is accelerating Snowflake. We had Ali at Databricks on stage yesterday, Palantir. So the whole data category in software is accelerating right now. Can you just pinpoint the 1 or 2 things that are causing that? Certainly, when I talk to enterprises, I consistently hear this theme about needing to better utilize their corporate data to make it AI ready. But something broad is clearly happening. And could you perhaps help the audience understand exactly what that is?

So I would say, your observations are correct that infrastructure software related to data, definitely benefiting right now. And there are still questions in enterprises' mind, hey, what do we do with SaaS and what is the long-term vision for SaaS? But you contrast that with data, though, right now, you mentioned some data warehouse companies. Data warehouse companies always are very good at, hey, I can ask these kind of analytical questions, my top 10 trends in the holiday weekend, just give me those answers. And then for data scientists and AI folks in those enterprises data warehouse technologies are helping them out with those questions.

From my perspective, so that's one piece that trying to get more value out of the data you have to make the business decisions. Now companies are focused on it rather than in '22, '23, you saw, Karl, there was a lot of efforts on optimization. Do I really want to do this right now? I have other supply chain and issues. If you're a manufacturing company, or if you're a financial services company, you had other concerns related to inflation and so on.

And you contrast that on the OLTP side. On the OLTP, now people are saying, okay, we have time to really modernize the application stack, as you saw in our results, they speak for themselves. And what we discussed, Mike and I, 2 days ago, is that where we are seeing is, of course, the high end of the enterprise where the consumption that we reported for 3Q, but also broad-based strength in Europe as well. And then our self-service motion, coming back to your developer point, we are seeing a nice customer growth to say, I finally have time to create a new application or modernize a new application, and that's both working in our favor. So you have data warehouse, the analytics phenomena, and you have the OLTP with us, a phenomena that's helping.

I'm not asking for guidance, that will come in 3 months, but maybe I'll phrase it this way. These drivers you're talking about, CJ, how durable are they? Is there anything that you can share with us, maybe it's anecdotally, backlog color to make the point that these drivers can persist next year or year after. Ultimately, what I'm getting at is, what's the duration of this data investment cycle you appear to be benefiting from today?

I would say, we are always -- Mike and I even spoke at this -- about this thing that how do we think about the future and so on, on consumption. What we saw is that we don't want to be at the highest level, say, this will continue forever, right? I mean, because we just don't know what we don't know, and you saw what happened in '22 and '23. But from my perspective, when I see -- speaking to customers, as they think about the IT budgets in next fiscal year, there is nothing that they are giving the indication that the budgets are going to shrink like what you saw...

I'm not hearing that...

Yes, in '22, '23. They are telling me that AI is still going to be a priority, right? And they still have to show the ROI. I'm talking about CIO, CTOs and so on. And I think from my perspective, we just have to, of course, execute on our side in serving the customers at the highest level and continue to provide right guidance that Mike is now on top of as we move forward. Mike, would you add anything?

So thank you, CJ. I would just do one thing on that, Karl, is that's the part of the industry, and we get a lot of questions. Keep in mind, the changes we've made internally to address those industry trends, we feel really good about the, call it, the durability of that. We've moved resources upmarket. We still have a huge amount to go get in the -- call it, in the Fortune 500 or 1000. So we feel like there's still a ton of room to run there as we continue to shift resources. So the industry trends are great. We also feel good about our change in resources to go attack that market.

Okay. Let's talk a little bit about more specifically the effect of AI on your results. My impression from listening to you the other night is that you put up that stronger-than-expected results. But not because of some direct AI lift. I think the way you were describing it was that it was a core strength, actually, which makes Mongo interesting to a lot of people in the audience because if you've put up that strong performance in the core even before that direct AI lift comes, well, that's interesting because then you've got a potential growth catalyst sitting in front of you.

So can -- maybe this is for you, Mike, can you draw that distinction in terms of where the strength came from core versus AI most recently?

Sure. So to that point, and we've said it for the last couple of quarters, we continue to see really good traction in AI native. We see the work going on in the large corporations related to AI, but it has not been a material driver to our results. And it was not in Q3 either.

The growth in consumption that we saw in the drivers, call it, in the core. It's been the large customers who are doing mission-critical workloads for their business. So if it's an insurance company, claims processing, if it's a bank, it may be check depositing, it's core workloads that we've seen. Do we see a lot of activity in customers in terms of testing? Sure. but that's not driving a lot of revenue. So we do think that, that is a future driver. Again, we believe it is not if, it is when, but it is not driving the results today, nor is it a big part of the guidance that we did for Q4.

And is the catalyst, Mike, that we're waiting for as simple as enterprises like UBS start writing more robust enterprise-grade AI applications that create a pull-through for database that maybe today, a lot of the AI applications are somewhat lightweight and they're not super database heavy. But sort of Phase 2 might be that catalyst? Is that conceptually the right way to think about it?

I think that's the right way. And they may be doing inferences as they're not creating a lot of data internally. But once they actually do it with their internal data, that will create a lot more data. And we feel that will be the pull-through.

Yes. And can I add just something to what Mike said. Agreed, here is why I'm personally paying attention being based both half and half in San Francisco area as well as New York is we are learning a lot from AI native company. Because AI native companies today, right, there are so many, and venture capitalist who are on the sidelines, as you remember in '22 and '23, started funding in 2024, whether it's a vertical-specific AI company or a foundational model company and so on.

And my observation, even when I was doing diligence on MongoDB and after joining MongoDB and speaking to them, Karl, is that they are saying, okay, to truly create a killer AI app, which AI native companies, that's the aspiration, right? What does really MongoDB have? And can we build on top of MongoDB, the next-generation killer AI company.

And so I ask them questions, hey, are you using our vector search functionality? If yes, tell me why. If not, why not? As you know that Dave and the team bought Voyage and have you thought about our embedding models and reranking models that improves your search accuracy and so on. And they're like, I didn't know that, let me try that out. That's an easy few lines of code change that I can do.

And the third most important thing is scaling. And if I become successful because everything that becomes big, start small in AI native companies, and some of them are getting traction, as you know. And what I see with them is, yes, we are actually growing a lot, CJ and we used the example of Mercor in the earnings script. And because we are growing and we are scaling, MongoDB is the right OLTP database for us.

That learning from the AI native companies, we want to take that to the enterprise to say, hey, because the AI team calls, when I talk to these large enterprises, what I find is that the AI team, as in the agentic team, is separate from the core database team and the core workloads team, and they are dependent on those workloads team to get the data via APIs and so on, and they want to move fast. Because they are like, hey, I have a lot of pressure to experiment on this agent X or agent Y. And then when I tell them, hey, do you know that this foundational model company is using MongoDB in this way or this Mercor is using it and this is why they are using it. They're like, ah, thank you for telling us because we just wanted to move fast, so we didn't really make an explicit decision.

Got it. CJ, let me ask about a subject that's somewhat relevant in the markets today. The markets are a bit heavy today. I think on additional media stories about the pace of enterprise agentic adoption. I think Dave and you, more recently have been on the more reasonable, I'd say, accurate, actually, end of the spectrum on that debate, hyping it versus being quite reasonable. Do you want to weigh in with your review? You talked to a lot of customers, not just since you've joined Mongo, but prior. Where do you think enterprises are in taking truly agentic apps out of pilot actually into production. And Mike said when, not if. Can you give your best guess as to when the when is?

I don't like to do that, Karl. But I would say in speaking to customers, this is not days and weeks, right? This is not days and weeks. It's a few quarters depending on. Because enterprises, if they want to transform their business, which is a very important point, using an agent, whether it's single-purpose agent or a multipurpose agent, you have scale, durability, availability, real-time learning, context switching for the agent versus human. There are a lot of factors to be considered. And yes, I created a pilot and it does something cute internally for productivity that I can write e-mail fast or I can do this Copilot or Codegen.

But to truly create an agent that transforms your business, right, or creates a new business for you. My viewpoint is still a few quarters away. I have -- in like I've been speaking to customers, as you know, for a long time, nobody has come said, CJ, this completely transformed my business today. And we are piloting, tinkering, I have this financial services firm I talked to, they said we have 20s of agents but they're still doing this 1 task, but nothing at scale.

Got it. Let's switch the subject maybe to competition in your space. There's always been intense competition in the database space for as long as I can remember. Still the case, but in your defense, what I would say is that 10 years ago, you were 1 of probably 5 or 10 at the time, no SQL database vendors, and you've actually been the one to emerge from the pack, well, a lot of them have still stuck at $100 million, $200 million or sold themselves. So you've won that race. But today, you're still looking at some competition.

So let's go through 3 categories, and Ben maybe went away in as well. One is a lot of us have been hearing about the popularity of Postgres relational databases, where Postgres running on Azure and AWS have partly, maybe not entirely solved some of the scaling issues that used to be a blocker years ago. Ben, do you want to take this one? When you contrast Mongo against Postgres, where do you live on this debate? Is it sort of room for 2 because the overall demand is so strong? Or do you feel like you've got an opportunity to take some share from Postgres?

Well, I definitely think we have a lot of opportunity, but let's just go back to what Postgres is because it's not just one, it's 30, 40 different flavors of this, right? And I talk to customers all the time and we started out with Postgres in AWS or you name your hyperscaler. And now for whatever reason, they want to go to a different hyperscale or they want to bring it back on prem, you can't just move that application. It's actually -- just because it's Postgres doesn't mean it's truly portable.

So one thing that really especially resonates with enterprises is that MongoDB actually has a truly portable database, right, without any application change. So we can move your data from one cloud to another, and bring it internally, go back to cloud, no application change. And so especially enterprises, Europe is big with sovereign cloud. We're seeing a lot of this portability be a massive driver of a requirement of like what data platform they're going to choose. So I would say that's number one.

And then number two is, and you touched on this earlier about the developer experience that we started out with that we really still to this day focus on, is that developer experience, that developer velocity, that efficiency. And so if you look at what we've been doing over the last few years, building up to this AI era is we haven't been bolting on other workarounds with different API layers to add new use cases. It's all part of our idiomatic API that we've always invested in, which is MQL. So we added search, part of the same query language, we added vector, same query language. We're keeping that developer velocity and efficiency in mind at all times. And that's just something that the competitors are not doing.

Got it. The second category, which we were thinking about yesterday because we had Ali at Databricks on stage is some of the OLAP vendors are stepping into the OLTP database space. And Ali was quite proud of their new Lakebase offering. So when you think about those guys, the likes of Databricks and snow taking early gentle steps into the operational database space, what do you think of that?

I think it's cute. Like look, like they tried for many years to build an organic OLTP, right? Because they saw it very early on that OLAP wasn't going to be fit for purpose for these real-time production applications that are going to be business mission critical as CJ was just kind of pointing out and why all these applications are in pilot. So they realized that they couldn't do it organically, so now they bought someone. But they didn't buy the A players. And so we're -- from a place of an OLTP landscape, I think we're 15 years ahead of where we need to be from an OLTP landscape for these AI application.

And I'll add a one thing as it is a question that comes up a lot, and we have great investors here. Like make no mistake, even when I was at Oracle, Oracle was known for OLTP. And we experimented a lot with data warehouses. I'm talking late 90s, early 2000s, and then you know what happened with business objects and all those firms back then, too. And OLTP and OLAP were always 2 sides within an enterprise, different buyers and based on the use case, what you're solving for.

And from my perspective, when somebody that is just focused on data warehouses, starts buying OLTP based companies, it's a validation of that our space is great, and it has durable advantage, and they are trying to expand the TAM. And then I'll just add to what Ben said, that even Postgres selling support for JSON and when you actually really look at that why they did that is JSON is popular with developers. Why? Because of MongoDB, and that's a bolt-on versus we just naturally work with that. And for AI high grounds, I would argue that us, being native JSON support and just how MongoDB works for it, even if you look at any of your popular ChatGPT today. They talk about images, videos and then there's so much unstructured data. So we are correctly positioned for them.

The third and last one is the hyperscalers. They're native databases. None of us can leave a meeting with Microsoft without such in company bragging about Cosmos DB and how customers are coming to them for their native databases. Has anything changed in terms of competitiveness versus the hyperscalers, Ben, CJ?

For my personal view of what I'm talking to customers about is it usually starts out with, it's really easy. I click it. It's in the console. It's available so they just start there, right? And even all the hyperscalers claim compatibility with Mongo. When we run our own compatibility test, we come out a lot less than that. And the way we do that, we actually published the benchmarks publicly. So it's available on GitHub, everyone can download the same test and do it themselves.

We have thousands of database schematic, so you can query against. When they look at -- when they do their benchmarking, we think it's sub-100 of the actual features of the MongoDB API to give them a better percentage of compatibility. When we take a conservative view of, let's say, 270 of the most critical, mostly used pipelines, we are into the 40s to 50s percent. So even when we give a little bit extra. So I just think that people start there because it's easy similar to why people start with Postgres, it's just like easy. So we eventually get it. I want to get it earlier, don't get me wrong, but we actually get it.

And on the high end of enterprise on that point, besides the features and functionality and scale out and this and that, recently, I mean, if you look at 2025, you had GCP outage, you had AWS outage and you had Azure outage. And speaking to customers, including large financial services as well as health care companies just recently in the last couple of weeks.

As Ben said that, hey, on one hand, it's easier for me to turn on and use the first-party database resiliency perspective, now you're locking in yourself. So if one of the hyperscalers is out, your workload is out. And MongoDB on Ben's first point, that I can -- are talking to this large customer, and they said, from a resiliency perspective, CJ, we chose MongoDB rather than the first-party hyperscaler because I can now replicate MongoDB from AWS to Azure without a problem. So not just an intra-region of AWS, US-East-1 and so on, but I can do that across clouds. And that is a massive advantage compared to the first part of it.

Mike, let's turn the conversation to you for a moment. Congratulations on the October quarter results.

Thank you. From all the employees at Mongo.

I think one thing that stood out to me in my judgment, the 3Q results were very good, but what I thought was really outstanding was actually your fourth quarter outlook, where you raised it quite substantially. And if I know you wouldn't encourage us to do this, but if we assume sort of a normal-ish beat on that guidance, you're getting a fairly marked acceleration in your total revenue growth rate in the January quarter. Can you unpack 1 or 2 things, Mike, that gave you that confidence for that acceleration in the January quarter? Because to me, that was the highlight of the print.

Sure. So let's break it up into Atlas and then non-Atlas if we could. So for Atlas, as we looked at the second half, when we guided Q3 we were confident, call it, around that mid-20% growth rate. What we saw coming out of Q3 was and we said, hey, it was very much what we thought, and it was very consistent throughout the quarter. The nice part is when that consumption builds, then that's the starting point for Q4. So when we forecasted Q4 then it's a higher base and we felt better about raising that to the 27%.

With the caveat, which is, hey, folks, it's the holiday season, happy holidays, everybody. There are -- we've always seen a little bit of unpredictability based on where days fall. So we just want to take that into account.

The big thing for us was around the EA business. And from that business, not only did we see a little bit better for multiyear deals, and I'll say this publicly, folks, this is not Poland. This is regular business that was coming due in the second half where it's tough to forecast. Are they going to do a 1-year deal? Are you going to do 3? Possibly even 5.

The great part about that is these are the largest customers at Mongo, and we love them all, committing to us long term, and we saw a much better commitment not only in the multiyear, but just then call it the run rate business. So when we built that bottoms-up pipeline, we felt better about that. So those were -- if you look at the Q4 raise, about 1/3 of that was Atlas and about 2/3 of that was EA.

Mike, the other thing that stood out to me was that since you came aboard, I'd say on the margin, you've been giving a little bit of color about how to model Atlas, but more directional. You went one step further on this call where you gave a point guidance for Atlas, which I think everybody appreciates. It leaves out a little bit of a guessing game, how to apportion total revenues.

But one interpretation of that is that you wouldn't be making that disclosure improvement, if I could put it that way, unless you felt fairly confident about the Atlas business. Was that the correct interpretation, Mike?

So I never want to give a number externally that I don't feel good about. And what we would say is we debate this all the time, and you can look at ranges, we felt really good about that number in terms of, hey, from a pragmatic point of view, that's where we think we are. Hopefully, things go better. Hopefully, they do, but that -- we felt good about giving that number.

The last thing the -- I wouldn't even call it a flaw because I think you can explain it. But one of the only metrics that stood out to a couple of investors that hit me, Mike, was that the direct customer count fell slightly for the second quarter in a row. But I think there's an explanation for that, but it might be good to just give you a chance to explain that one.

Yes. So thank you about that. And, Karl, we'll probably change this going to next year. Keep in mind that when we talk about direct versus self-serve, this is how we internally categorize those customers. Self-serve is not -- they started and self-serve and then they stay there forever. At some point, they will shift to a direct customer. Sales will take over. So what you have is a little bit of the apples and oranges. As we have added more sales to go direct, we've pushed that team up, focus a little bit higher. And then what happens is self-serve covers that gap.

So it looks like direct is actually going down. Folks look at the total customer count, that's what matters. The bifurcation between direct and self-serve is our own internal categorization of how we go to market and just pay a little bit of warning. In '27, folks, we were probably not going to give this number because of this exact issue. Look at the business as a whole, look at consumption growth, look at revenue growth and total customers. How we bifurcate internally is how we go to market. It's not a driver of the business.

And 8,000 customers added this year on year-to-date with 67% growth. But even adding 2,500 in the quarter, specifically, which, if you look at last Q3, that's a 40% growth on just new customer adds. Karl, I mean, you know how much obsessed I was about new logos and others in the past. These are very good leading indicators for the future.

CJ, Mike and Ben, thanks so much for coming to the event. I think, along with everybody here looking forward to watching the Mongo story unfold over the next 12 months.

Thank you for having us.

Thank you.

Good day, and thank you for standing by. Welcome to the MongoDB's Third Quarter Fiscal Year 2026 Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded.

I would now like to turn the conference over to your speaker for 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 third quarter 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. Following our prepared remarks, Dev Ittycheria, MongoDB's former President and CEO and current member of the Board will join us for Q&A.

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 and 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 July 31, 2025, filed with the SEC on August 27, 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'd like to turn the call over to CJ.

