Snowflake Aktie

Please welcome Head of Investor Relations at Snowflake, Katherine McCracken.

Hi, everyone. Welcome to Summit and welcome to Investor Day. Thank you for joining us here, whether you're joining in person or virtually. We appreciate you making the effort. So I'm going to kick things off with a quick overview of our agenda today. We will have presentations from Treder from Christian and from Brian, Sridhar will give an overview of really his vision for Snowflake and what that means for both our core data platform opportunity as well as our AI opportunity. Christian will then take the stage and go over a lot of the product announcements you heard from us this morning and really detail how those are fulfilling the vision that TRD will lay out.

And finally, Brian will come up here and share our financial outlook and the implications of that vision. We will wrap with a Q&A, so we'll take questions from the audience. Sridar, Brian and Christian will all be on hand to answer your questions. As a reminder, we will be making certain forward-looking statements today. So this is a statement on our non-GAAP financial measures as well as our safe harbor. Both are available on our Investor Relations website.

And with that, I would like to pass it over to Sridhar.

Thank you -- thank you, Katherine. I -- it's great to see all of you. And Summit continues to be an event who's scale I have trouble absorbing One of my end who came last year, Sara, thank me for inviting her to a concert. It's kind of funny to be in the world of data and be able to have that kind of excitement and impact.

I'll start with a big picture view of both the disruption and opportunity that AI provides for many companies. It's not like definitely in that list or most of us work is an endless sea of tabs. And we are responsible for figuring out how to organize over time, how to organize the information that we consume and then to figure out what to do with it. I joke to people that commence if they're on Chrome is life changing. But it's really -- that's really hard. And what we are beginning to see is AI changing the very nature of information work.

And what this means is that the data on new AI agents have access to. We'll get into what an agent is and so on is critical. Integrations with the different pieces of software that all of you use that I use that is also critical. And overall, for an organization, governance overall of this data, security, is also a big, big deal because these coating agents are immensely powerful, but also sometimes don't have good judgment about what's okay to do with what data. And so this means that for an analyst, somebody that wrote SQL for a living, things are just very different.

They go from effectively creating dashboards or writing one-off sequels to creating what looks closer to software within Snowflake, for example, we deployed skill packs that were specialized to different departments within Snowflake in a matter of like 4 weeks, and we've been continuously rating on them and using these kind of agent products, super, super intuitive for all of the nontechnical folks at Snowflake. Brian is going to talk to you a little bit about sort of the CFO experience doing that. And for our data scientists and our data engineers, they now think in terms of how do you automate creating an entire pipeline.

For them, even adding a single column in a table used to be like this endlessly tedious work of making stuff propagate across hundreds of files manually with people looking at over. That stuff is getting automated. And so for a lot of these end users, myself included, when I need to look at like sales data, I don't want to be writing SQL data integrations are seamless, which means that all of the information that I want us just available kind of thought analysis at our fingertips and deliverables for the smartest people that know how to take advantage of these products is no longer limited by how many hours they work.

It comes down to how effective are they at using agents to get their work done. In fact, 1 of the EDMs that we are trying to teach our software engineers at Snowflake is that they really need to be thinking of their work as being a tech lead of agents rather than an individual contributor that rights code 1 line at a time. It's a huge, huge mentality shift.

Now I'm not claiming that we saw all of this. I don't think anyone saw all of this. But I've talked previously about just the transformative power of even being able to access data faster even in a pre-coating agent era. And so we started investing into this in earnest. -- starting early 2023. And one of the things that, Christian, I, many others have consistently believed is that AI is going to make the value of and easy to use, connected and trusted data platform like Snowflake, even more than before. And -- these are our growth rate numbers over the past many quarters. And as I said, during much of this time, our focus very much was on how do we create the definitive data platform. that people would want to use if they wanted to get value from AI.

And all along this journey, we also worked really hard as a company, and I mean it, in discovering the basics of what does it take to create great products and launch them. Two years ago, I talked to you folks about how we are basically rethinking how we took new products to market about farming we teams.that brought every specialized function back into a small collapse team that could sit in one room and take a new AI product to market.

I talked last year. about how it was really, really critical that not just software engineers, but all of the solution engineers within our team. These are the presales folks that show the art of the possible with our customers that help them get projects done. I talked last year about how it was really important that they become AI native because they could just get more things done faster. And a lot of our success as a company. This was a trend 3 years ago, it was going down. clearly, it's not. If anything, it's going up and going up well, has come from this back-to-basics approach of we need to create great products. And we need to figure out as a company, as a team, how we take them to market.

And even in this pre-agent world, we are seeing the results of that effort which a lot of people had to painfully reinvent how they work because people are happy sort of doing their own specialization. It's awkward to suddenly say, you're responsible for the whole and you need to iterate a lot faster. And all of that work has been paying off in things like productivity numbers. We measure the productivity of our expansion account executives in terms of how many quality use cases do they win per unit time per quarter, per month. Similarly, we measure the effectiveness of our sales engineers, solution engineers by how many use cases did they help their customer take to production.

And so the number of use cases on per A, this is not the size of the AE team increasing, but it's per -- it's increased by 86% year-on-year as we look at the quarter that just transpired. And the number of use case go-lives per SE has increased by 58% year-on-year. These are hard numbers to move because the average AE wins a handful of use cases per quarter. And even now, the way I think about scale processes within the team, and it's often an awkward conversation is I routinely boil it down to what's the top design doing even within a population that, on average, clearly is doing better. And we press it very, very hard on what is the top decile doing that the rest of the team needs to learn.

And it's the process of continuous self-improvement that we think is really important for us. And in many ways, that's the structural transformation of Snowflake as a company. And now fast forward again. to now. I have talked about -- this is what my keynote was about. This is what Christian covered a lot of I think -- the future of work is very much all of us, all of you, me included, living in a new kind of environment. just like all of us got used to living in a browser or most of our work life or using our phones 24/7. What we see happening very, very clearly is that there is a new category, the agentic control plane. And that is going to be at the center of how work gets done.

A lot of companies are going to be competing for it. But for it to be effective, it needs to have amazing enterprise data in context. It needs to have all of the applications that particular user is using and has context for, these are the sales forces and the Workdays and the ServiceNows and the SAPs of the world. And obviously, the awesome models that seem to have no bound in their capabilities for what they can do. What -- we are very proud of is we have created products that can capture what this work is going to be. But in a way that is true to what Snowflake is. I'm going to know illusions. -- that competing with antropic on the quality of large language models that my team can create is a winning strategy. It's not. It's a failing strategy..

But on the other hand, we can go head-to-head with lot code when it comes to cocoa. -- and say, here are the reasons why we are actually an important part of every customers and increasingly every partner's data ecosystem when most of our partners are here. I've spoken to several executives already about how do we effectively have cocoa as a de facto implementation platform for all of the data work that their teams do, and this is hundreds of thousands of people in some of these organizations. And on the Coco side, we have like measurable proof on a product that is very young. So our services team delivered a sport migration 60-odd percent faster working on behalf of a Global 2000 hospitality customers.

And -- and a financial services firm saved over 500 on a job that they were doing. We often hear about migrations at this point, the number of things that are possible with cocoa honestly exceed our imagination. We hear of people doing things, doing migrations -- but honestly, we would not have thought about it. What we did do with it was set the details for creating a product that will truly be great when you work with Snowflake. To me, this is the other reality of the current moment, which is a little bit of what the judge says about what they saw. You know a quality product when you see one. when you use it. And having that bar for creating amazing products matters more than ever. Cowork is even more ambitious.

Obviously, it has its origins in Snowflake intelligence. But back when we first launched Snowflake Intelligence, which was November of 2024, we saw it as a place where analytic data came together. But part of what we are realizing, again, driven by large-scale use within Snowflake is that it can be so much more. Once you are able to access all of the common applications that you have, whether it's a Gmail, a drive and now even things like a salesforce. And you have a platform in which work can be abstracted [indiscernible] becomes very, very different.

And again, is available like right in your pocket or your laptop. We are earlier with these kinds of very large deployments of cover, but customers like Woo, tech-forward companies are figuring out how to use a combination of Coco and Cowork to transform how their teams operate. And in the analytics world, Cowork has already proved its metal with any number of large customers, folks like United Rentals or Domino's in Australia, are 1 of the largest banks, which is delivering a personalized solution for all of their exec staff using cohort. And as I said, an important element of all of this product work is leaning in to what is possible, starting with Snowflake as customer.

We have talked about Snowflake being customer 0 before, but I think we are practicing it at a very, very different scale and speed right now. Things are being codeveloped. And I want to show you one glimpse of how we are using these agent platforms to transform how work gets done internally by our teams. This is an example of our support team using Gogo to transform itself. Let's watch the video.

[Presentation]

And so benefiting from data gravity. We think we occupy a key position in the world of AI. And we are very cognizant of continuing to be world class in this. A lot of what Christian announced today was around continuing to be that trusted data platform, that governed data platform. And things like the Natoma acquisition are going to make that even more true in this world of agent KI and agent control planes. And we think there's a significant amount of opportunity areas like observability or data-intensive problems that are ripe for disruption from people that are willing to think from first principles about what software should be.

And honestly, we also get inspired by customers like Emmanuel that you saw yesterday. Came to us and said, hey, this is our data. We want to rethink how my salespeople should interact with that data. And I have the guts to say, "I'm willing to do that from first principles.

I think that's the disruption. That's the opportunity that's there in front of us. I've stressed this in the previous 2 investor days that I have done with you folks. Strategy is fine. We think supercharged great agent I products, AI control planes built on top of this incredible data foundation can be a great company, but I stress execution a lot. And that execution manifests itself in how are we able to move quickly in creating value. It's not lost on me or on Snowflake that we need to rethink speed when it comes to software. But living it is really important. .

I'll give you folks like another example that's like this role, right here. There's 1 in sir, just 1 that's been working on a Cocoa mobile app. This is one of these like remarkably productive people that knows how to chuggle balls at the same time. And yesterday, before we met a set of reporters. I had like this momentary Pang of doubt that I didn't know all of the launches that were going to happen at Summit, I had a list, but it's a long list. And as Christian where can I look, Question is helpful. use me 4 dock. It's not, not like really did 4. I pace it into Coco. MCP support has not yet been added. And so go thankfully, I put it into coworking. That part worked. So I had the list of launches, which was cool.

But the more cool part was I go back to the Slack channel after we did the interview with the press folks. And I tell this person, "Hey, when MCP support coming. They go into Slide 5. I usually like preferencing all my slacks with like low priority because people act faster than you really want them to and you're the CEO, but he's like, no, no, no, we'll get it to you. And 2 hours later, he's like MCP support satojust update the app, you got it. And sure enough, I base the same front back into the global app. It gives the same summary.

And so execution really, really matters right now. And on things like durable advantages need to be thought through. Christian speaks to some of these things. So we pay attention to that. If software is truly easier to create, what does that mean for the future of Snowflake? Where is the ongoing enduring value? What are the products that we can create, for example, that can make cocoa much better out of the box than a cloud code. And how can it make it even more better for everyone else in the company if a set of folks use it. These are the feedback loops. -- do bio. It's clear to me that Airbnb doesn't care about the cost of creating software going down because they create a network in the real world. And so companies need to be thinking about what's like the additional value, what makes these products better with usage.

So we spend a lot of time thinking through how do we execute to that kind of vision in addition to being a great data platform. We want to be efficient on the go-to-market side. It's an enormous team. We get enormous leverage. And we have talked to you many times about things like new logos. And this was a remarkable quarter for us because both the number of new logos that we won and the ACV, the total contract value that we got out of these new logos, both went up significantly. year-on-year. That's because there are a set of people who obsess about this motion. We obsess about getting these customers onboarded, getting these customers live. And that's the efficiency that I push for that we push for.

What are the happy accident that happened with Gogo Cowork and AI in general with Snowflake is the act of making these products broadly available to the entirety of employees at Snowflake basically led to this explosion of creativity and ideas. You didn't tell people, you can't use cocoa because, well, you're not an engineer like anyone can use cocoa, you can only access the data that you're supposed to see. We have governance controls on the data, but sure, you can build anything. And so we saw amazing things like JB, our Head of Sales. He built a streamlet app to look at his travel and entertainment expenses because you will seek of e-mails from Brian complaining about it. Let me just look at it -- and -- that clearly changed its mind about what is possible.

And so if you focus or to JV, he will talk excitedly to you about how we can shift right in a massive way and have more of Snowflake sales team focused on delivering projects for our customers. It's like we need to create outcomes faster. And software engineering, as I said, is undergoing a complete revolution. Anyone that thinks that's software engineering is about wipe coding. It's firmly stuck in early 2025. We are producing a set of not we, like the world is producing a set of rocket scientists that are way smarter and get way more done then the ordinary software engineer or even the excellent software engineer could do last year. And so by focusing on the basics of what Snowflake is about, what do we do?

We make software, we sell software? Run software. That's the SRs. They've gone through a complete change similar to what you saw with the support team. They have completely redone how they look at operational problems, again, built on top of built on top of cocoa. And we did this without buying new software. That's the magic also of the moment. And we are focused heavily on how do we make deployments go faster because I see that as a final remaining hurdle. Obviously, we work with partners, but we are also investing into a it's going to be a small team, I don't think of them as thousands of people. But these are folks that know the best of what Snowflake has to offer as a platform go deep to understand what it means to solve a customer's problem and solve it as quickly as possible.

And you saw the results of some of that with Sanofi yesterday on stage. These are among the healthiest collaborations that we've had with a very motivated customer. We're doing similar things for large banks. And we anticipate that we'll be leaning into something like this as the impact of products like Cowork becomes obvious, and people realize that their data teams like our own have to modernize themselves for them to be relevant in this age of AI. And the final comment that I want to make is that because we have invested so much in transforming ourselves. -- in being more effective as a company. And because of our ability to increase non-GAAP operating margin, but also bring SBC firmly under control. we feel confident enough to say that we'll be reaching profitability at the end of next year.

And Brian will walk us through more of the details of that. But I see this as the culmination of the work that we have done over the past 3 years to reinvent ourselves to be more driven, more product focused, more quality of obsessed. Continuously self-improving company. With that, I'm going to hand it off to Christian.

Hello, everyone. How are you going. So good to see Ron showed that was this morning. Now I ask them to see so many familiar faces. I assume most of you attended the keynote this morning. Okay. So sort of some clapping, good Coco is the answer. You got that so I will recap some of those innovations that we're launching at the conference. But I will also contextualize it for what is probably most interesting for all of you to think about it and how do you think about it as a company.

To get started, I use the exact same diagram visual that we are started with because it is truly a set of innovations that reinforce what we're trying to do here. The more we've thought about this picture of the enterprise, the clear we are that the elements are data, AI models, connectivity to enterprise systems and something that drives it. SEDAR in the keynote last night, so it's something that is resonating 1,000% with customers that we talk to, which is -- the differentiation is not the access to the AI models. The differentiation is the access to the right data. And that has created a sense of urgency in many of the customers that we talk to on, oh, I really need to go get my data estate in order. We have been saying for a number of years, you have all heard us say consistently,

no AI strategy without the data strategy and we're living it more and more on a regular basis. The question that I think many of you are usually trying to infer or to get us to provide color is, okay, how do we differentiate? How do we stand out from the alternatives that customers have. And Street just mentioned it, but I cannot emphasize enough the easy connect trusted. The keynote this morning had some fancy words, but it was the exact same easy connected and untrusted. And I've arranged the set of launches and announcements that we have into these 3 buckets. So with that, starting with easy, you know the answer, right? Coco.

Someone is whispering cocoa. And it is true. It is not only on brand to how we thought about differentiating for a long time. You've heard us talk a lot we may be willing to give up some use cases where someone wants to turn knobs all day long because we just want people focus on productivity, business outcomes, business value. And what has happened with cocoa is truly just we materially change that. I would like to say 10x that, but 10x doesn't quite capture it. I shared this morning at open flow, we added all these APIs. And now -- we went from open employees school, but it's hard to configure to -- I just ask cocoa you configure for us. Something that at the encouragement of Serge credit every single launch that we're doing has to come with how is the experience simpler with cocoa. And in some instances, in many instances, we're starting with cocoa first then you go build the UIs and the APIs and all of that.

Because in reality, if you can just ask, hey, give me governance, give me interactive analytics. And cocoa figures it out. It's easier to build for cocoa private interfaces where we turn on interfaces, then go and make it easier from a user experience or a UI. So the emphasis on cocoa is not unwarranted -- there are parts of their product that today at some, they're only visible via cocoa. And in reality, many instances probably you'll never need any other way to access it. So I cannot emphasize enough the role that it's playing for us. And as we established in our earnings call last week, it is that nature that is helping the entire of the usage and use cases for Snofi. We announced a number of capabilities this morning. The way I would think about it, I don't want to go too deep into the technology, we're trying to eliminate the differences between the form factor.

The beginning, Cocoa has a command line version, which is incredibly powerful but it's accessible to a smaller set of users because not everyone is comfortable with a terminal window and a bunch of shall commence. On the other side of the spectrum, we have cocoa in snow side, which that one, the usage is quite broad because it's in the phase of all of our users, but it's not as powerful because it didn't have the right sandboxing and security guarantees.

A lot of what is in here, and I'm happy to answer questions at the end, but I don't think we need to go into those details. A lot of it is eliminate the friction, bring the power of a command line interface, bring it into a desktop experience, bring it also into the hosted version of Snowflake. And now what we say is you get full power but you still can sleep well at night in terms of the scope of actions taken by coco are constrained, whether it's on-prem or a new machine or whether it's hosted. And the other thing that we're very excited is the Cocoa desktop. For those of you I know that you actually are tracking very much or very closely a lot of what we do.

Initially, when we had announced this research review call now work, it was all about we released the desk of internally to Snowflake. It took off like wildfire. Everyone transform how they work. And that's when we said, okay, maybe this is a different way of working. At the end of the day, we have clarity that desktop experience is cocoa. And I think we're going to also follow with something that, that 4 co-work, which is the more governed experience. But the data form factor has product market fit inside of Snowflake. We made it available in pole review a couple of weeks and at the conference is generally available. We expect this to drive some additional usage of CoCo.

And maybe I'll highlight here the Excel form factor, the VS Code form factor, as additional ways and services for our customers to be able to get value of cocoa. And it's not on this slide, but I mentioned it this morning may stage, which is we also put a cocoa plug-in into the cloud core marketplace. Of course, we would like to say that for data management operations, you don't have to use the interaction of Cloudco to cocoa. But if someone is already recommitted to cloud code, there's a very easy way to plug in, and we've already heard from some customers in that situation, hey, this is ideal. I can use cloud core for application development completely unrelated to data or Snowflake, but I can delegate to cocoa all of the data management activities. Street just mentioned migrations.

And the way to think about what's going on in migrations is truly a reboot based on what AI has enabled. You all have quoted this for years now on how quickly customers consume the contracts, how quickly they start with consumption -- and we are seeing a massive acceleration of time to migrate. Al caveat. Not everything in our migration is just what the technology needs to do. That piece, material acceleration. But sometimes there's things like I will not be able to run this test because it's end of quarter, end of year or I do a production freeze in the Q4 by fiscal year. There's a number of constraints outside of the pure technology. Those were working and some of the FD efforts that you mentioned help, but at least the pure coding testing, all of that is materially changed. The middle column in here, actually, I talked about Mincom, the 1 on the right, Spark.

We've been working on more and more compatibility. I've been the ones sharing with all of you that -- our engine is amazing. People that want to move from Spark, they want more compatibility. Guess what? In a world we're migrating from 1 type of API to another type of EPI is bottom line free we're starting to see different reactions. I mentioned this morning, there's a customer that wanted some legacy Spark API, and it's a pain to support that legacy Park API. We've been working on it, and we know that at the end of it is going to be compatible, but not super fast. And we're busy doing that. And in the meantime, the customer said, oh, we tried cocoa, we converted to Snowpark, and we're done, we're good, and it's 5x faster and cheaper by implication.

So we're actually -- we have a renewed push on Snow Park. We mentioned it to some of you that we are seeing increased momentum of Spark migrations, use of snowpack just because the pain that represented converting code is no longer as painful as it is, so it's just easier to do. And then the last piece that I'll mention is the first column here is the productization of an acquisition we made a company called Datometry. What that company does and what is now part of our migration suite, it lets us virtualize a Teradata experience. So we hear from many customers. I want out of Teradata, but I have all this stuff around it. have scripts and applications and reporting systems and changing all of that takes time.

That's why some migrations of Teradata we've done or 2 years, 3 years. What this virtualization lets us do is say, if this is Snowflake these are the apps we put a layer in between. And that layer -- let's say everything else in the enterprise environment, think that it is Teradata. It looks like Teradata has Teradata see esteradena scripts and a Teradata is a really rich function of product. To the rest of the ecosystem, it looks like Teradata. And what it's doing behind machines is translating into snow fleet. So when we did the acquisition, the converting it to support novel took a few months. I don't know how long ago, that was 6 months or so. Now we're ready to start accelerating migrations through this. So we're excited about the opportunity to help customers move off of their data quicker. Ben Feder mentioned it, co-work is super important.

We do believe that it is the enabler to change how people go about their jobs. I think both Rider and I talk a lot about these tabs because at least personally, we use a lot of different apps and you need to know where to go or what as opposed to, let's be more user-centric, and let's ask the questions or the request and something that you need to take action on, just say it and then let us systems figure out the details. That is what's being enabled.

That's why it's a big part of the shift of what we're doing here with the personal work agent. It's not about even more systems they move from tabs to different agents. No. I have 1 entry point. It knows me learns about me. It learns about what I like, what I don't like. I was mentioning to a few folks that -- now there are so many things that I go do some interesting and analysis. And then I say hey, I would love to get a refresh of this once a week. And I have coworked doing all these things all the time, and I'm just getting regular reports. And now we introduce automation. So I can say, oh, by the way, if you ever see this type of condition, just go take an action, e-mail someone else or do something like that.

So you can see how workflows are getting reinvented with the power of co-work. I have 1 slide on our artifacts and dashboards. This is, in my mind, what BI should look like if you were to start from a pure AI native perspective. It's not the goal of I have a dashboard and then I'll see if it went up and down and then you have to click 100x. No, you ask a question. And then you get the visualization that most helps you understand what happened, and that's what then you go and share with others. It's not the other way around. And by the way.

BI was amazing for when it was introduced, right? It changed the accessibility of the data, but it's still here's a set of static views and you need to figure out the answer. It should be the other way. You asked the question, we give you the answer, and here is a visual that helps you understand that. So that's what we're doing with artifact live data, govern data authority in cocoa published with co-work or made available to business Unicowork, and we have the way for you to pin them down and say, oh, I arrange these tiles, effectively, it looks like the modern version of a dashboard but it's curated for each user on what they want.

Cortex Sense, we introduced it this morning, I'll be the first 1 to say, it is early on, but the insight behind it is we have the ability to gather a lot of information that can help both cocoa and co-work produce better results out of the box. And I say out of the box as a contrast to today, if you curate enough semantic views and enough information, you can get all the results that you want with co-work and with Cortex agents. But what we're increasingly seeing is customers wanting -- I need answers now. I need to be able to roll something live as soon as possible. That's what Cortexense enables.

And in a few of the combinations we had last week, -- there was this question on how does it compare what you're doing relative to what a coding agent does i'll caveat is 1 evaluation set, it's not a fit issue. It's a real customer valid use case. But all of this will say mileage may vary, I still tried not generalizing where you don't have the power to generalize. But what you see in the front row is leading coating assistant, trying to interface with MCP for SQL and asking questions about the data. Second 1 is cocoa and co-work as we know it right now, -- and the last 1 is Coco and co-work with this run time content that Snowfly gathers to say, I know enough about the user, the data, all of this to say, here's additional information, and you see both material improvement in quality, but a lower cost. And if you're thinking like how can it be lower cost. There is a huge amount of cost and tokens that go into, oh, yes, my bad. I didn't get it right. Sorry, let's do it again.

That burns a lot of cycle as opposed to, if you know what the question is how do you answer the question? -- it translates to better economics. So we're extremely excited about this. And again, I'll caveat to we're in the early process of testing the different scenarios, different customers, et cetera. But this is a big differentiator for how we think about out of the box, our agents, Cocoon cowork just produce better results for customers. And Sreedhar mentioned the acquisition of Nat -- this is 1 of those 3 key elements on the Agentic enterprise. -- important differentiators, both for data administrators as well as for users. They're administrators.

One, it connects to 100-plus business systems out of the box. Number two, it enables those administrators to have policies on what agents using these MCP connectors can do? We were sharing the example at Snowfly the way we configure the e-mail connector of Natoma was you can ask your agent, Coco-Coto send an e-mail. If the e-mail is going to an internal recipient, it sends it. If the e-mail is going to an external recipient, it puts it in your drafts as a way to force humans to do -- that's a policy we chose -- the core thing is that Tom lets our customers decide what policy they want. If they just want to span people, that's fine. If they want to be more conservative, even for internal, they can do it. Third benefit is see and audit everything that's happening with these agents because it's the new wafer data to get pushed out.

Oh, we just sent a bunch of your sensitive data to a connector that was going to do, I don't know, Slack or e-mail, you want to know all of those things. And from a user perspective, instead of me authenticating with 100 different systems, I authenticate on to the gateway Natoma and that gives me authentication to the other 100 systems. We introduced data stream this morning. The goal of this is to move Snowflake upstream such that it can have a streaming solution, capture data when it's created, whether it's a sensor, a device, a website and be able to land it into Snowflake with almost no administration, very little management and very low latency.

We're very excited about this. It's in the category if it's early, but quite promising. Pillar #2 is connected. The integration, the interoperability that we're showing with iceberg, I would say, second to nobody. That's a hard statement to make, and I do it based on facts. We are truly committed. We are steering the iceberg standard, but we're also being amongst the first at implementing it. Right now, we're the broadest in terms of the implementation of the V3 spec, and we're steering the V4 spec. I mentioned this morning, we integrated all the rest catalog APIs into horizon to make sure that we can interoperate with data regardless of where it sits. Even if it's on data bricks,

We can read and write data other engines with glue and others, they can read and write data that sits in snowy. So this whole notion of I'm locked in, and I put my data like -- that's not excuse customers. Please use whatever gives you the best experience and the best performance, the best economics. And okay, this 1 is super important for all of you in this room. When we introduced iceberg and I think some of us regrets how much noise it costs. But 1 of the things that was factual was with Snowflake, you start in our format and you pay for storage. With iceberg, we always said it's customer-managed storage, but there's no reason for that trade-off. So we introduced and is generally available here at Summit. Snowflake manage storage for iceberg -- so you can still be interoperable, but we'll do the management of the storage, we'll give the economics. So that, I think, is going to be an even better tailwind relative to at least how we thought and modeled the adoption of iceberg. -- sharing, I think all of you know, I'm personally passionate about the network effect, personally passionate about the unsilo of data and healthcare organizations connect. I am very excited that we're finally breaking out of the -- its 2 parties, 1 directional, how do we do multiparty collaboration and symmetric -- it starts all with our data clean room.

This is productization and evolution of an acquisition we did a couple of years ago. But we're starting to see very interesting media use cases advertiser, buyer type of collaboration use cases that helps collaboration. And this already gives us some structural advantages, the more parties you have exchanging data via Snowfi. We also talked about zero-copy partnerships -- the marquee 1 that is -- went GA actually last month was with SAP. We have a few initial deals of people buying into this integration. And today at the conference, we announced the expansion of some of the Workday integration we've done, new integration with IBM and new integration with AVEVA.

And then I could spend as many hours as you want on the importance of trust, we gave it a decent amount of air cover this morning because I think this is what changes how snovik fits into the adoption of AI for enterprises. -- rolling out AI easy. -- rolling out AI in a way that people can truly sleep well at night is not as easy. Horizon, the catalog is well this comes together, Horizon context, is where we brought all the explicit semantics and information for agents to be able to work well together.

And again, there was a slew of announcements. I left a number of things out from this morning. There's a lot more during the conference. -- on how do we help govern agents with security policy, identity for agents, data movement, ex filtration protection, all of those. We also talked about adaptive compute I put it in here just because there's something I want all of you to be clear on. Massive performance improvement, but we're doing the exact same thing that we did with Gen 2 -- we priced it in a way that we're aiming for revenue neutrality.

So our customers get the benefit that is materially faster, but none of you need to go change your models, we're still good even though we're trying to push very hard for the adoption of all of this. Interactive analytics for responsive experiences, you can say this gets into the click house type of workloads. I am always very careful to make absolute statements, but -- it's incredibly competitive to the alternatives customers about there, and we're going to be making a big push to get an option of this. And with this, there's the Data Cloud as a whole. This is the data for enterprise data, manage governed part of the solution and wrap it up back into the -- it is part of the bigger picture that we share. Happy to chat more Q&A.

Hopefully, this was useful. And now we're going to turn it over to Brian. Thank you.

Thank you, so for super fascinating all the product releases and product velocity that we're seeing. And thank you to each for coming out today. There are some of you that have been around the story for a long time. There are some of you that are relatively new -- so I'll walk in and talk about the market, some of the revenue drivers get into GAAP profitability, capital allocation and so forth. So the market today is roughly $225 billion. We expect that market over the next 5 years to more than 2x to over $460 billion. AI is expanding our market opportunity. Last year, we went through this. And over the 5-year period, our market has grown roughly 30%.

We really have conviction on this when we look at our large customers. And so the top 25 large customers, they spend, on average, $34 million a year with us. That's grown over the last 2 years from $22 million a year. When we look at our Fortune 2000 customers, G2000 customers, in FY '26, they, on average, have only spent $2.4 million with us. We feel that we have the right to actually increase those customers up to our large customers' spending -- we'll jump into the core growth drivers of the business, primarily in the core data platform and our AI workload.

Let's jump into the core data platform. landing new customers is absolutely essential for us. When we land new customers, they don't add that much in year 1 or 2 from a revenue perspective. but it's fundamental to long-term durability of the business. And then also, when we land those customers, expanding them are really important. We have 1 of the best-in-class net revenue retention rates -- this really drives stability and expansion into our customer base.

We're able to expand our customers on a lot of the AI workloads that Christian Sridhar talked about. When we go to our customers, we sell business outcomes, which is really helpful. We got a lot of favorite charts in the deck, but this is 1 of my favorite charts. With the high gross retention in all cohorts expanding, you can see from FY '19 to FY '20, FY '21, those customers are delivering the majority of the revenue this year. And so with the land motion, then they expand over time with the high gross retention rate, this is really a powerful revenue engine for the company. We do that through a number of different ways, but I want to touch on migrations and use cases.

Migrations from FY '25 to FY '26 grew 1.9x. Use cases grew 1.7x. -- very meaningful increase. We actually are able to get this wallet share from a number of different sources. -- and where the real benefit comes in for us and our customers is when they're consolidating all this into a single platform at Snowflake. All right. Let's talk a little bit about sales compensation. We use sales compensation to incentivize growth. You can look in FY '24, we did not compensate on new customers. We made that change in FY '25 and it really paid off in FY '26.

We actually will go through and continue to make tweaks to the sales compensation model to get the most out of the sales organization to deliver the most for our customers. This is the fundamental pillars of our sales incentive compensation. We announced a new CRO in first quarter JV as we call them. there's really 2 core focus that JB has. One is stability and 2 is AI. From a stability perspective,

JB has been with the company for over 10 years. He actually -- we joke about it internally that JB actually bleeds blue blood because he's been here so long. He actually pioneered the use cases to customers, which is used throughout the entire sales force today. He's taken that and other things that he's learned and actually using that to leverage AI. Every rep today within the company uses Coco and co-work. When we go to a customer, we're actually using synthetic data and creating applications to deliver outcomes to our customers. We've actually changed the way that we're selling to our customers and do an outcome-based pricing.

So our reps have first-hand knowledge of the capabilities of our products and how to deliver that. We're extremely pleased with the first quarter that JB delivered and look forward to many more quarters. So AI accelerates growth. I got 3 charts up here. If you look at the chart to the left, the sales cycles are accelerating. When we -- when I went back and looked at the average days sales cycle for this last quarter, -- it was the lowest in the last 4 quarters. You would expect with more choices and more valuation out there that the sales cycle is actually expand and actually take longer. -- they're actually doing the ops of that.

So the sales cycles are accelerating. We constantly talk about how we are using our AI tools to actually get new customers to consume faster. We've taken that from 10 months down to 7 months. and we're continuing to see how we can decrease that. And then for all customers, migrations are accelerating. And so we've shown a 40% improvement. And so AI across the business is having a dramatic improvement on our time to ramp.

All right. Let's talk about a couple of customer examples. Before I dive into the specific examples, I'll talk about some broad trends that we're seeing. And so One of the things that we talked about on the last earnings call was there are secular tailwinds that we're actually benefiting from in the overall industry. And then second is cocoa is actually expanding our personas that we're selling into and allow more people to consume. And then thirdly, in our base business, we saw an acceleration of the base in the last quarter as it relates to cocoa and the secular tailwinds.

And so this particular customer was a large customer that was an equipment company, and they adopted Snowflake cohort. And they adopted cohort because they had over 1,600 locations -- and the reps we're having a tough time getting the information out to the customers and answer them in a unifying way. And so they use AI agents to actually build the responses with co-work and they're able to answer with greater consistency across all the 1,600 locations, faster answers and increase the customer satisfaction. If you look at the next example, this is a semiconductor company.

They originally bought deployed Coco to optimize their queries and save money. Their supply chain department then actually picked up on cocoa and start using them. And within the supply chain department, it was -- they were having a problem because it's really complex manual calculations on ordering inventory. Through the use of cocoa, they're able to decrease the time, increase the accuracy and reduce the cost. And so this is another example of how we've seen cocoa play out in our customer base. In both of these, as you can see, cocoa is actually increasing the consumption of those customers -- all right. Let's talk about how this is working at Snowflake. So I've joined Snowflake a little less than a year ago. And when I first got here,

I started playing around with AI tools. And I can tell you that CoCo has dramatically changed the way that it work. I worked so differently today than I worked a year ago. I think each of us with AI are trying to retrain the way that we actually work. And so I'll give a couple of examples on this. I've talked to some of you about my good morning CFO skill. So every morning when you come into the office, there's probably a number of websites you go through, report Cilag, structured data, unstructured data. When I get in the morning, I go into cocoa and I type good morning. And it basically takes all this data from structured to unstructured sources and actually puts it into an easy-to-read format within minutes. And I'm able to go through changes in the sales forecast. -- new hires that joined the company, customer releases and what's going on from a news perspective, major account wins, a number of different things. But not only can I do that.

I can turn it into visualization automation within cocoa. Our Natoma acquisition then allows me to connect it through our MCP servers to Gmail, Slack and so forth. -- at the application layer. So now I'm using cocoa really as a destination spot to actually work out of in the morning where it's bringing all this stuff together saving an immense amount of time and allow me to actually send e-mails out around the world, understanding all this data in 1 simple place. It's also changed the way that I actually work with my FP&A team. And so historically, you'd have a list of reports that you periodically get.

And on those reports, it'll always be like 1 number that you would look at. And you would say like, what's going on with this number. And you go back to your power user and say, -- could you go extract that data and actually give me another set of reports, so I can look at this data. And that process would go back and forth for probably 2 or 3 different times until you could draw a hypothesis about what the conclusion was -- now with cocoa, if I see something, I actually through natural language, not through a sec query, it does it for me, go and query the data and inquire what's going on. And I can drill all the way down to an account level to a product feature level and understand anything about the forecast.

And so it takes something that would take weeks to do down 2 minutes. We're also using cocoa in a number of different ways in the CFO organization. We have over 139 use cases deployed today. And as Sridarsaid, there's heroes popping up all over the company. People love to use cocoa and see what they can do with it. So they're automating and transforming the way they work across every aspect. Let me talk about how this is resonating with customers. I have the good fortune speaking to a lot of customers. Two weeks ago, I was in London, I met with over 20 CFOs. And there's 2 trends that are actually emerging. One is there's a different persona that we're selling to. And 2 is there's a big -- there's a larger sense of urgency.

And so when you take some of those use cases into a CFO and show them what you can do through natural language, not through a list aesthetic chart through a BI company. But what you can do through natural language and automatically drive that into visualization automation like the light bulb clicks like that. There's a couple of accounts today where it's a CFO of a $10 billion company, where they're going to do a big contract renewal and I'm actually the lead with him on the purchase. And so we're selling to a new persona. So we're constantly going in and selling to CFOs.

The second thing is the sense of urgency -- when I go to our customer executive center and meet with customers, not only are you seeing just the Chief Data Officer come in, you're seeing the entire management team, in some cases, the board, and in some cases, they're actually bringing in a whole list of partners -- and so it really is changing. I think when the newer models was released and now people are deploying AI more broadly, I really do believe there's a greater sense of urgency around deploying something.

All right. Let's get into margins and capital allocation. Streeter and I are 100% aligned. You can grow while getting operating leverage in the model. You can see in FY '25, we delivered 6.4% non-GAAP operating margin. And on the last call, we just guided to over 1 point -- we guided to 13.5% over 2x within the 2 years. And we're doing that by keeping non-GAAP product gross margin flat at roughly 75%. We talked about the AI products have a lower gross margin than our core. So how are we doing it? We're doing it really 2 ways. .

One is we're being extremely disciplined on headcount. And so last quarter, we reported absent of the observed acquisition, net head count increased only by 17 the quarter before that, only 37%. And so we're changing the way we work by using AI tools and necessarily not adding heads to get work done, but transforming how we're working. On the other hand, cloud spend has gone up a little. The offset of these 2 is giving us operating leverage in the model. The billing payment terms for the company have been really consistent.

Over 80% of the people pay in advance. You can see last year, it was -- 2 years before, that was 82%, but really remains consistent. There is some noise between billings growth and revenue though. And so you can see on the far left-hand side in FY '24, revenue grew 36% and and we had 29% billings growth. But in FY '26 has actually reversed. When our customers actually come to the end of their capacity -- or use the capacity that they purchase, they really have 2 choices. -- and we allow them to do either the following. They can actually do and end them and actually extend the current contract when the contract renewal comes up or they can actually do an early renewal. And so this cause some noise between billings and revenue. But over a long period of time, this actually normalizes out. So if you look at the 3-year, they actually are normalized.

All right. Let's talk about GAAP profitability. We're super excited to announce is today and the leverage that we're getting in the model while seeing the revenue growth that we have. So we announced that we'll be GAAP profitable in 4Q FY '28. There's 3 levers to do this: revenue, operating expense and SBC -- we actually went and just played with the bottom 2, operating expense in SPC. This is not a discussion about FY '28 revenue. And so we're seeing greater operating efficiency and operating expense. -- for modeling purposes to help you out, assume the same trend in SBC that you've seen for the following few years. And so we're at 41% of revenue. Last year, we were at 34% of revenue. And this year, we said we'd be 27% of revenue. So that should help you from a model perspective on how we're going to reach GAAP profitability in 4Q '28.

With that as well as we don't expect to do any large M&A. Okay. We'll jump into capital allocation. The primary areas. One is organic growth, R&D and sales and marketing. You heard Christian talk a lot about from a product perspective, what we're releasing into the marketplace and the velocity that Sridhar talked about in his presentation. We'll continue to do that. From a sales and marketing perspective, it's really adding capacity to the capacity model where needed. We have about $800 million left of authorization, or $4.5 billion that was announced earlier. And then from an M&A perspective, we have typically done small tuck-ins. -- on a buy versus build more of an aqua hire perspective. So in conclusion, we have a very large and growing market. We have durable growth drivers with the land and expansion motion that we have.

Our customers are fanatical about the products and services that we deliver. AI is accelerating all aspects of the business. And we've given you the framework today for us to reach GAAP profitability in 4Q 2028. So with that, I'll invite Street and Christian backup stage, and there will be some mic runners running around, and we have roughly, call it, 25, 30, 35 minutes, clock is still going up for some Q&A. So if you have some Q&A, please fire away.

We got 1 up here in Keith is going back Karl.

Okay. Great. Yes. Happy to kick it off, and thank you for today. Maybe this is for Sridhar and Christian. OpenAI did an event this morning. I'm sure you're too busy to have listened to the live stream, but they announced a new data analytics product. So the spirit of the question is how ambitious do you think the frontier model companies will be over time in vertically integrating down into the data layer? Or do you feel like the way this is going to play out in the next 3 to 5 years is that they'll partner with firms like Snowflake and your peers rather than go after it with first-party products. .

Yes. I can take a first cut at this. I think the market in front of them in the enterprise. -- which is to roughly get every company to rethink how work should get done, starting with things like software engineering is very, very large. I suspect that, that is where the bulk of their attention will go. Running products like Snowflake is it's a whole new set of both practical and operational skills. Having said that, I emphasize that software is changing so rapidly that people should not be in the business of making long-term predictions about what is possible and what is not. But that's my current best answer.

I don't think I have much to add other than a lot of what we talk about that acquiring data not so hard doing so with correctness with trust with all of that. That's takes some more time. But I share the alertness as Peer has seen still in all of us, which is just 1 to pay attention to what's there, what's working, -- in many ways, I am seeing the dynamics with the AI model providers similar to what has happened with the cloud providers, where, yes, there may be some overlap -- but at the end, they were more complementary than not at many customer sites. And so far, it seems to be very similar dynamics. .

Just building on what Christian is saying, absolutely, the cloud providers, as you know, have data platforms. And -- but they also quickly get into this mode of yes, we both need to partner and compete. So in certain sets of customers, we will be competing, and we'll sort of stay separate in that and be in our lanes -- while in others, we collaborate. We have an excellent working relationship with both the model providers. And I actually think that the world is headed to a place where most companies want certain amount of model independence. It doesn't -- you don't have to sit that hard to understand that being reliant entirely on 1 model provider, it introduces the same kind of dynamics that sitting on exactly what CSP does for your business, especially if it's large and varied.

And we didn't get as much into it, but we spent a fair amount of time making sure that both Coco and co-work work effectively across all models. And as others, you saw the partnership with SpaceX, but we also watch the open source models carefully, where if their performance rises up, that's actually it's very positive for us because we rent GPUs from the hyperscalers, and we have excellent infrastructure teams that can help us run that at scale. And it obviously produces just different margin profiles than working with the large model makers. It's pretty early for all of this. .

Sorry, but we're reaping on each other. Your comment we're starting to hear customers tell us Oh, I made a big commitment to this AI model company, but now I want to use the other one. And that dynamic, we saw with the cloud providers, and it's starting to benefit us, which is hey, you may have coming into Snowflake, we'll give you model choice. And that I think would make me pause on do I want to go all in with 1 company in a world where nobody knows what the world looks like 3 months from now.

Santa Singla, Morgan Stanley. I think as a management team, you guys have been very front-footed in terms of acknowledging that the world is changing and it's changing fast. And even with the presentation today, I think you gave us a clear sense of where you're making your bets and where you're going to invest behind products. I'd love to get a sense of having been at multiple easy analyst days, you guys have reached a tremendous amount of innovation in terms of products.

Can you give us sort of the real-time view of I got a good sense of where you're focusing going forward. Are there parts of the product portfolio that maybe we've discussed before? I don't know, I'm just container services, the data engineering portfolio that you're pulling back because the world is changing and this is where you want the team to focus is more of a kind of like a portfolio allocation question, Star and Christian can give us a sense of where we're headed.

Yes. I think 1 of the principles I live by is all of us can have theories for with rate product and what's going to achieve product market fit. But none of us are, in fact, capable of willing that into existence. And that's just how it goes. And by the way, all of the usual instincts that people have for how to bill PMF into existence, which is usually some variation off. I'll give them more attention, and I'll give them more people, like some unhappy combination of both of these typically produces the opposite outcome of actually trying to get product market fit. And so even in the world of AI, the thing that it all Christian, flatly in July, I was a sponsor, the first sponsor of the cocoa project. I told him, if by September, October, we didn't have traction. -- we should walk.

And because, as I said, everybody talks a big game about their ability to do things like super app announcements, Galore, but BMS is something very special. And so in areas where we perhaps had a thesis for what could be, I would put something like Nedavaxs into that. where we made a substantial investment. In my mind, substantial investments in early products are a mistake, but you can't change the past. We basically deconstructed that into what are the core capabilities that are -- that come as a result of that way of thinking. And Adia is just another way of saying, I want to share both data and code from a provider to a customer and have some rules for who can see what data and who can see what go.

And so we went, we deconstructed that into a set of capabilities, and we are not native apps as an end all be all concept for applications quite so aggressively. It's a very slim down team. And so we are being thoughtful about where do we pull away from -- and we -- Vivek is actually really good at extracting leverage from the teams. I mean the SR project, as like the Tata part of the project, which is, hey, you don't need to spend so many time dealing with annoying pages at 2 a.m. in the morning. And so this concept called Kitao,keep the lights on, a bunch of our teams have this -- that number has dropped by a lot.

And so we're very excited to you. It's like, okay, what am I getting for it? Or do we need to move some people from this team over to this other team where we think there is promise -- so that kind of reallocation is very active. And where there are this is not product and where there are tougher decisions to be made about disciplines changing. Christian and I came to the unfortunate and joint conclusion that tech writing didn't need to be kind of like this independent job. -- thing anymore. And we effectively disbanded the team. It gave us -- we got a bunch of grief because of it, but our take is like VMs are better at driving documentation today using a coding agent and a specialist whose job it is to right document. -- like looks obvious in retrospect, but when you make it, it's still painful.

So I think we're being pretty flexible about where we are allocating where we need to pull away from, and we'll continue to do that.

Actually, so 100%, I have more examples. For example, SPC as a third-party customer bring your workload, yes, has not gone the way we envisioned it. But I just shared the Cloud Agent sandbox. -- that is enabling a lot of power to cocoa. That is the same team and borrowing technology from what it was built there. The momentum with notebooks, the momentum with stream came from, hey, instead of pushing SPCS so hard as something external, make sure that you have a great rent environment. So some of that reallocation is happening for sure. I'll give you 1 other example.

The interactive workloads, that borrowed so many changes that have been done for UniStory which is why we were able to go turn something around way less than a year. And the performance of that technology is amazing. So yes, there's for sure reallocation and there is reuse of technology we built.

We'll go right here and then SP-13 I just looked up the data analytics announcement from Open AI. These look like lightweight skills that run on top of Snowflake. So we knew about this, I didn't quite make the connection. I think it's -- think of this more as a set of skills that let you answer our analytic questions. and can generate SQL for Snowflake but also a bunch of other platforms that have -- that were mentioned. I mean, first of all, it's not like under the data layer. It's more about how you use these from core -- and this is something that they've actually talked to us about. I mean, I have to give both the model makers credit for being really good partners. You saw that with Daniela yesterday. We work effectively together.Yes, there will be some cases where they would prefer, obviously, to have clot cowork rather than cocoa. -- snowflake co-work. That's fine. I think there's an element of maturity that we have about how we approach this relationship.

That's 100% calling into our MCPCconnectorwe gave them a quote on Friday. .

Alex Zukin with Wolfe Research. I think -- and you guys are roofing on each other, I'll riff on Carl and Sanjit. The question, I think, a lot of investors and even customers have like we watch the presentation. It's full of innovation, it's full of new products. I think we're having a little bit of a hard time or understanding the collapse of functionality and the consolidation of functionality across the models, the hyperscalers and apps. They all seem to be in this world of delivering you the answer that you want from any question that you asked. And so I guess -- I think I know the answer, the answer is Coco.But the question is Cocoa your way of driving a lot more consumption of the core? Or is it more about expanding beyond?

Like when Brian is talking about new personas that you're selling to, how those look, others feel the sales cycles, those lands how does this landscape -- like when is Coco the right answer versus cloud code versus codec versus whichever model Microsoft launched today?

First of all, I think like cocoa and Snowflake cowork are really like 2 sides of the same coin. They share an enormous amount of infrastructure underneath is just tailored for different personas -- all companies Snowflake need to have a clear eye view of what they're good at. Everyone can aspire to more, but you need to be very clear about what you're good at. And we are amazing at being a data platform. And cocoa is all about how do you get value faster from the data platform.

It's pretty much how do you bring -- it's equivalent of your AUM, how do you bring more assets to be managed by now like either directly into Snowflake or an iceberg, they're increasingly indifferent to those things. And then how do you take data through its value life cycle. And we feel very confident. And we have published benchmarks comparing clot code to cocoa on things to do with Snowflake. -- of Coco being a really good perhaps the best solution in the world for sort of working with snowflake.

You can see and say, like, that's not a big deal to your platform. It's not a big deal, but it's not like everyone has the equivalent of cocoa for the products that they are creating. -- still is a lot of work. And while we have clearly -- we have a lot of work to do in terms of driving cocoot option by each and every 1 of our customers. It's still pretty early. When we talk about the 7,000 customers. It will be more the case that there's like 1 user that's gone and done it. It's not that they have switched over to this agent take way of operating. .

And it's not like we have made migrations easy enough that anyone can migrate from anything into Snowflex. So there's work to be done. I would say in the near term, there's just -- that's where there is enormous potential for us. But it's sort of playing our game of -- be a good data platform, GoContrtes, everything that you can do with the data platform including creating products like cowork that people can get even more value from. And so in that sense, cowork is an expansion play from where we are, and it's early. And my aspiration, our aspiration there is that we get some mega deployment. And we first created AI products. And I'm positive. I said this last year here, usually like having clarity about priorities.

When we talked about -- when we talked about AI, I've always said create world-class products first, like that matters more than anything else. -- great products that customers love, get marquee folks to adopt the products that you create. And any such breakthrough is inevitably exceptionally difficult because you have to prove and you have to get the customer to trust you and then drive scaled adoption, then drive revenue. The margin will come if you have done 1, 2, 3, 4, right? Cork has to go through this kind of emotion. It's not flag intelligence. It's predecessor, which is mostly an analytic product, has done well. It placed us firmly on the AI MAP. You generated a lot of momentum and relevance for Snowflake as a product. cowork is like a giant step ahead in terms of the things that it can do.

But we very much have to prove ourselves in terms of getting the product deployed by large departments like we have with Snowflake. And the fact that I can get Brian to talk to every CFO and look them in the eye and say, this is all I transform, how we operate -- it's a huge asset. And it's the same for J.B. and team. But we have to translate that into the logos into the 10,000 user deployments. We have to get customers happy with the cost of spending money on AI. We have to convince them that the per unit cost of using cowork is a lot less than aim for some number of -- some amount of subscription software -- so that's the potential, but I'm the first person to say, like, that is early and we have to prove the scale use cases to you.

And between the 2 like cocoa sells itself. Why? Because we have, whatever, 14,000 customers that love Snowflake, I just go to them and say, everything you do with Snowflake is going to be 10x faster. They'd be foolish to not like go try it out. core, we have work to do to sell it and prove ourselves.

Ittai Kidron from Oppenheimer. So for interesting. Maybe I'm going to ask a little bit of Alex' question and the opposite of Carl's question a little bit earlier. Going back to Coco the first thing you said in your presentation right here right now is that data gravity is a major advantage that you have compared to everybody else. And on top of that context, which again, you have compared to everybody else, if we skip forward in time 2, 3 years, How do we think about what cocoa can really evolve and develop into the company? Because Carl asked a question of what happened if the bot companies go down into the data, .

I would argue that based on what you've said, you have a far greater improvement and advantage right here right now. Why not go aggressive upwards, not into the model itself, but more general coating agents and many other things that you can attach to the data and the context that you bring to the table that others don't have.

I mean it's a great question, but execution needs strategy for breakfast. I have the strategy, just have to get the other part right.

So what are you planning for us then 2 years from now, open up the kimono a little bit.

it's -- you should not be in the prediction game when things are improving by 20% every month. I'll just honestly tell you.

That's a toughest job for us. We're analysts to do -- that is,

I think, a part of the conundrum of the world right now. I read this in this amazing book where he basically says all history writing is teleological. People usually write history by assuming that the the path that you took to get there was preordained and then they write the history. I think it's just really hard to tell right now. I think we have clear ambition. We have a team that is willing to execute and live up to what we think other companies can be. We have value to show.

But the rest of it whether we do it with our own people that can help with deployment and set the stage for what is possible or whether we come up with a set of effective partners that can drive change through a lot of customers. I think that is part of like difficult execution.

Rob Owens from Piper. Great day from a new product perspective. One thing I'd love for you to double-click on is just the data streaming opportunity. And is this something that's customer-driven? Is this part of your bigger vision as to where Snowflake is going to fit in the future? And the answer can't just be cocoa. It's got to be something else?

Actually did -- this is very unique in -- please go .

No. So it's both. It's part of our core direction of travel, which is we want to help customers through the entire life cycle of data. And those are not empty words. It's truly the entire life cycle and there was a big gap upfront. Today, customers deal with technology that is frankly complex to manage and expensive to go from web logs and sensors and devices and apps and mobile phones all the way to Snowflake. .

So 1 piece is directionally. But the other piece is we do meet with our closest customers. We have a forum we call it the Black Lemon Council. And the signal was very, very clear. Like if you guys were to solve this, snowlityle, we'd love to do it. And as soon as we started sharing details, the interest is very high. And maybe the third piece that I'll say that is very interesting in that space is there's a technological disruption happening there, which is the original streaming systems all kept the data in memory. -- which made it insanely fast, but also incredibly expensive. And we've seen a number of entrants and players delivering something that has a separation of storage and compute.