Thank you, Jess, and thank you to everyone for joining. I'm honored and genuinely excited to speak with you as the CEO of MongoDB. This is an incredible company and stepping into this role is a privilege. I want to start by thanking our customers, partners and employees for everything you have done to build MongoDB into what it is today. I especially want to acknowledge Dev whose leadership and vision created a phenomenal company, which has strong momentum and a tremendous market opportunity ahead. Many have asked why I chose MongoDB, I had multiple opportunities to lead other technology companies but MongoDB stood apart. We are at a true inflection point driven by major shifts across cloud, data and AI.

MongoDB has the potential to become the generational modern data platform of this evolving era, an opportunity that comes once in a lifetime. I am a truly customer-obsessed leader. So during my diligence, I spoke with multiple customers. Across these conversations, the message was clear. MongoDB already powers core, mission-critical workloads were enterprises that are modernizing their technology stack. At the same time, MongoDB is uniquely positioned at the center of the AI platform shift. Few technology companies have that combination of durable core strength and emerging platform relevance.

Throughout my career, I have driven product to platform transformation at some of the most respected technology companies. Looking at MongoDB today, I see all the ingredients needed to build an iconic modern data platform company. World-class technology, a strong innovation engine, deep developer and customer pool and exceptional talent. We have everything required to become the generational data platform of choice in the AI era. Now onto this quarter's results.

Atlas performance was strong, accelerating to 30% year-over-year growth, up from 29% in Q2 and 26% in Q1. We generated total revenue of $628 million (sic) [ $628.3 million ] , up 19% year-over-year and above the high end of our guidance, driven by strength in Atlas. We delivered non-GAAP operating income of $123 million (sic) [ $123.1 million ] or a 20% non-GAAP operating margin. We ended the quarter with over 62,500 customers adding 2,600 in the quarter and 8,000 year-to-date, reflecting 65% growth in customer additions on a year-to-date basis driven by the strong performance of our self-serve motion. Q3 was an exceptional quarter that was driven by our continued go-to-market execution and the broad-based demand we are seeing across business.

At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while simultaneously expanding profitability. Now let me explain why I see such a large opportunity ahead for both core operational data and emerging AI workloads. Our core business is strong across self-served and enterprise customers even before any AI tailwinds. In my first 3 weeks, I've met with over 30-plus customers from AI-native companies to C-suite technology leaders at Fortune 500 companies.

Those conversations have only strengthened my conviction in MongoDB's opportunity. Customers already depend on us for mission-critical workloads today, and they are leaning in even further, betting on MongoDB to power the AI applications that will shape their future. The expansion opportunity in front of us is immense. We already serve more than 70% of the Fortune 100 and many of the world's largest banks, health care organizations and manufacturers run their mission-critical workloads on MongoDB.

Even with this foundation, there is still significant room to broaden our footprint within the enterprise. A strong example of this expansion opportunity is a major global insurance provider that has adopted MongoDB broadly across its enterprise. The company selected MongoDB Atlas to modernize several mission-critical systems, including its next-generation policy administration platform, analytics rating engine, unstructured data repositories and hundreds of supporting services.

Since moving its policy platform to Atlas, the insurer has expanded from just a small set of regions to nationwide and significantly accelerated the rollout of new products and distribution channels. Standardizing on Atlas has given the organization the scalability and reliability to improve customer experience, support more advanced data and AI capabilities and increased development velocity, all central to its transformation and growth ambitions. All of this momentum in the core business is happening before the AI wave has meaningfully impacted our results.

We are still early, but the signs are encouraging from AI-native start-ups building intelligent applications on MongoDB to large enterprises developing AI agents that will reshape how they operate. AI applications must connect what LLMs know with what companies know, which is their proprietary data, systems and real-time context. This is fundamentally an information retrieval problem, and it requires a very different architecture than the last generation of software.

Rapidly evolving AI models uncover new complex properties about entities and rigid tabular stores cannot deliver the real-time high accuracy performance that AI systems require. At the same time, AI is dramatically increasing the speed at which applications are built and iterated and fixed database schemas simply cannot keep pace. This is where MongoDB has a structural advantage. Our document model, natively, JSON is built for diverse class changing and interdependent data. Our integrated search, vector search and Voyage embeddings removed the need for brittle bolt-ons, and we are seeing industry-leading results.

Number one, on the Hugging Face retrieval embedding benchmark with Voyage MongoDB models and the #1 vector database on DB engines. Advances in our embedding and reranking models drive meaningful accuracy gains. Enabling AI applications to deliver more grounded responses with fewer LLM hallucinations, while lowering storage cost and query cost through smaller, more efficient embeddings. Because all of this is delivered in a unified platform that runs anywhere, customers can keep operational and AI workloads together, simplify their architecture and innovate faster.

As AI adoption accelerates, MongoDB's positioned not just to participate in the wave, but to help define it. we are already beginning to see this play out with AI-native customers like Mercor, which is redefining hiring with its fully automated platform that uses AI to assess and match talent with the opportunities they are best suited for. Mercor uses MongoDB Atlas to store the AI data behind its platform that directly connects professionals to AI model training and evaluation roles. Originally, a self-serve customer, the company is also utilizing Voyage embeddings and Atlas Vector Search. Atlas has scale to support Mercor's 50% month-over-month growth, allowing the company to keep its software engineering team lean and agile as it expands to over $10 billion in value. This is just one example of how customers are building AI-native applications and companies on MongoDB.

We are also seeing meaningful traction among large enterprises that are starting to build AI applications that have a material impact on their business. For example, a highly influential global media company aim to increase engagement via enhanced content recommendation for its vast repository of multimodal assets across its 70-plus websites. That existing stack powered by Elasticsearch hit a performance wall struggling with the complexity of new embedding models. Recognizing that [ rigid ] systems stifle innovation, the engineering team re-architected on MongoDB Atlas and MongoDB Atlas Vector search. Working with MongoDB experts to deliver a proof of concept in just weeks, they integrated Voyage AI models directly alongside their data. The solution scale effortlessly, cutting latency by 90% and reducing operational spend by 65% and driving a 35% increase in click-through rates, ultimately providing millions of global readers with a seamless, deeply personalized discovery journey.

The bottom line is that the business is performing exceptionally well. Existing customers are expanding with us and net new customer additions continue to show strength. Companies in nearly every industry and across every geography are choosing MongoDB because we deliver the features, performance, cost effectiveness, and AI readiness they need in single data platform. Given the continued robust performance of Atlas, along with the healthy underlying fundamentals we are seeing in the business, we are raising our financial guidance for the fourth quarter and the full fiscal year 2026 and reiterating our commitment to the long-term financial model outlined at our recent Investor Day.

Over the next few months, my focus is straightforward. Deepening customer relationships, advancing our innovation agenda as we build the generational modern data platform for the multi-cloud and AI era, scaling our go-to-market efforts and supporting our people so they can do their best work. I believe MongoDB is a company that has only begun to realize its vast potential and I look forward to unlocking this potential in the years to come.

With that, I'll now hand the call over to Mike to discuss the financial results and outlook in greater detail. Mike?

Thank you, CJ. I want to extend a big welcome to you from all of the employees at MongoDB. We are excited to have you join the team. I look forward to working with you to continue to execute on our business plans and drive meaningful shareholder value. I also want to thank Dev for the partnership and our time working together. I believe we accomplished a lot in a short period of time and appreciate all of your guidance and leadership. Best of luck in the next stage of your life journey.

Okay. Now let's move on to the financial results. I will begin with a detailed review of our third quarter results and then finish with our outlook for the fourth quarter and fiscal '26. I will be discussing our results on a non-GAAP basis unless otherwise noted. As CJ mentioned, we had another strong quarter as we exceeded all of our guidance ranges and are increasing our full year outlook across the board.

In the third quarter, total revenue was $628.3 million, up 19% year-over-year and above the high end of our guidance. Shifting to our product mix. Atlas revenue outperformed our expectations as year-over-year growth accelerated to 30% in the third quarter and now represents 75% of total revenue. This compares to 68% of total revenue in the third quarter of fiscal '25 and 74% last quarter.

In the third quarter, Atlas consumption growth was relatively consistent with last year's growth rates which drove the acceleration in revenue as well as growth in absolute revenue dollars for the third straight quarter. Atlas growth was driven by continued strength with our largest customers in the U.S. and broad-based strength in EMEA. This strength is being driven both by new workloads and growth of existing workloads. We believe these dynamics reflect our growing strategic importance to many customers and our ability to win more critical workloads due to the strength of Atlas. You can see that progress in our total company net ARR expansion rate, which increased to 120% in the third quarter, up from 119% last quarter.

Turning to non-Atlas. Revenue came in ahead of our expectations in the quarter as we continue to have success expanding within our existing non-Atlas customer base. Non-Atlas ARR, which reflects the underlying revenue growth of this product without the impact of changes in duration grew 8% year-over-year. We continue to see consistent trends in non-Atlas in the third quarter, which reflects the desire of some of our largest customers to build with MongoDB long term for their most mission-critical applications. We also benefited from higher-than-expected multiyear revenue in the third quarter as approximately 2/3 of the non-Atlas revenue outperformance versus the high end of guidance was attributable to multiyear outperformance.

We had another strong quarter for customer adds as we grew our customer base by approximately 2,600 sequentially, bringing the total customer count to over 62,500, which is up from over 52,600 in the year ago period. The growth in our total customer count is being driven primarily by Atlas which had over 60,800 customers at the end of the third quarter compared to over 51,100 in the year ago period. We ended the quarter with 2,694 customers with at least $100,000 in ARR, representing 16% growth versus the year ago period.

Moving down the income statement. Gross profit for the third quarter was $466 million, representing a gross margin of 74%, which is down from 77% in the year ago period. Our year-over-year gross margin decline is primarily driven by Atlas growing as a percent of the overall business. Although Atlas gross margins are slightly below the total company gross margins they continue to improve year-over-year.

Our income from operations was $123 million for a 20% operating margin compared to 19% in the year-ago period. We are very pleased with our stronger-than-expected operating margin results, which benefited from both our revenue outperformance and lower-than-expected operating expenses. Net income in the third quarter was $115 million or $1.32 per share based on 86.9 million diluted shares outstanding. This compares to net income of $98 million or $1.16 per share on 84.2 million diluted shares outstanding in the year ago period.

Turning to the balance sheet and cash flow. We ended the third quarter with $2.3 billion in cash, cash equivalents, short-term investments and restricted cash. During the quarter, we spent $145 million to repurchase approximately 514,000 shares which was executed under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at $144 million, and free cash flow was $140 million, which compares to $37 million and $35 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.

We remain confident in our ability to drive higher and more consistent free cash flow going forward. Before we go into our guidance for the rest of fiscal '26, let me recap some of the enhancements we have made to our approach to guidance since I joined MongoDB. Importantly, we are providing more visibility into our expectations for Atlas growth as well as non-Atlas ARR growth each quarter. That being said, we will continue to be prudent in our forecasting of multiyear deals and only include those deals where we have very clear visibility. Our goal is to give you a more transparent view into our expectations for the business and our approach to guiding the non-Atlas business.

Now let me share some of the assumptions driving our outlook for the rest of fiscal '26. Number one, we are continuing to see strong momentum in Atlas, which has experienced relatively consistent consumption growth through the first 3 quarters of the year. And comparable seasonal patterns as compared to fiscal '25.

We are seeing strength with existing customers, along with momentum in new accounts as customers large and small increasingly recognize the strategic value of Atlas. As a result, we now expect Atlas to see approximately 27% revenue growth in the fourth quarter of fiscal '26, which is higher than our previous expectations of growth in the mid-20% range. This outlook reflects our continued confidence in Atlas while taking into account the historical seasonal variability and consumption patterns during the holiday period.

Number two, we continue to experience steady ARR growth in our non-Atlas business and have good line of sight to several large multiyear deals we either already have or expect to close in the fourth quarter of the year. Based on these dynamics, we now expect our non-Atlas business to grow in the upper single-digit percent range year-over-year in the fourth quarter.

Number three, we continue to make strategic investments in engineering, marketing and direct sales capacity to drive continued growth. Some of these planned investments have taken longer to implement than expected and have shifted into the fourth quarter of fiscal '26 and fiscal '27, which has benefited our operating margin during fiscal '26.

Fourth, we continue to make progress on free cash flow conversion, which is now expected to exceed 100% for fiscal '26.

Finally, we will continue to execute our share buyback program to help offset dilution from employee equity awards. In addition to our buyback, this past quarter, we began settling the taxes due on the vesting of employee RSUs with cash instead of issuing new shares. We also expect to receive over 1 million shares of stock for the cap calls associated with our 2026 notes that mature in January 2026. All of these actions will help us manage share count for the long term and illustrates our commitment to being good stewards of your capital.

Now let's shift to guidance in the fourth quarter and fiscal '26. For the fourth quarter, we now expect revenue of $665 million to $670 million, which equates to 21% to 22% year-over-year growth. We expect non-GAAP income from operations to be in the range of $139 million to $143 million for an operating margin of approximately 21%. We expect non-GAAP net income per share to be in the range of $1.44 to $1.48 based on 86.5 million diluted shares outstanding.

For fiscal '26, we now expect revenue to be in the range of $2.434 billion to $2.439 billion, an increase of $79 million from the high end of our prior guide and representing full year revenue growth of 21% to 22%. We are raising our non-GAAP income from operation expectations by $109 million at the high end and are now targeting a range of $436.4 million to $440.4 million for an operating margin of approximately 18%.

We expect non-GAAP net income per share to be in the range of $4.76 and to $4.80 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the fourth quarter and fiscal '26 assumes a non-GAAP tax provision of 20%. While we will provide detailed guidance for fiscal '27 on our fourth quarter call, I would like to comment on how we are thinking about a few metrics as we sit here today.

First, we remain committed to the long-term model presented at our Investor Day in September and continue to make great progress against all of the objectives highlighted at the event. We have seen strong margin expansion and free cash flow performance in fiscal '26. And both of these metrics are tracking well above the long-term targets we discussed in September. As we look ahead to fiscal '27, we will continue to make strategic investments to focus on driving growth going forward. With these planned investments and the timing of head count adds, we continue to target 100 to 200 basis points of margin expansion on average and 80% to 100% for free cash flow conversion outlined in our long-term model.

Second, our non-Atlas business is on track to exceed our prior expectations for fiscal '26 due to the stronger performance, including greater-than-expected large multiyear deals. Given this outperformance and our current bottoms-up forecast for fiscal '27, we currently do not expect non-Atlas multiyear transactions to provide either a meaningful headwind or tailwind to revenue in fiscal '27.

To summarize, we had another very strong quarter. We are pleased with our ability to drive both revenue growth across the business while increasing our operating profit expectations 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, Lisa, we would now like to open the call up for questions.

[Operator Instructions] And our first question of the day will be coming from the line of Sanjit Singh of Morgan Stanley.

Fiscal year '26 has turned out to be quite the year for MongoDB, so congrats to the team all around. CJ, I wanted to start with you since this is your first earnings call, heard you loud and clear in terms of what the goal is here to make MongoDB a foundational data platform for the AI era. In terms of making that happen in your kind of first 45 days on the job, maybe even less than that. Are there some initial things that you're looking at some kind of things that might fit in the sort of quicker win bucket? And then longer term, what is -- what are some of the changes you think that the company can make or evolve to get to that -- to [ see or place ] in that sort of AI era?

Thank you, Sanjit. Here is -- this is my day 28 on the job, and I have been speaking to customers as well as our innovation team, including our Voyage AI team as well as our core database teams. The first thing I would say is the opportunity for MongoDB to be that data platform for AI workloads is very real because you need real-time operational data, you need the right context, you need to make sure that you are keeping up to date between the proprietary data of the company as in the enterprise as well as the LLM learnings that the LLM model brings to the table. And most importantly, when I think about all of that combined together, MongoDB has all the elements needed to be the right foundational platform for AI workloads. In speaking to customers, it is still early. There are various co-pilots when it comes to productivity types of applications that are happening inside of an organization, whether it's a bank or a health care organization or a manufacturing organization.

But what I have not seen is truly AI agents running in production that fundamentally transform the business or serve customers better. There are many, many pilots still going on. When I contrast that with the AI native companies, and there is a really good fast growth at scale, AI native company that currently switched from Postgres to MongoDB because Postgres could not just scale.

There is another AI company that highlighted that is using our embeddings as well as our vector database besides our operational platform. So when I combine all this together, Sanjit, what I see is, as truly scaled agentic platforms where you can have enterprises creating agents that transform their business, MongoDB has a very important role to play. And from a low-hanging fruit standpoint, I would argue that our embedding model and reranking model is something that customers can start with today, then they can move on to our vector database and use us for also real-time operational store. So that's how I'm thinking and some of my initial customer conversations have validated that theory.

Understood. I know it's early, so great to get that perspective. And then one follow-up for me, sort of a mark-to-market question. The calendar year '24, fiscal year '25 workload sort of improved in quality versus the prior year. I just want to get a sense of your sort of view on how the calendar year '25 workloads are shaping up as they will unlikely be a factor in terms of thinking about growth next year? And just so in terms of the quality of the workloads this year, can you give us a sense of the quality of those workloads?

Sanjit, it's Mike. So what we'll say there is, as we said during the prepared remarks, and we saw this in Q2 as well, what we're really seeing is strength in the larger customers. It's not only from new workloads, but it's from the existing workloads. We don't want to bifurcate between which calendar year those were added. What we'd say is that we continue to see growth in the larger customers. They are growing longer and they're getting bigger and growing for longer, which is great. And we're seeing that across both the United States and then broad-based in EMEA as well.

And as Atlas gets bigger and bigger, all of those kind of munch together because they're expanding, they're adding. So what we'll do is we'll focus on the growth in our larger customers, especially in the U.S. and EMEA without going into each year. I hope that helped.

And our next question will be coming from the line of Matt Martino of Goldman Sachs.

Nice to see another quarter of acceleration. CJ, I appreciate you're only a few weeks in, but I'd be curious to hear what customers are telling you is top of mind for MongoDB. What are the repeated themes in customer conversations as you take a fresh lens to the business?

Absolutely, Matt. First thing I would say is that the modernization effort, whether it's a workload that may be just running on-prem, in a large enterprise or a workload that is moving to cloud or sometimes to multiple clouds for resiliency that transformation in speaking to a large telecommunications company, a large health care company, a large tech company, and I can cite you many other examples. I was pretty overwhelmed to understand that those transformations are still going on. There is just a recent conversation I had with CTO of a large telecommunications company who said that they are moving 1,300-plus applications to another hyperscaler and trying to determine which workloads are best suited for MongoDB.