The data is in cloud storage -- it's materially cheaper, a little bit slower, and many customers have said, many of you companies that are evepresented here in the room saying, we're totally fine with that trade-off. All of these things come together, that's what led to the opportunity. And what not only here reminds me Lions, but just said, like the thesis is there. Now it's on us to make sure that it's a great product and delivers on the thesis.

I'll stress that point again. I think the act of recreating something is people tend to look at it much more as there's something over here. It's a product. It's some system that works. It's a company. And they think they can essentially deconstruct that and somehow get to that point. many of you know, I spent a lot of time at Google. You have endless arguments with lari about what innovation meant. And part of the thing that he drilled into my head that stays with me to this day is you will never win by aspiring to be someone else. You have to find your path. It's a journey that matters. And that journey usually starts with a brilliant new insight. Much of streaming was designed for all of you, was designed to minimize the amount of time that it took to get data from the stock exchanges of New York; two, the data centers in New Jersey. This is like all of your teams wanted to squeeze that millisecond out. So everything was in memory, everything was RPC, like remote procedure calls, machine to machine, like people optimize the heck out of it. Obviously, it's sort of expensive. That insight that's been had a few times before, is that most people don't really care that data shows up in 20 milliseconds.

If it's 400 milliseconds it's fine. Is it 10x cheaper -- that's like that's the core underpinning of data stream, which is still a bright new insight. By the way, if the model companies were to want to disrupt Snowflake, it has to be some new thesis like I can think about this just very differently. It's not, I'm going to compete with Snowflake and operate at 22.5% margin, whatever the margin they want to operate at instead of something -- that's kind of maybe it works for bases once upon a time with the retail industry that was unwilling to see the Internet. It's just not something that works in a general way. And so it starts with this bright insight for -- this is how you rethink -- I mean this is the origin story of Snowflake.

All of you folks know it. It started with that 1 core kind of thesis. But still, it's the journey that matters, whether we can create a successful product, get people to adopt it. There's a lot of hard work ahead.

Brad Zelnick, Deutsche Bank. Great summit. I mean it feels like an innovation blizzard this year. So hats off to the entire team. I guess my question is in a world where enterprises are overconsuming tokens, beginning to question ROI and even putting in curbs and usage limits, it was great to see Christian, in your slide comparing Cocos co-work and the promise of Sense ahead versus general-purpose cogen. How does Snowflake position itself to be insulated from an inevitable wave of token optimization to come and even be part of the solution and to be able to benefit from it. .

Yes. I think absolutely, how much like how much tokens are used, what models are used, what cost is a big issue. But I also think there are lots of really good technological solutions that we feel confident. This is where having like control over the harness, the thing that's actually executing the plan is so very important. One of the things that 1 of the engineers and I got together a few weeks ago, was a skill that would generate like a plan for how do you solve a complex problem. And part of what you can do when you do things like that is you can have subagents work with smaller models. And similar, I'll point back to our advantage, if there's an open source model that's perfectly great at some job that also happens to be hosted by Snowflake, we can use 1 of those models. You don't always have to use like the marquee name models for every job. In fact, we make these models available within every model garden and people run a lot of jobs using much smaller models as well.

Similarly, I think techniques like skill compilation, a skill is an English language recipe, but 90% of the time, the skill is actually doing something fairly deterministic. You don't need a fancy element to do the deterministic part. And so there's an experimental project for -- for the most common use cases or value skills, how do you compile that thing down into code where basically the code is involved instead of the big giant LLM interpret English. So I see a slew of techniques like this show up as there are concerns about cost. And honestly, we also want to put them into products like Coco and co-work as we continue to innovate with them.

One of the things, ironically that Christian and I are quite happy about is that things like Snowflake optimization is a lot easier with Coco. And even though people optimize more with cocoa, we actually get more consumption anyway because they just do a whole lot more. And that's the benefit of of general purpose Swiss Army nice like tool that coco is. And we have already worked on things like per user limits per account limits for how much some tool should be used in fact, I'm having a conversation with a company about deploying cowork for 3,000-odd sales folks within that company. And part of the guarantee that they want is a per user limit. And our model, which is pure consumption.

We don't charge a per user fee for co-work is actually very beneficial here because customers end up getting the best of both worlds. They can both place a limit on how much 1 user can consume. But if users don't consume anything at all, they spend 0. So you see little innovations like that actually be helpful, especially in a consumption model that starts at 0, that does not have a seat-based license. -- many of the coating agent providers do like a blended seat plus token pricing. And I'm actually pretty happy that we stayed away from those.

100% happy that we set away from per seat. The other thing is -- it doesn't fully inoculates your question is actually very insightful and valid. But we did learn a lot on when someone is consuming with no -- let's go and have a conversation with the customer and make sure that it's valuable consumption. If there's -- I have lots of medium-sized regrets, but if there's 1 big 1 from a tamales, when things were going amazing and all of you were revenue models. We never went to and ask the customer.

Are you getting value out of this? And we learned that and we're not going to let it happen. Is there a risk of some technical disruption that changes Sure. We'll cross the bridge, but at least correlating value with spend matters a lot. And that's where the control the striatal about matters so much for us.

Srini, all these products take off, low 30% growth doesn't really feel right. I mean, it feels like this is a market, your primary competitor is growing at twice as fast as you. So when you think about ultimately what you think these new solutions can do to help accelerate growth? I know you're not giving guidance here, but it doesn't feel like you're a crew's altitude from where the rest of the industry is at right now. .

I don't know what to say. Absolutely. We aspire for more, but showing is then you're doing. .

Right -- our guidance is based on rooted observed behavior, and we did conservatively guide up this last quarter. As we see things happen, that's when we'll have to update our guidance. .

And having said that, I can't resist the ask for a GAAP profitability guidance from said person you mentioned.

Mike Cikos with Needham. Given the larger number of personas you guys are addressing the applicable use cases, -- can you talk to the population growth within your existing customers? Like you're obviously not a seat-based model, but I was trying to make an analogy to your NRR. How is that seat growth trending within the existing customers. Are we actually seeing an acceleration in the number of seats for those organizations?

I do think that we're seeing a broadening of the reach of Snowflake -- but as retrade who was talking about co-work, it is harder to go beyond our core audience. But those examples that we're quoting on the , we have customers say, "Hey, I'm going to put this in front of. 500 users, 600 users, 3,000 users, it is happening and it reaches different functions and disciplines. So I still think that it's early on in our journey with co-work convincing customers to deploy something to every employee organization. It takes time, it takes effort, and we're very early on. So from a number of people that in an organization that leverages Snowflake, we're very underpenetrated. But to be clear, we just started on that journey. .

And this is something we track pretty extensively internally in the context of Snowflake intelligence/ which is the number of unique users for each of the customers that we have. How do we drive that up, how do we get entire departments to adopt it. The journey is still early. Hopefully, we'll have more updates for you in the coming quarters. .

We got time for 1 last question. And so Keith, if you could give the microphone to someone for the last question, then I'll wrap it up.

Appreciate it. Adam Tindle, Raymond James. I recognize the announcement on GAAP profitability is going to play well to this audience, but I want to ask a challenging question on that red -- you outlined a generational growth opportunity. Brian talked about the TAM here. Your business is accelerating. Why is GAAP profitability important at this juncture and why not pour more investment in now versus being governed by this promise? .

Because it isn't clear that simply throwing more humans at problems gets more things done. That's like my honest assessment. I think more of my energy should go into having each and every 1 of my engineers think and act like the person that like reliably got the feature out into hours. That is going to drive more leverage for us than simply hiring more people. And the other thing that you should also take into consideration is similar to the point about tech writing. There is a transformation of the workforce itself that is going on, where pretty much every team, this is not just engineering ourselves is going to be quite different.

And we are going through a process internally for what does it mean to have a particular function operate in a true AI forward way. What does the team structure look like? What do the job definitions look like? And how do you go from here to there. And so Bine,what looks pretty conservative. There's a massive amount of churn and reinvention that are going on. I think, as I said, I will end with like scale no longer needs to be driven by the number of humans that you have working on some problem, getting super linear scale from highly effective people what business is going to be about.

With that, I want to thank our IR department and all the other snowflakes who make this event possible, and thank you for your support. Have a wonderful day and enjoy the rest of the Summit.

Thank you all.

Good day, and welcome to the Q1 FY '27 Snowflake Earnings Conference Call. Today's conference is being recorded.

At this time, I'd like to turn the conference over to Katherine McCracken, Head of Investor Relations. Please go ahead.

Good afternoon, and thank you for joining us on Snowflake's First Quarter Fiscal 2027 Earnings Call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; Brian Robins, our Chief Financial Officer; and Christian Kleinerman, our Executive Vice President of Product, who will participate in the Q&A session.

During today's call, we will review our financial results for the first quarter of fiscal 2027 and discuss our guidance for the second quarter and full year fiscal 2027. During today's call, we will make forward-looking statements, including statements related to our business operations and financial performance. These statements are subject to risks and uncertainties, which could cause them to differ materially from our actual results. Information concerning these risks and uncertainties is available in our earnings press release, our most recent Forms 10-K and 10-Q and our other SEC reports. All our statements are made as of today based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

During today's call, we will also discuss certain non-GAAP financial measures, see our investor presentation for the definitions of the non-GAAP financial measures, and a reconciliation of GAAP to non-GAAP measures and business metric definitions, including customer count and adoption. The earnings press release and investor presentation are available on our website at investors.snowflake.com. A replay of today's call will also be posted on the website.

With that, I would now like to turn the call over to Sridhar.

Thank you, Katherine, and thank you all for joining us today. AI is fundamentally reshaping how work gets done, and Snowflake is at the center of the transformation. Across industries, organizations are moving toward a future bed employees and intelligent agents work side by side to accelerate decisions, automate complex workflows and unlock entirely new levels of productivity and innovation. With Snowflake, that future is already taking shape.

Our platform brings together the four elements organizations need to become an agent enterprise, a unified governed data foundation, access to leading AI models, connectivity across enterprise applications and workflows and a unifying agent control plane that turns intent into governed action. That control plane is becoming real through Snowflake Intelligence and Cortex Code or CoCo, as it's affectionately known. Snowflake Intelligence gives business users a natural language interface to enterprise data context and actions, while CoCo gives builders a natural language way to create applications, pipelines, agents and workflows directly on Snowflake.

Snowflake is uniquely positioned to help customers become agentic enterprises as evidenced by our Q1 results. Product revenue came in at $1.334 billion with growth accelerating to 34% year-over-year, up from 30% last quarter and 26% a year ago, marking our strongest sequential dollar growth in company history. Our net revenue retention rate increased to 126%. And with our continued focus on executing with discipline and operational rigger, our Q1 non-GAAP operating margin expanded over 300 basis points year-over-year to 12%.

I want to take a moment to touch on our outlook. Based on a combination of strength in our core data platform business, a meaningful uplift from AI capabilities, including CoCo and Snowflake Intelligence, we are increasing our FY '27 outlook from 27% to 31% year-over-year growth. Brian will share more details on our guidance in his remarks. Thank you to all of our Snowflake for the hard work and dedication to deliver these results.

Across our business, AI is strengthening Snowflake on multiple levels simultaneously. First, AI is accelerating consumption in our core platform as customers migrate workloads to Snowflake faster in order to access the data context and governance needed to power AI, securely and at scale. Second, Snowflake Intelligence and CoCo are seeing the fastest adoption of any new products in our history, opening new opportunities for growth as the first major product surfaces of the agented control plane. And third, adoption of these AI products is increasing core platform consumption as customers move from questions to answer from prompts to pipeline and from ideas to production workflows on Snowflake.

Customers who are adopting CoCo are growing even faster, and we expect that momentum to continue as adoption expands. The strength of our Q1 results reflects the powerful flywheel effect of the agentic enterprise. Importantly, this momentum starts with the strength of our core business. Our 13,912 customers start to Snowflake because our AI data cloud is easy to use, seamlessly connected for collaboration, entrusted with enterprise-grade governance and security. In fact, 42% of our customers are data sharing on Snowflake with at least one stable edge. This underscores the part of the platform to connect organizations, partners and applications around a single governed source of truth. That interconnected foundation becomes even more valuable in the age of AI. Snowflake isn't just software. It is a circulatory system, connecting modern enterprises, enabling data applications and AI agents to move secularly and seamlessly across organizations.

This combination of connectivity, governance and ease of use is why enterprises continue to choose Snowflake as the cornerstone for their data and AI strategies again and again. To take Holiday Inn Club Vacations, a leading vacation ownership company, they choose Snowflake to power their data and AI modernization, citing our simplicity, built in AI and machine learning capabilities, a strong partnership as reasons for their selection. With Snowflake, they are now positioned to scale analytics and operations across their business.

And Health, the leading AI-driven platform for construction and design selected Snowflake to accelerate their next phase of growth, enabling faster access to insights across the business. With Snowflake, House will significantly improve data processing performance, reduce pipeline maintenance and free up engineering resources to focus on building new products. Going forward, they'll be investing in natural language query processing and self-serve analytics to make data more accessible across the organization.

On our existing customers, continue to go all in on Snowflake. After nearly 2 years and one of the most complex data warehouse migrations in financial services, one of the largest banks in the United States, completed their Teradata migration on to Snowflake. This migration represents one of many legacy platforms they intend to move to Snowflake. Their teams are now building AI-powered regulatory intelligence, natural language analytics and data discovery directly on top of a platform they already run at massive scale.

Then there is Nestle, one of the world's largest consumer goods companies with more than 2,000 brands globally, operating in 185 countries. They're expanding their use of Snowflake to power their enterprise digital transformation. As part of this, Nestle is reimagining its operations end-to-end with data and AI as key enablers: building enterprise data products used by over 50,000 users across 150 global capabilities. This enables a real-time connected view of the business, allowing teams to make faster and more proactive decisions.

On one of the world's largest wealth management firms built a Cortex powered agent called Osteo data, and deployed it to their entire executive leadership team. Over 60% of business inquiries that were previously routed to analysts for manual data pools are now answered instantly on demand, leveraging their existing data in Snowflake. We also saw Global 2000 companies like Global Payments, Depository Trust and Clearing Corporation, DTCC and Blue Yonder, expand their use of Snowflake to support growing workloads, accelerate AI-powered insights and drive further value for their end customers. This continued expansion is reflected in our large customer growth. In Q1, 8 customers surpassed $10 million in trailing 12-month revenue. We now have 64 customers spending more than $10 million on a trailing 12-month basis.

As AI strengthens demand for our core platform, it is also expanding Snowflake's opportunity to deliver a new generation of AI-powered products and experience. Snowflake is uniquely positioned to lead in the next phase of enterprise AI because we already sit at the center of our customers' data, business context, AI models and workflow. What customers increasingly want is simple, one place to get work done. A place where a business user can ask a question, understand the answer and trigger the next step. And where a developer can turn an idea into an application, a pipeline, an agent, auto workflow without leaving Snowflake. That is what we mean by the agentic control plane.

It's the governed layer where intent becomes action, grounded in the customers' enterprise data, business context, model, applications and security policies. Snowflake Intelligence is the business user surface of that control plane. CoCo is the builder interface. Together, they help customers move from insight to action and from prompt to production, all within Snowflake trusted governance model.

In fact, accounts using Snowflake Intelligence more than doubled quarter-over-quarter as more organizations embrace a governed, conversational way for business users to ask questions, get answered and act on enterprise data. And CoCo is already in use with more than 7,100 accounts, giving builders a natural language way to create applications, pipelines, agents and workloads directly in Snowflake.

Just recently, our partner, Infinite Lamda was preparing for a major customer pitch. One of our engineers use CoCo to build a true customer 360 application in just 5 hours, bringing together customer data, churn insights, recommended actions and live dashboards into a single experience, and they showed it to the customer, the reaction was immediate. After the meeting, in CEO called me and said, you are changing this industry.

Providence, one of the largest health systems in the United States is using Snowflake Cortex to surface insights from clinical notes and patient records in seconds. With CoCo, they are now building these workflows directly in Snowflake, enabling care teams to access critical information faster while maintaining privacy standards. And Thomson Reuters, the global provider of legal, tax and regulatory intelligence uses Snowflake Cortex, including cocoa to power AI-driven legal and compliance workflows.

By leveraging CoCo to build and deploy intelligent applications directly within Snowflake, its teams can turn complex regulatory data into actionable insights in seconds, while accelerating product development. This approach maintains the fiduciary grade governance and reliability required for high-stakes professional use. CoCo is contributing meaningful AI revenue while also driving increased engagement across the broader platform. This tangible momentum together with continued strength in our core platform is reflected in our increased FY '27 outlook.

Today, with the announcement of our intended acquisition of Natoma, we are extending the Snowflake agented control plane beyond data envelopment workflows into the everyday applications where work happens. With Natoma users can do things like send e-mails, summarize lack conversations, check calendars and open JIRA tickets without ever leaving Snowflake Intelligence or CoCo. The important point is not just convenience, it is control. These actions happen from a government environment with enterprise security, permissions, observability and policy enforcement built in.

This will extend Snowflake leadership in AI governance by ensuring companies can safely manage not just their data, but also the actions AI agents take across business workflows. As we continue to innovate to support our customers, we are also leading the AI transformation from within. With Snowflake Intelligence and CoCo our teams are revolutionizing how they work. Across our global support organization at Snowflake, CoCo now analyzes incoming customer cases before an engineering engages, surfacing diagnostic insights and likely root causes upfront. Alongside the use of AI accelerated investigations. This has driven over 25% faster case resolution times and a 25% increase in Crete throughput per engineer.

By using CoCo engineering team that runs Snowflake Cloud deployment has freed up capacity and moved resources to product innovation, while reducing complex case resolution time by nearly 30% and cutting engineering time spent per ticket by roughly 40%. Across our data organization, CoCo's double developer productivity as measured by and line accord per engineer and has automated more than 100 workflows across finance, marketing, sales and HR in just weeks. Through this operational transformation, our teams are moving with greater speed and focus to capture the AI opportunity in front of us.

In Q1, we delivered over 20% more product capabilities to market than we did a year ago, underscoring both the pace of our innovation and the breadth of platform expansion underway across Snowflake. We are also strengthening our go-to-market organization to support our next phase of growth. Following a seamless transition, our new Chief Revenue Officer, Jonathan Boulier, J.B., is positioning Snowflake to scale in the AI era. J.B. brings more than a decade of experience at Snowflake, deep knowledge of our customers and platform and a strong operational focus as we continue to evolve our go-to-market motion.

That strong execution is translating into continued customer momentum and broader product adoption of the platform. In the quarter, we added 616 net new customers, up 38% year-over-year. We're also seeing customers deploy and scale workloads at a faster pace. The number of new cases, individual projects managed on Snowflake deployed in the quarter increased 114% year-over-year as customers move more workloads into production on the platform. At the same time, the number of use cases on per account executive increased 86% year-over-year, underscoring both growing customer demand and improved sales execution across the organization.

We're also continuing to strengthen our ecosystem as we deepen our strategic partner relationships and extend the reach of our AI data cloud. Just today, we announced an expanded collaboration with AWS through a new $6 billion multiyear agreement to accelerate enterprise AI adoption globally, leveraging Graviton compute and AI services. The announcement comes as Snowflake surpassed $7 billion in lifetime AWS Marketplace sales, reflecting the growing demand for AI and data workloads running on Snowflake.

During the quarter, we also announced an expanding $200 million partnership with Open AI. And just recently, we brought the joint capability from our landmark partnership with SAP to general availability, enabling customers to unite mission-critical business data across their core data systems within our AI data clock.

Before I close, I want to acknowledge our Co-Founder and Chief Architect, Benoit Dageville, who will be stepping away from day-to-day operations in mid-June and continuing as a member of Snowflake's Board of Directors. Benoit is one of the greatest technical visionaries of our industry. His leadership and innovation helped invent the modern cloud data platform and laid the foundation for everything Snowflake has become today. The impact he's had on this company, our customers and the broader technology landscape is extraordinary, and we are deeply grateful for his continued guidance as we enter this next chapter. Our product organization will continue to be led by Christian Kleinerman.

For the past several years, you've seen AI emerge as a tailwind for our business. Q1 marks an important shift in this journey. The combination of Snowflake's trusted enterprise data, rich business context, leading AI models, and secure connectivity into enterprise applications creates a unique opportunity. Snowflake Intelligence and Cortex Co are the two primary ways customers experience that opportunity, one for business users, one for builders. Together, they allow customers to move from intent to action in a governed environment positioning Snowflake to win a new market, the agentic control plane. We are benefiting from AI as a secular tailwind while also monetizing first-party AI capabilities. Through the combination of rapid innovation, strong go-to-market execution and operational discipline. We are well positioned to deliver accelerating growth and margin expansion.

With that, I'll turn it over to Brian to walk through the financial details. Brian?

Thank you, Sridhar. In Q1, year-over-year product revenue growth accelerated approximately 400 basis points to reach 34%. Growth benefited from a meaningful increase in AI revenue and an acceleration in our core data platform business. AI is a driving force behind our momentum. AI serves as a catalyst for our core data platform business. With an AI-first mindset, customers are moving to the cloud and to Snowflake with increasing urgency. This tailwind is evident in the pace of new customer additions. As Sridhar mentioned, our net new customer additions increased 38% year-over-year. We added 13 Global 2000s compared to 4 in the same period last year.

Snowflake's AI workload is now a significant revenue engine in its own right. AI products like Cortex Code are expanding our opportunity with existing customers as CoCo encourages faster, more consumption of the data platform. We now have 79 customers spending more than $1 million on a trailing 12-month basis. 46 customers crossed the 1 million threshold in Q1 compared to 26 in the year ago period. Remaining performance obligations grew 38% year-over-year compared to 34% in Q1 of last year. We continue to see customers favor Q4 renewals. As a result, we expect bookings to be increasingly weighted towards the fourth quarter. We remain committed to delivering both growth and margin expansion. In Q1, non-GAAP operating margin expanded over 300 basis points year-over-year to reach 12%. Strong revenue growth and disciplined hiring both contributed to the outperformance in non-GAAP operating margin. We added 190 employees this quarter compared to approximately 400 added in the year ago period. Of these 190 employees, 173 joined Snowflake through the Observe acquisition. Excluding Observe, organic hiring was limited to 17 people in the quarter.

In Q1, we used approximately $300 million to repurchase 1.7 million shares. We have approximately $800 million remaining of our original $4.5 billion repurchase authorization. We ended the quarter with $4.4 billion in cash, cash equivalents, short-term and long-term investments. During the quarter, we entered into a 5-year $6 billion contract with AWS more than doubling our prior contract signed in FY '23. With this agreement, AWS has committed to an expanded go-to-market investment in collaboration. This agreement marks an important milestone in our ongoing partnership with AWS and its impact is fully incorporated into our outlook.

Moving to our outlook. As always, our forecast is based on existing consumption patterns. There are no changes to our forecast methodology or guidance philosophy. Given the strength we've observed in both our core data platform business and AI business, we are raising our guidance for the year. For FY '27, we now expect product revenue of $5.84 billion, representing 31% year-over-year growth. In Q2, we expect product revenue between $1.415 and $1.42 billion, representing 30% year-over-year growth.

Our Observe acquisition is progressing well, consistent with our initial expectations, Observe contributed less than 1 percentage point of product revenue growth in Q1, and we continue to expect the acquisition to add approximately 1 percentage point of revenue growth -- product revenue growth for the full year.

Turning to margins. We expect 75% non-GAAP product gross margin for FY '27. We expect Q2 non-GAAP operating margin at 12.5%, and we are increasing our full year non-GAAP operating margin guidance from 12.5% to 13.5%. We are reiterating our non-GAAP adjusted free cash flow margin guide of 23%. Our full year outlook for both non-GAAP operating margin and non-GAAP adjusted free cash flow margin continues to include approximately 150 basis point headwind related to our Observe acquisition. This impact is unchanged from last quarter. Our intended acquisition of Natoma will bring 20 employees to Snowflake.

Before turning to Q&A, I'd like to briefly revisit my priorities for FY '27. Last quarter, I outlined two key priorities. First, driving growth and margin expansion; second, supporting ongoing excellence in our go-to-market motion. We are executing well on both fronts as AI strengthens every element of our business. Since last quarter, we've seen a step function change in our AI revenue opportunity led by Cortex Code. AI is only transforming how we operate internally enabling greater productivity through a combination of slower hiring and more cloud spend.

On the go-to-market side, we're incredibly pleased with the response to our new CRO. J.B. brings a wealth of experience and a proven track record of success at Snowflake. He understands how to deliver great outcomes and win with individual customers. More importantly, he knows how to drive that success across the broader organization. Finally, next week, we'll host our Investor Day in conjunction with Snowflake Summit, conference in San Francisco. If you're interested in attending, please e-mail [email protected].

With that, I'll pass the call to operator for Q&A.

[Operator Instructions] And we'll go ahead and take the first question.

This is Sanjit Singh from Morgan Stanley. Sridhar, I've been covering consumption software companies, consume software companies for a long time. In a normal year, we typically don't see the sequential dollar growth that you guys are posting up. Typically, you don't see raises through the full year or Q2 guides the way we're seeing with this set of results. But the simple question is like what sort of inflected in the quarter on like two fronts, I would say, maybe from a market backdrop demand perspective? And then from like a -- within the Snowflake portfolio, between, let's say, maybe vibrations, organic customer expansion in the core data platform and then the AI story. Can you talk about where specifically you're seeing the inflection?

Absolutely. So I would break this up into three parts. First, AI is accelerating the value that people can get from the data that they have put into Snowflake or that they can put into Snowflake. So we saw like a healthy secular tailwind for our core data platform. And part two is really that agentic products, the control plane products like Snowflake Intelligence, and Cortex Code, CoCo, came into their own in Q1. Recall that CoCo went into GA on February 5. So just as we were opening up the quarter. And we've seen very strong traction with both the products. And the really interesting thing with Cortex Code is that it, in turn, drives more consumption on the core data platform simply because it's much easier to get projects done, whether it's a pipeline or creating a new agent or setting up a new dynamic table or even honestly, a migration. So it's driving the second order effect as well. But it's really -- this is the 1, 2, 3, and that's why I like to think of this as AI compounding Snowflake strength in data. And I'll hand it off to Brian for the mechanics of how these came together in our forecast for the quarter and the year. Brian?

Yes. Thanks, Sridhar. I'll impact that a little in terms of impact. And so Cocoa had the largest driver to the increase in our forecast. As a reminder, when we forecast, we only forecast Observe behavior. And as Sri mentioned, that just happened in the quarter. And so this quarter, we had a very unique opportunity to layer CoCo in the model, and that's reflected throughout the remainder of the year. We also saw acceleration in our core business, and that informs our outlook as well. And so there's no change to our guidance philosophy, where 3% we view as a really strong beat.

And we'll take the next question.

It's Kirk Materne with Evercore ISI. Congrats on the quarter. Sridhar, I want to dive a little bit more into CoCo just in terms of -- how does that sort of change your customers' ability to get more data out of the platform at a faster rate? Can you just dive into that a little bit more? And then can you also just talk about how having a product like CoCo maybe changes the go-to-market model a little bit. You said, obviously, JV had a great first quarter. Just wondering how having these genic products also sort of shapes your thinking around the go-to-market efforts as we go through the rest of the year.

Yes. So CoCo is a is a general-purpose coating agent that has a set of features that are specialized for Snowflake and data platforms. We have published benchmarks that show that CoCo can outperform even the frontier model when it comes to doing operations within Snowflake. And over the past quarter, we've actually expanded it to support other data platforms like Amazon Glue or Airflow or DVT cloud, and in fact, even Databricks. So it's incredibly powerful.

And in terms of how it impacts our customers' ability, our ability, our partners' ability to get things done faster, is any kind of coding transformation and the migration is one such example can be made faster with CoCo. We have a migration team that is busy creating, we call them harnesses, they are ways of structuring the process so that a complex migration can be broken down and attacked methodically. We work very closely with both partners and customers and help them get these migrations done faster. And I previously talked about how many -- some of our partners are even switching their entire business models from charging for time and material to being able to charge for outcomes.

In addition, something like creating an agent to run insight snowflake intelligence, just goes a whole lot faster because we have created workflows within CoCo for the entirety of the agent creation pipeline. In fact, this has gotten so demystified that even somebody like me can go from a data set to things like Cortex Analysts and search instances to creating an agent to running an eval on it. That's the life cycle of creating an agent. It's like an automation platform for everything having to do with Snowflake.

There is a ton of activity within Snowflake and outside by partners, for example, to build even more complicated skills and processes on top of this. I very much think that this is early. And in terms of how it's affecting our go-to-market, first and foremost, I think this products like Snowflake Intelligence have made the entirety of our go-to-market team, AI native in a way that honestly would not have been even -- like we could not even imagine it a year ago because we have a lot of government data. And so our solution engineers, our sales -- our account executives even can show the power of Snowflake. There's nothing like pulling your phone out to show what Snowflake Intelligence can do as every CEO that's met me in the last 9 months. Now that's one of the things that I always do.

Our solution engineers are able to build much more realistic demos and prototypes and even actually get projects done for their customers very, very quickly, showing our customers what is possible with CoCo. And similarly, our internal teams, whether it's the support team that I talked about are our SRE team, our site reliability engineering team that runs our production systems are our services team. They have 95-plus percent adoption of CoCo, which leverages them enormously when they are creating they're creating products. And CoCo and counting agents have also changed things like enablement. It's a lot easier to learn, and you can literally ask a coating agent how to do something, have you right have it right to example for you for you to examine it, thinker with it and then write a more complicated example.

A professor a friend of mine called coating agent self-categogical. They come with that learning built-in which means that a product feature released in CoCo can be used by someone in services literally the same week. And it's that rapid iteration that's also benefiting us. And so that's the virtuous loop that we are on. We think we can get projects done faster. We think we are also, honestly, very early in the world of agentic development that are new techniques being developed, honestly, every week. And our ability to get more and more complex projects done on top of these coating agents is just enormously powerful. And I think we're setting the standard for what data work and more is going to be like, both with CoCo, but also with Snowflake Intelligence and things like MCP are a further unlock into what is possible with these agents.

And we'll take another question.

It's Karl Keirstead with UBS. I'd love to continue the conversation on CoCo, if that's okay, for a question to both Sridhar and Brian. Sridhar, it's pretty evident that customer spend on Cortex Code and even broadly models like Claudia bending spending higher given the token or usage-based pricing. I think a lot of investors are worried that it's going to reach a point where customers might try to govern or throttle the use of these tools to try to contain spend. I'm just curious, are you anticipating that to happen? Perhaps the answer is that the value add is such that you are unlikely to see that?

And then maybe for Brian, I think there might also be a perception that products like Cortex Code come at generally a lower gross margin than the rest of the business. But one thing I noted from your guidance for the full year is that you stuck with the product gross margin guidance of 75% despite a big apparent uptick in Cortex Code, which suggests perhaps that the gross margin drag is minimal, if any. And I'd love to ask you to comment a little bit on that.

I'll start, and then I'll hand off to Brian. Cost is always an issue that we pay attention to. This is true in Snowflake Intelligence. This is also true in Cortex Code. But what helps significantly is the fact that these are products in which you can either get things done that you are never able to before or get things done 10 times and sometimes like more than that faster. Those are not normal things. And to give you concrete examples, a very large bank that we work with has told me that while they spend several hundred million dollars on data systems as a whole, it's a very large bank. The amount of money that they spend on the human capital that powers all of these various pieces of software and link them up is 3 to 4x that. And anything that makes that part of the labor force 10x more effective is always incredibly welcome. .

Having said that, when we want to roll, for example, Snowflake Intelligence out to 10,000 users, cost governance is absolutely an issue just like it's an issue in Snowflake when I want to roll products out at scale. And so we are doing things like cost limits at an account level or at a particular agent level or you want to be able to restrict how much tokens a particular user can be spending. Of course, it quickly comes back to having exceptions for very talented users that are actually worth the tokens that they are using. And that's the kind of infrastructure that we are really good at creating. And so we feel very good about being able to do that. Plus there is a lot of innovation that we are driving within these coating agent products themselves. As I said, they handle very complicated task, but not everything is complicated.

If you want to summarize, for example, I mean, I did something a couple of days ago, to summarize black threads. And perfectly small models from this raw are enough for that. You don't need the latest and greatest focus models for summarizing slack threat. We are building those kinds of capabilities natively into Snowflake, so that it can be efficient in what kind of models that it uses. But my short answer to your question is they're creating incredible value, but we are not resting on that. We are creating the controls that one needs in order to keep costs manageable as things continue expanding.

Then I'll let Brian take the AIN margin question.

Yes, Karl, thanks for the question. You're absolutely right. Our AI products have a lower gross margin than our core platform. The one thing that we want to do with our AI products when we launch a new product like CoCo is make sure that we develop a great product that we get massive adoption. And we've seen really good adoption with CoCo. We're up to roughly about a little over 7,000 accounts have adopted CoCo.

With that said, we're offsetting that and keeping the same product gross margin, 75% for the full year. in lower bandwidth cost, i.e., I talked about the AWS contract. And so we're offsetting it there. So that's how we're able to do that. We're committed to find efficiency to be able to maintain that 75% gross margin.

And we'll take the next question.

Raimo Lenschow from Barclays. Congrats from me as well, that's an amazing quarter. The question I have is more for Brian. Brian, like if you think about the last couple of quarters, you've been telling us about like the beat cadence that you think about. Obviously, this quarter, you beat by much more and CoCo is helping there, but it's also a consumption model. Like how do you think about this going forward? And how should we think about the guidance philosophy that you have here? Maybe you can help us there, but congrats from me as well, amazing quarter.

Thank you. Let me emphasize that there's been no change in guidance philosophy and we view a 3% beat is a very solid beat. The difference that happened this quarter was CoCo was launched in the quarter. And we base guidance on Observe behavior, and so we didn't have any Observe behavior for guidance for CoCo. And so we -- we had a unique opportunity now since we've been able to watch that for a quarter to layer that in now for the full year, and that's what we've done. And then we also saw the acceleration of the Core, and we've included that for the full year based on what we've seen.

And we'll take the next question.

This is Matt Hedberg from RBC. Congrats from me as well. I had a question. There's been a lot of talk, especially from an autonomous AI perspective, the importance of context engineering and harness engineering. And Sridhar, you mentioned that in your prepared remarks. I guess I'm wondering what role does Snowflake have in that? And how do we think about that from a moat perspective from some of the AI labs?

Yes. The data that is stored in Snowflake is among the most valuable pieces of data for a particular company. This is the -- it's called the gold layer and typically has the most important information at Snowflake, for example, all of our revenue information or consumption information and information about the different departments are all kept in Snowflake. But on top of that, the dashboarding platforms that are written on top of Snowflake have an amount of additional context as well. And we see what they do. So our ability to provide context to AI is exceptional. And we are also busy creating products that can use this to make the act of getting value from AI even faster.

I talked earlier about how we have workflow automation for the entire life cycle of creating an agent. We want to do more than that. We want CoCo to be the place where it is fastest to get value from the data investments that you have made. And Christian is working on a key effort on this side as well. Christian, do you want to add additional context.

Yes. And briefly, Matt, I think your question is insightful. We have a track record of using metadata and activity inside of Snowflake to drive better results. Oftentimes, it used to be query optimization and performance. And we are now using that same type of civil activity in Snowflake to provide better context to AI. We will be showcasing at Summit some of the differences of how out-of-the-box results are better with CoCo and Snowflake Intelligence as opposed to other agents.

This also points to the overall strategic value of Cortex Code because if a number of data users from within an enterprise are using these agentic coating platforms, in order to create end-user products. It could be skills, it could be dashboards, it could be agents. We also then have the ability to essentially learn across these. And so we have created memory concepts where use of these products within Snowflake makes Cortex Code itself much better for future use. That is part of the flywheel effect that one gets from having great agentic coating products.

And moving on to another question.

It's Brent Thill at Jefferies. Sridhar, just on the sales and marketing side, if you the backlog observed, we didn't have a really big S&M hiring quarter. And I'm just curious, based on the demand and everything you're seeing, why not lean a little harder in the go-to-market side. Maybe you are behind the scenes. And I think this maybe also ties into the transformation that took place in the quarter with the new head of sales. So maybe if you could just tie it all together in a go-to-market view from your perspective, that would be great.

I think part of what we need to understand right now is that there are many, many places in which AI is making Snowflake a lot more efficient. I thought in my prepared remarks about how we have greatly increased the number of use cases that we have won, which is primarily an account executive driven activity. We've also had significant increases in individual productivity year-on-year. This is because of -- because of AI, their ability to learn faster, pitch products that are more relevant to their customers, and also have solution engineers create prototypes that are directly relevant and in the context of the customer. So as an organization, we are just becoming a lot more effective. We will continue to invest in all of the key functions that are responsible for driving Snowflake forward, the supply exchange ring. This applies also on the sales and solution engineering side. But it is counterbalanced by the large amount of efficiencies that we are getting in a number of other functions that are very amenable to AI automation like support like Saudi, like technical documentation. Basically, a lot of information functions and information exchange functions have gotten a whole lot easier -- and this is also where teams are being very, very effective in deploying things like CoCo on Snowflake Intelligence for these kinds of use cases. We will absolutely continue to invest wherever we get strong leverage.

And we'll take the next question.

Alex Zukin from Wolfe Research. Just congrats on an amazing quarter. Sridhar, maybe for you, actually, both of them probably for you. If you think about the profile of a customer a year ago versus now a customer that's using Cortex Code. What are you seeing in terms of the uplift on spend? And with the acquisition of Natoma that you announced, it seems to me that Cortex Code was just the beginning. Maybe it's the first agent that you're kind of going to launch. And you're not stopping there. Maybe there's a number of other ones that are coming. So can you just help us think about how that changes the potential spend profile of the customer over time?

I mean, among the biggest like impact that products like CoCo have with our customers is simply one that of expectation. I talked again in my remarks about how we did a 2-year Teradata migration. We are engaged in more migrations, but the time lines for doing those now run between a quarter and 2 quarters. Why both my team and the customer expects and demand state. And we have the ability to deliver against that. I think the -- both the inpatient and hunger or what people can do with data along with the expectation for how quickly we can get them done. I would say that's like that's a huge sea change. Christian?

With what you're saying, Sridhar, there's a massive backlog -- what customers want to do -- so just helping them do it faster, just as they get to the next set of work sooner.

That's right. Even our own data teams, for example, typically had backlogs that ran into multiple years. In fact, the standard request, all of you know, this is sort of funny, but not. If you had a request of a data team, the answer usually is like that's nice, take a ticket and waited. But we are now in a situation where they can actually crack through that backlog just a whole lot more quickly unlocking value. And to go back to the question about Natoma and it's important and coding agents, it is important to understand that Snowflake Intelligence and Cortex Code are built on the same underlying technology with just different tools having different capabilities that are exposed to end users. They use the model garden underneath that powers all of these models. They share what's called the harness. This is the one that is working on top of the model, deciding what tools to call. And increasingly, they are also going to be sharing the same run time. We have a cloud run time product that is in public preview. It means that all of the power that you expect from running CoCo locally can now be executed in the cloud in a governed manner. And I'm already running agents in this cloud agent platform on that ability to launch things, for example, autonomous agents because you no longer need to have your laptop open for something to run is pretty remarkable. It's all being built on the same infrastructure for the harness, further on time as well as things like session memory. And Cortex Code and Snowflake Intelligence are just two manifestations of the same product.

And the reason MCP and Natoma are a big deal is they now bring the context entirety of SaaS application context into these products. And so I've done deep research reports, for example, that I've shown Christian that can now look for information from Snowflake, from the web, from Google Docs also from Slack and synthesize that into something that is astoundingly meaningful. And these also let you take action instantly. You can slack somebody, you can compose e-mails and send it and you can take actions on the underlying applications, and that's the promise. We basically have a builder version and an end user version of these products. Obviously, the names make them sound more different than they are, that's something that we are working on. But the amount of power and flexibility that these coating agent products offer is pretty remarkable. And in my mind, the right analogy here is that a coding agent, yes, can write code, but at its core, it's an abstraction agent. It can let you do things at a high level that previously you sort of had to sequence out one by one. And I think that's the power that comes from them. Christian?

And one other comment on very important to highlight that it does that tool visibility with governance and auditability because our mission is to help every organization leverage AI in the context of the data but with governance, with security and trustworthiness. So that fits entirely into our mission.

And we'll take another question.

Brad Reback, Stifel. Sridhar, with the success you're having here with CoCo and Snowflake Intelligence, is that fundamentally changing the competitive landscape when you're going into new customers? Are you now seeing LOMs more than some of the older competitors?

We come with a unique value proposition. As you folks know, even in the world of data, that cloud service providers have had products. We have very successful partnerships with them. In fact, we just announced a $6 billion partnership with one of them. Our value prop has always been very clear. We are about customer choice. We are also about a certain amount of independence from the mechanics of the cloud providers, a snowflake implementation works fine on AWS. But it can also work on Azure. We have similar really good partnerships with the leading AI labs, both Anthropic and OpenAI. We collaborate very closely with them to create great AI products, but also to create safe AI products.

And similarly, Cortex Code and Snowflake Intelligence provide model choice. We run fine on both the models. We also host a whole cities of other models ourselves and as things like open source models become more important. We always act on behalf of what is right for the customer, which I think positions us in very good stead with all our customers.

And I'll just add on to that. Just from a sales execution perspective within the quarter, the achievement was great in all geographies and all industry verticals. This was the most net new customer adds that we had in company history. And so it's just really a solid quarter all around.

And we'll go ahead and take the next question.

This is Koji Kada from Bank of America. So when I talk with partners and customers of Snowflake, I hear the same thing over and over again. Snowflake is my trusted enterprise data and AI vendor with governance and security guardrails as key differentiators. I think about that a lot. But the AI world is moving so fast. And assuming the competition out there gets better with all this. What makes you confident that Snowflake's position as the trusted enterprise data and AI partners secure over the long term?

Because there are a set of deep infrastructure capabilities that just take a lot of time to develop, whether it is role-based access control and role-level access control at massive scale. -- our world-class replication that provides for things like disaster recovery, amazing organization support and there are dozens that I'm missing, Christian, do you want to add something?

No, I think that, that piece on data masking and role-level policies, all of the government security configuration identity makes it such that customers have already configured Snowflake to have trusted access to the data. and AI just amplifies that as opposed to alternatives are just going to get them to reinvent the wheel and rebuild all of this, which doesn't make much sense.

And it's also important to understand that we are also not sitting still our ability to create products like Snowflake Intelligence and Cortex Code, but also all of the second order effects imagine having autonomous agents that can automatically figure out if there are anomalies in your data so that you don't have to be running those jobs outside are to be able to do governance not with endless tedious sets of SQL statements that you write, but more with the policy that specifies that this is how you want your enterprise governance to be done, and we take care of all the details and the mechanics of running these things behind or creating new classes of applications that sit on a substrate of Snowflake data powered by AI. These are all things that we make possible. And I think, honestly, that is also what we have to do. Your core thesis that people will be able to add these features or stitch them together is true, but we are also developing great new capabilities at brick neck speed also powered by AI. I think that is what it takes to succeed today.

We have lots of new controls and policies will be showcasing next week, including amazing mechanisms to simplify them.

And that does conclude the question-and-answer session. I'll now turn the conference back over to Snowflake for closing remarks.

Thank you, everyone. To recap, AI is accelerating consumption across our core platform and our native AI products, Snowflake Intelligence and Cortex Code are scaling rapidly, already contributing meaningfully to revenue in their own right. These AI capabilities are establishing Snowflake as the agentic control plane for the enterprise, connecting data, models, applications and workflows in a trusted environment where intent becomes governed action. We are continuing to execute accelerating growth, expanding margins and deepening our customer relationships while winning many new ones.

We believe that Snowflake is uniquely positioned to lead in the era of the agentic enterprise and continue to see enormous opportunity ahead. AI is compounding Snowflake's advantage in data.

Thank you. That does conclude today's conference. We do thank you for your participation, and have an excellent day.

All right. Continuing the afternoon sessions at TNT day 2. I'm Sanjit Singh. I cover the infrastructure software practice on the Morgan Stanley research team. Thrilled to have the Snowflake management team CEO Sridhar Ramaswamy; and Chief Financial Officer, Brian Robbins, Sridhar, Brian, welcome back to the T&T conference.

Thank you. Excited to be here.

For important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. So we've got 35 minutes and Sridhar, we got a lot to talk about. Between a durable core business. We got Snowflake Intelligence out. We got Cortex code, and we've got to figure out how all this will translate into an attractive growth in free cash flow story.

I wanted to start the conversation with the core business.

So when I was going at various conferences with AWS or other the hyperscaler conferences, the sort of rallying cry that I heard was you had to get your data state ready to prepare for AI. It seems to me like those initiatives really got operationalized in calendar -- and so when you look at the core business, how it's sustained over the past year, was at these data modernization is that drove that durability and that strength in the quarter? Or were there other additional factors that you would call out?

Data monetization continues to play an important role, but we've fundamentally been limited by how quickly we can do these modernization. And I'll come back to this topic because it's a really important one. But as 2025 progressed, people were beginning to understand the value of agent AI because we had started doing Snowflake Intelligence initially prototypes and POC and not the folks right off the public preview started using the product. And it's a magical product.

It looks forward to what could agent systems with reasoning do with different kinds of data sets and truly the power of agent AI on top of data states that were on Snowflake. So that's a strength that continues to pull in terms of what drives the core business. And migration to be honest with you, is this problem that our industry as a whole, not just Snowflake, has struggled.

For a very long time, these tend to be long, complicated, messy with lots and lots of details. I've been involved in migration projects were like 100 people from Snowflake deployed, 100 people from the customer. It's an 18-month project like total pressure cooker and drama. But we are making remarkable progress in migration also. And I expect this year, for example, technically, I think we'll be able to get through most aspects of migration. Thanks to the power of coating agents. Thanks to the rapid progress that's being made here. But we're very much looking at a world where the core continues to be very strong.

And if anything, products like Snowflake Intelligence are demonstrating how much more value you can get from data. So that's a string that actually pulls the whole ecosystem forward.

Yes. That makes a lot of sense. And what all the investors in this room to really understand where you're taking the business, Sridhar. And I wanted to take a quote from the last earnings call in which you said Snowflake is an evolution for a company to govern and analyze their data to a platform where they build AI native applications and workflows. Given what you've released to market and the core business, and what you've delivered over the last 18 to 24 months. What will it take for Snowflake to make good on this evolution?

Yes. If you think about for just data access, and what it means for an enterprise to have its data estate in gear? It often means that you need to have a trusted set of data products within the company. But just as importantly, you also need to have it be secured. You need to make sure that it is auditable because for a lot of financial institutions.

It's not just enough to say you're controlling who as it is. You also have to say who actually looked at the data and having things like governed access, so it's the right people can see the data is also incredibly important. This is what we've been working on for a very, very long time.

This is the foundation of Snowflake and because we have often been that analytic layer that supplies data for every important function, most of the interesting companies in the world, we're super well positioned to do this. And what things like Snowflake and tell what AI then provides are the tools for you to take advantage of this data. It's still a read-only application.

What we are beginning to see, and this is what Cortex Code demonstrated to us internally because it's a desktop app, things like setting up MCP servers got a lot easier. We could set up MCP servers to Atlassian. We could set it up for other systems. -- all of a sudden, what Brian and I got were a set of things and call them an application, if you want, there's incredibly fluid access to data, plus the ability to take actions institute without needing to think about what you were doing.

To me, that's the future of how we are all going to act on data. That salesperson, not only are they going to know, hey, what do I pitch to this customer the next time I talk to them if they actually win that use case, they're going to be able to update that use case right within a product like a Snowflake intelligence. And I think that's where applications are going to be headed, where the -- both the access and the uptake is pretty much seamless.

If you look at the ecosystem and think about some of your classic competitors as well as some of the AI in, you seem to be pursuing a similar vision in terms of becoming a genetic app platform. So what gives Snowflake the right to win to become the destination for the next wave of modern AI application?

This is a great question. And I already talked about some of the strong benefits that we have around data and on governance that sets us up very nicely. A lot of it is going to come down to how you execute. And this is where products like Cortex core become really important. Our original intention with it was to have an agent coating platform that would make Snowflake a lot easier, a lot faster to use. -- moving intelligence when you have an end product like I have on my phone is a great product. But to set it up, took months the first time we did it in the summer of last year. We said we need to be using AI to make things like that go much faster. It's an example of a coding problem.

So we were able to create a product that started delivering 10x improvement in how you could deploy things on top of note. It greatly ease the burden, for example, of setting up an agent because not only could you set up the first version but you could run an eval on is this actually doing it right? Or if you got a problem, someone didn't like an answer, you're able to go change it. And that's been a huge unlock for us.

And Cortex Code also pretty much made the entirety of the Snowflake team aware of the power of AI and what it can do on top of governed data. so much so that it's gone from being a coding agent that writes SQL or Python or other things to being much more of an abstraction agent. We are rethinking a lot of our workflows in terms of acting on these governed data sets to get us the data that we want and to be able to make the update that we want. And it's an experience deeply born out of what we ourselves have gone through. And that's the thing that we're turning around and bringing to our customers.

We don't have outside of the fact that we run the best analytic data system on the planet, we have to earn our right to be that layer that comes from creating great products. No one has anything guaranteed in a world like the 1 that we live in today, where there's so much change happening. We have to help create that history, and it comes down to can you create great products that your users love.

That's a great perspective. And I want to continue to dive in, in terms of the Cortex Code unlock. Before we get there, let's bring Brian in the conversation and do a pulse check on where we are in terms of the business -- so if I look back to Q4 results, the takeaway from my point of view is that business is in a healthy place. Product revenue growth improved to 30%, your RPO accelerated. You saw in your largest delever, signed another 7, 9-figure deals.

So Brian, what were the factors at play allowing the company to land 7, 9-figure deals in the quarter? And how many of those deals are already baked into the consumption run rate?

Yes, thanks. I think it's important to note, we did reaccelerate revenue in fourth quarter, had a $9.8 billion RPO balance or 42% year-over-year. And really, the deals that we talked about, we signed one deal over $400 million, and then we had 7 deals, 9 figures. And first and foremost, thanks to the sales team to sign a $400 million deal in today's economic climate is very difficult. But what that really told us was these companies are actually betting on Snowflake's data and AI strategy and the benefit that they're currently getting today with Snowflake.

And so our sales team was in there showing all kinds of different use cases. And so these were all existing customers, and so they're already consuming with us today. And this is an expansion of what they're doing with us. And so I think the real testament is betting on our data strategy, our AI strategy and the positive business outcomes that they're generating.

Large customers betting bigger on Snowflake. It's great to see. The other element -- the theme coming out of Q4 is that free cash from our margins did come down to 23% adjusted free cash flow margins versus the 25% that you delivered in fiscal year '26. So outside of the observed acquisition, which was about 150 basis points headwind, what other factors should investors think about to understand the free cash flow margin trajectory?

Yes. Absolutely. So in FY '26, we guided 25% free cash flow margin. In FY '27, we guided 23% we made acquisition of Observe. We think the observability market is just another data problem that we can help solve. And so there's about 150 basis points headwind related to that acquisition. And so and coming up with the guidance, we want to give out a number that we felt comfortable with and that we could overachieve.

Great. Let's return back to the Cortex Code conversation. I know we've talked about it a lot, but if we just sort of step back. When the announcement came on the general availability of Cortex Code, I think many were confused as to why Snowflake was getting the AI market. I have to raise my hand including myself. I think I start to get it out coming out of Q4 results. But can you shed light on why Snowflake built its own coding agent? And can you hit on the major ways that Cortex code combined with intelligence can unlock growth and productivity across the business.

Yes. Coding agents are increasingly critical to every system. As I said, one of the things that Snowflake has always struggled with is -- how do you make projects go faster? And I've experienced this myself, I think her with our product all the time, setting up an SI agent used to be hard. And I also saw that it took my own data team 2-plus months to set up a sales agent. It was born out of the conviction that a coating agent that was native to Snowflake that understood all of the nuances of Snowflake. Different deployments, for example, are different. And a business-critical addition of Snowflake has different features from a regular enterprise edition.

And not every feature is available in every geography. So you can't have a generic coding agent that's going to know all of this stuff. And we also felt that being the place where all of the builders that wanted to build on Snowflake. Gathered to do stuff was strategically important for the company. And those are the original pieces for Cortex code.

And it more than exceeded our expectations in terms of the results that it delivered in everything from what does it take to set up an open flow pipeline. This is a normal thing trying to move data from one place to another. That's incredibly easy out of the buck or to be able to do the media governance activities that your admins have to do but are still very tedious to do and things like Snowflake intelligence. All of that got faster.

With the net result that, for example, all of my field team can create custom POCs for practically any customers, speed up implementation of every project -- by the way, they have access to other things like Cursor and Clotcode, but this was so native to Snowflake that they got value out of it. And it also had a funny other side effect that really illustrates the power of data. We made cocoa, as we call it, available to everybody in the sales team. It's set off this explosion of creativity within the company that honestly we had not anticipated.

People that I would normally not think of as coders, like sales exact, they started writing applications. It opened the possibility of like how much could be done if you democratize access to it? And then another funny thing happened. It turns out that coding agents are also abstraction agents. We increasingly saw people write skills that started automating complicated problem.