So the whole multi-cloud or a public cloud transformation is still going on. And just my intuitive sense in speaking to these customers will be going on for at least next 5 to 7 years. So that specific TAM still very much exists for MongoDB. Now these are the same set of customers, while they are trying to modernize their application stack, they are also experimenting, I would say, because I've not seen agents at scale that are customer facing or sometimes even employee-facing, they may have 10, 15, 20, but not that many compared to thousands of applications they run.

In those AI applications area, they are experimenting sometimes with our embedding models or with our vector database or using MongoDB for real-time operational database. So that second aspect, which is still fairly early, but we are very well positioned as you think about AI workloads in enterprises and large enterprises.

And last but not the least, spending time, as you know or you may know that I spent half of my time in New York City and half of my time in Silicon Valley and speaking to my network in Silicon Valley with AI-native companies or digital-native companies, what I hear from them is that certain alternatives on relational database just do not scale because AI workloads are fundamentally around unstructured and semi-structured data. And then they decide sometimes explicitly to use MongoDB. So I put this in 3 buckets. One bucket is our core and still the cloud transformation, digital transformation, modernization, whichever term you want to use, our core will still continue to grow. As people create AI agents at scale, MongoDB has a role to play and for AI-native companies and some at scale are already using MongoDB because the alternatives in relational world just do not scale.

So those are my like 3 buckets and initial mental model on how these conversations are proceeding and what we can do for them.

Really clear. And then, Mike, just a quick follow-up for you. It was good to see the outperformance on both Atlas and non-Atlas, but with op margins now about 200 basis points shy of your midterm framework, how should we think about the philosophy around reinvestment? And any considerations around non-Atlas and the ability to expand margins as we look out into fiscal '27?

Yes. Thanks for the question, Matt. So I'm sure everyone's focused on '27. So what we'd say is we will guide '27 on the next call. What we would say is, and it's built into the guidance that you have in Q4, and I also talked about it on the prepared remarks, we are continuing to invest, and we will continue to invest. Some of the investments that we wanted to make, especially around engineering, marketing, less so, but certainly around sales capacity has been pushed into Q4. So you should expect to see OpEx continue to grow in fiscal '27.

But we also want to make sure, and that's why Matt, we took the time to say, "Hey, we want to reorient you to what we talked to you about in September -- we still expect to see margin expansion. But you really see it in the fiscal '26 numbers is that is coming mostly from revenue growth. That is the expectation next year. We'll continue to grow revenue. We're going to continue to invest in the business, but the business model will continue to drive that expansion. So you should expect to see us continue to invest, especially across those 3 areas.

And our next question will be coming from the line of Karl Keirstead of UBS.

Okay. Great. Thank you. First of all, CJ, welcome aboard. I'm excited to work with you over the coming years. I had a question for you. So it seems as if you're describing these good set of numbers as strength in the core, essentially even before that AI tailwind kicks in. I'd love if you could define what you think is fundamentally driving that core strength? And do you feel like it's possible that actually Mongo is already getting an AI tailwind in the sense that there's a heightened focus on modernizing your data in advance of AI, such that this core strength is actually AI-related?

Karl, great to hear from you and looking forward to seeing you on Wednesday. I would say the core strength from my perspective is workloads that are -- need modernization has a lot of unstructured or semi-structured data and ideally suited for MongoDB. Now when it comes to AI, could AI potentially drive more modernization efforts? That is possible but not deterministic. As in we see -- as we shared in the remarks, that in the high end of the enterprise, the consumption of the workloads we acquired maybe a year ago, 1.5 years ago, that continues to move up in the right direction as our go-to-market teams are focused on the high end of the enterprise. We also saw broad-based strength in Europe. And that is pretty much to the core business like the large insurance company on the claims engine and other things that I spoke about related to policies.

So I particularly see that as, okay, is that -- does that mean that if core is modern, it helps with AI workloads, absolutely, that is true because they are not mutually exclusive. And Karl, one thing I would say, this is my personal experience in building AI technologies in the past. That the AI team is typically a separate team from the core data team. And AI team relies on the core data team. And if the core data team moves slow, then AI teams get really frustrated because innovation velocity is how they measure themselves on. So my personal experience was, hey, when the core team is not agile there schemas are not flexible, it actually slows AI down. So that is definitely some facts behind your theory that it is potentially the AI revolution, which we are still in the early stages, is driving modernization in the other part of the enterprise.

Okay. And then, Mike, for you, I think everybody on the line appreciates the more definitive guidance on Atlas for the following quarter. So thank you. I wanted to ask what's driving that? Is it simply a function of you and just in your new -- relatively new seats, wanting to be more transparent in the guidance? Or Mike, is there something actually changing in Atlas such that now that it's at scale, it's becoming predictable enough that it now makes more sense to give precise guidance?

So thanks, Karl, thank you for the question. I would say it's probably a little bit of both. One is, hey, we want to give you folks a little bit more visibility to what's behind the guidance that we provide. That was number one. Also, as Atlas gets to be, gosh, now almost a $2 billion business, we feel better about the forecasting. The team has done a wonderful job forecasting that part as well. So when we gave the number for Q4, we want to make sure and give you the visibility. But we also have a pretty good view of what we hope it would be, understanding that, keep in mind, Q4, we want to be prudent because there are some seasonal holiday patterns that can be somewhat unpredictable, and we've seen that play out in the past Q4s. So I just want to note that for the guidance that we just gave.

And the next question will be coming from the line of Raimo Lenschow of Barclays.

CJ, all the best from me as well. I had 2 questions, one for CJ, one for Mike. CJ, on the -- one of the core things in terms of adoption of Mongo will be on the developer side because they're -- at the end of the day, developers are like a big driver of like what's getting used, et cetera. At the moment, a lot of AI is on the West Coast. What's your thinking around like getting AI -- getting developer engagement up with Mongo to kind of go against that Postgres kind of narrative that happens a lot in the valley. And then, Mike, for you, like since next year EA is not seeing benefits from all the year. Should we anchor our numbers on the ARR performance? And is that the right way to think about it?

Thank you, Raimo. Great to hear from you. I'm going to first ask -- there is a little bit of historical context in terms of your point on the West Coast. I'm going to ask, our previous CEO, Dev Ittycheria to talk about reclaim the Bay, the initiative that him and the team started, and then I'm going to specifically talk about how I think about it on the West Coast.

Raimo, it's Dev here. As CJ mentioned, we've talked about this in previous calls, but we made a concerted effort to reinvest in the Bay Area because during COVID and post-COVID, we felt that we had neglected that region. And obviously, there was a whole new corpus of AI-native companies that were getting launched. So there's been a real concerted effort both in terms of putting more feet on the street, putting more marketing efforts in terms of supporting that part of the world. Investing more in the start-up community and also in the venture community to get people to understand the true value proposition of MongoDB. We've done things like hackathons and other events in that area as well. And so the team's really focused, dedicated to really supporting and servicing these early AI native companies, and that is starting to yield some results.

And we feel really good about the progress there, but I'll let CJ talk about what happens going forward.

Thank you, Dev. And this is the reclaim the Bay in San Francisco on the West Coast. It is 100% true Raimo, that there is a lot of investment with AI-native companies, and we could benefit from increased mind share and being in front of them as in the developer community that you talked about, which is a super important community to us on the West Coast. So me spending personally time on the West Coast house. I do also have deep network in the West Coast community, both venture community as well as tech companies at scale. And I've already started leveraging that network to get their feedback. We are really excited in this quarter, as in the 4Q, we are relaunching our .local after a few years in San Francisco on January 15, where we are going to invite companies that have built on MongoDB, some great speakers on why they should build on MongoDB and show hands-on experience to the developer community in that conference on January 15. And what I see is just speaking to many CEO founders as well as developers of smaller companies or midsized companies, all these efforts of the marketing investment that Mike and Dev originally approved is going to start yielding results as we move into the next fiscal year.

And Raimo, thanks for the question. It's Mike. On non-Atlas next year, we wanted to make sure we've had a lot of questions about the multiyear headwind. So thank you for the question there. We are not guiding for fiscal '27. However, sitting here today, I would steer you more towards -- if you look at the full year revenue growth of non-Atlas it's about 4%, somewhere in that mid kind of low single digits is probably a good range to think about for next year as we sit here today.

And our next question will be coming from the line of Brad Reback of Stifel.

Great. I'm not sure who this is for, but on the commentary around new customer strength within Atlas, are you seeing new customers ramp faster for net new workloads than they have been historically? And if so, why?

Brad, my initial observation is that the team -- engineering team has done a fantastic job when they launch 8.0 and all the subsequent point releases that allows Atlas to be adopted faster and remove the friction, whether you are coming via our self-serve channel or whether you are a large enterprise moving onto Atlas. So that's one thing I would say. And I'm going to ask Dev to provide commentary as well from a context perspective.

Yes. I think what I'd also say is that, Brad, is that I think we've -- the self-serve team has really removed the friction to enable customers to onboard more quickly and more easily. And given the performance -- price performance gains that we've seen in 8 and now even better in 8.2, I think that's really driving a lot of the traction we're seeing in our new customers they quickly see the performance benefits and they're scaling nicely. And so that's allowing us to continue to acquire customers efficiently.

And one last thing on that, Brad. If you look at the revenue from that, it hasn't changed materially. It's still, keep in mind, a pretty small number when they first onboard, so it's not going to move the needle much. We haven't seen much change in that cohort over the last couple of years.

Great. And then, CJ, a quick follow-up for you. Philosophically, how do you think about M&A as it relates to Mongo? What types of things, if anything, you think you need to acquire?

Brad, you know me well, and I'm a big believer in organic growth. The team, Dev and the team have laid a very strong foundation on our technology platform. I think Voyage AI in February was a brilliant acquisition, where we got unbelievable team in Palo Alto. And my goal on behalf of MongoDB is to always believe in our own teams and our technology. We participate in a large market and where it makes sense, where we can get a particular adjacent technology or a great team that can help us accelerate the road map, we would always consider that type of M&A.

And our next question will be coming from the line of Alex Zukin of Wolfe Research.

CJ, maybe for you. I mean, you shared, I think, a lot of thoughts about your initial vision. You shared the 3 pillars of the core, the enterprise AI opportunity and the AI natives. I just want to maybe lean in, where do you see your particular skill set of network offering kind of not the lowest hanging fruit, but your ability to make kind of the biggest impact in, call it, the next 12 to 24 months? Like where do you really see that incremental opportunity for growth inflection?

I would say, Alex, and -- you are aware of the enterprises and the customer obsession I have and the relationships that I have formed over many, many years with technology leaders at large companies. So, from my perspective, there are 2 areas where I can benefit our go-to-market teams immensely. Number one is Fortune 500, where MongoDB can still penetrate even at a higher rate than it is penetrating today, both within the existing accounts as well as the new accounts we get. So that's Fortune 500. And then I was with our sales teams in Europe, and there are many customers that they are targeting, including existing customers, large banks, manufacturing companies and so on, where they're trying to expand where my personal relationships with those technology buyers can help. So that's bucket number one is make no mistake, high end of the enterprise as in Fortune 500 and Global 2000.

Number two, on the other extreme would be AI-native companies, lived in Silicon Valley for a very long time. I understand where venture community is investing, folks who are creating, whether it's domain-specific AI companies or foundational companies have relationships there as well across [ 101, 280 and 237 ], and that's where I also plan to -- I would say, plant the seeds in a correct fashion so that over time, that becomes a meaningful business for MongoDB if we are the underlying infrastructure for those companies. So those are the 2 extremes that I'm going to spend personally a lot of time on.

Excellent. And you mentioned Voyage AI the acquisition this year being kind of a crown jewel in the portfolio. Maybe just help us understand with the AI native, specifically the opportunities there, are those starting -- are you guys landing with Voyage? Are you landing with Atlas? Are you landing with both now at a more kind of constant pace? Help us understand kind of that incremental differentiator.

Yes. I would say one example, and this in my remarks, I shared that there is a super high growth AI company that is doing very, very well and will become a very large company. I have absolutely no doubts about that. They were not able to scale with Postgres and few other technologies, Redis and so on that they were using, and they moved completely to MongoDB and seeing that week-over-week and month-over-month growth is super inspiring. And I spoke to the hyperscaler where this workload is running and they are seeing the same that, wow, this company is doing really well. So that's built on MongoDB because Postgres had scaling issue.

The other extreme, I spoke to a fairly successful AI native company that is doing decent ARR, growing very fast. And when I said, hey, have you considered MongoDB to the founder, CEO, who is very technical. And he said, CJ, we didn't, we built our own vector database and so on. And while I was speaking to him Alex, about 10 days ago, he basically said, once he looked at the portfolio, he said, let me start with embeddings first. So we are going to try. Of course, we have to prove it to him why our embeddings improves his accuracy on search and so on and improve the performance.

So he said, let's start with embedding models first from Voyage AI once that works CJ, I'm willing to replace my vector DB that we have homegrown created it with MongoDB and oh, by the way, if that works well, eventually, I'm willing to swap out my operational database as well and use MongoDB. So in those kind of scenarios where they are already on a certain track we can land with Voyage AI embeddings. And I'm also seeing in a very large customer of MongoDB, I spoke to somebody who is running the AI initiatives, and they love the Voyage AI embeddings and reranking model, and they've already approved it for 2 big workloads. So we can absolutely land with that is the short answer.

Sounds like a beautiful synergy.

And the last question for the day will be coming from the line of Ryan MacWilliams of Wells Fargo.

The consumer app development environment is getting stronger as new iOS app development has surged multiyear highs. We think it's due to agentic coding, And I know it's early but on the enterprise side, are you seeing stronger product velocity from your customers in building their enterprise applications?

I'm going to ask Dev to provide his opinion, and then I'll provide mine.

Yes, I think what we're seeing is we're clearly seeing a lot of, I would say, prototyping and iteration. I would say the enterprise requirements still have a pretty strong and stringent requirements around security and durability and performance. So while there's a big difference between coming out with the prototype and having a production-grade system that an enterprise can truly rely on trust. And so there is still a lot of work required to make those applications enterprise class.

But clearly, with the advent of [ cogen ] tools, the rate and pace of software development is only going to increase. And as I think we said in the past, that's one of the big reasons why we think AI is a tailwind. It's just that the ability to produce more software, [indiscernible] more database and more and more strategies has been encapsulated in software. So from that point of view, we think that's all good news for us.

Yes. And the only thing I'll add on is, when I speak to customers who I've been speaking for a long time, in regulated industries, which is financial services, which is health care, which is public sector, the requirement for an AI agent to be in production versus prototype are vastly different, and they are looking for governance, auditability, this and that, while the innovation and the need for the speed is very high. So I have not seen -- like customers will tell me, CJ I have 10 agents in production, 15 agents in production. And when I really asked them, I say, are they really customer-facing? Can they be audited on the probabilistic outcome they derive? The answer is, oh, we are still working through that. That doesn't mean that it will not happen soon, but it will never happen.

But I still feel we are fairly early. And even the environment on which they are building agents, they are telling me they try one, it doesn't work, they move on to the next one. So the churn for some of these AI companies that deliver these tools is also very real. And that's why I'm very encouraged by the MongoDB opportunity. We have the platform for operational data. We have the best vector database and we have the embedding models where they can comfortably at enterprise scale, build a real AI agent using MongoDB platform.

Excellent. Really appreciate that detail. And then for Mike, on the Atlas 4Q growth guidance. I appreciate the color there. Just a quick clarification, on this 4Q Atlas guidance, should we expect results closer to the pin or a guidance philosophy consistent with your historical precedent?

Yes. So thanks. I don't want to go into the golf analogy. And besides Ryan, you know I like hockey analogies better. What I would say is that, hey, we are -- we feel really good about Atlas. It's had a great year so far. We feel good about it going into Q4, we remain excited about the growth. That being said, we are being prudent for Q4 as a holiday -- as the seasonal holiday patterns, hey, they can be somewhat unpredictable and we've seen that play out in the past Q4s. So what I would say is, hey, we just need to be prudent as we enter the holiday season.

Thank you. That does conclude today's Q&A session. I would like to go ahead and turn the call back over to MongoDB's President and CEO, CJ Desai, please go ahead.

Thank you, Lisa. In summary, we delivered an exceptional third quarter, highlighted by accelerating Atlas growth, robust customer additions and significant operating margin outperformance. We are raising our revenue and operating income guidance for the fourth quarter and full fiscal year 2026 and reiterating our commitment to the long-term financial model we outlined at Investor Day.

Our results underscore that MongoDB's core business is firing on all cylinders even before any meaningful AI tailwinds. At the same time, we are uniquely positioned to become the generational modern data platform for the AI era, all while driving durable, efficient growth. Thank you, everyone, for joining, and thank you for listening.

This does conclude today's conference call. You may all disconnect.

Good day, and thank you for standing by. Welcome to the MongoDB's Third Quarter Fiscal Year 2026 Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded.

I would now like to turn the conference over to your speaker for 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 third quarter 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. Following our prepared remarks, Dev Ittycheria, MongoDB's former President and CEO and current member of the Board will join us for Q&A.

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 and 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 July 31, 2025, filed with the SEC on August 27, 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'd like to turn the call over to CJ.

Thank you, Jess, and thank you to everyone for joining. I'm honored and genuinely excited to speak with you as the CEO of MongoDB. This is an incredible company and stepping into this role is a privilege. I want to start by thanking our customers, partners and employees for everything you have done to build MongoDB into what it is today. I especially want to acknowledge Dev whose leadership and vision created a phenomenal company, which has strong momentum and a tremendous market opportunity ahead. Many have asked why I chose MongoDB, I had multiple opportunities to lead other technology companies but MongoDB stood apart. We are at a true inflection point driven by major shifts across cloud, data and AI.

MongoDB has the potential to become the generational modern data platform of this evolving era, an opportunity that comes once in a lifetime. I am a truly customer-obsessed leader. So during my diligence, I spoke with multiple customers. Across these conversations, the message was clear. MongoDB already powers core, mission-critical workloads were enterprises that are modernizing their technology stack. At the same time, MongoDB is uniquely positioned at the center of the AI platform shift. Few technology companies have that combination of durable core strength and emerging platform relevance.

Throughout my career, I have driven product to platform transformation at some of the most respected technology companies. Looking at MongoDB today, I see all the ingredients needed to build an iconic modern data platform company. World-class technology, a strong innovation engine, deep developer and customer pool and exceptional talent. We have everything required to become the generational data platform of choice in the AI era. Now onto this quarter's results.

Atlas performance was strong, accelerating to 30% year-over-year growth, up from 29% in Q2 and 26% in Q1. We generated total revenue of $628 million (sic) [ $628.3 million ] , up 19% year-over-year and above the high end of our guidance, driven by strength in Atlas. We delivered non-GAAP operating income of $123 million (sic) [ $123.1 million ] or a 20% non-GAAP operating margin. We ended the quarter with over 62,500 customers adding 2,600 in the quarter and 8,000 year-to-date, reflecting 65% growth in customer additions on a year-to-date basis driven by the strong performance of our self-serve motion. Q3 was an exceptional quarter that was driven by our continued go-to-market execution and the broad-based demand we are seeing across business.

At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while simultaneously expanding profitability. Now let me explain why I see such a large opportunity ahead for both core operational data and emerging AI workloads. Our core business is strong across self-served and enterprise customers even before any AI tailwinds. In my first 3 weeks, I've met with over 30-plus customers from AI-native companies to C-suite technology leaders at Fortune 500 companies.

Those conversations have only strengthened my conviction in MongoDB's opportunity. Customers already depend on us for mission-critical workloads today, and they are leaning in even further, betting on MongoDB to power the AI applications that will shape their future. The expansion opportunity in front of us is immense. We already serve more than 70% of the Fortune 100 and many of the world's largest banks, health care organizations and manufacturers run their mission-critical workloads on MongoDB.

Even with this foundation, there is still significant room to broaden our footprint within the enterprise. A strong example of this expansion opportunity is a major global insurance provider that has adopted MongoDB broadly across its enterprise. The company selected MongoDB Atlas to modernize several mission-critical systems, including its next-generation policy administration platform, analytics rating engine, unstructured data repositories and hundreds of supporting services.

Since moving its policy platform to Atlas, the insurer has expanded from just a small set of regions to nationwide and significantly accelerated the rollout of new products and distribution channels. Standardizing on Atlas has given the organization the scalability and reliability to improve customer experience, support more advanced data and AI capabilities and increased development velocity, all central to its transformation and growth ambitions. All of this momentum in the core business is happening before the AI wave has meaningfully impacted our results.

We are still early, but the signs are encouraging from AI-native start-ups building intelligent applications on MongoDB to large enterprises developing AI agents that will reshape how they operate. AI applications must connect what LLMs know with what companies know, which is their proprietary data, systems and real-time context. This is fundamentally an information retrieval problem, and it requires a very different architecture than the last generation of software.

Rapidly evolving AI models uncover new complex properties about entities and rigid tabular stores cannot deliver the real-time high accuracy performance that AI systems require. At the same time, AI is dramatically increasing the speed at which applications are built and iterated and fixed database schemas simply cannot keep pace. This is where MongoDB has a structural advantage. Our document model, natively, JSON is built for diverse class changing and interdependent data. Our integrated search, vector search and Voyage embeddings removed the need for brittle bolt-ons, and we are seeing industry-leading results.

Number one, on the Hugging Face retrieval embedding benchmark with Voyage MongoDB models and the #1 vector database on DB engines. Advances in our embedding and reranking models drive meaningful accuracy gains. Enabling AI applications to deliver more grounded responses with fewer LLM hallucinations, while lowering storage cost and query cost through smaller, more efficient embeddings. Because all of this is delivered in a unified platform that runs anywhere, customers can keep operational and AI workloads together, simplify their architecture and innovate faster.

As AI adoption accelerates, MongoDB's positioned not just to participate in the wave, but to help define it. we are already beginning to see this play out with AI-native customers like Mercor, which is redefining hiring with its fully automated platform that uses AI to assess and match talent with the opportunities they are best suited for. Mercor uses MongoDB Atlas to store the AI data behind its platform that directly connects professionals to AI model training and evaluation roles. Originally, a self-serve customer, the company is also utilizing Voyage embeddings and Atlas Vector Search. Atlas has scale to support Mercor's 50% month-over-month growth, allowing the company to keep its software engineering team lean and agile as it expands to over $10 billion in value. This is just one example of how customers are building AI-native applications and companies on MongoDB.

We are also seeing meaningful traction among large enterprises that are starting to build AI applications that have a material impact on their business. For example, a highly influential global media company aim to increase engagement via enhanced content recommendation for its vast repository of multimodal assets across its 70-plus websites. That existing stack powered by Elasticsearch hit a performance wall struggling with the complexity of new embedding models. Recognizing that [ rigid ] systems stifle innovation, the engineering team re-architected on MongoDB Atlas and MongoDB Atlas Vector search. Working with MongoDB experts to deliver a proof of concept in just weeks, they integrated Voyage AI models directly alongside their data. The solution scale effortlessly, cutting latency by 90% and reducing operational spend by 65% and driving a 35% increase in click-through rates, ultimately providing millions of global readers with a seamless, deeply personalized discovery journey.

The bottom line is that the business is performing exceptionally well. Existing customers are expanding with us and net new customer additions continue to show strength. Companies in nearly every industry and across every geography are choosing MongoDB because we deliver the features, performance, cost effectiveness, and AI readiness they need in single data platform. Given the continued robust performance of Atlas, along with the healthy underlying fundamentals we are seeing in the business, we are raising our financial guidance for the fourth quarter and the full fiscal year 2026 and reiterating our commitment to the long-term financial model outlined at our recent Investor Day.

Over the next few months, my focus is straightforward. Deepening customer relationships, advancing our innovation agenda as we build the generational modern data platform for the multi-cloud and AI era, scaling our go-to-market efforts and supporting our people so they can do their best work. I believe MongoDB is a company that has only begun to realize its vast potential and I look forward to unlocking this potential in the years to come.

With that, I'll now hand the call over to Mike to discuss the financial results and outlook in greater detail. Mike?

Thank you, CJ. I want to extend a big welcome to you from all of the employees at MongoDB. We are excited to have you join the team. I look forward to working with you to continue to execute on our business plans and drive meaningful shareholder value. I also want to thank Dev for the partnership and our time working together. I believe we accomplished a lot in a short period of time and appreciate all of your guidance and leadership. Best of luck in the next stage of your life journey.

Okay. Now let's move on to the financial results. I will begin with a detailed review of our third quarter results and then finish with our outlook for the fourth quarter and fiscal '26. I will be discussing our results on a non-GAAP basis unless otherwise noted. As CJ mentioned, we had another strong quarter as we exceeded all of our guidance ranges and are increasing our full year outlook across the board.

In the third quarter, total revenue was $628.3 million, up 19% year-over-year and above the high end of our guidance. Shifting to our product mix. Atlas revenue outperformed our expectations as year-over-year growth accelerated to 30% in the third quarter and now represents 75% of total revenue. This compares to 68% of total revenue in the third quarter of fiscal '25 and 74% last quarter.

In the third quarter, Atlas consumption growth was relatively consistent with last year's growth rates which drove the acceleration in revenue as well as growth in absolute revenue dollars for the third straight quarter. Atlas growth was driven by continued strength with our largest customers in the U.S. and broad-based strength in EMEA. This strength is being driven both by new workloads and growth of existing workloads. We believe these dynamics reflect our growing strategic importance to many customers and our ability to win more critical workloads due to the strength of Atlas. You can see that progress in our total company net ARR expansion rate, which increased to 120% in the third quarter, up from 119% last quarter.

Turning to non-Atlas. Revenue came in ahead of our expectations in the quarter as we continue to have success expanding within our existing non-Atlas customer base. Non-Atlas ARR, which reflects the underlying revenue growth of this product without the impact of changes in duration grew 8% year-over-year. We continue to see consistent trends in non-Atlas in the third quarter, which reflects the desire of some of our largest customers to build with MongoDB long term for their most mission-critical applications. We also benefited from higher-than-expected multiyear revenue in the third quarter as approximately 2/3 of the non-Atlas revenue outperformance versus the high end of guidance was attributable to multiyear outperformance.

We had another strong quarter for customer adds as we grew our customer base by approximately 2,600 sequentially, bringing the total customer count to over 62,500, which is up from over 52,600 in the year ago period. The growth in our total customer count is being driven primarily by Atlas which had over 60,800 customers at the end of the third quarter compared to over 51,100 in the year ago period. We ended the quarter with 2,694 customers with at least $100,000 in ARR, representing 16% growth versus the year ago period.

Moving down the income statement. Gross profit for the third quarter was $466 million, representing a gross margin of 74%, which is down from 77% in the year ago period. Our year-over-year gross margin decline is primarily driven by Atlas growing as a percent of the overall business. Although Atlas gross margins are slightly below the total company gross margins they continue to improve year-over-year.

Our income from operations was $123 million for a 20% operating margin compared to 19% in the year-ago period. We are very pleased with our stronger-than-expected operating margin results, which benefited from both our revenue outperformance and lower-than-expected operating expenses. Net income in the third quarter was $115 million or $1.32 per share based on 86.9 million diluted shares outstanding. This compares to net income of $98 million or $1.16 per share on 84.2 million diluted shares outstanding in the year ago period.

Turning to the balance sheet and cash flow. We ended the third quarter with $2.3 billion in cash, cash equivalents, short-term investments and restricted cash. During the quarter, we spent $145 million to repurchase approximately 514,000 shares which was executed under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at $144 million, and free cash flow was $140 million, which compares to $37 million and $35 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.

We remain confident in our ability to drive higher and more consistent free cash flow going forward. Before we go into our guidance for the rest of fiscal '26, let me recap some of the enhancements we have made to our approach to guidance since I joined MongoDB. Importantly, we are providing more visibility into our expectations for Atlas growth as well as non-Atlas ARR growth each quarter. That being said, we will continue to be prudent in our forecasting of multiyear deals and only include those deals where we have very clear visibility. Our goal is to give you a more transparent view into our expectations for the business and our approach to guiding the non-Atlas business.

Now let me share some of the assumptions driving our outlook for the rest of fiscal '26. Number one, we are continuing to see strong momentum in Atlas, which has experienced relatively consistent consumption growth through the first 3 quarters of the year. And comparable seasonal patterns as compared to fiscal '25.

We are seeing strength with existing customers, along with momentum in new accounts as customers large and small increasingly recognize the strategic value of Atlas. As a result, we now expect Atlas to see approximately 27% revenue growth in the fourth quarter of fiscal '26, which is higher than our previous expectations of growth in the mid-20% range. This outlook reflects our continued confidence in Atlas while taking into account the historical seasonal variability and consumption patterns during the holiday period.

Number two, we continue to experience steady ARR growth in our non-Atlas business and have good line of sight to several large multiyear deals we either already have or expect to close in the fourth quarter of the year. Based on these dynamics, we now expect our non-Atlas business to grow in the upper single-digit percent range year-over-year in the fourth quarter.

Number three, we continue to make strategic investments in engineering, marketing and direct sales capacity to drive continued growth. Some of these planned investments have taken longer to implement than expected and have shifted into the fourth quarter of fiscal '26 and fiscal '27, which has benefited our operating margin during fiscal '26.

Fourth, we continue to make progress on free cash flow conversion, which is now expected to exceed 100% for fiscal '26.

Finally, we will continue to execute our share buyback program to help offset dilution from employee equity awards. In addition to our buyback, this past quarter, we began settling the taxes due on the vesting of employee RSUs with cash instead of issuing new shares. We also expect to receive over 1 million shares of stock for the cap calls associated with our 2026 notes that mature in January 2026. All of these actions will help us manage share count for the long term and illustrates our commitment to being good stewards of your capital.

Now let's shift to guidance in the fourth quarter and fiscal '26. For the fourth quarter, we now expect revenue of $665 million to $670 million, which equates to 21% to 22% year-over-year growth. We expect non-GAAP income from operations to be in the range of $139 million to $143 million for an operating margin of approximately 21%. We expect non-GAAP net income per share to be in the range of $1.44 to $1.48 based on 86.5 million diluted shares outstanding.

For fiscal '26, we now expect revenue to be in the range of $2.434 billion to $2.439 billion, an increase of $79 million from the high end of our prior guide and representing full year revenue growth of 21% to 22%. We are raising our non-GAAP income from operation expectations by $109 million at the high end and are now targeting a range of $436.4 million to $440.4 million for an operating margin of approximately 18%.

We expect non-GAAP net income per share to be in the range of $4.76 and to $4.80 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the fourth quarter and fiscal '26 assumes a non-GAAP tax provision of 20%. While we will provide detailed guidance for fiscal '27 on our fourth quarter call, I would like to comment on how we are thinking about a few metrics as we sit here today.

First, we remain committed to the long-term model presented at our Investor Day in September and continue to make great progress against all of the objectives highlighted at the event. We have seen strong margin expansion and free cash flow performance in fiscal '26. And both of these metrics are tracking well above the long-term targets we discussed in September. As we look ahead to fiscal '27, we will continue to make strategic investments to focus on driving growth going forward. With these planned investments and the timing of head count adds, we continue to target 100 to 200 basis points of margin expansion on average and 80% to 100% for free cash flow conversion outlined in our long-term model.

Second, our non-Atlas business is on track to exceed our prior expectations for fiscal '26 due to the stronger performance, including greater-than-expected large multiyear deals. Given this outperformance and our current bottoms-up forecast for fiscal '27, we currently do not expect non-Atlas multiyear transactions to provide either a meaningful headwind or tailwind to revenue in fiscal '27.

To summarize, we had another very strong quarter. We are pleased with our ability to drive both revenue growth across the business while increasing our operating profit expectations 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, Lisa, we would now like to open the call up for questions.

[Operator Instructions] And our first question of the day will be coming from the line of Sanjit Singh of Morgan Stanley.

Fiscal year '26 has turned out to be quite the year for MongoDB, so congrats to the team all around. CJ, I wanted to start with you since this is your first earnings call, heard you loud and clear in terms of what the goal is here to make MongoDB a foundational data platform for the AI era. In terms of making that happen in your kind of first 45 days on the job, maybe even less than that. Are there some initial things that you're looking at some kind of things that might fit in the sort of quicker win bucket? And then longer term, what is -- what are some of the changes you think that the company can make or evolve to get to that -- to [ see or place ] in that sort of AI era?

Thank you, Sanjit. Here is -- this is my day 28 on the job, and I have been speaking to customers as well as our innovation team, including our Voyage AI team as well as our core database teams. The first thing I would say is the opportunity for MongoDB to be that data platform for AI workloads is very real because you need real-time operational data, you need the right context, you need to make sure that you are keeping up to date between the proprietary data of the company as in the enterprise as well as the LLM learnings that the LLM model brings to the table. And most importantly, when I think about all of that combined together, MongoDB has all the elements needed to be the right foundational platform for AI workloads. In speaking to customers, it is still early. There are various co-pilots when it comes to productivity types of applications that are happening inside of an organization, whether it's a bank or a health care organization or a manufacturing organization.

But what I have not seen is truly AI agents running in production that fundamentally transform the business or serve customers better. There are many, many pilots still going on. When I contrast that with the AI native companies, and there is a really good fast growth at scale, AI native company that currently switched from Postgres to MongoDB because Postgres could not just scale.

There is another AI company that highlighted that is using our embeddings as well as our vector database besides our operational platform. So when I combine all this together, Sanjit, what I see is, as truly scaled agentic platforms where you can have enterprises creating agents that transform their business, MongoDB has a very important role to play. And from a low-hanging fruit standpoint, I would argue that our embedding model and reranking model is something that customers can start with today, then they can move on to our vector database and use us for also real-time operational store. So that's how I'm thinking and some of my initial customer conversations have validated that theory.

Understood. I know it's early, so great to get that perspective. And then one follow-up for me, sort of a mark-to-market question. The calendar year '24, fiscal year '25 workload sort of improved in quality versus the prior year. I just want to get a sense of your sort of view on how the calendar year '25 workloads are shaping up as they will unlikely be a factor in terms of thinking about growth next year? And just so in terms of the quality of the workloads this year, can you give us a sense of the quality of those workloads?

Sanjit, it's Mike. So what we'll say there is, as we said during the prepared remarks, and we saw this in Q2 as well, what we're really seeing is strength in the larger customers. It's not only from new workloads, but it's from the existing workloads. We don't want to bifurcate between which calendar year those were added. What we'd say is that we continue to see growth in the larger customers. They are growing longer and they're getting bigger and growing for longer, which is great. And we're seeing that across both the United States and then broad-based in EMEA as well.

And as Atlas gets bigger and bigger, all of those kind of munch together because they're expanding, they're adding. So what we'll do is we'll focus on the growth in our larger customers, especially in the U.S. and EMEA without going into each year. I hope that helped.

And our next question will be coming from the line of Matt Martino of Goldman Sachs.

Nice to see another quarter of acceleration. CJ, I appreciate you're only a few weeks in, but I'd be curious to hear what customers are telling you is top of mind for MongoDB. What are the repeated themes in customer conversations as you take a fresh lens to the business?

Absolutely, Matt. First thing I would say is that the modernization effort, whether it's a workload that may be just running on-prem, in a large enterprise or a workload that is moving to cloud or sometimes to multiple clouds for resiliency that transformation in speaking to a large telecommunications company, a large health care company, a large tech company, and I can cite you many other examples. I was pretty overwhelmed to understand that those transformations are still going on. There is just a recent conversation I had with CTO of a large telecommunications company who said that they are moving 1,300-plus applications to another hyperscaler and trying to determine which workloads are best suited for MongoDB.

So the whole multi-cloud or a public cloud transformation is still going on. And just my intuitive sense in speaking to these customers will be going on for at least next 5 to 7 years. So that specific TAM still very much exists for MongoDB. Now these are the same set of customers, while they are trying to modernize their application stack, they are also experimenting, I would say, because I've not seen agents at scale that are customer facing or sometimes even employee-facing, they may have 10, 15, 20, but not that many compared to thousands of applications they run.

In those AI applications area, they are experimenting sometimes with our embedding models or with our vector database or using MongoDB for real-time operational database. So that second aspect, which is still fairly early, but we are very well positioned as you think about AI workloads in enterprises and large enterprises.