Somebody came up with their own template for how they wanted to get ready for a forecast call. Someone else came up with a different template for the exact information that they wanted to have for a customer that's visiting Snowflake. And so because we made things so easy as like this explosion of capabilities that became available to everyone in the company, and it really gave us a new perspective of what is work going to look like.

In this future, Brian can not only just look at a piece of data, he can e-mail a set of folks within the company all within the same interface. And if I have a question, I don't need to go to an analyst, I can set up a crown job to take your pick. Tell me what launches are coming up next week. It's 10 minutes of work. So we think having a powerful coding agent on top of structured data on top of well organized data is a massive unlock for every enterprise. We are living it, but it also gives us a glimpse into the future of where is work itself going.

And I think these are all profound experiences, not for one person, not from me, for the entirety of 6,000, 7,000 people to go through, it's given us the kind of purpose, but I think it's very hard to achieve just by just using floating Easy for me to say, AI AI, AI. But unless you have lived it, you can actually feel it.

And the other thing finally that is done is letting us imagine, reimagine, whole categories of jobs. Decorating as we knew it is not really a thing of the past. We now need people that know the product and can also produce the documentation. We no longer now think of enablement as people making slot. We think of that as a transformation from what a product manager creates to what a sales executive would like to see.

We have people that are creating PowerPoint deck straight from information that's in Snowflake so that they can get ready for a customer presentation. None of these are things that I would have predicted. And trust me, I would not have given cloud core licenses to my sales team. That's just not something that you do in the regular course of business. And that's the power of actually investing in the tech and living and breathing the stuff that you talk about.

Now when I go to our customers and talk about what Cocoa Cortex Code can do for them. Both I and the thousands of people within Snowflake can speak from the lived experience of what AI actually does to work. It's been transformative for us. That's also what gives me confidence about how can Snowflake actually take the jump from being this analytic layer to one that feels increasingly confident that it can create new kinds of experiences. I don't even want to call them applications. They're something else. That is going to be all about fluid access to data, fluid access to actions you can take all of the 40 tabs that all of you are 400, depending on who you are, that you struggle with kind of melding into one fluid hole where you get what you want and you get to do what you want.

I told your CTO, Christian, after last earnings call that in another lifetime, I used to build data pipelines. It was a miserable experience. so miserable that it may become a sell-side analyst on Wall Street. But now it seems like it might be the job to have.

And so I saw dude on Reddit, who basically said, he kind Cocoa to his data pipeline, his data source is destination tables and how it found about that had been sitting in this system for a year that you had not even realized existed.

Yes. So let's talk about how Cortex code from a monetization perspective, how is that priced? Is that going to be a stand-alone opportunity? Is it more of a halo effect on the broader business?

So it comes -- it's not a separate product. So it's something that you can attach to a snowflake account. And it just -- you just draw down from the consumption that you have. My primary goal with cocoa was to drive snowflake adoption. Everything that you want to do with Snowflake should get a whole lot easier, a whole lot faster. That will continue to be the top goal for us with the product because it has such a large impact on the business.

But here's the thing. It now gives us access to how people are using Snowflake and the collective knowledge within an enterprise. This is what both SI and Cocoa do. It gives us a glimpse into what they're doing, which means that all of the things that we can do to make this product better flows back into the product.

Increasingly, in a world -- we live in a world where, let's face it, the foundation models are getting better at generating software by the day. It's not an unreasonable paranoia for all software people definitely need to think that software is going the way of media, which is the cost of making software is going down to 0. And so what is the special value add that you have. it's your knowledge of the customers' data.

It's your ability to take that knowledge and put it into the tools that you give them, that is your own special secret sauce. It's basically the equivalent of what made, let's say, searched, a great product because it's a feedback loop, we always show the ads that users wanted to see because we were the only ones that saw what users wanted to see and what they wanted to click on.

That's the kind of feedback effect that I think is going to be essential for companies to survive in this world where software costs are going to 0. So it is a much more profound influence than we built this little coding agent on the site that's going to help someone do their jobs a little bit faster.

Yes. That's great insight.

When I talk to investors about the growth opportunity for Snowflake, I build -- the conversation is really around like a siloed manner of what's the growth opportunity at warehousing. Data engineering, application services, what's going on with the AI portfolio. In reality, these opportunities sets are probably interlinked. They're all -- it's a single string that you pull on because data in Snowflake is data that we can help you get in great shape for AI, data that we can help you govern very easily.

It's the thing that we can then make you easily develop agents on top of and agents, in turn, give you a lot more insight into what's going on within your enterprise and will absolutely soon turn into what you would previously think of applications.

I think of that as a continuum -- and I think of Snowflake Intelligence on top as going to the business user, while Cortex core at the bottom, delivering the programming capabilities needed to make this platform smoother, but there's absolutely a convergence between where these products are headed Snowflake intelligence is just cortex code at a slightly higher level of abstraction. Brian doesn't want to see the SQL query or the Python code, he wants to understand and actually act on the data, and that's where those 2 converge.

Understood. And maybe if we just go back to the point, you hit on it earlier, but just to pinpoint it about why Cortex code is the right mousetrap to unlock all this value with this toe platform as opposed to a third-party agent, whether it's from the model providers, it's a question that we get since earnings, I just love for you to pinpoint that.

I mean, first of all, I said, we are living in a world where the cost of software is going to 0. And we'll be the person that thinks that someone else's front end should be the 1 that's touching, accessing all of their data, all of their interfaces and some of that they're safe. I grew up at Google. Our first rule for competing was on the front door, otherwise you're tossed. I think the same applies in enterprise software as well. Anyone that thinks that are going to run a successful business with the monstrous capabilities of coding agents. Okay, swarming all over them. I think it's smoking the good stuff right? I actually think of this as an existential investment that we had to make.

By the way, we didn't bet the company on it. That's the magic of today. my cocoa developers developed with cocoa. That's like the magic of today where most I can do all of features on top of cocoa using cocoa. That's the insanity of the world that we live in, in terms of how powerful these agents are I think vacating something like this is foolish. Do I think that we're going to keep up in a fair fight with Open AIR on topic and be it somehow a general coating agent for everyone? I don't present that at all.

But on the other hand, simply vacating this space seems like a really dumb move to me. I'm glad we invested early. You once there is a certain amount of momentum behind the market leader, it becomes even more difficult to catch up in any way, shape or form. I think of this as a critical investment. But as I said, if I just take the value of what this product has done to teach the entirety of my team about AI and what good data means to them.

Like just that would have paid for the small number of engineers that worked on the product, we got so much more.

Also add that we're aligned with our customers, right, because it's a consumption-based product -- and so you can use it if you get value out of it, continue to use it. And so not only are we taking it from the data science and the data engineer, this is more personas. So everybody in the company can basically talk to their data in natural language. Why we have right to win is because Snowflake is the data layer and then we actually have the security, the governance, audibility, the -- all that built in the role-based access.

And so -- so for me, when I access the data, I get all the data of the company, if it's a financial analyst or a sales rep, they're just getting that portion of data within the company. So I think those things are important for adoption and our alignment with our customers being purely consumption is, I think, the right way to go.

That's a huge point, which is that AI on Snowflake, the products that we sell are all consumption products. I don't go to our customers and say, "You need to cough up XYZ million to get our AI bundle. Everything comes with it. we make money if they get value from the product. In fact, they're adding other features like per user caps because they want predictability of how much AI products are going to cost, which we are very, very happy to add.

I think this starting from 0, positions us very, very differently from subscription companies that basically have to create a package and sell the package. I think it's very hard at this point in time to convince customers that they have to make big outlays for a package in a world where software costs is going to nothing. Models are getting better and better.

I think people are much more comfortable making a bet on a data platform that also is absolutely keeping up with what's going on AI. And that's the reason why you see the many 9-figure contracts that Snowflake has.

Yes. It's pretty exciting. Brian, one of the interesting things coming out of Q4 is that as revenue growth improved in the quarter, there was no real increase in headcount. And naturally, I think headcount came down by a little bit. So if we play this forward, how confident are you that Snowflake's ability to grow is now decoupled from the growth in headcount.

Super interesting, historically, like with capacity curves and things you'd look at within the business, like people, times productivity equal revenue growth, but those have completely become decoupled now in what we're doing. So fourth quarter, reaccelerated revenue to 30%. We actually did a reduction force in fourth quarter, about 200 people related to some of the efficiencies in the G&A groups coming out of our AI tools. So we only added a net 37 people in the entire quarter.

If we sort of look at it on the other side of the coin, while the growth improved, you had a headcount reduction, margins were higher, operating margins were higher. When we look to fiscal year '27, you guided down product margin by a touch. And so is the takeaway here that AI revenue streams are structuring lower margin as those revenue streams scale to protect EBIT, you'll be forced to do things like ongoing headcount reductions.

Yes. So I'll just add 1 thing. First of all, it separate the 2. I think, again, what AI cocoa have shown is that it's a deconstructed work. All of us are in the business are figuring out how we can work differently and way more efficiently. My data team is the 1 that produces all of the products that we all use is genuinely worried that they will run out of like the entirety of their road map in the next couple of months. they're busy figuring out, okay, what is that road map? What should that look like? What are new products that we could be creating? I think this investment in AI is not just an abstract investment to create future business I think it's also admitted into what could work be. And again, I think that's a pretty profound impact.

Yes. So to add on just a couple of things. In FY '26, we guided gross product gross margin. We guided that in FY '27 as well. In order to launch AI products from infancy, they don't have the same gross margin as the core business does. And so the #1 thing we want to do is make great products, make them easy to use, get adoption so we get revenue, then we'll work on the gross margin perspective. What we have done though, within the core, we're constantly looking for areas where we can save and get more efficient.

So we're offsetting some of the AI dilution that we have with some of these new products in the core business. And so for the year, we gave 75% product gross margin guidance.

That's great context. In the area of public cloud, there was like I would argue a healthy competition dynamic between the hyperscalers and the third-party software ecosystem with the major highriscalers. When I think about the deals that you've done with both open AI and anthropic. They, as you think about how Snowflake wants to navigate its relationship with the leading model providers who at some point may want to try and compete with Snowflake, would that strategy be similar to how Snowflake partnered with the hyperscalers? Like what's going to be the difference in this era versus working with hyperscalers?

I think it's going to be very similar. I think part of the maturity that both the hyperscalers and we had to arrive at was understanding that we're going to compete in some situations. But that the value that we create together in many other situations was going to be hugely accretive to both the parties that are involved I think it's the same with the model provider. They have different strengths. They have different presence when it comes to things like cloud. And we are very happy to be partnering with them. They will continue to mature our own growth.

Awesome. Let's talk about maybe the state of play with the big 3 hyperscalers. So historically, Snowflake has partnered very effectively with AWS. In more sitting years, I think Azure has also been on the upswing and when we think about maybe with Google Cloud and the momentum it has with Gemini, that's always been a knife fight in my opinion, between Snowflake and Google. Do you see an opportunity to partner more effectively with Google and that becomes an emerging channel for the business?

Yes. I think GCP kind of anchored on big query, which made the prospect of collaborating with Snowflake, a tough one for them. And with the rise of Gemini, which is world class and their increasing confidence with what -- who they are as a company on the cloud side. We have already seen better collaboration between the teams, and I absolutely expect this to be an area that gets better and better with time because they have differentiated value, and it's no longer about bit.

There are many situations in which Snowflake plus GCP as a whole is hugely positive for the customer and we both lean into it.

Could you maybe comment on the state of relationship with Azure in particular? and how that's going in terms of you guys working more...

We have a really good relationship with the Microsoft team as a whole, Azure and fabric as well, we collaborate very, very tightly with the fabric them. You can create iceberg tables, for example, in Snowflake and have -- they have it be stored in one lake in fabric. You can also read on lake table straight from within trade from within snowflake.

We have a lot of excellent product collaborations, Snowflake intelligence agents can be exposed via Microsoft team. So it's a very healthy multilevel collaboration between the teams. And I think this has really improved over the past 18 months. And so folks like ArondScott and Satya have all contributed to it, and we are all very, very grateful for them.

Yes, I think a couple of quarters, we talked about their Snowflake business on as your accelerating. So that's great to see. I wanted to hit a couple of topics with Brian, but I do want to go to the audience to see if you had any questions for the mast. you just raise your hand, a microphone should get to you.

Just going back to this concept of owning the front door. So my understanding is your frontier models are going to be equivalent because you're just powering Cortex AI with the leading anthropic and open-end models. But how do you get the distribution beyond the current sort of users of Snowflake to get that broad enterprise footprint when you're saying what open and anthropic trying to get these sort of like enterprise-wide deployments.

I think both with Snowflake Intelligence and with Cortex Core, the initial thrust very much is the Snowflake user base. It's really important for all companies to know which side the cart is and which side the horse is. Snowflakes. And so we are pretty careful about how we position ourselves. We're not going to have Cortex code go up against broad use better clock code or, let's say, Cortex can provide. But on the other hand, there are teams that are dedicated to Snowflake that spend a lot of their time in Snowflake and making them a whole lot more efficient with Snowflake is very helpful for them.

Like-- I mean, like, look, at the end of the day, Cortex Code is powered by the frontier models, and there are many things about it that work out of the box because of that power I've had people tell me that it's perfectly good editing PowerPoint files. And my team uses them for editing communities configurations. We're using them a lot in situations that are very different from the original goals that we had envisioned.

On the other hand, I'm not pretending that I'm competing to be the enterprise-wide coding agent. That's just not true. But being that effective coding agent, for example, for all data is actually a pretty good place to be for a company like Snowflake and it plays to our strength.

Great. So maybe I want to wrap up the conversation around the team's perspective on capital allocation. Unfortunately, it's been a tough year in terms of share prices for software companies in 2026. Including with Snowflake. And so a couple of questions for you, Brian. Given the market, do you anticipate having to issue more stock-based comp to retain employees.

And as a follow-up, what is the team's message with respect to share repurchases and the level of share dilution investors should expect on an annual basis? And how much of a priority is it getting some meaningful GAAP profitability?

Yes, absolutely. One of the things that's really important to the company is GAAP profitability. And so when Sridhar took over as CEO, he put a plan in place to actually help achieve that. And so 2 years ago, our SBC was 41% of revenue. This past year, FY '26 is 34% and -- and we said on the call that we're targeting 27% this year. So we actually have a plan to actually get to GAAP profitability, primarily through SBC. That's really the only differentiation. As you know, we generate a lot of free cash flow. We did 25.5% this past year. We guided to 23% this year. And so happy with what we're doing there and how we're moving forward.

From a capital allocation perspective, we do have a share buyback authorization -- we have $1.1 billion remaining on the buyback. We historically bought in the open market in previous quarters. And then we also do some small acquisitions, typically tuck-ins, more acquires we did a larger 1 this past quarter with the Observe acquisition.

Well, that went fast. But thank you so much, Brian, Sridhar for giving us an update on the Snowflake business where you're taking the business going forward. And it seems like this exciting things ahead. So thank you very much.

Thank you.

Good day, ladies and gentlemen. Thank you for joining today's Snowflake Q4 FY '26 Earnings Call. My name is Tia, and I will be your moderator for today's call. [Operator Instructions]

I would now like to pass the call over to your host, Katherine McCracken, Head of Investor Relations. Please proceed.

Good afternoon, and thank you for joining us on Snowflake's Fourth Quarter Fiscal 2026 Earnings Call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; Brian Robins, our Chief Financial Officer; and Christian Kleinerman, our Executive Vice President of Product, who will participate in the Q&A session.

During today's call, we will review our financial results for the fourth quarter fiscal 2026 and discuss our guidance for the first quarter and full year fiscal 2027. During today's call, we will make forward-looking statements, including statements related to our business operations and financial performance. These statements are subject to risks and uncertainties, which could cause them to differ materially from our actual results. Information concerning these risks and uncertainties is available in our earnings press release, our most recent Forms 10-K and 10-Q and our other SEC reports. All our statements are made as of today based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

During today's call, we will also discuss certain non-GAAP financial measures. See our investor presentation for the definitions of the non-GAAP financial measures and a reconciliation of GAAP to non-GAAP measures and business metric definitions, including adoption. The earnings press release and investor presentation are available on our website at investors.snowflake.com. A replay of today's call will also be posted on the website.

With that, I would now like to turn the call over to Sridhar.

Thank you, Katherine, and thank you all for joining us today. This past year has been transformative for every business. A year ago, we were talking about the promise of AI. Today, the promise is real, and Snowflake sits at the center of the enterprise AI revolution. Across the market, AI is reshaping the software landscape, redefining categories and competitive dynamics. In our view, this is creating a clear separation between systems that demonstrate intelligence and platforms that can deploy safely and at scale. The winners will be the platforms that combine trusted enterprise data, govern business metrics, secure execution and broad model choice and make all of it easy. That's exactly what Snowflake was built to do.

We deliver the data foundation enterprises rely on across clouds and across data types with the performance, reliability and operational simplicity required for mission-critical workloads. As AI agents become central to how work gets done, those same capabilities become even more valuable because agents are only as powerful as the data they can access and the governance and security that surround it.

You can see that leadership in what we shipped this year. With Snowflake Intelligence, we brought enterprise-grade agency capabilities directly to business teams. With the general availability of Cortex Code, we extended that to builders accelerating the entire data life cycle and helping customers move faster from development to production. Most recently, we expanded Cortex Code CLI to Encompass data systems as we work towards simplifying how all of them are used in practice. The general purpose agency capabilities of Cortex Code CLI, combined with our [indiscernible] data on Snowflake are already driving meaningful operational impact, just week suffer launch. Snowflake Intelligence and Cortex Code are meaningful steps in Snowflake's evolution. On the platform where enterprises govern and analyze their data to the platform where they build and run AI native applications and workflows.

Turning to our results. Product revenue in Q4 grew 30% year-over-year to reach $1.23 billion. Remaining performance obligations totaled $9.77 billion with year-over-year growth accelerating to 42%. Our net revenue retention was at a healthy 125%. Thanks to AI we are both scaling revenue and becoming operationally more efficient. Fiscal '26 non-GAAP operating margin reached 10.5%, expanding more than 400 basis points year-over-year reflecting our continued focus on operational trigger. Stock-based compensation declined meaningfully from 41% of revenue in fiscal '25 to 34% in fiscal '26 and we expect it to further decrease to 27% of revenue in fiscal '27.

This year's results are a testament that the AI Data Cloud continues to deliver tremendous value to our more than 13,300 customers across every stage of the data life cycle. Built a deep product cohesion, Snowflake is easy to use, seamlessly connected for collaboration, grounded in the security and governance enterprises trust. As we innovate, we remain maniacally focused on driving great business outcomes for our customers. That focus is why leading our organizations continue to choose Snowflake as the foundation for their data and AI strategies. We added 2,332 net new customers this year and we are seeing more and more businesses move over to Snowflake.

[ Seagate ], for example, is modernizing its data foundation to better support its mission of powering data-driven innovation at global scale. By consolidating a massive data environment on Snowflake, the company is moving away from legacy infrastructure onto a platform built for scalability, reliability and predictable cost enabling teams across the business to access high-performance AI analytics and make faster, more informed decisions. Our core business remains strong, and AI is expanding workloads across our platforms.

Capital One is a great example of how we are deepening our relationships with key customers. As Capital One scales its AI initiatives, they are leveraging Snowflake to unify proprietary data, optimize engineering workload and deliver AI-driven analytics across the enterprise. Key to our growth is the strength and momentum around our AI products. This quarter, we delivered the largest sequential increase in accounts using AI, bringing the total to more than 9,100 accounts. And in just 3 months, Snowflake Intelligence has scaled from a nascent offering to an essential capability for over 2,500 accounts, almost doubling quarter-over-quarter.

For example, [ Perdamator ] Europe, a global automotive leader is leveraging Snowflake Intelligence to revolutionize its operations. By enhancing enterprise search with [ EVs ] knowledge chatbots and streamlining contract management through Document AI, Toyota has fundamentally shifted development time lines, reducing AI agent deployment from months to weeks, creating a significant competitive advantage.

And United Rentals, the global leader in equipment rentals is using Snowflake Intelligence to power a new business intelligence agent that helps teams across more than 1,600 branches get real-time answers from their financial and operational data using natural language. The agent enables faster, more consistent decision-making for frontline managers. United Rentals is also using Snowflakes Cortex Code to accelerate the development and testing of additional AI agents, scaling trusted intelligence across the business. And that's just the start of what Cortex Code can do. It's a truly transformational coding agent that's already helping our 4,400 customers build and scale AI-powered applications and massively accelerating their ability to deploy production-grade AI.

The Chief Technology Officer of one of our partners evolved consulting described Cortex Code impact on their business, saying, "20 days, 21,000 operations, over 600 hours of work delivered. That is 16 workweeks compressed into less than a month. Development cycles that used to require extensive research, trial and R&D bugging now flow naturally through AI [indiscernible] iteration. We're using this capability to accelerate how we bring new workloads on to Snowflake for our customers." Cortex Code meaningfully expands the surface of AI development on our platform and reinforces Snowflake as the enterprise AI foundation.

As we look forward, we continue to see immense opportunity to support enterprises across the data life cycle, and we're innovating rapidly opportunity. This year, we launched over 430 product capabilities, underscoring the strength of our product velocity. We are gardening how data enters and flows through snowflakes. Snowflake OpenFlow, not generally available, makes it easier than ever to bring in structure, on structure, but our streaming data into the platform. We have also deepened how applications are built on Snowflake, now generally available. Snowflake [indiscernible] is a roll class operational database built directly on to the Snowflake platform, enabling developers to build undrawn production-grade transactional applications with the performance, reliability and ecosystem of [indiscernible] fully managed and governed within Snowflake.

This transforms Snowflake from a system you analyze with into a platform that you build on. And our recent acquisition of [ Observe ] a market-leading observability platform extends the value that Snowflake can develop. By integrating observability directly with data and AI products, we reduce complexity and enable faster, more reliable operations at scale. This expands our opportunity into the $50 billion IT operations market and position Snowflake to lead in next-generation AI-powered observability. At the same time, we are strengthening the ecosystem around the platform. Our landmark partnership with SAP is delivering incredible value, helping customers like expand energy, unite mission-critical business data across their core systems within our AI Data Cloud.

Our deepened partnership with [ Entropic ] is already helping customers like Intercom see significant impact. Snowflake provides the secure governed data foundation that Intercom's AI is built on. By applying direct AI capabilities to this data, including their use of [ Entropic ] cloud models, Intercom automates customer support at scale. This allows you to handle significantly higher support volumes with greater consistency and lower operational burden, especially for large complex customers. We also recently announced a $200 million expanded partnership with OpenAI, it brings OpenAI models natively into Snowflake to help our customers innovate faster while keeping their data secure and governed. And through our partnership with Google Cloud, customers now have access to the latest Gemini models natively within Snowflake further expanding model choice and availability.

As we innovate, we are scaling efficiently. Work is fundamentally changing, and we are leading this transformation both within Snowflake and across the industry. In many cases, we are creating entirely new AI native systems built directly on Snowflake. Across our business, Snowflake Intelligence and Cortex Code are already delivering measurable results. Our service delivery team can complete customer projects up to 5x faster, improving response accuracy by more than 25% and compress implementation cycles from days to hours. To drive 40% to 50% higher project margins and enabling customers to go live more than 40% faster.

We have seen our site reliability engineering investigations that once required ours across multiple engineers now resolved in minutes, dramatically reducing resolution times and further strengthening Snowflake's reliability. And we have built agentic capabilities that help our sellers prioritize accounts, automate research and generate personalized outreach projected to recoup the equivalent of 90 full-time engineers of productivity this year. Our finance team is working on automating travel and expenses analysis, proactively curbing auto policy behavior, an initiative that is expected to drive millions in annual savings.

Under seeing this transformation within our customers as well. We are leveraging agents not just to analyze information, but to automate complex workflows and in some cases, retiring entire categories of previously used software systems. Take Sanofi, for example, AI-powered workflows built on Snowflake with partners like [ Elementum ], are replacing the traditional software systems used for processes like software license and invoice management. By running these workflows directly in Snowflake, Sanofi's streamlining operations while keeping its data securely within the platform. This is where the enterprise is heading. And we believe Snowflake is uniquely positioned to become the control plane for the agentic era.

We have built the conditions that make agents safe, scalable and enterprise-ready covering a single enterprise-wide source of truth. Governed metrics and shared business definitions, cross-cloud and cross-domain interoperability, built-in security, auditability and governance. Our continued rapid innovation tight go-to-market alignment and operational discipline are all in high gear to capture this opportunity, and we see a long runway of durable high growth and continued margin expansion ahead.

Now I'll turn it over to Brian to take us through the financial details.

Thank you, Sridhar. Q4 was a strong quarter across revenue, bookings and margin results. Product revenue grew 30% year-over-year. Our results were driven by stable growth in our core business and a step-up in growth contribution from AI workloads. We saw no decline in our net revenue retention rate, which remains at 125%.

Q4 sales execution was outstanding. Remaining performance obligations accelerated for the second consecutive quarter. We signed the largest deal in Snowflake's history greater than $400 million in total contract value and signed 7 9-figure contracts compared to 2 in the same period last year. These strong commitments represent Snowflake's strategic role in our customers' long-term data and AI strategies.

And we've consistently emphasized durable growth depends on 2 fundamentals: landing new customers and expanding existing ones. We've delivered on both. We delivered another strong quarter of new customer wins, adding 740 net new customers, up 40% year-over-year, including 15 Global 2000 organizations. At the same time, we've proven that we can drive meaningful customer expansion. We now have 733 customers spending more than $1 million on a trailing 12-month basis growing 27% year-over-year and a record number of customers crossed $10 million in trailing 12-month spend, bringing a total of 56 customers above this $10 million threshold, growing 56% year-over-year.

Turning to our margin results. FY '26 non-GAAP product gross margin was 75.8%. We are demonstrating that we can scale while driving efficiency. FY '26 non-GAAP operating margin was 10.5%, and FY '26 non-GAAP adjusted free cash flow margin was 25.5%. Earlier this month, we closed the acquisition of Observe, which we acquired for approximately $600 million in a combination of cash and stock. With Observe's offering, we're unlocking new expansion opportunities within our customer base. The impact of the acquisition is reflected in our outlook.

In Q4, we used $150 million to repurchase approximately 668,000 shares at a weighted average share price of approximately $225. We have $1.1 billion remaining on our repurchase authorization and ended the quarter with $4.8 billion in cash, cash equivalents, short-term and long-term investments.

Before moving to our outlook, I'd like to share my priorities for FY '27. First, I see a clear opportunity to drive both growth and operating margin expansion. We are investing in our key growth drivers as Street are related, we deployed more than 430 product capabilities to market this year. We'll continue to expand operating margins as we drive greater efficiency across the business. Second, it's clear that our go-to-market motion is working. My focus for this next year is on ensuring stability and ongoing excellence. We've established a financial framework to support continued product velocity and sales execution.