And last but not the least, spending time, as you know or you may know that I spent half of my time in New York City and half of my time in Silicon Valley and speaking to my network in Silicon Valley with AI-native companies or digital-native companies, what I hear from them is that certain alternatives on relational database just do not scale because AI workloads are fundamentally around unstructured and semi-structured data. And then they decide sometimes explicitly to use MongoDB. So I put this in 3 buckets. One bucket is our core and still the cloud transformation, digital transformation, modernization, whichever term you want to use, our core will still continue to grow. As people create AI agents at scale, MongoDB has a role to play and for AI-native companies and some at scale are already using MongoDB because the alternatives in relational world just do not scale.

So those are my like 3 buckets and initial mental model on how these conversations are proceeding and what we can do for them.

Really clear. And then, Mike, just a quick follow-up for you. It was good to see the outperformance on both Atlas and non-Atlas, but with op margins now about 200 basis points shy of your midterm framework, how should we think about the philosophy around reinvestment? And any considerations around non-Atlas and the ability to expand margins as we look out into fiscal '27?

Yes. Thanks for the question, Matt. So I'm sure everyone's focused on '27. So what we'd say is we will guide '27 on the next call. What we would say is, and it's built into the guidance that you have in Q4, and I also talked about it on the prepared remarks, we are continuing to invest, and we will continue to invest. Some of the investments that we wanted to make, especially around engineering, marketing, less so, but certainly around sales capacity has been pushed into Q4. So you should expect to see OpEx continue to grow in fiscal '27.

But we also want to make sure, and that's why Matt, we took the time to say, "Hey, we want to reorient you to what we talked to you about in September -- we still expect to see margin expansion. But you really see it in the fiscal '26 numbers is that is coming mostly from revenue growth. That is the expectation next year. We'll continue to grow revenue. We're going to continue to invest in the business, but the business model will continue to drive that expansion. So you should expect to see us continue to invest, especially across those 3 areas.

And our next question will be coming from the line of Karl Keirstead of UBS.

Okay. Great. Thank you. First of all, CJ, welcome aboard. I'm excited to work with you over the coming years. I had a question for you. So it seems as if you're describing these good set of numbers as strength in the core, essentially even before that AI tailwind kicks in. I'd love if you could define what you think is fundamentally driving that core strength? And do you feel like it's possible that actually Mongo is already getting an AI tailwind in the sense that there's a heightened focus on modernizing your data in advance of AI, such that this core strength is actually AI-related?

Karl, great to hear from you and looking forward to seeing you on Wednesday. I would say the core strength from my perspective is workloads that are -- need modernization has a lot of unstructured or semi-structured data and ideally suited for MongoDB. Now when it comes to AI, could AI potentially drive more modernization efforts? That is possible but not deterministic. As in we see -- as we shared in the remarks, that in the high end of the enterprise, the consumption of the workloads we acquired maybe a year ago, 1.5 years ago, that continues to move up in the right direction as our go-to-market teams are focused on the high end of the enterprise. We also saw broad-based strength in Europe. And that is pretty much to the core business like the large insurance company on the claims engine and other things that I spoke about related to policies.

So I particularly see that as, okay, is that -- does that mean that if core is modern, it helps with AI workloads, absolutely, that is true because they are not mutually exclusive. And Karl, one thing I would say, this is my personal experience in building AI technologies in the past. That the AI team is typically a separate team from the core data team. And AI team relies on the core data team. And if the core data team moves slow, then AI teams get really frustrated because innovation velocity is how they measure themselves on. So my personal experience was, hey, when the core team is not agile there schemas are not flexible, it actually slows AI down. So that is definitely some facts behind your theory that it is potentially the AI revolution, which we are still in the early stages, is driving modernization in the other part of the enterprise.

Okay. And then, Mike, for you, I think everybody on the line appreciates the more definitive guidance on Atlas for the following quarter. So thank you. I wanted to ask what's driving that? Is it simply a function of you and just in your new -- relatively new seats, wanting to be more transparent in the guidance? Or Mike, is there something actually changing in Atlas such that now that it's at scale, it's becoming predictable enough that it now makes more sense to give precise guidance?

So thanks, Karl, thank you for the question. I would say it's probably a little bit of both. One is, hey, we want to give you folks a little bit more visibility to what's behind the guidance that we provide. That was number one. Also, as Atlas gets to be, gosh, now almost a $2 billion business, we feel better about the forecasting. The team has done a wonderful job forecasting that part as well. So when we gave the number for Q4, we want to make sure and give you the visibility. But we also have a pretty good view of what we hope it would be, understanding that, keep in mind, Q4, we want to be prudent because there are some seasonal holiday patterns that can be somewhat unpredictable, and we've seen that play out in the past Q4s. So I just want to note that for the guidance that we just gave.

And the next question will be coming from the line of Raimo Lenschow of Barclays.

CJ, all the best from me as well. I had 2 questions, one for CJ, one for Mike. CJ, on the -- one of the core things in terms of adoption of Mongo will be on the developer side because they're -- at the end of the day, developers are like a big driver of like what's getting used, et cetera. At the moment, a lot of AI is on the West Coast. What's your thinking around like getting AI -- getting developer engagement up with Mongo to kind of go against that Postgres kind of narrative that happens a lot in the valley. And then, Mike, for you, like since next year EA is not seeing benefits from all the year. Should we anchor our numbers on the ARR performance? And is that the right way to think about it?

Thank you, Raimo. Great to hear from you. I'm going to first ask -- there is a little bit of historical context in terms of your point on the West Coast. I'm going to ask, our previous CEO, Dev Ittycheria to talk about reclaim the Bay, the initiative that him and the team started, and then I'm going to specifically talk about how I think about it on the West Coast.

Raimo, it's Dev here. As CJ mentioned, we've talked about this in previous calls, but we made a concerted effort to reinvest in the Bay Area because during COVID and post-COVID, we felt that we had neglected that region. And obviously, there was a whole new corpus of AI-native companies that were getting launched. So there's been a real concerted effort both in terms of putting more feet on the street, putting more marketing efforts in terms of supporting that part of the world. Investing more in the start-up community and also in the venture community to get people to understand the true value proposition of MongoDB. We've done things like hackathons and other events in that area as well. And so the team's really focused, dedicated to really supporting and servicing these early AI native companies, and that is starting to yield some results.

And we feel really good about the progress there, but I'll let CJ talk about what happens going forward.

Thank you, Dev. And this is the reclaim the Bay in San Francisco on the West Coast. It is 100% true Raimo, that there is a lot of investment with AI-native companies, and we could benefit from increased mind share and being in front of them as in the developer community that you talked about, which is a super important community to us on the West Coast. So me spending personally time on the West Coast house. I do also have deep network in the West Coast community, both venture community as well as tech companies at scale. And I've already started leveraging that network to get their feedback. We are really excited in this quarter, as in the 4Q, we are relaunching our .local after a few years in San Francisco on January 15, where we are going to invite companies that have built on MongoDB, some great speakers on why they should build on MongoDB and show hands-on experience to the developer community in that conference on January 15. And what I see is just speaking to many CEO founders as well as developers of smaller companies or midsized companies, all these efforts of the marketing investment that Mike and Dev originally approved is going to start yielding results as we move into the next fiscal year.

And Raimo, thanks for the question. It's Mike. On non-Atlas next year, we wanted to make sure we've had a lot of questions about the multiyear headwind. So thank you for the question there. We are not guiding for fiscal '27. However, sitting here today, I would steer you more towards -- if you look at the full year revenue growth of non-Atlas it's about 4%, somewhere in that mid kind of low single digits is probably a good range to think about for next year as we sit here today.

And our next question will be coming from the line of Brad Reback of Stifel.

Great. I'm not sure who this is for, but on the commentary around new customer strength within Atlas, are you seeing new customers ramp faster for net new workloads than they have been historically? And if so, why?

Brad, my initial observation is that the team -- engineering team has done a fantastic job when they launch 8.0 and all the subsequent point releases that allows Atlas to be adopted faster and remove the friction, whether you are coming via our self-serve channel or whether you are a large enterprise moving onto Atlas. So that's one thing I would say. And I'm going to ask Dev to provide commentary as well from a context perspective.

Yes. I think what I'd also say is that, Brad, is that I think we've -- the self-serve team has really removed the friction to enable customers to onboard more quickly and more easily. And given the performance -- price performance gains that we've seen in 8 and now even better in 8.2, I think that's really driving a lot of the traction we're seeing in our new customers they quickly see the performance benefits and they're scaling nicely. And so that's allowing us to continue to acquire customers efficiently.

And one last thing on that, Brad. If you look at the revenue from that, it hasn't changed materially. It's still, keep in mind, a pretty small number when they first onboard, so it's not going to move the needle much. We haven't seen much change in that cohort over the last couple of years.

Great. And then, CJ, a quick follow-up for you. Philosophically, how do you think about M&A as it relates to Mongo? What types of things, if anything, you think you need to acquire?

Brad, you know me well, and I'm a big believer in organic growth. The team, Dev and the team have laid a very strong foundation on our technology platform. I think Voyage AI in February was a brilliant acquisition, where we got unbelievable team in Palo Alto. And my goal on behalf of MongoDB is to always believe in our own teams and our technology. We participate in a large market and where it makes sense, where we can get a particular adjacent technology or a great team that can help us accelerate the road map, we would always consider that type of M&A.

And our next question will be coming from the line of Alex Zukin of Wolfe Research.

CJ, maybe for you. I mean, you shared, I think, a lot of thoughts about your initial vision. You shared the 3 pillars of the core, the enterprise AI opportunity and the AI natives. I just want to maybe lean in, where do you see your particular skill set of network offering kind of not the lowest hanging fruit, but your ability to make kind of the biggest impact in, call it, the next 12 to 24 months? Like where do you really see that incremental opportunity for growth inflection?

I would say, Alex, and -- you are aware of the enterprises and the customer obsession I have and the relationships that I have formed over many, many years with technology leaders at large companies. So, from my perspective, there are 2 areas where I can benefit our go-to-market teams immensely. Number one is Fortune 500, where MongoDB can still penetrate even at a higher rate than it is penetrating today, both within the existing accounts as well as the new accounts we get. So that's Fortune 500. And then I was with our sales teams in Europe, and there are many customers that they are targeting, including existing customers, large banks, manufacturing companies and so on, where they're trying to expand where my personal relationships with those technology buyers can help. So that's bucket number one is make no mistake, high end of the enterprise as in Fortune 500 and Global 2000.

Number two, on the other extreme would be AI-native companies, lived in Silicon Valley for a very long time. I understand where venture community is investing, folks who are creating, whether it's domain-specific AI companies or foundational companies have relationships there as well across [ 101, 280 and 237 ], and that's where I also plan to -- I would say, plant the seeds in a correct fashion so that over time, that becomes a meaningful business for MongoDB if we are the underlying infrastructure for those companies. So those are the 2 extremes that I'm going to spend personally a lot of time on.

Excellent. And you mentioned Voyage AI the acquisition this year being kind of a crown jewel in the portfolio. Maybe just help us understand with the AI native, specifically the opportunities there, are those starting -- are you guys landing with Voyage? Are you landing with Atlas? Are you landing with both now at a more kind of constant pace? Help us understand kind of that incremental differentiator.

Yes. I would say one example, and this in my remarks, I shared that there is a super high growth AI company that is doing very, very well and will become a very large company. I have absolutely no doubts about that. They were not able to scale with Postgres and few other technologies, Redis and so on that they were using, and they moved completely to MongoDB and seeing that week-over-week and month-over-month growth is super inspiring. And I spoke to the hyperscaler where this workload is running and they are seeing the same that, wow, this company is doing really well. So that's built on MongoDB because Postgres had scaling issue.

The other extreme, I spoke to a fairly successful AI native company that is doing decent ARR, growing very fast. And when I said, hey, have you considered MongoDB to the founder, CEO, who is very technical. And he said, CJ, we didn't, we built our own vector database and so on. And while I was speaking to him Alex, about 10 days ago, he basically said, once he looked at the portfolio, he said, let me start with embeddings first. So we are going to try. Of course, we have to prove it to him why our embeddings improves his accuracy on search and so on and improve the performance.

So he said, let's start with embedding models first from Voyage AI once that works CJ, I'm willing to replace my vector DB that we have homegrown created it with MongoDB and oh, by the way, if that works well, eventually, I'm willing to swap out my operational database as well and use MongoDB. So in those kind of scenarios where they are already on a certain track we can land with Voyage AI embeddings. And I'm also seeing in a very large customer of MongoDB, I spoke to somebody who is running the AI initiatives, and they love the Voyage AI embeddings and reranking model, and they've already approved it for 2 big workloads. So we can absolutely land with that is the short answer.

Sounds like a beautiful synergy.

And the last question for the day will be coming from the line of Ryan MacWilliams of Wells Fargo.

The consumer app development environment is getting stronger as new iOS app development has surged multiyear highs. We think it's due to agentic coding, And I know it's early but on the enterprise side, are you seeing stronger product velocity from your customers in building their enterprise applications?

I'm going to ask Dev to provide his opinion, and then I'll provide mine.

Yes, I think what we're seeing is we're clearly seeing a lot of, I would say, prototyping and iteration. I would say the enterprise requirements still have a pretty strong and stringent requirements around security and durability and performance. So while there's a big difference between coming out with the prototype and having a production-grade system that an enterprise can truly rely on trust. And so there is still a lot of work required to make those applications enterprise class.

But clearly, with the advent of [ cogen ] tools, the rate and pace of software development is only going to increase. And as I think we said in the past, that's one of the big reasons why we think AI is a tailwind. It's just that the ability to produce more software, [indiscernible] more database and more and more strategies has been encapsulated in software. So from that point of view, we think that's all good news for us.

Yes. And the only thing I'll add on is, when I speak to customers who I've been speaking for a long time, in regulated industries, which is financial services, which is health care, which is public sector, the requirement for an AI agent to be in production versus prototype are vastly different, and they are looking for governance, auditability, this and that, while the innovation and the need for the speed is very high. So I have not seen -- like customers will tell me, CJ I have 10 agents in production, 15 agents in production. And when I really asked them, I say, are they really customer-facing? Can they be audited on the probabilistic outcome they derive? The answer is, oh, we are still working through that. That doesn't mean that it will not happen soon, but it will never happen.

But I still feel we are fairly early. And even the environment on which they are building agents, they are telling me they try one, it doesn't work, they move on to the next one. So the churn for some of these AI companies that deliver these tools is also very real. And that's why I'm very encouraged by the MongoDB opportunity. We have the platform for operational data. We have the best vector database and we have the embedding models where they can comfortably at enterprise scale, build a real AI agent using MongoDB platform.

Excellent. Really appreciate that detail. And then for Mike, on the Atlas 4Q growth guidance. I appreciate the color there. Just a quick clarification, on this 4Q Atlas guidance, should we expect results closer to the pin or a guidance philosophy consistent with your historical precedent?

Yes. So thanks. I don't want to go into the golf analogy. And besides Ryan, you know I like hockey analogies better. What I would say is that, hey, we are -- we feel really good about Atlas. It's had a great year so far. We feel good about it going into Q4, we remain excited about the growth. That being said, we are being prudent for Q4 as a holiday -- as the seasonal holiday patterns, hey, they can be somewhat unpredictable and we've seen that play out in the past Q4s. So what I would say is, hey, we just need to be prudent as we enter the holiday season.

Thank you. That does conclude today's Q&A session. I would like to go ahead and turn the call back over to MongoDB's President and CEO, CJ Desai, please go ahead.

Thank you, Lisa. In summary, we delivered an exceptional third quarter, highlighted by accelerating Atlas growth, robust customer additions and significant operating margin outperformance. We are raising our revenue and operating income guidance for the fourth quarter and full fiscal year 2026 and reiterating our commitment to the long-term financial model we outlined at Investor Day.

Our results underscore that MongoDB's core business is firing on all cylinders even before any meaningful AI tailwinds. At the same time, we are uniquely positioned to become the generational modern data platform for the AI era, all while driving durable, efficient growth. Thank you, everyone, for joining, and thank you for listening.

This does conclude today's conference call. You may all disconnect.

Hello, and thank you for standing by. Welcome to MongoDB Leadership Transition Conference Call. [Operator Instructions]

I would now like to hand the conference over to Jess Lubert, Vice President of Investor Relations. Please go ahead.

Thank you, operator. Good morning, and thank you for joining us to discuss the MongoDB leadership transition outlined in the press release published earlier today. Joining me today will be Dev Ittycheria, MongoDB's current President and CEO; and CJ Desai, MongoDB's incoming President and CEO.

During this call, we may make forward-looking statements, including statements related to our market and future growth opportunities. 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, 2025, filed with the SEC on August 27, 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.

There will be a question-and-answer section following our prepared remarks. Please be advised that the purpose of this call is to discuss the leadership transition. We will not field any questions related to our financial results or the announcement in today's press release that we expect to exceed the high end of our FQ3 guidance ranges. We plan to address these questions on our Monday, December 1 earnings conference call.

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

Thank you, Jess, and good morning, everyone. As you've seen in the press release published earlier today, after a great deal of reflection, I've made the decision to retire as President and CEO of MongoDB.

Over the past 11 years, this role has required deep focus and commitment, which often meant putting other parts of my life on hold. I'm eager to be more present for those moments, from everyday time with my family and friends to experiences and ventures we've postponed for far too long. This was not an easy choice.

MongoDB has been my professional home for more than a decade and it's been one of the greatest honors of my career to lead such an exceptional team and company. When I joined MongoDB in 2014, the company had a bold vision to disrupt the database industry with a document model that empowered developers to build modern applications faster and more easily than ever before. That vision wasn't just about better technology. It was about unlocking innovation for customers. Today, nearly 60,000 customers around the world trust MongoDB to power some of their most critical applications. What started as an ambitious idea to become a global business, scaling from roughly $35 million to more than $2.3 billion in annualized revenue.

This success was achieved through relentless innovation, discipline and teamwork, which has not only transformed MongoDB into a global software leader with a strong track record, but also laid the foundation for the company to win in the AI era. As I look to the future, I'm confident that now is the right time for a new leadership to take MongoDB into the next phase of growth. The business has strong momentum and has the foundation in place to achieve meaningful scale in the years ahead. That's why I couldn't be more thrilled to welcome CJ Desai as our next President and CEO.