Now let's look to our outlook for FY '27. In Q1, we expect product revenue between $1.262 billion and $1.267 billion, representing 27% year-over-year growth. For FY '27, we expect product revenue of approximately $5.66 billion, representing 27% year-over-year growth. We expect Observe to contribute approximately 1 percentage point of product revenue growth in FY '27. As always, our forecast is built on using existing patterns of consumption. There are no changes to our forecast process or our guidance philosophy. Our outlook is supported by continued strength in our core business and further growth in AI workloads.

We expect FY '27 non-GAAP product gross margin of 75%. We're guiding Q1 non-GAAP operating margin of 9% and FY '27 non-GAAP operating margin of 12.5%. Our hiring this year will be weighted to the first quarter, reflecting the addition of 178 employees from Observe. We expect non-GAAP adjusted free cash flow margin of 23% this includes an approximate 150 basis point headwind related to our acquisition. As in prior years, we expect our bookings will continue to be weighted to the fourth quarter, and we expect next year's non-GAAP adjusted free cash flow seasonality to mirror FY '26.

Finally, we'll host an Investor Day in conjunction with our Summit Conference the week of June 1 in San Francisco. If you're interested in attending, please e-mail [email protected].

With that, I'll pass the call to the operator for Q&A.

[Operator Instructions] The first question comes from the line of Sanjit Singh with Morgan Stanley.

Congrats on reasserting 30% product revenue growth in Q4. I had 2 questions, starting with Brian, and then hopefully for you, Sridhar. Brian, on the guide for fiscal year '27, basically implied sustained growth around 27% throughout the year. And just sort of just want to get your perspective on the durability of that 27% given that it's a consumption model, sort of sustained growth off of a really good year this year. So just sort of the confidence in that.

And then for Sridhar, as we go into the first 4 years of Snowflake Intelligence and an expanded product portfolio, I was wondering if you can give us a sense of where we are in terms of momentum with the areas of the business outside of the core. I think we got an update on the data engineering revenue run rate or growth rate several quarters ago. So once you get an update on that and when we sort of stand with the AI portfolio exiting this year and going to fiscal year '27.

Thanks, Sanjit. I'll go first. From a guidance perspective, we guide based on the observed customer behavior up until really the point of earnings. And the guidance, if you sort of double click into it this year, it's really based on the high stable growth that we see in our core business. It's also the growing contribution from AI workloads. Then finally, we called out in the prepared remarks, there's 1 percentage point of growth from our observe acquisition.

I'll turn it over to Sridhar for the second part.

And to just reiterate on top of that, our overall guidance philosophy hasn't really changed. We continue to be very stable with respect to that. I see products like Snowflake Intelligence, now with 2,500 customers as a major driver of growth across all aspects of the data life cycle. I think what products like Snowflake Intelligence, and I never tire of showing every single CXO and CEO that I meet Snowflake Intelligence on my phone but any access that it offers is truly magical to critical business information. And that reinforces the need for enterprises to adopt Snowflake to get their data estates in gear so that they can bring the transformative power of things like Snowflake intelligence to that data.

The really important thing also to remember about Snowflake Intelligence is that it works fine on all open data. You can build Snowflake amazing agent with using Snowflake intelligence on data that is sitting in [ S3 ] managed by [ Glue ] are sitting in other places. Any open data ecosystem is supported by Snowflake Intelligence, and that's really very powerful. But Cortex Code is the real game changer for us because it is a massive accelerant for every part of the data life cycle. What I mean by that is we can build open flow pipelines to bring in data from complex systems into Snowflake at a fraction of the time that it used to take before. Similarly, building [ DBT ] pipelines to run data engineering on that data or to build dynamic tables, or debug performance issue with either of these now is again 10x faster.

And what's magical about [ cocoa ] is also the ability to actually build Snowflake intelligence agents faster. I think that's the unlock of AI using AI to make things go faster. And we see this, as I said, of having transformative effects on our business, I'll give you folks an anecdote. One of our partners wrote to us after using Cortex Code CLI and said that all this time, they had been using shales to dig, and we just gave them bulldozers.

Let's go to the next question, Mark Murphy.

The next question comes from the line of Mark Murphy with JPMorgan.

So the bookings and RPO figures look very robust [indiscernible]. It looks like the biggest bookings figure in the history of the company actually by a pretty wide margin. I want to just want to ask first, can you describe the $400 million deal in terms of the customer type because I don't -- it's a gigantic contract. I just don't think we've heard anything like that.

And second, I'm curious if you see some sustainable new drivers kicking in there for bookings like maybe thinking back on achieving a faster product GA cadence is something you've done or what -- is this a little more temporary 1 time, you had the hiring surge several quarters ago, and I think you've been incentivizing reps a little more heavily on bookings this year. So I'm just wondering if you can comment on this.

I can start, Brian can add on bookings and multiyear contracts are a clear indication of the trust that our partners have in their future with Snowflake. And yes, the product acceleration and velocity goes a lot towards convincing customers that we are a platform for the future. We didn't do anything particularly special in the quarters. Yes, we did adjust the compensation plan to also take bookings into account last year. But in many ways, that represents a reversion back to how things were 2 years ago. And we plan to continue that this year. So it's very much business as usual.

I do think that the $400 million, $400-plus million deal that we signed is an indication of the importance that we deliver to that large financial services customers. We have previously talked about deals in the $250 million range. I think it represents a maturity of Snowflake as a durable provider, not just today of data services, but also into the future. Brian?

Well said, Sridhar. I would say when the big contract over $400 million, it was an existing customer. So it's already built into the run rate. We did sign 7 9-figure deals as well. And so just to reecho what Sridhar says.

Just Q4.

Yes, just Q4. And just to reecho what Sridhar says, it's really a buy-in from our customers on our product road map and AI strategy and the positive business outcomes that we're delivering for their business.

The next question comes from the line of Brad Zelnick with Deutsche Bank.

Great. And I'll echo my congrats. Sridhar, I guess this one's for you. Just coming away from sales kickoff and now the first full year with go-to-market under Mike and its command, what are you going to do differently in the field to win and drive upside in fiscal '27?

Mike's had a year. He has had a very positive influence on the sales team. But I think what drives momentum for the whole company and absolutely the sales team are great products that let our sellers, our solution engineers deliver value for our customers. And I have never seen more excitement from our sales force about the products that we create. We have had multiple people. I'll let Christian chime in because he gets a lot of these accolades.

We have had multiple people come and tell us how Cortex Code is absolutely transformational in what people can do with Snowflake. Many folks come and tell us that they've never felt as much excitement about the product that we have created since when the original product was created. And Christian had a section of Cortex Code heroes that highlighted their experience. I'll let him say it since you're the one that ran that.

Yes. super quickly, like partners, customers and our internal field are all incredibly excited about the results in with Cortex Code. The original value prop of Snowflake, which is change what's possible in terms of ease of use is just gone like 10x with Cortex Code. We showcased a number of [indiscernible] where people are building pipelines faster, transformation faster insights faster. And I think we're only at the beginning of what is possible.

One of our sales leaders, who I assure you, would be the last person to declare himself to be a software engineer. Built a stream lead application deployed it on Snowflake and had his team use it. That's how easy Cortex Code makes it to use data from Snowflake.

The next question comes from the line of [ Curt Matter ] with Evercore ISI.

This is [ Shrug ] on for Kirk. Sridhar, observability is a big market, right? How does Observe fit into that topography? And what were you seeing in the market and in the company that it made sense to bring them in-house?

Observability, especially in the world of AI is a big deal. As you point out, it's a very large market, a $50 billion-plus market, which means that it has many different angles of expertise that go into it. And AI observability in particular with agents is a big, big deal. I'm sure many of you use agents and no one is ever going to accuse a coding agent of not being chatty. There's just volumes upon volumes of text that then need to be distilled into things like skills into things like what went right and what went wrong and so we see this as a critical data problem. And we also see it as a natural extension of our overall role as a data platform.

Observe was built on top of Snowflake. So it inherits the excellent data and compute foundation that Snowflake has. And for a lot of our customers, especially ones with very large volumes of data, observability as traditionally done has become a little bit of a sore point with respect to just the sheer cost of it. And this is where Observe is able to offer a value prop that is factors away, not like 10%, 20%, factors more efficient. I think those are the kinds of customers that are going to benefit enormously. There is a huge overlap between potential customers of Observe and customers of Snowflake and it's really that 1-2 punch Observes built on Snowflakes on our job of integrating it is very simple. Observe has an excellent value prop for a large set of customers that also happen to be Snowflake customers -- that was the -- ultimately the thing that made both Jeremy and the observed team want to be part of Snowflake.

We are very excited for what's ahead.

Christian, anything to add?

That's great.

Let's move on to the next question.

The next question comes from the line of Raimo Lenschow with Barclays.

This is Sheldon McMeans on for Raimo. As you keep making the Snowflake platform more accessible to users and your solutions, you certainly have an exciting opportunity to expand users in consumption. But there is also a risk of maybe sticker shock as AI agents proliferate or new users create more applications and workloads on your platform. So how are you working with customers to help reduce the risk of cycles of strong growth and optimization? And just a little bit on do you feel like customers truly understand kind of the potential consumption uplift they can have as they leverage your agents more?

It's a great question, but one that we've spent a lot of time thinking about. Let's make sure we examine the counterfactual for some of the early agent products. Several of them were launched as part of subscription bundles and many companies that offer agent platforms see them as an extension of their existing subscription model. At Snowflake, we charge based on consumption. And we, therefore, offer a very predictable model. I'm also of the firm belief that products have to show value right out of the gate.

And I [indiscernible] you, our personal example where our sales agent replaced a legacy dashboarding system that we were paying close to $5 million for. And so it delivered ROI out of the gate because that moved to be a set of stream leads and Snowflake Intelligence. And this is where we feel like we are very, very value aligned, but we are not stopping there. We know that our customers still want price predictability even with Snowflake Intelligence. So we will be launching features like a per-user cap on top of Snowflake Intelligence so they can feel like there is a clear upper limit to how much they can get charged with an agent. We think models like this that are consumption-based with clear user cat and account cap offer the best of both worlds, which is consumption pricing with price predictability and we'll continue to innovate rapidly in this area because we think these agents can deliver huge value. And absolutely, we don't want our customers to have sticker shock. We want to be predictable. And we will provide the controls that are necessary to make for wide deployments of Snowflake Intelligence.

We've also done things like integrate Snowflake as a whole with identity providers so that even the task of things like configuring users to be able to use our products like Snowflake Intelligence, is a whole lot simpler than ever before. Christian and my vision is effectively that every single employee of every enterprise customer we have should have access to a set of agents that provide them with all the key business details that they need to run their part of the business.

And only get billed for what they use, which is always correlated with amazing outcomes.

Very clear. And a quick follow-up. So you certainly talked about your robust AI agent strategy is progressing well, but there's also the idea of other agentic workflows leveraging Snowflake for critical steps in their process. Can you speak to this latter area and how that's evolving for you? And do you see that as a fiscal year '27 growth opportunity? And do you see it mainly going through your zero-copy partnerships? Or would there be another pattern that would emerge there?

Could you clarify your question, please?

Yes, [indiscernible] workflow that's done in a different platform that may be need for leverage some data in Snowflake for a step of the process.

Well, interoperability has always been a key part of how we operate. And over the past 2 years, Christian and I are very proud of the fact that we have executed flawlessly on an interoperable data strategy. We support iceberg as a first-class construct within Snowflake. We support iceberg where we manage the rights. In fact, we recently announced. We support iceberg where we even manage the block storage so that our customers get the best of all worlds. They get the manageability that they get with Snowflake while feeling confident that another engine can read that data.

And what we have done over the past year is use interoperability to drive additional workloads for Snowflake because as I said earlier, you can -- we can run sequel queries on any open data through things like catalog linked databases, you can also create agents that are sitting on any open data. And this kind of interoperability is really key for Snowflake to succeed. No customer wants to get into a situation where they cannot -- where they do not have options. So we offer interoperability at the storage level. Certainly, people can write SQL and access the data. So we offer interoperability at the [ JDBC ] level. And one level above that, we make semantic models available to others. We introduced semantic views, but anyone can read semantic views.

And finally, our Snowflake Intelligence agents also double up and can be [ MCP ] servers that can be used by other agents as well. And so offering interoperability at every layer of the stack is central to what we do. But we also focus on creating world-class products that lead the way that are easy to use and set up that make all of this way, way simpler than what anyone else can do. We don't see any contradiction between the two.

The next question comes from the line of [ Koji Liva ] of Bank of America.

I wanted to ask about the $9.8 billion in RPO, which is growing 42%. I mean, really, really nice there. And so instead of asking you where you saw strength I'm most curious if you could talk about any air pockets where you were surprised that they didn't contribute more, why you think that happened? And how you think those pockets get better from here?

Koji, this is Brian. There wasn't any -- we called out the big contract in the quarter for over $400 million in the 7, 9-figure deals, but there wasn't anything in the quarter that happened where I thought there's areas that we overexceeded or underperformed. Overall, we had a good sales execution quarter and the RPO, as we talked about a little earlier, is just really points to the business outcomes that we're driving for our customers and then buying into Snowflake long term.

Overall, I'm just -- I have to add that I'm incredibly proud of our sales team for delivering both across consumption, in terms of driving use cases both the wins and our services team for driving more and more of them to production. And of course, what the sales teams got done in terms of these monumental contracts overall. It was a stellar year by those folks, and we are all very grateful.

Yes, yes. And maybe just a quick follow-up here. I wanted to ask about platform usage visibility and predictability, maybe compare and contrast today versus a year ago, if that has changed at all? And if it has, what has been driving that change?

Could you clarify your question? What did you mean by platform usage and visibility?

The usage of your platform by your customers. How much more predictable is it today versus a year ago, if at all?

We continue to have among the most sophisticated systems for consumption prediction. And we obviously calibrate ourselves on how well we do something like a 0.5% deviation is 1 part in 200. And for us, that's sort of a big deal. That's the level of sophistication that there is. And there is a similar methodology that is being applied for contract prediction, the [ TACV ] prediction as well, and it's an area where I expect us to see -- where I expect us to get better and better over time.

And another area that we are actively working on which has a little bit less predictability is one that goes from use cases to consumption. It's an active topic for us. It's a little bit of a research project because we are not always privy to what our customers do. But we feel very good overall about our ability to model the business and be able to see where it goes. Of course, you also want the surprises that are not part of your models. There is no model that would define the birth of Cortex Code or its adoption by 4,400 customers. We are happy when things like that happen. But when it comes to the core, we are very, very buttoned up among the best teams that I've worked with. I worked with a lot of them at Google and other places when it comes to predictability of our business.

The next question comes from the line of Matt Hedberg with RBC Capital Markets.

Congrats from me as well. You guys are checking a lot of boxes. You're accelerating at scale. Sridhar, you went through a number of new AI product announcements. And it looks to me like you're starting fiscal '27 organically, a couple of points higher than you did at this point last year.

So I guess investors want to know, is AI-related products, is that some or all of the kind of the upside that you're starting to see in this model because it certainly feels like you guys are well positioned from these trends. I'm just wondering, is it starting to inflect in the model?

So the other side of this is that our models are predict based on observed behavior. And we think that there is a lot of upside. As I said, there's no way that they can take into account the impact of [ cocoa ] because the historical data simply is not there. We see the benefit of things like [ cocoa ], vividly because we can see how quickly projects finished when they're being done by our services team. We also see when our partners take these products and are able to do truly transformative things.

And you can ask me, am I over using that word? I point you to a block post that's one of our partners, [ James Dinkel ] wrote, where he said that they were basically moving their business model as a whole from charging for time to offering fixed fee services. And a lot of that predictability came because they use Cortex Code to drive the vast majority of the migration. So we see a lot of upside to where the business can go.

And on top of this, part of what you've learned even over the past few weeks with Cortex Code is the impact that it can have on every function within Snowflake. Our product managers now have their own version of this to be able to predict -- to be able to look at everything from what are the launches coming out next week or what are the bugs that have been filed against their products. There's even someone that wrote a Christian feedback but to give them feedback about how Christian would react to a product proposal. The level of innovation that we are seeing across the company is pretty inspiring. And that gives a lot of confidence about how we approach the year.

Please go ahead.

I was just going to add on to what Sridhar was talking about prior. Go ahead, Matt.

You can finish your answer, Brian. I was just going to wonder, it looks like gross margins are down about 1 point this year. I'm curious with all the investments that you're making, do you feel like mid-70s is kind of a stable place for kind of gross margins, especially as we look at a couple of years forward?

Yes. Great question. One of our objectives when we launch new products is really, first and foremost, is to build great products. Two, we want to make it easy to use. And three, we want to drive revenue after that. Once we get there, we'll look at optimizing the margins for that. We have launched a lot of new AI products. The margin profile for those right now aren't as high as the core business, but we're offsetting that by finding more efficiencies in the core business. And so that's really sort of the component of that. We'll do what's right to drive growth, and we'll balance it all the way down the line at the operating margin level.

And things like margin improvements are coming both at the gross margin level but definitely also at the company level to just tell you folks about a couple of projects that we did that have had a big impact. One of the folks basically optimized all our free pools across all our deployments using AI because they got way better visibility into that data. That actually -- free poosl basically, we have to maintain free pools of compute so that our customers don't have to wait when they want to spin up a new warehouse and somebody found out a very clever way to look at the data and to optimize it. Or we have done a number of things around things like storage life cycle policies. When does the table need to be in nearline storage versus more like place storage and things like that.

So there are a lot of wins to be had with AI, both above the gross margin line, but definitely at an operating margin line as well. To be honest, it's a matter of prioritizing what you put your time into because the world is so rich with opportunity.

And Matt, just to emphasize that point. Just in fourth quarter, we saw a lot of benefit with AI that we had a small reduction in force and about 200 people in the company were impacted. So if you look at our fourth quarter net adds on a headcount basis, we only added 37 people. So AI has really changed the framework for investing in growth. It's no longer tied to headcount.

The next question comes from the line of Brent Thill with Jefferies.

Sridhar, all the SaaS things are selling off on the big AI labs taking the stack. As you know, I guess when you think about the advantage you have with the platform of having Gemini, OpenAI and Entropic available natively. First, do you think your customers understand that yet? And second, I guess, are you seeing that show up in demand given that you have all 3 of the top supported natively?

I think it's useful to step back and look at the impact that AI as a whole is having on software. We spent a lot of time looking at this. We live this and our take is that overall, the winners are going to be the companies that provide that single source of enterprise truth. No AI model is going to help you if there are 4 sources of the truth. Similarly, having built-in security, auditability, trust or even governance over access, who can access what data set is critical.

Obviously, you do need the best model, but there are at least 3, if not 4 best model providers right now, and we work with all of them. I think our secret sauce, which has existed since the beginning of the company is packaging all of this into a cohesive product that is easy to use. And do you see this play out with things like Snowflake intelligence and Cortex Code working together which is Snowflake Intelligence is a pretty cool product, but Cortex Code makes it 4 to 10x faster to be able to deploy those agents.

I think we are really seeing a lot of nice synergies come together as we go into this journey of agentic AI. And it is this combination of capabilities plus the fact that we have always been trustworthy steward of all enterprise information that I think make us a great party for every single enterprise to be working with.

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

Just excited to see the progress around Cortex code, and it seems like you're combining the best of what AI can do today along with the [indiscernible] Snowflake. As it makes it a lot easier to build agents on the Snowflake platform. It seems like there's a lot of different vendors that are trying to be the place for users to build agents. So from a technical perspective, what do you think are some of the advantages that Snowflake has to be the best place for users to build agents. And then have you seen any increase in quarry volumes from Cortex Code users today?

Our mission for a number of years has been to be that data platform that makes data easy to get value from. This is what we did when Snowflake first came out. This is what we have always been doing. In fact, our motto always has been easy, connected and trusted so that data within an enterprise is easy to use, but also present wherever you need it to be -- whatever you needed to be present.

And it's that thing that I think it's that quality that gives us an advantage when it comes to creating agents. As I said earlier, we are also believers in interoperability. It is perfectly fine if someone wants an agent and be able to use MCP to call into a Snowflake Intelligence agent. But I think we are uniquely positioned to be that central place where that 360-degree view is possible for a number of our customers we are stewards of their most important data. The goal layer, as it is called, in analytics, I think that positions us exceptionally well to also be the ones that are providing agents for accessing the data and we are heavily leaned into technologies like MCP. MCP works both ways. You can use MCP to read from an agent, but we can use MCP to read data from other systems, and we are beginning to see use cases like that come alive as well.

And we have done a number of studies, Snowflake Intelligence, absolutely drives more usage, more queries. And -- but we tend to focus on what's the value that we are creating. At this point, I'm slightly indifferent about whether we get more of Snowflake Intelligence revenue from running a query or from running the model. It's all about creating amazing experiences and making it easy to do so. Christian?

We definitely see in the telemetry activity on the platform being increased based on the ease of use that boast of the Intelligence and Cortex Code bring.

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

Maybe Sridhar, a quick one for you and then I have a follow-up for Brian. Last quarter, you spoke to kind of how January and February consumption trends would be the most important to determine the fiscal year guide. Maybe just talk specifically about kind of what you saw post holiday in January. And specifically, even coming out of February, that give you the confidence on what looks like a stronger guide this time versus last year? And then I've got a quick follow-up for Brian.

Well, Brian, did say earlier that when we guide, we try to take every ounce of data possible into that guide, that's what we have done. And we also clarified that the guidance process is a pretty strict one that focuses on historical information and our ability to -- our ability to reliably predict the future. So in that sense, it is taking everything into account. And if you were to ask me what's the difference between last year and this year at the beginning of last year, Snowflake Intelligence was a glimmer in our eye and 1 year later, not only did we launch Snowflake Intelligence and get it adopted we've also -- we are also being at the forefront of how you use agentic AI to massively accelerate how a data platform is being used.

I think all of that is going to culminate into how we perform this year. But as far as the guide is concerned, it is very much about using every bit of data that we have until this moment, Brian?

100% correct. What was your second follow-up question?

Yes, I was just going to ask if any update on the Snowflake AI ARR and then the free cash flow margin guide, obviously digesting the observed acquisition maybe just the puts and takes there and how we should think about that trajectory?

Yes. Just on free cash flow overall. The seasonality will follow prior years. We collect the majority of our cash in the fourth quarter. It's been greater than 60% in the fourth quarter for the last few years. Observe -- we guided to 23%, Observe was a 150 basis point headwind. That's included in our numbers. The revenues included, the op margins included as well as the free cash flow. And then we just want to get guidance that we felt comfortable with that we can perform against.

That concludes today's Q&A session. I will now hand the call back over to Sridhar for closing remarks.

Thank you, everyone. Snowflake remains at the center of the enterprise AI revolution, and we see significant opportunity ahead. To recap, AI has moved from promise to reality and Snowflake is built to win this era by combining trusted enterprise data, governed metrics, secure execution and broad model choice so that customers can deploy AI and agents safely at scale.

We are rapidly transforming from the platform for governing and analyzing data into the platform where customers build and run AI native applications and workflows, making it easier for both business users and builders to go from ideas to production. This strategy is working. Our rapid pace of innovation and strong go-to-market execution are driving continued product revenue growth, and we see a long runway of sustained durable growth ahead. Thank you.

That concludes today's conference call. Thank you. You may now disconnect your lines.

Okay. Let's get started. Day 4. A ton of familiar faces given that I've been looking at you and you've been looking at me for 4 days now. And you guys should feel good that this many people are here on day 4 after I know because I saw them, many of them were up quite late at the first Camel bar last night to heroics to you guys.

I love having Snowflake here. Sridhar and Brian. And, Brian, by the way, congratulations on the role. Nice to have you here with different stripes. Thank you formidable team. Obviously, Snowflake just reported. So we'll have a chance obviously to talk with Brian about some of the aspects of the quarter.

But maybe we'll start with you, Sridhar.So we had you here last year, that was a great discussion we had. When you look back on 2025, what in your judgment were a couple of the things that you thought went really well? And were there any things that didn't quite go as planned?

Thank you for having me. Great to see all of you. So both last year and this year. The 2 main objectives for the Snowflake team and me were around accelerated project product velocity. And taking these products to market with like a globally dispersed sales team. That's the essence of Snowflake. And people ask me, what do I worry about when it comes to AI making snow flight better is getting these 2 functions to be better.

And they are both tricky things. Product velocity as anyone that's watched big companies put lots of effort and things now is a lot more than just putting people to work on problems. It's making good choices. It's having taste. Products are still magical. None of us can quite tell why a good product works the way it does and the so-so products kind of annoying.

And so I'm very pleased with just the craft and speed that has gone into products with products like Snowflake Intelligence. Everyone wants to create an intake solution. So a little bit of a buzz phrase right now. But our ability to create value quickly to make that entire life cycle of creating an instance of Snowflake Intelligence agent. And for me to be able to show it to you, and hey, this is what it does and for you as a user to be able to relate to it and go. That's great, and I love that. That's still magical. I think we got a number of those things, right? Also a number of companies that we acquired, Datavolo, for example, has turned into open flow in Snowflake. And the team is doing exceptionally well.

And we took on a broader lens of being there for the entirety of the data life cycle, a number of product efforts, all within that tight supply umbrella -- part has gone well. On the go-to-market side, it's been about just taking these new products, making sure that we get our sales team on it, bring in specialist resources where it made sense. But always with an eye towards how do you drive the broader team to have more and more skills. There's only so many specialist teams any of us can afford to have.

I think that's actually that's coming along. It's a much more quantitative and deliberate team for the past 2 years, which I think is very beneficial. In terms of what could have gone better. I like -- I'll tell you a little story me growing up. As you know, most people that have done exceptionally well in the U.S. go through this country like in India, called IT Metro, I mean, IT entrance examination. These are terrifying exams hundreds of dozens of people take them and a couple of thousand qualifier. And I came in some 35th or 36th -- a long time ago. I told it to my dad. He said, really, why 36? And so that's a little bit of what can be better I get it.

Is the time of infinite opportunity? I think the speed at which products can be developed is truly remarkable. You see people like open AI and on topic do it day in just think differently. They're not beholden to any of the failure patterns or any of the ways of working that traditional software companies are. So we need to adopt in a very big way, both my product and engineering team, but also the go-to-market team. I think the things that they're expected to do are going to be very, very different driving change at speed through thousands of people and telling them they have to like earn their living differently.

Yes, they're just really hard. Okay. Let's talk about some of the broader trends. Sridhar, I do, and I know everyone in the audience does when we're talking to customers and we're talking to partners, we always hear this refrain that especially as we prepare for this AI era. We've got to do a better job aggregating synchronizing, utilizing our corporate data. It's like such a common theme. And you can see that reflected in the shares of most data software stocks where -- the stocks have done well because this group appreciate that trend. And in most cases, the growth rates are accelerating. So it's pretty clear something really interesting is happening.

If you were to dive into like a couple of interesting demand trends that you're seeing in the last pick your period, last couple of quarters, what's starting to change a little bit that you would encourage this group to keep their eyes open to.

Yes. I think the broader theme here with AI is something I call, it's the beginning of the industrialization of thought. We have all industrialization, first of all, plays out over like many decades, if not centuries. But what is I think unique about this moment is that ability for these language models to plan to be able to execute things that only like a human could have done. And for the better part of the last 50 years, age of computing, let's say, data systems have always been a little bit of a back office thing, honestly, no one cared. You just wanted your quarterly earnings report. You didn't care that it went through 800 people with lots of people editing little spreadsheets. So there's a little bit of a cottage industry.

What Snowflake Intelligence are products like that dramatically reveal and on the consumer side, products like ChatGPT dramatically show is if you have the right data. you can do match. The kind of things that you folks can get done with the deep research port, for example. I'm sure all of you know that you had analysts doing that kind of work and it will take them a week to produce the equivalent of a well done deep research report.

But much of that technology pretty much has been trained and only uses the open web. That's a simple explanation for why is data is such a big deal. If you want the caliber of thinking if you want the caliber of planning that wows you when it comes to these products, these AI products, whether it's Gemini, Auto, ChatGPT research are ones from Anthropic for your own enterprise. You need high-quality data. And I think that's the excitement.

Yes, it's faster access to data, but more and more smart CEOs also realize that having this data. in platforms like Snowflake, where they're readily accessible, they're readily transformable is also the basis for transforming their business because you can say things that were previously done by human passing paper or PDFs around is now more automatable. And that's why this idea of an AI ready platform is such a big deal. And that's the pool that we see for Snowflake demand yourself because data in Snowflake is data that's AI ready first to Snowflake intelligence, but there are many other things to come, but they all build on this notion of data transformation, thought on transformation.

When I talk to customers and I ask them specifically how they're going about this. There's multiple paths -- where some just want to get their data into the cloud infrastructure of their choice or into platforms like Snowflake. But then I talked to others and some in the audience join me for a discussion with the UBS IT folks. We're trying to deploy something different that's more of a data mesh where we're trying to keep all our data where it is, not make copies, not move at all and utilize it at rest. So it feels like there's multiple avenues to go to modernize your data stack. Does -- do any of those paths benefits no like more than others? I'm sure the former does, but do you still benefit when customers go down the path that UBS does?

Well,. First of all, no company should attempt to do mass transformation of everything that it does in 1 day. This is something I explicitly tell all of our customers to never do. You just bring too much risk. It's something I would never do. I don't -- I no longer accept 2-year projects from my teams without clear deliverables, honestly, every month. Like 2 years is too long, I should not -- no one should trust anyone like that. So being incremental is very much a thing.

On the other hand, that is a reason for the secular movement of computing over to the cloud. On-prem systems involve, first of all, boom and bust capital investment cycles. And increasingly, that is not where the center of attention from software engineers from great companies is -- and many of the systems that are on-prem and software are also firmly in the realm of value extraction, not value creation.

There's a reason why people migrate away from those systems because if they want to increase the amount of compute that they want to put on a problem by 5%. That helpful vendor will come until you have to pay twice as much because they're very much in that phase of how do I extract every single dollar from every single customer.

While on Snowflake, you don't even have to tell me that you want to spend 5% more compute on some problem because your team found it to be interesting. There are these kinds of secular reasons for why cloud computing platforms like Snowflake are indeed preferred by Lat Am. We also have the best tech that can act on top of the data to be able to create things like AI and agentic solutions. There's a lot that you have. but absolutely a heterogeneous world and things like open format -- absolutely, we can read data from hardware systems if they expose it as an S3 API. The real world is messy and complicated, and we will play nice with it. But our secular advantages are also strong, and it will only compound from here.

Yes. I certainly, when I'm talking to customers since a growing interest in migrating more of their data into the cloud, so that syncs -- I said. Maybe this is actually a bit of a segue to run. So I think you are quite clear that Snowflake benefited in the July quarter from a number of large migration activity. I think you narrowed it down to a lot of in telco customers. And maybe the results that you put up last night didn't have that same degree, but there's just quarterly variations. So how would you describe like the pacing of that migration activity where it can surge in 1 quarter be more normal in the next quarter?

Yes, absolutely. With a pure consumption model, the quarterly results have a little lumpiness in by default. So Q2 was a very, very strong quarter for migrations. But think about you have thousands and thousands of companies, and they are planning their migrations around your quarter end. It's around when they're doing their transformation internally. So when we report, we're snapping the topline. And depending upon where all those companies are on their migrations is what we recognize from a revenue perspective.

Unlike a SaaS company that actually once it gets built, it's daily recognition, it doesn't really matter as much on usage. And so what we really like to point people to is the FY guidance. And we're really happy with the quarter, reported 29% year-over-year revenue growth. There was nothing in the quarter that was unexpected. We did mention on the call one thing, there was a hyperperscaler outage that caused roughly $1 million to $2 million worth of headwinds. But everything else played out pretty much as expected. And then we raised our full year guide by $51 million to reflect what we're seeing inherently in the customer behavior over all those migrations.

Sticking on this migration thing though, Brian, when I take your 4Q January guide, and you would probably discourage me from doing so, but I'm assuming, call it, a 2- to 3-point beat, you're going to land at a place where actually the product revenue growth rate reaccelerate in the fourth quarter. So are you seeing anything in the January quarter that's a little bit of a reversal of the trends you saw in October, where you're seeing some goodness maybe a little bit more migration activity.

Yes, absolutely. When we report earnings with the consumption model, you can imagine being a data company. We look at the data every day and have all these machine learning models and numbers of people actually doing daily forecast. And so we take all the observed customer behavior into effect when we give our guidance. And so what you're seeing is what we've observed over the last 90 days up until when we report. And so we've seen an improvement overall in migrations over the last 90 days.

Okay. Only color I'll add on is I think we like as humans, we like to see binary outcomes, meaning it's tempting to call something an acceleration or deceleration. But there like we have eyes on the prize is to be close to that 30% mark, which to me is a great place to be in Obviously, we had 1 quarter that went a little bit more than that, another quarter that's slightly less than that. But to me, to be able to operate a realm is great. If anything, it should be challenging Brian, on what it will take in to hit 40%.

Okay. I may do that and that was hypothetic. But in terms, there's not that many software companies that your scale that are growing at 30%. So I'm with you. Let's get back on the AI side. One subject that interests me is not so much how customers are behaving in this AI era, but Sridhar, how you and the engineering team are incorporating AI into Snowflake's product set to actually improve your own query speed. In the same way that a number of hardware changes have occurred over the years, the chipsets are getting better, improving query speeds -- how is Snowflake actually embedding AI in your core product to drive price performance improvements for your customers?

What did you mean by query speed here?

Just customers that are hosting data in Snowflake are querying it for business intelligence reporting needs. Your embedding AI in a way that perhaps they can interface more easily with their data and query it a little bit faster?

I would break this up as 2 separate questions. There are a large suite of improvements that we make to performance in Snowflake period. Some of them come from things like newer generation of chips that the hyperscalers are Intel for that matter, produce. They will often involve price performance trade-offs meaning with a new chip, you might be able to get 20% more performance, meaning query finish faster. But on the other hand, the chip itself might cost you 10% more per unit of time. We also make a lot of software improvements that make queries just go faster.

I would -- these are generally almost orthogonal to AI. And we have struggled to figure out how to roll this out in the previous years. And we have had discussions with many of you about how we roll out performance improvement and so on and so forth.

But one of the geniuses in my team, they came up with this idea of a new generation of warehouse. -- which delivers a lot of these performance improvements but price -- neutral. That's the Gen 2 warehouse. And the idea very much is that it's a win-win. We don't see any reduction in the amount of money that we make. At least that's the aspiration. It's a complicated modeling problem to price correctly.

But on the other hand, our customers have a lot of the work that they do, just go fast. They don't have to do anything. And that's the kind of trade-off that we are increasingly headed to where we can carefully apportion the -- a bunch of benefits to customers, but also have a throttle how much do we want to pay in terms of a price hit on our side. There are second artifacts that become difficult to model. If you let your customers do a whole lot of queries just much, much faster then they often do more of them because you can just analyze things better, you can model things better, but even though taking that into account, I'm very happy with Gen 2 because it kind of removes this question of what's the tradeoff that we need to make in terms of rolling out improvements in the core platform.

Now part 2 was more about how are you using AI to make the act of using Snowflake, configuring Snowflake, optimizing Snowflake a whole lot better this is actually a really exciting area for us as a whole. We have 1 product. It's in private preview. It's called Cortex Code -- idea very much is it's a data agent come as part of Snowflake is able to handle pretty complicated task for you so much so that much to the tender of my -- nears, I can write prototypes in an afternoon. Because it's much more oriented towards the outcome you want and it helps guide you along the way. Yes, it will be used to optimize queries.

But you'll also be used to do things like configure a complicated connector like open flow to extract data from an Oracle system and put it into Snowflake a whole lot faster. But it goes back to my point this is a net in which product development needs to be rethought in a fundamental way, but product rollout and how people like solution engineers or services engineers use software also needs to change in a big way. And we think it's going to have a dramatic effect on things like migrations. You touched on that earlier.

During the entire time -- Dave known Snowflake, 2.5 some years, migrations have been gated by the capacity of the Snowflake team and our partner team to handle them safe. Each migration is a high state exercise because there's some critical system that is sitting behind a business owner saying, you better be exactly the same before an -- but we think AI can be a huge axle rent in making those go faster. They all follow the same bucket of how do you use AI to make the act of doing these complicated data jobs just a whole lot faster and safer.

That's interesting. So that could be an accelerant to that migration activity in coming years.

I think there are step changes to be made. I've consistently talked about it for the previous 3 quarters. I have a few pet projects that I personally pay attention to AI-driven migrations is one of them because the potential is just...

Maybe a couple of thoughts on some other developments in the space. Fred asked you this question back in the summer, you may not remember, but I pointed out that a lot of the SaaS ponies, the app vendors that this group pays attention to are all in various ways, Salesforce might be a good example. Stepping from the roots as workflow automation SaaS firms into the data arena. It feels like every SaaS company is attempting to become in part, a data company as well. What are your thoughts on that transition? And is there any part of the database that they would or of have to win and beyond which might be a little bit out of their wheelhouse.

I mean just to put a little bit of historical context into the most SaaS vendors were operated basically transactional systems. These are systems of record. You go into Workday if you want to file PTO or if you want to hire someone new, somebody goes and can entry there. Reporting for these folks was always an afterthought. I either on the ADR team, we had a reporting team. But to be honest, that reporting team was a little bit of a tax on my regular team. I'd rather them work on how to make more money, not give more stats to advertisers. That was a general attitude that all SaaS vendors had about reporting and analytics. And it's part of the reason why platforms like Snowflake, that's specialized in being very good at analytics even came off age because we were very good at doing that.

And over the past many years, we have established ourselves as a place with different kinds of data can be brought together, juxtaposed to get more of the 360 view of what's going on within an enterprise that we all create. With AI especially, but even before that, with analytics becoming more and more prominent, people are beginning to understand that the mechanics of having deeper insights on how a system functions or how processes of functioning is an essential part of making this more efficient.

There's more and more of a realization that there indeed is a closed loop around data. And AI accelerates this because people now understand that if somebody, Snowflake has a copy of all of the most critical data about a company, it can be the place where decisions can be made about what do you optimize, what do you do next?

And hence, the many, let's call it, aspiring data clouds and one seems to come up every other month or so -- and roughly, in terms of our right -- first of all, we view -- we don't view this as a zero-sum game. I think there's lots of value to be created. We have gone and basically done bilateral partnerships, let's see, with Salesforce, with ServiceNow, with SAP, with Workday, several others are in the works for basically these kinds of agreements. And the idea very much is, by doing this, these folks are able to create products that can make money. Because this data is indeed valuable. But we make money as well because with these bilateral agreements, we can take the data juxtapose it is other data. Our customers like you end up getting a lot of value from using Snowflake it's not a zero-sum game. There will be agent solutions developed on top of Snowflake. There will also be agent solutions are developed on top of the platforms that these folks provide and it's a little bit of made the best product win. And we feel good about where we are because we've been doing this for a very long time.

Sridhar, you've always had a rival some large like Google and Microsoft. But let me just give out a smaller one that's hit my radar and I think others that click house. So I think they're well known for low latency analytics, jobs, especially certain log events -- what are your thoughts on that? And where is Snowflake on its journey to frankly launch eaters that can frankly do that?

Well, interactive analytics are an interesting category. And as you correctly point out, one that Snowflake hasn't always paid attention to. And a number of our customers even are a large one whenever they want to serve data from Snowflake. By that, I mean, put data that's in Snowflake in front of users like you with very tight latency requirements. If you're looking at a trading screen and you want to see some summarized data, you have a low tolerance for that thing taking even a second. You wanted to paint immediately. It's not an area that we paid attention to.

We think it's a natural adjacency to what we are doing. We have actually introduced a product feature called Interactive Analytics that is focused on high-performance analytics, our aspiration very much. And I can speak to someone that's run load tests on these systems is proud to be like sub 200 milliseconds for simple queries so that it can be deployed at scale.

And the underlying Snowflake technology is sort of truly amazing, and we can support hundreds of queries per second, which can translate to millions, if not more, of users right on top of Snowflake, they involve different trade-offs from our regular snowflake systems, but this is what we are really, really good at. There's a crack team that's working on it. and it's coming along well. I think it will be an interesting category for us.

Yes. I look forward to seeing that next year. Brian, a new set of eyes on the margin structure at Snowflake. My view is that there's actually pretty good EBIT margin potential at this room. Maybe that's one of the things that actually attracted you to the platform. But Sridhar and team have built an at-scale $4 billion to $5 billion revenue company, yet in my view, it's got an EBIT margin structure with room for improvement. Do you share that view? And where do you think the improvement over the next several years can come from?

Yes. Absolutely. I'm a big believer, and I think you can look at the company's now past, GitLab and VeriSign that you can grow, but you can do that responsibly. And so Sridhar and I are 100% aligned. One of the things that I just recently done was we sat down and gave out the annual operating plan to all the and really driving accountability by using AI, getting more efficient and just not throwing more bodies at a problem. And so this is just a ginormous market. It's a super interesting market. It's changing all the time. And so we're very, very focused on growth, but we'll do that responsibly.

Yes. Okay. We've got a minute, too, and it might be good for you to ask Brian, anything on your mind related to the print. I give you a chance I think we have a hand up Malcolm. Yes, I think you can just shout it out.

[indiscernible]. Your thoughts on that with regards to the opportunities.

Yes. I would phrase this much more as data and Snowflake has gravity, and we are making it easier and easier to bring data into Snowflake. But our super power with that data is that layer of governance and security that we put in. People that bring data into Snowflake often will set up fine grain permission. And we can handle that at absurd scale. Tens of thousands of roles, intricate relationships between both modeling a complex company that has 100,000 people. And their applications become interesting is there are a whole set of folks within these enterprises that say, I want to build a slick interactive application, but I don't want to relitigate decisions about governance and who has access to what data how can I make it super easy for it just works of the Snowflake system. Streamless was one such attempt added. And honestly, like this was 2, 3 years ago, we didn't do such a great job of making it performing and easy to use.

Again, AI is a big game changer here, part of our thrust with coding agents. now and again, I've done this, you pretty much write 2, 3 sentences since I have this data set in these tables, help me make a stream let to visualize the data, outcome the options and you can tinker with it and so on. But we also recognize there is now an entire ecosystem of companies that have specialized in rapid applications.

All of you folks, I'm sure, know about companies like Fire. They have a great new product called Zero which is their coding assistant, you can develop just beautiful react apps with very, very little programming. We announced a private preview with them just a few weeks ago, and we are in to get it out. where you can build an app in a vessel environment, but deploy it securely into Snowflake just a push button that says deployed to Snowflake and then you now have a modern react customizable app that is running within Snowflake security perimeter or base rules or base pharmacies, all of that stuff. And your teams then don't have to worry about, well, do I have to manage a separate hosting environment. Do I have to worry about permission? it's increasingly that kind of stuff that we want to do. There are many others in the space, whether it's a rule or a lovable -- we see a slew of these kinds of partnerships coming that marry the best of app technology with the incredible staying power and gravity of data and secure governance.

Why don't we leave it there? I think we're out of time. Sridhar and Brian, thanks much for coming here for our event.

Good afternoon. Thank you for attending today's Snowflake Q3 Fiscal Year 2026 Earnings Call. My name is Jen, and I will be your moderator for today.

[Operator Instructions]

At this time, I'd like to pass the conference over to our host, Katherine McCracken. Please proceed.

Good afternoon, and thank you for joining us on Snowflake's Q3 Fiscal 2026 Earnings Call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; and Brian Robins, our Chief Financial Officer.

During today's call, we will review our financial results for the third quarter fiscal 2026 and discuss our guidance for the fourth quarter and full year fiscal 2026. During today's call, we will make forward-looking statements, including statements related to our business operations and financial performance. These statements are subject to risks and uncertainties, which could cause them to differ materially from our actual results. Information concerning these risks and uncertainties is available in our earnings press release, our most recent Forms 10-K and 10-Q and our other SEC reports.

All our statements are made as of today based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During today's call, we will also discuss certain non-GAAP financial measures, see our investor presentation for a reconciliation of GAAP to non-GAAP measures and business metric definitions, including adoption. The earnings press release and investor presentation are available on our website at investors.snowflake.com. A replay of today's call will also be posted on the website. With that, I would now like to turn the call over to Sridhar.

Thanks, Katherine, and hi, everyone. Thank you all for joining us today. As every company transforms to embrace the AI era, Snowflake remains at the center of today's AI revolution. We have delivered yet another strong quarter, thanks to the hard work and dedication across our team to help our customers realize value through all their end-to-end data life cycle, and effectively harness AI's potential every step of the way.

Our continued focus on operational rigor and close knit products and go-to-market execution has helped us maintain strength across our core business and innovate rapidly to bring new capabilities to market. We are executing with urgency and focus and maintaining deep partnerships with our customers that enable us to capture the opportunity in front of us and sustain durable momentum. Product revenue in Q3 was $1.16 billion, up 29% year-over-year. Remaining performance obligations totaled $7.88 billion, with year-over-year growth accelerating to 37%.

Our net revenue retention remained stable at a very healthy 125% and we added a record 615 new customers this quarter. As we continue to deliver strong revenue growth and healthy results, we are increasing our growth expectations for the year and reiterating our margin target. As I've shared, Snowflake is on a mission to empower every enterprise to achieve its full potential through data and AI and we are making incredible progress against that mission every day.

We continue to double down on what makes Snowflake unique, delivering an AI data cloud that's truly enterprise-ready with a radical focus on our customers. Snowflake is intuitive and easy to use, seamlessly connected for collaboration and built with the security and governance that enterprises trust as their foundation. That's why customers like Coca-Cola Consolidated, PayPal and thousands more are transforming their businesses with Snowflake. And it's why more organizations than ever are going all in on Snowflake as their foundational data and AI platform.

Already, Snowflake is the cornerstone for our customers' AI strategy. In Q3, more than 7,300 accounts are using our AI capabilities every week. Just recently, Morgan Stanley named Snowflake its Strategic Partner of the Year, recognizing how our AI Data Cloud is accelerating their transformation and driving AI innovation across one of the world's leading financial institutions.

And with the general availability of Snowflake Intelligence, we are seeing the fastest ramp in product adoption in our company history. Already 1,200 customers are harnessing next-generation agentic AI capabilities to drive real business impact at scale. Snowflake Intelligence is transforming how businesses interact with their data, turning natural language into real-time actionable intelligence. For example, [indiscernible] Imagine, a global SaaS platform for financial services, use Snowflake Intelligence to build an AI agent that now handles staff equal to 8.5 full-time employees.

The agent helps users manage and query data, make faster trading and risk management decisions and automate customer case resolution, increasing transparency across teams and with clients. And Fanatics, the global leader in sports merchandise and e-commerce, uses Snowflake intelligence to connect billions of fan data points across shopping, collectibles and gaming platform for more than 100 million fans worldwide. This Unified Data Foundation helps Fanatics better understand its customers, boost sales and grow its advertising business, following the launch of new Fanatics advertising audience network this year.

This momentum has enabled us to achieve a major milestone. $100 million in AI revenue run rate achieved 1 quarter earlier than anticipated, thanks to our pace of innovation, cross-functional collaboration and early adoption among many of our marquee customers. Because we operate as a consumption-based business, this number reflects real-world enterprise usage. It's a direct signal of how customers are using our AI capabilities in production to create value today.

What's more, our AI capabilities are strengthening our customer relationships and expanding the value we deliver across every stage of the data life cycle. AI is a key driver of the strength that we see in our core business. In Q3, we landed a record number of new logos and continue to build strong momentum with AI influencing 50% of the bookings signed this quarter. We also deepened relationships with existing customers as 28% of all use cases deployed during the quarter incorporated AI.

But being enterprise-ready is not just about innovation, it's also about reliability. When a major cloud service provider experienced an outage this quarter, our disaster recovery capability seamlessly transferred more than 300 mission-critical workloads to backup systems, ensuring business continuity for our customers when it mattered the most.

Our commitment to making business critical capabilities, just work continues to resonate with our customers. And so we've built on this strength by expanding not only our product capabilities, but our ecosystem. This quarter alone, we announced new partnerships with Workday, Splunk, Palantir, UIPath and more to deepen integration, enable secure and seamless data access across the systems our customers use every day and unlock new innovations like agent to agent collaboration.

More recently, we announced a landmark partnership with SAP to unite mission critical business data with the Snowflake AI Data Cloud. We are already supporting customers like AstraZeneca to access and analyze real-time data. These partnerships amplify our ability to deliver value to joint customers and extend our go-to-market reach. Our progress is clearly resonating with our global community.

During Snowflake's annual world tour, over 40,000 customers, partners and prospects, joined us across 23 events, a record-breaking turnoff representing a more than 40% year-on-year increase in participation from last year. More recently, our annual build Dollar per Summit saw a 43% increase in attendance year-over-year underscoring the growing excitement and engagement across our global audience. Behind this incredible momentum is our relentless focus and continued delivery against our product strategy.

Throughout the quarter, Snowflake maintained a rapid pace of innovation, bringing our total GA product capabilities to 370 year-to-date, a 35% increase over last year with AI being front and center. As I shared, Snowflake Intelligence continues to set the tone for enterprise-grade agentic AI.

Just recently, we announced that Snowflake is the official data cloud provider for USA Bob [indiscernible] skeleton, powering their journey to the upcoming Olympic Games. The team is using Snowflake Intelligence to unify and analyze data across its performance ecosystem to optimize, push performance and equip coaches with data-driven insights to create a competitive edge on the eyes for a metal worthy perform.

At the core of our AI philosophy is customer choice and flexibility, empowering organizations to leverage the world's leading models securely on their own enterprise data. As you may have seen a few weeks ago, we announced a partnership with Google Cloud to make the latest Gemini models available to our 12,600-plus customers within Cortex AI and Snowflake Intelligence, further enhancing access and customer choice.

To drive even more tailored innovation, we introduced Cortex AI for financial services, a comprehensive suite of AI capabilities and partnerships that empower financial services companies to unify their financial data ecosystem, deploy AI models, applications and agents securely and meet the rigorous security and compliance standards for regulated industry.

Even as we supercharge the data life cycle with AI, we remain committed to strengthening our core data foundation. To ensure that Snowflake will continue to deliver the trusted performance and scalable data platform our customers rely on every day. Key capabilities like Snowflake OpenFlow are making it easier than ever to bring in structured, unstructured, batch or streaming data into Snowflake. [indiscernible] GO, which is using open [indiscernible] to simplify and speed up how it ingests data across its EV charging network. By consolidating multiple data pipelines into Snowflake, eVgo has reduced latency, improved reliability and gained a more complete view of its customers and charging stations. And we are continuing to extend our value through strategic acquisitions. We recently acquired the technology behind cytometry software migration solution, which will enable our customers to move from legacy data warehouses to Snowflake at lower cost and with minimal disruption, further simplifying their journey to our AI data cloud.

We have also agreed to acquire Select Star to enhance our Horizon catalog and deliver a more complete view of an enterprise's data estate. We believe this richer context will empower agentic AI experiences like Snowflake Intelligence to better understand enterprise data and uncover deeper insight. As we scale, the breadth and depth of our product capabilities, we continue to maintain tight integration across sales, marketing, product and engineering to effectively launch and scale new offerings and deepen our customer relationships.

This alignment is driving tangible results. Q3 marked a strong bookings quarter underscored by accelerating RPO growth and healthy customer retention. At the same time, we are investing in and strengthening our strategic go-to-market partnerships. In addition to those I've already mentioned, today, we've announced an expanded partnership with Anthropic. This brings native model availability into Snowflake and also introduces a new joint go-to-market motion designed to accelerate enterprise AI adoption.

We also continue to build our strong relationships with major cloud providers. In fact, Snowflake has already surpassed $2 billion in sales through AWS Marketplace in a single calendar year and was just recognized with 14 AWS Partner award wins, more than any other ISV provider. This underscores the extraordinary demand for Snowflake's AI data cloud.

Momentum is also accelerating with our global systems integrators. Accenture just launched a Snowflake Business Group, committing to train over 5,000 professionals on Snowflake solutions to help joint customers realize AI value faster. Already, Accenture and Snowflake are helping customers like Caterpillar, unlock the full value of their operational data. This collaboration is improving quality in manufacturing, providing timely insights for finance and helping teams share knowledge and solve complex challenges faster.

As you can see, this was a milestone quarter for Snowflake defined by exceptional advances in product innovation and incredible customer momentum. As we deepen our strategic partnerships with the world's leading cloud service providers, AI model developers, SaaS providers and global system integrators. We're unlocking new levels of performance, accessibility and AI-driven insight for our customers while expanding the value and impact of the Snowflake platform across industry. I'm incredibly proud of our team for their efficiency and discipline they continue to demonstrate across the business.

Our operational rhythm remains strong and as we invest strategically for long-term growth, we are building the foundation for sustained scale and high durable growth. To help lead us through this next phase, I am pleased to introduce Brian Robins as our new Chief Financial Officer. Brian brings extensive experience as a CFO across high-growth software companies and a deep understanding of scaling financial operations with discipline. Brian, why don't you take us through some of the financial details.

Thank you, Sridhar. It's a truly exciting time for me to be at Snowflake. In Q3, we delivered strong results across revenue, bookings and margins. Our product revenue grew 29% year-over-year, fueled by durable growth in our core business and continued expansion into data engineering and AI workloads. Together, these factors contributed to a stable net retention rate of 125%. Financial services and technology verticals led growth in Q3. We continue to see significant opportunity to expand within our existing customer base. Our Global 2000 customers now totaled 776 with each of these accounts spending, on average, $2.3 million on a trailing 12-month basis.