CJ was selected following an extensive search by the Board and an executive search firm due to his remarkable track record of delivering growth at scale, most recently at Cloudflare and before that at ServiceNow. CJ maintains extensive product, engineering and go-to-market strengths and his experience driving enterprise adoption will be invaluable to MongoDB's next stage of growth.

Importantly, CJ also shares many of the same values that define our culture here at MongoDB. I will be working closely with CJ over the coming months to ensure a smooth transition. I plan to remain on the Board, where I'll continue to serve as an advisor and committed supporter of MongoDB.

To our employees, customers and shareholders, thank you. I'm immensely grateful to our customers for their partnership and trust to the exceptional employees whose passion and ingenuity has built MongoDB into the company it is today and to our investors for their enduring confidence in our vision. MongoDB story has always been about the people who dream big and deliver even bigger and I'm confident that our company's best days are still ahead.

Thank you, Dave. First, I want to say how honored and excited I am to be joining MongoDB. I have long admired what Dave and the team have accomplished, the strength of the business, the clarity of the mission and the passion of the people here. I have been a customer and partner of MongoDB for a long time.

MongoDB has long been the partner of choice for applications that transform businesses, and I believe the company is exceptionally positioned to power the next wave of AI applications. Throughout my career, I've had the privilege of helping companies scale, whether by driving innovation, operational rigor on the expansion of the customer base, or the go-to-market execution. What drew me to MongoDB is the incredible potential ahead in a large market. The company has built something truly special and I believe we are just beginning to tap into what is possible. By staying relentlessly close to customers, delivering category-defining products and platform, and executing at scale, I'm confident we can seize the enormous opportunities ahead. I'm eager to get started, to listen and learn from our employees and customers and to build on the strong foundation that Dave and the team have created. Together, we'll focus on executing our strategy, driving durable and profitable growth and delivering long-term value for all our stakeholders.

I want to thank the Board for their trust and confidence and Dave for his partnership during the transition. I look forward to leading MongoDB in its next phase with energy, focus and respect for everything that has been built here.

With that, we'll open the call for questions.

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

Perfect. Dave, I'm going to miss you. And congrats again for this kind of great journey, 11 years there was -- I still remember our first meeting at the IPO kickoff. And CJ, welcome back in -- on my side of -- on this side.

The question I had, obviously, first of all, for Dave, we're in very interesting times with AI and like huge growth in applications are expected, et cetera. Like from your perspective, like why was now the right time because it's kind of like the world is changing. Is that kind of -- was that kind of the motivation and that change would have taken a few years? Or like how should we think about that?

Thanks, Raimo. And I do remember the first IPO prep session. So it's been a blast to work with you over these 8 years. In terms of timing, as I said, I've been CEO of this company for 11 years, 8 years as a public company, and it's all consuming. It truly is a very intense job.

And as I thought about what to do next, and as part of our succession planning process, the Board asked me to consider staying on for another 5 years. It just became clear that after spending time discussing this with my family and the Board that it was time to make a change. There were just certain things I wanted to do that I just didn't have the chance to do.

But I also want to be clear, I was not going to go anywhere without finding a suitable successor. And I'm so thrilled to find CJ. We frankly have spent a lot of time together over the last couple of months getting to know each other, CJ learning about the business, and I couldn't be thrilled about CJ taking on the role. I think he's well suited. He's got rare growth at scale experience. There's not many people in the software industry who have taken a company a little over $1 billion in revenue and turned it into over $10 billion in revenue.

He's also recently accelerated growth at Cloudflare where that company is really executing well. And I think he can do the same here. And so I'm super excited by what CJ can do, and I'm not going anywhere. I'm going to stay on the Board, and I'm also going to serve as advisor to CJ to help him ramp as quickly as possible.

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

A huge congratulations to both of you. Maybe this one for CJ. Kind of similar to Raimo's question, but why -- for you, what's the most exciting thing about the opportunity at Mongo? And if you look at your ability to kind of harness your enterprise go-to-market shops and your product strategy, dimensions, where do you feel like there's a tremendous amount of opportunity to unlock in the story at the moment?

Thank you, Alex. Mongo participates in a very large growing market. And it has also a very clear architectural advantage to become heart of the architecture from modern workload, including AI workloads perspective. So as Dave was answering Raimo's question, from my perspective, there are so many new workloads being defined, or there are so many workloads being changed to leverage AI, both whether it's an AI-native company, whether it's a digital-native company or whether it's a large enterprise. So we have reached this inflection moment where Mongo can truly become the heart of this next phase of re-architecture that's going to happen in all of customer base across Fortune 500, Global 2000 and AI native companies and start-ups.

So the potential is massive. Mongo's architecture was not force fitted for AI workloads. It existed for AI workloads. And that's what really excites me that we will continue to innovate, with scale the go-to-market in large enterprise and the business has strong momentum this year as we announced today. And then how can we accelerate that even further as we expand Mongo's reach all the way from developers who love us, and they love building on us, and I have built on Mongo and it's a great database. I started my career in the database industry. So I have a lot of appreciation for how it has been done in Mongo from an overall architecture perspective.

And second, on the go-to-market aspect, the opportunity is still massive in the large corporations who are going to now redefine their workloads to leverage AI. Every single one is doing that, and we are just getting started. And it's on the beginning stages from my perspective on how to leverage truly AI and Mongo is perfectly positioned.

Our next question comes from the line of Tyler Radke with Citi.

Yes. Congrats to both of you, Dave, it's been a pleasure. And CJ, glad to see you turn up as CEO here. So CJ, I wanted to ask you just -- as you think about how Mongo is positioned on the AI front, I mean, clearly, there's been a lot of investor conversation around the Postgres technology. And I know, Dave, it's probably sick of answering questions on that. Hopefully, that's not why he's stepping down.

But just given your perspective, you were at ServiceNow and when ServiceNow acquired RaptorDB I imagine you were involved in some of that process, which was a Postgres technology. So how do you sort of think about the limitations of Postgres, and how Mongo can kind of further influence its role in these AI applications because it seems like you have a pretty unique perspective given the RaptorDB acquisition.

So Tyler, great to hear from you. What I would say is when I truly look at the technology of Mongo and I've spent some time through evaluating the recent innovations from Mongo on 8.0 and post 8.0 releases. What I would say is that this is the right database to build or modernize an application for AI.

All the relational databases where I started my career, they tend to be very rigid, not flexible and do not handle the unstructured data really well. When you think about AI applications of the future, it will still be both structured and unstructured data. And as the business changes, the foundational models improve and all of that, Mongo would still be on the right side of that equation as many, many of our customers want to scale their application for AI workloads. So that's what really excites me.

In the past, yes, there was a reason why we chose Postgres a couple of jobs ago and that was because we wanted to maintain the SQL aspect of it, and it was mainly structured data. And even there are AI native companies who are using Mongo to build applications, including some prominent AI companies that are leveraging Mongo today as they build applications on top of foundational model.

And our job with the team is to continue to make sure that Mongo is top of their mind, in front of them, to understand the advantages of Mongo to be flexible, scale-out and a great architecture on which they can continue to build on.

Terrific. Congrats to both of you.

Our next question comes from the line of Kash Rangan with Goldman Sachs.

Congratulations, Dave, on a spectacular career. I think Raimo was talking to you about his MongoDB IP experience. I will bring up the BladeLogic IP experience, I think, 2005, 2006. So it's been a pleasure working with you. You've built a tremendous value over the years. So I really appreciate it.

And a big congratulations to the incoming CEO, CJ. I realize that there's a lot more we're going to be learning over the next several quarters. So it's hard to ask to a strategy question and whatnot. But I will still ask you something, CJ, that as you look at the database landscape, it is being increasingly populated and crowded by the hyperscalers that are able to blend their incumbency with compute, be it Microsoft with their database as a way to leverage their more vertically integrated relationship, and that probably applies to AWS, Google as well.

So as you enter this new role, what are the things that you're going to be focused on to ensure that the thesis of MongoDB, which has been to be a cloud-neutral database, which is what Dave led with, do you see any changes, refinements to that approach, especially as the broader again, hyperscalers and AI compute-centric world are increasingly muscling their way with their databases, which are also NoSQL databases.

Kash, great to hear from you, and thank you. From my perspective, really, Dave and the team nailed the cloud transition really well. Many, many database companies or enterprise software companies were not able to transition to, one, being cloud-agnostic and two, still work in multi-cloud environment with Atlas, almost 8 years ago, they nailed that really well.

And I think that advantage will continue to exist. When I speak to customers, most of them and with the recent outages in multiple hyperscalers that literally happened 3 weeks ago, multi-cloud world is here to stay. And Mongo with a cloud-agnostic architecture and how seamlessly it can work in a multi-cloud environment is a competitive moat for MongoDB. So that's the first thing I would share.

Second thing, even AI workloads, given that if you are running applications in multiple clouds, if it is compute-centric, maybe you're running it in AWS. If it's something else, you are running it in Google, whether you want to leverage their security models, whatever the case might be, for AI application, the same competitive moat will remain, and it is our job from an innovation perspective to stay in front of our customers' requirements and lead our way as they transition to AI workloads for years to come.

Our next question comes from the line of Rishi Jaluria with RBC.

Dave, it's been an absolute pleasure working with you on this name. I really appreciated you navigating through all the kind of different environments, technological shifts. And really positioning MongoDB well for, honestly, the next decade. So it's been an absolute pleasure.

CJ, I never had the pleasure of working with you, but obviously heard amazing things about you and your reputation proceeds you, so very much looking forward to this. One question I did want to drill into is, and correct me if I read it wrong, but this morning, an interview with CNBC, it sounds like CJ, you were talking about kind of the goal or aspiration of getting MongoDB a $5 billion-plus business and growing it profitably and reaching that scale over time. I know, obviously, early to talk kind of about these long-term strategic road maps.

But would love to kind of hear some color is, as you think about scaling MongoDB from where it is today to that sort of scale, what are kind of the building blocks in your mind? And maybe some of the high strategic priorities over the next, call it, couple of years to actually get to that sort of scale. And I'm sure, obviously, there's ambition going well beyond that over time. But I'd love to hear kind of the thinking that informed that and what's really top of mind and most exciting for you?

Thank you, Rishi, and very nice to be speaking to you and say hello to all the friends at RBC.

What I would say is the potential in my opening remarks for Mongo and that's what excited me is still massive because this is the market I entered as my first job from a technology standpoint and market has been around, as Dave has shared, for 50-plus years. And this particular architecture of MongoDB is the right architecture for 21st century workloads. So that's number one.

Number two, when I think about the potential, the cloud adoption in the enterprises, customers, very large customers, all the way to Fortune 100 is still going on. As you have seen the recent numbers from Amazon, Microsoft, then Oracle and others, you see that the cloud adoption is still growing, and Mongo would definitely benefit from that.

Number two, from a strategy perspective, AI is absolutely a tailwind for Mongo, as developers create these applications or customers and large organizations make a decision on what standard to use for AI application, Mongo has the right architecture to build that.

And then when I think about geographies spending time with the go-to-market leadership team, recently, there is still a lot of potential that exists in many regions of the world where they are building new applications for AI, you could even take a country like India, you could take many, many cities where AI natives are still building applications and the developers want to use Mongo. So between large enterprise motion, certain geographies where Mongo is underpenetrated. And number three, a large market where we'll continue to have additional market share is what actually excites me, Rishi, on the path to $5 billion plus that we can drive MongoDB.

Our next question comes from the line of Mike Cikos with Needham.

Dave, it's been an honor. CJ looking forward to the continued partnership now. Very simply, and I appreciate the commentary we just got on the strategy you're looking to drive here, CJ. Would just love to get a sense, given the timing, there's been a lot of change in the last year. We have a new CFO on the seat with less than a year here. You coming in. Obviously, Dave, will be supporting you in that transition. But just curious, given we had the Analyst Day earlier this year, can you just talk to whether or not we should expect any changes to those targets given the fact that we just got those recently? That's it for me.

Mike, this is Dave here. First of all, thanks for the question. And I'll take the first part of that question. In terms of the changes we made earlier this year, obviously, we're super thrilled to have Mike Berry as our CFO. He has made a huge impact in a very short period of time. His breadth of experience and just domain expertise has just helped us just become a better company. And he's brought in also some new leaders into the organization that are also having a big impact. So I think he's going to be a huge asset to the company and to CJ going forward.

We also brought in some other leaders recently and promoted some people. So in terms of the foundation, I think the foundation is strong, but I'll let CJ talk about what he -- how he plans to go forward from here.

Yes. Thank you very much, Mike. And I absolutely echo what Dave just said around Mike Berry being the CFO, and I look forward to partnership with Mike and the team as we move forward. Overall, the leadership team is in a great place. We are currently extremely focused on the last quarter, once I'm in the seat, it's Q4, and we will share more details around the plans on December 1.

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

So we've seen the rise of agentic coding this year through tools like [ bot code ] and Codex. CJ, I'd love to hear about how you think that plays into the future for the Mongo story, whether it's more app development and more accelerated database creation or the potential to drive further app modernization and new workloads onto the cloud.

Let me try and just take the first -- first give some comments and then I'll have CJ add as much color as he wants to. I think it's important to understand through every platform shift, the cost of building applications came down. And so what that created was an explosion of apps. You saw that from the mainframe to client server, client server to Internet mobile and then from Internet mobile to the cloud. Almost every meaningful company expresses their business strategy through software.

And now with the -- now we're in the era of AI, the cost of building application is going to come down even further, which means that there's going to be more software, more use cases where that software can be used because now you can blend the physical and the digital world with AI. And then you can address use cases that require reasoning that we can never really programmatically implicate that into software before, I think you're just going to see an explosion of software.

So to me, AI is going to be a big tailwind for our business because the more software there is, the more databases you need. And so I think just at a macro level, this is just going to be a big tailwind. And then to what I said earlier, I think architecturally, we are well positioned for the AI era just by being a native JSON database, LLMs emit and consume JSON, MCP is built on JSON.

JSON is well designed to handle the complicated messiness and constantly evolving nature of data in the modern world. And so given all those ingredients and being built on a distributed platform, I think we're well set up. I don't know, CJ, if you want to add any more color.

Yes. I think, Dave, obviously, you articulated this perfectly. And from my perspective, there is a role that Mongo plays, whether you create an agentic workload or whether you re-platform your database to take advantage of brand-new agents or just agents talking to other databases.

So I think Mongo is perfectly suited for this transition. This transition like we saw with cloud, I mean, started about 2007, '08 and still going. So this AI transition will be going on for 10, 15-plus years. That's what happens in every transition. And we will make sure that we listen to our customers. We listen to our developers on what we can do and the role they want Mongo to play so that we are at the heart of that architecture for AI workloads.

Our next question comes from the line of Madeline Brooks with Bank of America.

This is Madeline on for Brad of Bank of America. CJ, a question for you. You've had just an incredible career playbook of really transforming deep tech companies into enterprise scale. And I'm just wondering for the current sales motion or channel strategy. What do you think needs the most leverage from both the product, combining that product and marketing messaging that you've historically done so well.

Thank you so much for your question. I think Dave has shared penetration in Fortune 100, 70-plus percent, penetration of 70% in Fortune 500, similar numbers when you look at Global 2000 on decent penetration. My perspective is, though, there is still a lot more potential in those existing customers for the workloads to take advantage of Mongo. That is a massive opportunity to work with these customers, developers already love us, and we have great communities that are being built all over the world.

But in these large enterprises, which I definitely consider a strength of mine, working with these large enterprises and being focused on them, there is still so much potential as we think of Fortune 100, Fortune 500 and Global 2000 on how they can leverage Mongo to be the standard for workloads of the future and today.

Yes. If I can just add, the -- we talked about how we're moving upmarket and putting more resources upmarket. When I started spending time at CJ and started comparing notes on customers, CJ almost finished my senses about the people we knew and the common contacts. I mean, CJ has clearly been a very customer-obsessed leader. He spends a lot of time with customers. In fact, he has far more contacts in the C-suite of the Fortune 500 than I ever did just as a virtue of ServiceNow and Cloudflare being more top-down selling. And so I think his connections and his customer obsession is really going to help us do exactly what he just said is expand the wallet share of MongoDB in those large accounts.

Our next question comes from the line of Miller Jump with Truist Securities.

Congrats to both of you, CJ on the opportunity and Dave on the next steps in your journey. relational migration is an opportunity we've heard about for some time now for MongoDB, CJ earlier, you mentioned ServiceNow's database transition and kind of having that SQL structure they were looking to support through it. How do you think about the opportunity to convince customers to migrate previously legacy relational apps to MongoDB? And how does the need for that shift change in the current environment with all the AI innovation?

I -- Miller, thank you for the question. What I really think is having started the career in the database industry and for a database technology, what I would say is, from my perspective, databases are very, very sticky, okay? They are very, very sticky and people use them for a very long time unless they have to change.

I think both cloud as well as the AI transition, truly are the inflection points, I mean, that drove Mongo's growth in a significant way when the cloud transition happened and Mongo captured that opportunity in 2017 with Atlas that was just starting out. And similarly, when you want to leverage AI, you figure out what is the strength of this foundational model, X, Y and Z that you may be using across cloud A or B.

And then you say, okay, how much of this is going to be structured data, unstructured data. Do I still need to maintain my stored procedures or I really need to scale this workload for a rapidly changing business environment, that is exactly when you'll say it is time for me to revisit my database decision. Because even the lot of shifts to the cloud for existing workloads were mainly lift and shifts, people were just trying to move to the hyperscalers and very few customers re-architected the application. AI forces them now to really think, I was just speaking to a customer a few weeks ago, and they are revisiting 30% of their workloads for AI-driven innovation and they want to re-architect.

So I think the re-architecture is happening right now. And it is our job as MongoDB to ensure that customers understand the why and how Mongo is ideally suited for those applications.

Our next question comes from the line of Siti Panigrahi with Mizuho.

Great. Can you hear me?

Yes.

Okay. Great. Dave, congratulations on a successful tenure and CJ, congrats on your new role. You talked about some of the opportunity there. And CJ based on your due diligence and also working in an operating role, how are you thinking about balancing the growth and profitability given the opportunity ahead?

I would say there is a tremendous opportunity, and Mike Berry, our CFO, did a wonderful job at Investor Day on saying it is absolutely our goal to get to a Rule of 40. We had that at Cloudflare and continue to deliver on that. So growth being durable but also profitable is extremely important, and that's the foundation Dave and the team have laid that Mike announced at the Investor Day in September, and our plan is to continue to do that, that we want this company to grow durably for a very long time to come, while being profitable on our path to $5 billion plus.

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

Congrats, Dave, congrats to CJ. One of the things that Mongo has been innovating on is not just on the product side of the equation, but also on the go-to-market side, they've been in the leaders in terms of software companies pushing into consumption-based sales. And CJ, I just want to get your perspective on like the go-to-market side, you've come with an excellent product, operational background. As you think about what will need to drive success in terms of sales in the AI era, I'd love to get your initial thoughts there.

Absolutely. You are correct that Mongo did nail that transition also really well on consumption-driven model so that as customers not only expand their existing workload, but new workloads and use consumption as in pay as you use it. This needs to be continued because with AI workloads or cloud workloads, whatever they are, it could be a billing application, it could be a CRM application, whatever the application that is being built on Mongo, we just want to continue to build on that solid foundation from my perspective.

So it was learning for me just to understand and even at Cloudflare, we switched to consumption model for certain class of products, and I understand that well. So Dave, do you want to share?

Yes. No. Sanjit, first of all, thank you. It's been great working with you all these years. I'm obviously very proud about how we've evolved our go-to-market model. I do believe that as we talked about, we're moving upmarket and moving more of our dedicated sellers upmarket because there's so much opportunity there, we're also going to continue to innovate on how we acquire customers through our PLG motion.

So that's an area that I think we will continue to focus on because that's a way to not only effectively acquire new customers, but also acquire some of those early AI native customers who ultimately can become very big customers of their own. And so I think you're going to see that. We also leverage our partner channel to extend our reach in places that are just -- we didn't -- couldn't really either help accelerate our growth or extend our reach in places that we just couldn't deploy dedicated resources.

I know CJ and I have already talked about certain parts of the world where we want to -- we should be investing more. And I think CJ will be looking at all those opportunities very, very carefully. So I think there's lots of opportunity in front of us. And the good news is that I know CJ will push as hard as possible to grow the business.

Our final question comes from the line of Rudy Kessinger with D.A. Davidson.

Dave, congrats, certainly a phenomenal run here at Mongo and wishing you the best on the next chapter. CJ, welcome. Glad to have you on board here.

CJ, my question is for you. I guess, if we think about AI workloads, certainly, the adoption of AI workloads, the rollout of AI workloads, I think it's taking much longer to materialize, and many of us on this call maybe hoped for several years ago. Is there anything Mongo can do in your view to maybe pull forward the demand for AI workloads and adoption? Or is it really just something that where you're going to have to wait for the market to kind of come to you?

I definitely still believe when I speak to large or very large customers, whether you look at Fortune companies or even digital natives, most of the early AI applications are very employee-facing productivity-based and very departmental. Nobody has so far outside of AI native companies, created an amazing agentic implication that is customer-facing or a partner-facing or matters to their core business or accelerates their core business.

So I think it is just going to take time. There are, of course, what I learned at Cloudflare, there are also quite a bit of security concerns. So on what will it -- what can happen if you have agents that are facing, take credit card payments, et cetera, et cetera, and I can go in details. But that's basically the journey that we have to go through, like the cloud journey that we all went through, where it just takes time. People will start with 1 agent, 2 agents, 3 agents.

And what Mongo can do is make sure that we are on top of mind for all those developers, we continue to show our advantages of how flexible, scale-out architecture that can truly scale really will matter to them as they build this new AI applications. And I think just constant, I wouldn't call it as far as reeducation, but constant education on strength of Mongo's architecture is what would help us as you see truly scalable customer-facing agents that are being built by these enterprises.

Ladies and gentlemen, I would now like to turn the call back over to Jess for closing remarks.

Thank you, operator. As previously noted, we plan to report our finalized third quarter fiscal 2026 results on December 1, 2025. Following our results, we plan to participate in the UBS Technology Conference on December 3, the NASDAQ London Conference on December 9, the Barclays Technology Conference on December 11, and the Needham Growth Conference on January 13. CJ plans to participate in all of these events and look forward to seeing you there. Thank you.

Thank you.

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

Please welcome MongoDB President and CEO, Dev Ittycheria.

Thank you. It's great to be back here in New York City at MongoDB.local NYC. And I'm really grateful for all of you to spend time with us today. I know all of you have super busy schedules. So it means a lot that you want to devote today to be with us. I also want to take a moment to thank all our incredible sponsors, especially our global sponsors, AWS, Google Cloud and Microsoft as well as our gold sponsors, Accenture, IBM and Infosys and all the other sponsors who really made this day come together. Please take a moment during breaks to go to their booths and get a sense of how they're using MongoDB to partner and build better solutions for all of you.

We have a fantastic day ahead. There's over 50 sessions, deep dives around the technology, customer case studies, hands-on workshops, and there's multiple opportunities for you to earn skill badges throughout the day. And to make most of the day, if you haven't already done so, I recommend you scan this QR code to really understand what's all going on -- happening on this floor and the floor upstairs, so you can make sure you can go to the events that are most -- and activities that are most important to you.

Just to put things in context, this is MongoDB.local NYC and we do this all around the world. So this is going to be one of over 20 events we do in North America, Europe, Asia and Latin America. So our goal is really to meet our customers where they are. We run a truly global business. And since the last time we were on this stage, we've had tens of thousands of people attend these events all around the world. And now we're back here in New York. This year has been marked by some great milestones. I just want to give you a couple in terms of giving some sense of our business. When I first joined MongoDB, we're a fairly small company, still with a decent name, but still unproven. And we had roughly 1,000 customers, and we were doing a little under $40 million that year. Just to put things in perspective, now we have nearly 60,000 customers who -- and Wall Street estimates that we'll do about $2.4 billion in revenue this year. To put that in perspective, we do now in 1 week more than we did when I joined 11 years ago. That gives you a sense of our growth.

And the point is that our growth is as much about you as is about us. It's really a reflection of all the incredible work you're doing to use MongoDB to run and transform your business. And to give you a sense of the kinds of companies we have, we have companies of all shapes and sizes for all kinds of use cases across almost every industry and every geography using MongoDB, including over 70% of the Fortune 500 as well as AI-native startups you may have heard of and even some you have not heard of. And they've all recognized that the relational database is not best suited for today's modern requirements. Companies like Toyota Connected that powers critical vehicle safety systems around the world, McKesson that manages highly regulated pharmaceutical supply chains, Coinbase that operates reliably through immense market volatility and electronic arts that delivers always-on gaming experiences. These companies realize they need a modern, highly flexible and scalable solution for today's challenges.

So I think it's not hubris to say that we are the world's most popular modern database, and we're consistently ranked at the top of developer surveys that highlight this. Now the reason for our popularity is that the alternative out there is the relational database. And you have to remind everyone that this was originally conceived over 50 years ago when the world was a very different place. And not many people know this, but the founders of MongoDB were also the founders of a company called DoubleClick. DoubleClick was one of the first web-scale apps that have to serve billions of ads per day to consequently that to deal with massive amounts of data. So they observed the constraints and limitations of the relational model firsthand over 20 years ago.

The first constraint is that the relational model is incredibly rigid. It's designed for a world that was very uniform and very predictable. Well, we know that's not the case today. It's also very brittle. It's hard to make changes when your business needs to respond to new opportunities or new threats, and it's awfully challenging to scale. So the MongoDB founders became tired of investing more and more time and money and effort to work around these constraints. So they said this got to be a better way. And that epiphany was what we call the document model.

The document model built on JSON as we believe, we would argue, is the easiest way to organize and work with data. It allows you to model and represent the messiness, the high interdependence, the constantly evolving nature of data of the modern world. It has the flexibility to handle a wide range of data types, which is so important when 70% of the world's data is unstructured. It offers agility to easily make changes as your data model and your business do the -- your data model, as your business changes, and it also enables you to do that when the pace of change is only accelerating. And because Mongo is built on a distributed architecture, it offers unparallel scalability and performance. And we think that the pace of change is high today, it's only going to get faster with AI. If your business is built on a technology that doesn't adapt well to change, you simply will not be able to keep up with the competition.

So the principles of the document model are even more relevant today as we transition to a world powered by AI. So at the risk of being trader being a cliche, we are entering into a new era of how everyone will use this technology to transform their business. And hopefully, what I'll try to do is explain why the database you choose matters more now than it ever did before. And the first generation of AI tools have been really focused on what I call end user productivity, making developers more productive with code-gen tools, making customers support personnel, more productive with conversational chatbots, making business users more productive by summarizing documents, synthesizing them and creating new documents but this is just the beginning.

The next wave of AI applications are all about agents. And instead of single-purpose chatbots or autocomplete on steroids, agents are most similar to the way humans actually think. We focus on an outcome, and then we decide what are the process and steps and tasks we need to take to get there and agents operate the same way. So they won't just make us more productive, they'll actually drive true business and industry transformation. .

So just to level set of what an agent does, we should use this -- I use these definitions. I believe agents can be understood as systems that perceive, decide and act in a continuous loop of desired outcomes. They could be a simple shopping agents, a travel planner, a financial analyst, a legal assistant or anything you can imagine across a wide variety of domains. And so while a large language model provides the reasoning, it is the database that gives the agent memory and state. So in the perception phase, this database supplies the agent with context such as user preferences, prior actions, constraints and enabling it to understand more than just the immediate input.

In the decision phase, the database helps with planning by providing accurate facts so that the LLM can help the agent decide what to do. And in the act phase, the database captures the outcomes of the new state of things. Consequently, the database is the agent's memory and source of truth, allowing it to operate coherently across multiple cycles and even coordinate with other agents.

So to make this come to life, I'm going to give you an example that I think everyone could understand. Imagine you and your partner want to go on a vacation, you decide, you know what, we want to go to the Amalfi coast and we want to take some time and enjoy the scenery there. So you use your AI agent to define what are your budget constraints and what are the dates that you want to travel, and then you start the process to book your vacation. Now the agent may also know that you prefer boutique hotels, restaurants may be off the beaten path that you like morning flights and that you tend to favor water excursions over long car drives.

With this context, the agent builds a tailored plan that fits your constraints and your preferences really without your involvement. And as the itinerary takes shape, the database acts as the ledger, recording the actions have been taken, the cost and timing of flight to Naples, the booking of a hotel in Positano and a dinner reservation in Rabelo. These are all log the database. And because all the state is stored, the agent can reason clearly about what remains, how much budget I have left what days are already committed and what choices have already been made.

So the database is not only the memory of the past but also drives the constraints so that each new decision fits into the overall plan. Now God forbid, if that Naples flight is canceled, the agent will immediately see what other flights are available, understand the existing itinerary and replan around those immovable or important events or if a coveted restaurant table suddenly becomes available, it secures the reservation and reshuffles the itinerary accordingly. So the point here is the database is at the center of making that agent adaptive and proactive turning what used to be a brittle booking engine into a capable and responsive travel manager that can orchestrate an enjoyable vacation within time constraints, budget constraints and even factor in challenges in any unanticipated events.

So you can see how memory, context, state and real-time data enable you to build a powerful agent that can transform your business or even your life. Now obviously, I've shown you a very simple example, but as the technology matures, there's no question that agents will transform every industry and every company. But the funny thing is, given all this promise, this hasn't happened yet. In fact, in a recent MIT study, it highlighted that 90% of corporations have failed with their AI projects. And the interesting thing is not because of the LLMs but because of the lack of the ability to use memory for complicated AI use cases, being able to store and retrieve memories and applying that context to problem solving and decision-making is a massive factor for delivering high-value and transformative use cases.

So if memory is so crucial, you need to think very, very hard about the database that can deliver on these capabilities. So then the obvious question is, what does the ideal database for AI look like? Well, let's go through the requirements. It obviously has to model the complicated nature of the modern world and the world that's constantly changing. It needs to perform sophisticated search and retrieval across raw data, metadata, embeddings, et cetera, to find the right information quickly and easily. And none of this matters if you can't trust your database. It needs to support an environment where the intensity of usage is far higher than ever before as agents don't take breaks, they don't go to lunch, they don't go home at night and they certainly don't go on vacation. So these would be massively performant and scalable and incredibly secure.

So given these requirements, let's take a look. Well, we know that JSON is the best way to model the real world. And this is what MongoDB delivers. We are a native JSON database, the ability to handle all types of data and adapt and change very, very quickly. But what's also interesting is that JSON has become the language of AI. LLMs are trained on JSON. MCP emits and consumes JSON. Other adjacent technologies use JSON. So bring this kind of dynamic data into MongoDB is so easy and so seamless.

Second, our advanced search and retrieval methods go far beyond simple queries. We use both keyword and exact matching, but also you can search on meaning and intent. So you can do a hybrid query of vector similarity search, a keyword search and search on metadata filters, all in one pass. So this means that developers can build AI-powered agenetic applications without having to stitch together multiple separate databases or search engines. So this is a far simpler path to more relevant and accurate search results. And then embedding something that people have only started to appreciate now are the bridge between a customer's private data and the LLMs. And what's clear is that the higher the quality of the embedding models, the better the LLMs can reason to improve their output. .

And through our acquisition of Voyage AI at the end of last year, we are providing the industry's leading embedding and reranking models by optimizing most contextually rich domain optimized embedding. So what said in another very simple way, MongoDB sits at the gateway of meeting of an AI system. And last but not least, as we said, we have thousands of customers. Our platform is battle tested, pushing on performance, on availability, on security. And I mentioned earlier that over 70% of the Fortune 500 rely on MongoDB. And that's not by accident. That's not something a new company or a new technology can replicate overnight. It takes years of investment, a focus and experience to do that.

So what's interesting is you can see MongoDB doesn't just check the box on the list of requirements, it actually defines them. And what's clear is that our architecture wasn't retrofitted to meet the requirements of the modern agentic world because we were actually built that way from the start based on the document model. So if I go back to my original question, if you ask AI, what's the ideal database that will power the monogenetic world? And I encourage all of you to ask your favorite frontier model, I think it will look an awfully lot like MongoDB. .

And these are just a few of the thousands of customers who are putting us at the center of their AI journeys from large incumbents to native AI start-ups. And you'll hear from some of them today. So companies of all sizes are building their AI future with MongoDB. And again, I want to emphasize that memory and state are key to developing transformative agentic AI applications and think of the right database is even more critical today than ever before.

So we have a great day planned for you. What you're going to hear today is about all about our latest release, MongoDB 8.2, it's our most feature-rich and performance release yet. And you're also going to hear about the next generation of encryption, we call queryable encryption, which is an industry-first security capability that no other platform has.

You'll also hear about Voyage's leading embedding models, natively integrated into MongoDB, delivering state-of-the-art accurate and trustworthy models to build more reliable applications. And our new application modernization platform is something we're super excited about. It's engineered to help customers cut the risk, cost and time of updating and migrating legacy applications. So our approach is an agentic approach to leverage it to transform code and schemas. And we believe enterprises will be able to move 2 to 3x faster from their legacy system to MongoDB using this new approach that we call it app. So there's obviously a lot of us very excited about what we're doing here today.

So what I'd like to do next is to welcome who I affectionately call the wizard, one of our most senior product leaders [ Oz Olivier ] and he is going to give you a lot of detail about the foundation that we're building that talks about MongoDB 8.2. Os?

Thank you, Dev. Now I'm a little biased because I've seen firsthand what our teams have put into the database. But I generally do believe MongoDB is the strongest foundation you could have for the agentic future Dev just described. MongoDB 8.0 shipped just last October, and it's already become our fastest-adopted release ever. More than half of Atlas clusters are already running it. That's because 8.0 delivered more of what your applications need now and what they will need in the AI era.

Compared to our previous release, 8.0 achieved 36% more read throughput for read-only workloads 59% higher throughput for bulk updates. And for common time series workloads, we're seeing an incredible 200% faster time series reads, thanks to advanced block processing. But 8.0 wasn't just about performance. We made the database easier to operate at scale as well. For this, we've added new insights into how queries run so you'll see bottlenecks before they ever slow you down. And once you shorted, we've made it easier to keep up with your applications by making re-shorting up to 50x faster. And if that's not enough, in 8.0, we also optimize the database across concurrency, memory management and query execution. So every application will run better just by upgrading.

8.0 set a new bar. So of course, we couldn't wait to raise it again. That's why today, we're announcing MongoDB 8.2. As we enter an agentic world, performance isn't just about pleasing human users. It's about machines talking to machines, applications calling agents, agents calling other agents, all in real time. Not only do agents not take lunch, they never sleep. It's an unforgiving workload where every millisecond, every throughput gain matters more than ever. MongoDB 8.2 takes another big step forward in performance to meet the challenges of this new era. In 8.2, unindexed queries are up to 42% faster, which means even when requests are unpredictable, responses will stay quick and reliable.

Rate reversals, the kinds of queries that dig into more complex data structures are about 20% faster, making operations smoother end to end. And time series bulk inserts run nearly 3x faster, so you can ingest massive streams of machine-generated data without hitting a wall. We're also, as always, focused on security. And we steadily advanced queryable encryption, a true industry first. The enterprise-grade security technology protects sensitive data at every point in its journey. Most database vendors only protect data at rest or in transit over the network. But when data is in use and being processed, it's often left exposed to administrators and service operators. Unique to MongoDB, queryable encryption closes that loop. It's the first fully integrated database technology that keeps data encrypted, not just when it's at rest and in transit, but also -- and here's where the big difference is, while it's actually in use in memory.

With queryable encryption, MongoDB ensures your data can only be viewed by the authorized application while still remaining searchable with expressive query types. With 8.2, we've expanded what queryable encryption can do. We started with support for exact match of quality queries and expressive range queries across numbers and dates. Now in 8.2, we've added substring support, letting you search encrypted data with prefix, suffix and partial matches. Imagine the possibilities. A doctor or a nurse searches by patient name and autocomplete finds the records, but the data behind it never leaves its encrypted state.

A bank analyst flags accounts by the last 4 digits of a social security number without ever exposing the value in memory. This is a breakthrough that keeps your most sensitive data secure at every single step. Across performance and security, these are all really exciting improvements. And we know many of you want access to them right away, even if you're not building on Atlas. Bleeding edge builders don't have the time to wait. So starting with 8.2, every incremental release will now be available across Atlas, Community and Enterprise Advanced. This gives you the freedom to put new and advanced capabilities to work the moment they're ready. And for those applications that are sensitive to change, I need to stay on a release longer, we've got you back here, too.

Currently, major releases are supported for 3 years. Starting with MongoDB 8.0, I'm very excited to announce that we're extending long-term support from 3 to 5 years. It will give you more stability when you need it and the freedom to innovate on your own schedules. Having great upgrade options does not only apply to new features. It also applies to where and how you want to run MongoDB. Because with MongoDB, you don't just get a database. You get a consistent foundation that runs and everywhere your applications need to be, whether you're developing on a laptop, running in a data center or using Atlas in the cloud. It's the same skills, the same features and the same experience. You only needed to learn one database, you only need to learn one API.

Now if you choose to run in Atlas, you can run on AWS, Azure or Google Cloud. Atlas has more than 120 supported regions worldwide across all 3 major hyperscalers. You can keep data close to your customers, meet governance requirements and integrate with native cloud services wherever you operate. But Atlas can do a lot more than just run on different clouds. Atlas supports a single cluster spanning across multiple clouds. So you can leverage each provider's unique features and specialized infrastructure from a single deployment. This is especially important today when it comes to the fast-evolving AI landscape. This flexibility ensures you can always take advantage of new AI capabilities regardless of what cloud they're available in.

So whether you're managing MongoDB yourself or relying on Atlas as a fully managed service, you stay in control, free to deploy, move and scale in the way that best supports your growth today and in the future. With MongoDB, you get the foundation you can count on with performance to power demanding applications, security that protects your most sensitive data and the flexibility to run anywhere. There's so much more to tell you. So please attend the session later today to learn more. Performance and reliability aren't just features of our latest releases, they're the nonnegotiables our customers depend on.

So let's hear from one of them now. McKesson is a leading pharmaceutical distributor who provides prescriptions for over 1/3 of North America. The applications that they run on MongoDB play a vital role in making sure essential medications reach the patients they need. Please join me in welcoming [ Brian Schmidt, Pendrick O'Carney ] in conversation with MongoDB's very own Melissa Plunkett.

McKesson has been called the largest health care service provider that you've never heard of Upendra, can you give us a sense of McKesson's scale and impact and your individual roles in the mission.

Absolutely. Thank you for this opportunity. McKesson is the largest drug distributor within the United States. We serve about 40% of the Rx products within the United States. 1/3 of the medication is supplied by McKesson across the North America. And within McKesson, I work as a principal product manager, and I have been with the company for about 15 years. .

Brian Schmidt, principal IT architect. I've been working with the DSCSA systems, which is one of the systems we're going to talk about today, but -- in addition to McKesson being the largest, we're one of the oldest too, well over 200 years old, which is kind of astounding in today's market, right, where you have all these new up and coming companies. We've been around for a very long time in this industry, and we continue to be successful.

Fantastic. Thank you so much. Now there was a regulation passed in 2013, the Drug Supply Chain Security Act or DSCSA, that you just mentioned, Brian, for those unfamiliar with it, what does this regulation require? And at a high level, without getting into a lot of technical details just yet. What kind of challenges does that create for a company like McKesson?

Yes. I think -- so DSCSA is a new regulation to help patient safety. Back in 2013, the states were starting to organize around coming up with ways to protect counterfeit drugs. And so a coalition formed at the federal level to help us make sure we had a unified market in the space. And really, what it means is every pharmaceutical product drug every bottle has a unique serial number within the globe. So if I get a particular bottle, I can scan that and authenticate that drug all the way back to the manufacturer, if I needed to, enhancing the patient safety and the efficacy of the drug. .

Fantastic. Now I understand that this is a pretty massive scale. We're talking 1.2 billion serial numbers, and I believe that is annually. So it's billions of drug bottles where accuracy is critical because as I understand it, even small errors could impact patients. Is that correct?

That's correct. So for DSCSA, one of the regulations is that the supplier has to send us not only the product, but the data. And if the 2 don't match, the challenge is that product is now as if it didn't exist in the supply chain. So you can think of a lot of drugs where we've had shortages over the last decade or so. Every bottle matters and when you come to a pharmaceutical supply chain. And so not only that, but when we send our product to our customers, we have the same responsibility. So our dispensers cannot receive or use that product unless we send them the accurate information. And it kind of changes the mindset because now you have a product in the data is just as important as the product itself to be usable.

Amazing. So now I understand the McKesson's first approach involved a specialized SAP compliance system together with PostgreSQL. But that setup couldn't serve your use case at scale. So we'll get to how MongoDB comes into play in a moment. But Brian, do you first walk us through at a high level what the limitations were and what kind of solution you ultimately had to build.

Yes. So we attempted at first that come up with 1 system to solve all of our problems. And what we realized at our size and scale -- it was almost -- it was -- it just became impossible. Once we started overlaying all of our use cases from receiving, picking, packing shipping and then trying to service our customer with the data that they're required, all the use cases just started to combine, and we couldn't scale on the platform. And so that's where we pivoted and moved to federating our information to be able to take our warehousing information, put that on its own repository and also take our customer information to put that on its own repository to help alleviate that bottleneck in that scaling.

Fantastic. Now as I understand it, you built 2 repositories. So you had this central data repository or CDR, and then DSR which is the distributed serial repository. So with the pain points for that first approach, what led you to go with that dual system?

Yes. So part of it was we had lots of requirements, so let me step back. DSCSA was a very new information, new regulatory requirement. We are out to build something that didn't exist. There's no off-the-shelf software that can go and buy or look at. So with this, all the new challenges in. We went with DSR to help break down the recalls from our warehouse management. So we have about 60 warehouses across the nation. We do about 2 million verification calls every evening in the supply chain. And with all those -- all that activity, we decided to go ahead and put a distribution-facing system to be able to handle those calls. .

On the customer side, we have customer use cases where we have to supply information to 350,000-plus customers every day. And all that activity, unfortunately, happens all in the morning. So any time from 7 a.m. to noon is when everybody is wanting their information. And so it made sense to help separate that -- those systems out to individual use cases to make sure that we're able to be successful. .

Fantastic. So now you've decided on the CDR, DSR approach. So what tipped the scale toward MongoDB?

So a couple of different things, right? One of the key things was our data in the DSCSA world is very hierarchical in nature. And obviously, the document database fits that model very, very nicely. It was a very good fit. We also took a look at some of the performance metrics between MongoDB and other systems. And with our access patterns, and I want to make that key as access patterns is key here. We were able to get much more performance-related metrics out of the MongoDB platform than some of the others that are out there. In addition, we have requirements to also have on-prem systems as well as in the cloud systems. So allow our developers to go ahead and code once and deploy anywhere we needed to in order to make our business successful.

Fantastic. So now Upendra, systems built big day. So once you went live, how did it actually perform in the real world? .

I think it's kind of overwhelming and the kind of scale that we achieved, I think a big thank you to MongoDB as a partner, along with our partners as well. The GoLab has been as smooth as it can get. I think it's a moment that we all should be incredibly a bit proud of because if you see DSCSA, it is all about trust. It is about protecting the integrity of the supply chain so that our people that rely on our medication to manage their chronic conditions or flight infections, can trust, feel safe that the medication that they're getting from is safe, it is authenticated and that one that they can trace back from the manufacturer all the way to the pharmacy. .

Fantastic. I mean that is a huge accomplishment. You're really serving your patients well. And an enormous jump in scale, I understand it's 300x what you expected with no disruption. And so that means a lot of your patients. What does it mean for the team?

What it means it's a good question because if you think about it, DSCSA, right, one, it is not just another regulatory project that we can just check market. It is about leading the way. It is about setting the standard for how pharmaceutical companies should approach compliance, that is with integrity, with innovation and with a deep sense of responsibility. So having had this opportunity to work on critical projects like this that serve kind of the humanity. It's an incredibly proud moment.

Fantastic.

And one of the things you mentioned is the success we've had with this platform. So if you imagine shipping 1/3 of the pharmaceutical medications across the U.S. every second counts in the middle of the night. We did a study where even 1/3 of a second would add an additional 15 people across the network. So if you have a system that cannot perform or be performing, it drastically impacts the operational perspective of actually meeting patient requirements and getting the pharmaceutical drugs there. On the CDR side, just to give some metrics around this, we went from servicing 1,000 requests, right, before we went live to the next day, go doing 300,000 requests in the next day. And MongoDB was so performant, we did even notice it. There's no blip on the map. The outlets performed fantastic. And we're very proud of the success and what we're able to achieve in that area. .

So glad we could partner with you in that. And I have to ask back looking at the journey that you've gone through with advice would you give your peers in the audience, Brian, if they're tackling similar large-scale mission-critical challenges like this.

Yes. When I think about the entire journey, I think one of the keys was for me was partnering with MongoDB because they not only took the time to understand us, but they were really there for our success. I think when I was working with our account manager, Daniel Johnson, he said, "Brian, I just want you to be successful. We're here to help you be successful. And that made a big difference because with the DSCSA project, we are working with lots of vendors, lots of different technology. And MongoDB partnership stood out because they were able to pivot when we wanted to pivot. They were able to accommodate and be able to provide that leadership when we need it. And it was -- is very good, and I highly recommend anybody else to engage with you guys.

I'm so glad to hear that. Now all of this is going. It's fantastic. But I have to ask you, what's next? Where do you see the system in the underlying technology expanding in the future?

Well, I think having accomplished, having met the FDA deadline of August 27, it is now about how can we future-proof our operations, how can we leverage the technology to meet those ever-growing challenges of the health care industry and the patients alike. And we have quite a few things that are lined up. We are embarking. We are about to complete a use case with the Mongo team on the GenAI use case. And then we have a few more use cases that are lined up, and we are so excited to start our journey of proofing our operations now.

Yes. I'd just like to add, Mongo was key for us to accelerating our AI journey at McKesson. Being in technology, I think we've all heard of blockchain, if anybody remember those days when it was very popular. Blockchain was going to save the world. AI was kind of seeing somewhat similar in our technology space, but being able to partner, you guys helped us accelerate and see real-world results. And that's the key is real world results that can speak to the business, and that's just -- that was just fantastic.

Fantastic. Thank you both so much for sharing your story with us. Upendra, Brian. Thank you, guys.

Thank you everyone.

It's not often than we get to hear about how large-scale technology choices filter all the way down to the individual patients. Many of us right here in this room, and there are our safety. We really appreciate McKesson sharing their story with us today. Now McKesson's journey shows what it takes to run mission-critical systems at massive scale. Now we want to show you how those same capabilities can extend even further into powering a new generation of intelligent AI-driven applications.

Please welcome my friend and colleague, Frank Liu, MongoDB Staff Product Manager.

Thanks, Melissa. AI is a broad term. But if I were to define it with a single sentence, it would be something like this. AI helps us create applications capable of understanding the world as we humans do. And this will really bridge the gap between computers and people. What does that actually look like in practice? Let's start by taking a look at some different applications that leverage AI.

First up is retrieval augmented generation or RAG for short. RAG finds the most relevant information in a knowledge store using vector-based retrieval. And that information is then fed to a large language mall to generate accurate grounded responses. The ability to chat with developer documentation is a great example of this. Next up, our recommendations. This is the ability for a system to intelligently surface content that a user would like to engage with. If I'm shopping on e-commerce websites and add a coffee machine to my shopping cart, a good recommendation system will recognize that. I might also like to buy some coffee beans and maybe a milk frother as well.

Last but not least, we have agent memory. Now earlier, Dev talked about agents and how they are critical to unlocking transformational change with AI. These agents require memory to maintain context and adaptive feedback much like humans do. A real-world use case is an autonomous digital campaign marketing team. And in this scenario, a team of highly specialized independent agents, such as a VP of Marketing, a market researcher, a copywriter, graphic designer, et cetera, et cetera, collaborate, to plan, create and launch a complete marketing campaign from a single high-level prompt.

They use both individual and shared memory to coordinate tasks, maintain brand consistency and learn from their results over time. Bringing any of these use cases to life depends on 1 critical component, and that is the quality of your embedding models and rerankers. This is often the difference between potential and production. And before we go any further, let's do a quick refresher on embeddings and reranking. And embedded model is used to convert everything from PDFs to images, to audio or even code into an array of floating point values that capture meaning for software to process. The key thing to note here is this -- two embeddings that are close to one another in a high-dimensional space correspond to data that is related.

Now as for rerankers, they compare each retrieve document directly to the user's query. You can think about it this way. You first retrieve results using embeddings and a reranker then reorders those results to improve accuracy. Stronger embedding models and rerankers improve the quality of our retrieval. And this results in a more relevant and grounded results for RAG; b, recommendation systems that actually drive more engagement and improve user satisfaction; and c, agents that remember the contents of past conversations.

So embedding models and rerankers are so important, where can I get great ones? Well, I'm glad you asked. MongoDB Voyage offers best-in-class embedding models and rerankers. We have your typical general purpose text embedding models, which turn pure text into embeddings. In this category, the Voyage 3.5 series outperforms competing models from the likes of OpenAI and Cohere, all while being more cost efficient.

We also have powerful multimodal models where typical multimodal embedding models are only capable of vectorizing a single photo or a single text string, Voyage multimodal 3 is capable of vectorizing interleaved text and images. They can also capture key visual features from screenshots of PDFs, slides, tables, figures, you name it. And this eliminates the need for complex document in parsing while still maintaining retrieval accuracy.

Innovation is the name of the game when it comes to AI, and embedding models, quite frankly, are no different. We recently released voyage-context-3, a breakthrough in precise chunk retrieval with global document context. This contextualized chunk model is the very first of its kind. Our model process is the entire document in a single pass and generates a distinct embedding for each chunk, delivering superior retrievable performance. .

On the reranker side, our recent reranked 2.5 release sets an industry standard for reranking with instruction following capabilities. Voyage models are designed for high accuracy and quality. And when you start using them, I think you'll find that we've done a lot of work to make them as developer friendly as possible. And even though great embedding models and rerankers are critical for building grounded, reliable AI applications, you still need other components to bring AI to life. With other database providers, this is an extremely complex process.

You had to have your source of data, a database for structured data and a vector database. You also have to feed that data into your embedding model and then be sure to update both data stores. Then you had to have your results reranked outside of the database. Now all this made for a very, very complex and multi-vendor environment.

Now you can do everything with MongoDB. This native integrated experience reduces friction for you and accelerates your AI application development journey. Search and vector search have been in Atlas for a while, but we understand that a lot of development starts locally. And as such, I'm pleased to announce that search and vector search are now available for community server and enterprise server. With these 2 releases, we're opening up our text and vector search capabilities for development, testing and production in your own environments in addition to Atlas. .

Now I'd love to show you all of this in action. Please welcome Apoorva Joshi to the stage to help me do that.

All right. Apoorva, I heard you are a big fan of the movie, The Devil Wears Prada, is that right?

Guilty as charged, guilty as charged. I watched it way too many times for me to even admit. But I just can't get enough of Meryl Streep and her iconic stairs, like when she glares at Anne Hathaway at 32 minutes or oh, my favorite, cerulean sweater scene, you know what I'm talking about at 54 minutes, 11 seconds or...

Hold up. That's actually [indiscernible] you know the actual time stance. I mean you really have seen this movie a lot.

Well, I have to admit, I did have a little bit or a lot of help, right? All I really wanted was to find my favorite themes from all of my favorite movies. So I built an app for it, and AI app for it. It's why you do these days, right?

Of course, you did. Of course, you did. .

You want to see.

I would absolutely love to.

Let's do it.

But before you begin, I'm thinking maybe we shouldn't use The Devil Wears Prada since this is being live streamed and I really don't think we want MongoDB to get sued.

That's a good thing. That's a good point. Okay. So why don't I use the next best thing? Are you ready for it? Previous MongoDB.local keynotes. Are you really telling me you don't watch them all the time? What's better than curling up on your couch on a Friday night, watching Dev, our CEO talk about the greatest database of all time.

Look, you don't have to tell me twice. I do it every week, every weekend. So...

See -- so okay, jokes aside, there's this term that Dev always uses something like critical movement. So how about we see -- if he talks about that in last year's keynote. So here's my app. I already uploaded last year's keynote through it. So I'm just going to go ahead and select that and I can start searching for my favorite moments. In this case, we want to look for critical moment. It's crucible moment, my bad. But while we're here, how about we look at what that means in Dev's own terms, right? So I'm going to click on that top search result here. It has the time stamp for me to skip too.

So let's see what Dev has to say about Crucible Moments.

The most well-known venture capital firms in the world, and it's also one of our earliest and largest investors in the company as a term they called a Crucible Moment. And they define a crucible moment as an inflection point where a choice you make today has an outsized bearing on the years and decades to come.

[indiscernible] in case this ends up being a crucible moment in my life.

Look, crucible moment aside, this demo is super impressive. Can you tell us a little bit more about how you built it? .

Sure. Yes. So the app contains a lot of the components that you were just talking about, right? I upload a video to it, and it first extracts frames from it, then it uses Voyage AI's latest multimodal embedding model to embed trim to capture what's happening within it. But can you believe that it took me, it costs me less than $1 to embed a whole 70-minute long video.

That is a great price point for a best-in-class embedding model.

Right? I think so, too. And these embeddings are what make these frames searchable without using the exact keywords in the frame descriptions. And as you can see, I'm even able to match a text query to text within a frame image, which is something typical multimodal embedding models often struggle with. I've then implemented hybrid search using MongoDB's latest rank fusion aggregation stage, which allows me to combine vector search on the frame embeddings and full-text search on the frame descriptions.

So this really gives me the best of both worlds, which is semantic understanding from the embeddings and precise keyword matching from the full-text search. And this really helped me improve the search quality within this application that I built.

This is awesome, Apoorva. The demo itself looks really smooth, but I also imagine there's a lot of other things that are happening behind the scenes. Now could you tell us a little bit more about what makes this search so responsive?

Yes, 100%. So speed is the name of the game, right? And there's many MongoDB features that you can use to optimize the applications for not just real-time performance, but also massive scale. And one of my favorite features, something I use all the time is this feature called quantization, which essentially shrinks full precision float32 vectors into smaller, lower precision vectors consisting of int8 and binary values.