Many of these customers are still in the early stages of their Snowflake journey with ample room for further growth. Q3 was an excellent quarter for go-to-market execution. We achieved strong booking results, signing 4 9-figure deals. This represents a record number of large deals signed in a single quarter. Our focus on new customer acquisition continues to show yield. As Sridhar mentioned, it was a record quarter for new customer wins, adding over 600 new customers. Our ability to expand with existing customers and bring new ones onto the platform, underscores the strength of our business model. Equally important, we continue to operate with financial discipline, delivering healthy margins as we scale. Q3 non-GAAP product gross margins was 75.9%. Non-GAAP operating margin expanded more than 450 basis points year-over-year to 11%, reflecting our continued focus on driving greater efficiency across the entire company.

As a reminder, we intentionally front-loaded our sales and marketing hiring in the year. Non-GAAP adjusted free cash flow margin was 11%. In Q3, we used $233 million to repurchase 1 million shares at a weighted average price per share of $223.35. We still have $1.3 billion remaining on our original authorization for $4.5 billion through March of 2027. We ended the quarter with $4.4 billion in cash, cash equivalents, short-term and long-term investments.

Moving now to our outlook. For Q4, we expect product revenue between $1.195 billion and $1.2 billion, representing a 27% year-over-year growth. We expect non-GAAP operating margin of 7%. We are raising our FY '26 product revenue guidance. We now expect product revenue of approximately $4.446 billion, representing 28% year-over-year growth. We are reiterating our FY '26 margin targets. We expect non-GAAP product gross margin of 75%; non-GAAP operating margin of 9%; and non-GAAP adjusted free cash flow margin of 25%.

Before moving to Q&A, I'd like to share my perspective on my first 60 days here at Snowflake. Three key takeaways have truly stood out: First and foremost, I've been incredibly impressed by the caliber and energy of the Snowflake team. There's a sense of winning energy in every meeting and profound pride in their daily work. Specifically, the depth of the bench within our finance organization is exceptionally strong and really support our next phase of growth.

Second, I prioritize spinning my initial weeks meeting with customers. The customers I spoke with were fanatical about Snowflake and the transformational impact our platform has had on their business. They are placed in the AI data cloud at the absolute center of their strategic initiatives, underscoring our essential role in their future.

Finally, the velocity of our product releases and innovation engine is world-class and consistently sets us apart. Snowflake sits at the intersection of a massive market opportunity and I could not be more excited to be part of scaling this phenomenal team and sees an amazing growth ahead. As we look forward, my focus is on continuing to deliver efficient growth. I believe that continued alignment across our finance, go-to-market and product teams will enable us to balance growth with disciplined execution. With that, I'll now pass the call to operator for Q&A.

[Operator Instructions]

Our first question comes from Sanjit Singh with the company, Morgan Stanley.

I had one for Brian and one for Sridhar. Brian, first for you, when we look at the growth rates on product revenue this quarter, really attractive at 29%. It was just about 3% beat slightly below 3% beat [indiscernible] versus the midpoint of guidance. But at the same time, when I look at your Q4 guide, is probably the best sequential guide I've seen from the company in a couple of years. So I was wondering if you could help us square that.

And then for Sridar, like really impressive in terms of getting to that $100 million AI revenue run rate. You mentioned on the press release that Snowflake Intelligence 1 of the fastest adopting products. So wondering if you can give us a color on the types of customers that are taking on your AI products, some of the use cases that Snowflake Intelligence is unlocking. And also if you could comment on kind of Cortex AI adoption.

Thanks, Inge. I'll answer the first part of the question on the financials. We're happy with the performance this quarter. We delivered 29% year-over-year revenue growth. the quarter pretty much played out as expected. There is really only 1 surprise in the quarter, and that was a hyperscaler outage, which impacted our revenue approximately $1 million to $2 million within the quarter. I think it's really important with the consumption model that not to view quarterly beats as the best signal of the fundamentals within the business. The quarter, as you mentioned, we raised our fiscal year guidance by $51 million or $4.446 billion. And the FY guide is really the most meaningful signal. And I think the guide really reflects the underline behavior that we see in our customer base going into the fourth quarter. Sridhar, over to you.

Yes. No flake Intelligence amplifies the investments that our customers have made in putting high-quality data into Snowflake. To take our own example, we created a data agent on all of the sales information that matters from a sales team, whether it is consumption information or the Workday hierarchy itself of who is managing home information about customers, their use cases, and it's been a magical unlock for several thousand people because things that they needed to painfully find dashboards for, they can have answered immediately. Plus, you also get the benefit that unlike a dashboard, which is a 2D representation of a pretty complex space, you can ask questions that cut across any dimension, analyzed data in ways that previously were simply not possible before. And so we have a slew of customers, whether it is the USA Boxer team, our Fanatics. Our folks like ServiceNow or TS Imagine that are using this to create data agents specialized for some areas. So anyone that is working in a particular function, for example, has all of the data that is relevant to them available from a single interface and right on their phone or laptop computer.

It is that unlock of access to this data that is driving adoption. What I can tell you is like I -- whenever I have dinner with CIOs or with CEOs, and we are talking about them often, they turn out to be Snowflake customers and they end up showing off Snowflake Intelligence on my phone, usually to show them information that I -- we have about their companies, like how much they're spending, what use cases they have deployed.

And the first thing that comes from them is they want this for their own business. That's the attraction of Snowflake Intelligence, which is it puts all of the data that matters to you right at your fingertips -- and unlike before, this data is not confined to analysts. This is to every single business user within a company, and that's the big unlock file.

The next question comes from Kirk Materne Run with the company, Evercore ISI.

Sridhar, I was wondering if you could just talk about the go-to-market. You guys mentioned you had a really nice quarter. And I was particularly interested in the 600 million new customer wins. And I realize you all land and small and then grow with your customers. But -- with AI coming on in Snowflake Intelligence, are you landing with more products now, meaning is it still landing with the core data warehouse and then expanding? Or are you all able to land with multiple products at once and then grow from there. I'm just kind of curious about whether your surface area is growing within some of these new customers.

Kirk, thanks for the question. Well, I think things like intelligence now play a key role in making the power of data come alive every single time we are pitching a new logo. 1 of the magic of recent advances in AI is our ability to do demos or POCs, proof of concepts, that are hyper customized for each customer.

Often, we will generate a synthetic data set that they will mimic an oil producer or a pharmaceutical company and show them the art of the possible previously when people got onto Snowflake, it was for an abstract need. It was to make data more efficiently accessible so that you could do more analytics.

Now we do the work to show them what is possible with a product like Snowflake intelligence on top of their data. It just makes the value of the transition from previous systems onto Snowflake even more clear and those are some of the stats that we've been sharing with you, which is AI having helping hand, it's not the dominant thing but definitely having a helping hand in more than -- in close to 50% of the new logos that we acquired. I would say definitely opens up our aperture.

On the other hand, I would add that products that are lower down the stack, products like OpenFlow are taking off because they actually help make the other side the data life cycle more efficient. I've used open flow. It's pretty magical to be able to sink data, whether it's from an Oracle OLTP system or from Google Drive onto Snowflake, I think true investments like that are also helping us substantially in just accelerating what people do with us. previously, we used to be just the analytics provider, but we can be there from soup to nuts with products starting with Openflow, but then things like Snow Park, obviously, our analytics engine, then ML and then AI. It's where this breadth of offering and the complete data offering will end up playing a larger and larger role.

Our next question comes from Brent Thill with the company, Jefferies.

Sridhar, good to hear the news on AI bookings influenced. I guess many are now turning to the go-lives. And when do you expect this batch of go-lives to go up that then helps re-influence the -- even more excitement on the platform? How do you think about the trajectory and -- does that -- does it have a bigger ramification in the back half to '26 then as those deals go live?

Well, you're seeing it live, right? We gave guidance for Q4. It's a pretty hefty beat and raise. And that is driven by what we see in consumption trends. As you know, we tend to be pretty disciplined about how we forecast and guide. These are based on machine learning models. Unsurprisingly, that predicts the future. and we are disciplined in following that.

On the other hand, we track the other side, which is how many use cases are we winning, what is the time duration from a win over to a technical implementation over to a go-live and accelerating go-lives will continue to be a priority. And we're using AI pretty heavily in making some of these use cases go live a whole lot faster as well. and all of these feeds into the forecast and guides and the general optimism that we convey to you.

Great. And if I can just for Brian, on Anthropic, the $200 million partnership [indiscernible], is that in backlog or what goes in the backlog from that relationship?

The $200 million is a buy side that we're buying from Anthropic.

And in some ways, obviously -- our confidence in being -- in having AI drive more and more of our revenue, it is a commitment. But as you see the front side of things like the AI consumption revenue ARR that we announced, the $100 million ARR, that's what gives us confidence that partnerships with Anthropic, which include a buy, but also a broader go-to-market motion will continue to accelerate the overall business.

The next question comes from Brad Zelnick with the company, Deutsche Bank.

This is Dan on for Brad. Just wanted to ask maybe Sridhar to start, just if you can kind of help frame the impact that migrations had to product revenue this quarter versus last quarter? I know there were some kind of unique circumstance last quarter where some positive things came together to drive a pretty strong result. But just in general, I think across all of the cloud names, we've seen pretty strong momentum this year. And just as you look at kind of the visibility and pacing here that you have into that maybe just the sustainability of what you're seeing on that side?

And then maybe one for Brian, just on operating margins. I think 4Q operating margin was guided maybe a couple of points below where you guided 3Q and maybe a little down from what was implied in the guide last quarter. Anything just to unpack on op margins into Q4 for us to think about as we build our models.

I'll start. We are super early with migrations. I think you folks heard Matt Garman say today that he thinks maybe like they are 15% to 20% of the way through kind of on-prem legacy migrations. And that's positive news for Snowflake. And I see AI, I see products like Snowflake Intelligence exert both a powerful tool because the data that's in Snowflake just became more valuable because it can be used to drive business a whole lot more effectively, but I also see AI play a big role in pushing migrations forward.

In other words, making the act of migrating from legacy systems go faster. And this is where tuck-in acquisitions like the acquisition of cytometry, which makes products that make migrations go faster, easier are also helpful. We keep a close watch on migrations through the entirety of the use case life cycle, and it's something that we are continuously looking to accelerate, bring better techniques. It's an area that I've been personally involved with throughout the year, and we continue to make very solid progress.

Yes. Just real quickly on the 4Q guidance. All I would say is that 4Q is a little tricky in the sense given the 4Q guidance and annual guidance at the same time. And so don't read too much into that. There's nothing intended by meant to read into that.

Next question comes from Raimo Lenshow with the company, Barclays.

One question to stick to the one question rule. Sridhar, zero-copy comes up a lot in the conversation. And like every vendor is now talking about like, "oh, we're doing 0 copy that helps to kind of -- help us play better with everyone else in the ecosystem, et cetera." How do you think that will impact you? Is it kind of -- does it drive more adoption? Does it impact how much you can monetize? Can you speak to that, please?

Yes. Zero copy generally comes out in the context of SaaS vendors who are under a lot of pressure from their customers to share data. Many of them are busy creating data products on top of the data as a way to monetize. And Zero Copy or sometimes bidirectional data sharing agreements come up in that context as a faster, more efficient way for people to share data with each other. We see these as a win-win. We have these agreements with, let's say, ServiceNow, Salesforce, SAP with the recent partnership as well as Workday. These products continue to drive our broader mission to be at the center of all of the data needs that our customers have and they just make the process of data collaboration between the SaaS vendors and Snowflake just a whole lot easier.

And we are very happy with these agreements. And what this means is that Snowflake will continue to be the place for our customers to get like that single -- that stable single pane of glass sort of view on everything that matters to them. And obviously, with agent AI and agent systems now, the value that you can get from the data is tremendous.

I can tell you from personal experience that I'm not thinking when I'm looking at my sales data agent, about whether this data comes from Workday or from Salesforce or from our own systems, I can focus on the logic of what needs to get done. And the rest of this stuff works as though it is magic. And so zero-copy agreements just make data flow more smoothly and I think are a big step forward for everybody involved, Snowflake, but most importantly, our customers.

Our next question comes from Mark Murphy with the company, JPMorgan.

This is Ari on for Mark Murphy. Congrats on the strong quarter and continued momentum. I know you've touched on this [indiscernible] throughout the call here, but we spoke to a Fortune 150 customer, recently and they described Snowflake as the most important piece of their AI and data strategy and explicitly stated that Snowflake budget is now tied to their AI budget, and they're kind of broadening their adoption of products on the Snowflake platform. So my question is, are you kind of seeing that sort of tying explicitly from customers of their Snowflake investments to the AI investments? And if so, how is this influenced in the buying habits? Are they entering into larger, longer-term contracts, are they adopting more products or just any new customer patterns you're seeing emerge?

Yes. The strongest pattern that we have had to work hard and earned this year is to be that genuine player when it comes to enterprise AI. And no amount of talking can make you that, you need project, products that produce the magic. And so building on earlier products like Cortex Analyst as well as Cortex Search, Snowflake Intelligence [indiscernible] agentic platform that can use these different subproducts flexibly is the big unlock for us. And what you're also seeing is a number of these customers have tried to string together agent systems by, let's say, creating MCP servers on tables and sticking them into our foundation model, and then they realize that solutions like that don't actually work all that effectively.

Part of what we provide are systems that can help them thoughtfully structure the data that then needs to be exposed to an AI agent and a careful amount of tuning that makes sure that these systems are failsafe, that they're reliable and can actually answer the questions they are supposed to. We also work with our customers on things like unheralded, but really important things like eval where they can judge ongoing performance so that they know that they're actually making their systems better.

It's a combination of all of this expertise. Yes, the partnership with the big foundation model providers to bring the best models as part of Snowflake. Combined with our unrivaled expertise in data and modeling to help them create AI products that deliver value. And if you combine that with products like Snowflake Intelligence that now like are clearly valuable and useful for every business user, I think that's the narrative shift that you're seeing in a number of these companies. And agentic AI is still evolving. We have a lot more -- we have a lot more to do. That's part of the reason why I keep repeating being in the center of enterprise AI because we are already the holders of the most valuable data that many of these enterprises have and then we are bringing the power of AI to get even more value from this data.

Your next question comes from Kasth Rangan with the company, Goldman Sachs.

This is Matt Martino on for Kash. Sridar, I want to stick with the AI topic. The number of customers leveraging Snowflake AI is accelerating very, very quickly within your installed base, and you are going to pull forward that $100 million in AI revenue, which very few of your peers have been able to do. From your perspective, what about the Snowflake platform is allowing customers to really accelerate their AI journeys? And maybe secondarily, do you see the market increasingly standardizing around a smaller subset of platforms to handle all their data requirements given your commentary about Snowflake really sitting at the center of the AI opportunity.

Yes. I think to take on your second question first, I think there is a lot of complexity in the data space. I know of the number of different tools that not like the company itself has had to use to have an effective data -- to have an effective data strategy and with things like Snowflake Intelligence and [ stream lit ], which we are very heavy users are we are just able to do more with Snowflake. And again, investments like Openflow or even Postgres are going to expand the aperture of what we are -- of what we are able to tackle as the data platform.

Next question comes from Alex Zukin with the company Wolf Research.

Maybe for either of you, Brian or Sridhar, clearly, the momentum that you're describing is showing up in bookings. So I just maybe better understanding the confidence and conviction around -- and maybe the direction of travel for the expansion rate as we continue to see some of these go-lives and an explanation of how the consumption patterns, particularly as you start to see customers leverage the AI portfolio and the other -- and the multiproduct portfolio more broadly?

And then, Brian, any timing elements last quarter, it seemed like there was a little bit more of a onetime bump or boost to product revenues from consumption from some very large deals in the quarter, but then this quarter, you also had super large deals. So is there something where they maybe happened a little bit later. And last quarter, they happened a little bit earlier that maybe drove that beat magnitude cadence to be a little lower.

I can start with the first one. The virtuous cycle of Snowflake customer is one in which they sign a deal. It has a certain amount of slack capacity that is built into it, that our teams then use to expand into use cases that can deliver value for our customers. And to actually address a previous question that I had left unaddressed, that was the first part of the previous question. Part of what drives broad adoption of AI with Snowflake is that we make it easy to do.

It's not a brand-new system. You don't have to resolve the existing problems like governance and access control. And we have made it super easy to first build chatbots and then to build more complex agentic systems like Snowflake Intelligence, which is why some 1,200 -- 1,200 customers are already using Snowflake Intelligence. And as we expand and deliver value, these then naturally result in more confidence in more conviction on the part of the customer that they're getting value from Snowflake. And remember, in all of this, they don't have to make any pre-commits towards AI, the value that they get is like it has to be delivered by the products that they build on top of Snowflake. This risk-free approach driven by our consumption model is what makes AI super attractive for our customers on top of Snowflake. We make it easy to use, we don't require them to commit and then they naturally expand out the ones that are creating value.

And I'll just touch on the second part of your question, and I'll hand off to Brian. Large deals that we sign don't tend to have immediate impact on revenue within the quarter. If anything, as soon as a large deal is signed, they typically get a better discount. So it tends to be slightly negative with respect to revenue. But as I said, these are long-term cycles. Our customers on average sign deals with us once every 2.5 to -- 2.5 years-ish on average. It's not really directly tied to consumption and within a quarter, and I would not read too much into timing constraints like that. Brian?

Yes. Absolutely, Sridhar. I guess I would emphasize that product revenue is still the leading indicator of our business, and we saw that in really the migrations and increased use case wins. We're also happy with the developments in AI and also the data engineering workloads. We look at the consumption patterns up until today to inform our view of Q4. The quarterly beats are less indicative, especially in a consumption model, I would really look at the FY guidance as the best indication of the long-term business trends for a consumption model.

Our view of the business over the last 90 days has improved. And I think you can see that in our annual raise. This is also represented in the $7.9 billion in RPO, 37% year-over-year growth and all the new customer adds that we talked about in the prepared remarks.

Our next question comes from Patrick Patrick Colville with the company, Scotiabank.

I guess, Sridhar and Brian, one for both of you, please. You passed the $100 million consumption thresholds, really impressive to see that. I guess what do you see as the next milestone? And then could you just remind us what does that $100 million actually include? Is that equivalent to the Cortex suite? Or are there other products that go into that $100 million of consumption that you achieved this quarter?

Yes. The $100 million is primarily the product suite, but it's the whole stack. It is Cortex AI and AI SQL is accessible from SQL also as a [ Rest ] KPI. And then the products that stack up on top of that Cortex Search and Cortex Analyst, which are our unstructured and structured data products, respectively, on then Snowflake Intelligence, which builds on these building blocks to provide an agent solution for data products. That's roughly the suite. In terms of the next milestone, I think much broader adoption of Snowflake intelligence is certainly that we are driving. There is no reason for us to not have every single data set that is in Snowflake, be AI-ready and you're already seeing this play out in the collaboration space where instead of sharing a data set, you can, in fact, share an agent on top of that data set so that the recipient on the other side can straight out just start asking business questions of this data without needing to build dashboards and so on.

Obviously, in many situations, this data flows through programmatically and will be combined with other data. But my point is making all data in Snowflake AI consumable and making the act of making that AI consumable is something that we will be -- honestly be spending a lot of time on.

But the second and the third order impacts that I alluded to earlier, the pull-push analogy that I used I think that's where the impact is going to be a lot more profound. I think migrating from legacy systems, bringing data into Snowflake using products like open flow are being able to write data engineering workloads using our coding agents. All of those are going to get accelerated. I think that's where you're going to see like tremendous value that our customers can realize and tremendous potential for us as a business.

Next question comes from Brad Reback with the company Stifel.

Sridhar, the results are very impressive. The booking is super great. The op margin obviously down-ticked on the first half sales and marketing investment. As we look forward into next year and beyond, how do you think about balancing the huge opportunity in front of you and the ability to drive margin expansion?

I think we live in fortunate times where this is not an either/or. We clearly invested pretty heavily in our sales and marketing teams in the first 2 quarters because we saw a tremendous opportunity. And what we're going through now is a maturation of the folks that are here, and we expect them to aid us substantially, but we have also invested equally heavily in how do we make sure that we upskill our own labor force, whether it is engineers our solution engineers. We have rolled out coating agents for the folks. I talked earlier about how we want to make it super easy for every single rep, every single solution engineered to be able to do custom demos, custom POCs for our customers. Obviously, we have a big services team as well, making then AI native is a big transformation.

So we will -- the way we look at next year is, yes, we will continue to invest in the business, but I think there is also substantial gains to be had in just how efficient we are as a company. And I don't think of this as an either/or. We have had pretty healthy expansions in things like operating margin, but also things like SBC year-over-year, and we will continue to press hard on those things.

Yes. I'll just echo what Sridhar said. We can do both. It's not one or the other. Obviously, it's a really big market, and we've delivered impressive growth and we'll continue to do innovations in our product to drive that revenue growth, but we'll do that responsibly.

Our next question comes from Mike Cikos with the company, Needham.

Great. And Brian, congratulations again on the new role as CFO of Snowflake. Looking forward to working together here. My question comes back to -- I think there's been a couple of different attempts throughout this call with understanding, frankly, the magnitude of the product revenue upside relative to the prior quarter, where, to be frank, last quarter was more significant, but I really I attributed or I thought that you guys positioned it this last quarter, really saw some very large customer migrations, which is outside your control.

And so the question is, when we think about the increased confidence you're talking about for the year, the traction for data engineering and AI. Is it fair to think that 3Q here was just a strong execution quarter but maybe a more normalized return to typical migration activity? And then secondarily, just while we have everyone on the phone here, Brian, I would love to get your perspective on whether the guidance philosophy has changed at all the margin.

Yes. I'll start with the first one. We've consistently told all of you that we view a 3% beat as a very good beat and anytime we do much better than that, we go back. Obviously, the ML models recalibrate and we calibrate ourselves back to the 3% beat. So -- and there is also a natural variability in a consumption business because this is literally the agglomeration of 12,000-plus enterprises deciding what they want to do with their data futures. And so I view the Q3 beat is actually still a very solid beat at some -- some 2.5%. And yes, the Q2 beat, and we are upfront with you about it, had some large migrations that also had onetime activities, but we also have cautioned to you that large migrations are lobby and not all that easy, not all that easy to predict.

And that's roughly where we are. And as we look at things like Q4, we approach it the exact same way. We do the best job that we can of trying to figure out where we are going to -- where we are going to land and use pretty much the same guidance philosophy as we have before. Brian?

Yes. Thanks, Sridhar. Just to echo what Sridhar said as well, the quarterly variability is not the right way to evaluate the consumption model. Companies that do migrations, they don't do those due to our quarterly earnings calls. They basically -- we snap the chalk line and where they're at and their migrations are at. And so we really point you to the full year guide. And based on the behavior that we've seen up to the earnings call, we have the confidence to raise our full year guide, the $51 million to 28% annual growth year-over-year.

Just from a guidance philosophy perspective, there's a number of things that I did when I first joined, but one of the things that they would spend a lot of time with the team that wrote all the AI models. It does the forecasting on a daily business of our revenue. Super impressive team, very detailed, and I can assure you that there will be no change to the guidance [indiscernible].

The next question comes from that Matt Hedberg with the company, RBC.

Just a quick one for Sridhar. The $100 million AI run rate is super impressive. Wondering if you could give us just a rough sense for how quickly that's growing? And then maybe more of a detailed question. On the heels of crunchy data, curious if you can comment about just now that you've had more time, how customers thinking about that long-term balance of OLTP and OLAP within Snowflake?

Yes. As I said, AI revenue is predominantly driven by the Cortex product suite, including Snowflake intelligence. This is among the fastest products to get adopted by our customers because, as I said, the value is very, very clear as soon as someone uses Snowflake Intelligence. So we expect to -- we expect for this to continue to grow quite well. We don't really want to guide to it or hint at that right now.

With respect to Crunchy data, it will take us a couple of more months to get the product into GA, but all of the early conversations that we have is that customers are very welcoming of pulses support within Snowflake. They view Snowflake as an incredibly robust and reliable data platform. And for many kinds of applications, having them be hosted as part of the overall Snowflake deployment makes perfect sense for these folks. For what it's worth, Unistore, which is our HTAP product, is also doing well. It addresses a different segment of the transactional data space. And we will continue to have -- we will continue to have both of these, but I think bringing Postgres to market will be an important step forward for us, especially for things like agent solutions that need an OLTP store to function effectively. So there are a number of those kinds of use cases that we are actively working with our customers on.

Our last question comes from Tyler Radke with the company Citi.

Really impressed to see roughly $1 billion of RPO bookings in the quarter. I was hoping you could talk a little bit about the 3 9-figure deals that you added in the quarter. How are those to structured from an operation perspective and are you expected to see significant growth in those deals and how they got -- were they large expansions.

And then just a follow-up for you, Brian. Anything we should be thinking about as it relates to FY '27, whether it's headwinds or tailwinds in the model? I know you're not giving guidance, but just as we think about new products, optimization headwinds, anything you'd call out.

Well, as a matter of fact, Tyler, we had 4 9-figure deals this quarter. All of these folks are customers that are spending significantly with Snowflake and are very positive about additional value that they can bring. But bookings are an indicator of how much a customer thinks they're going to spend in the coming years. Product revenue is the best indicator of how our collective customers are going to be spending on Snowflake next quarter. And so that's the thing that I would look at. Brian, do you want to take the last question?

Yes. Tyler, as you mentioned, we'll guide to FY '27 on our next call. But what's really important is consumption after the holiday season, is the most important input for FY guidance for next year. And so we'll need to see the consumption behavior unfold in January, February, and that will give us better visibility to deliver that on our next earnings call.

At this time, I'd like to pass the conference back over to our host, Sridhar, for closing remarks.

Thank you, everyone. Snowflake remains at the center of today's enterprise AI revolution. And via Snowflake, our focused on empowering our customers throughout the end-to-end life cycle for data. This is an incredibly exciting time for the company as we continue to reimagine what's possible with AI and push the boundaries of innovation to lead in this new era. We continue to execute strongly as evidence of our product revenue growth and strong outlook for the remainder of fiscal '26, and we see a long runway of durable high growth and continued margin expansion ahead. Thank you all.

That will conclude today's conference call. Thank you for your participation, and enjoy the rest of your day.

You want to hear a short story before we get into this meat of AI and data analytics discussion. The time frame is 1995 to 1989. I know he looks really, really young. But the time frame is a 1985 to 1989, 2 guys go to school on either side of the same street. One, and you will guess who it is that we're talking about. And you get through the other person importantly, equally importantly, one person studies Computer Science at the top line college in India, just possibly fucose hurt to get in.

The other guy settles for maybe a top 10 school but mechanical engineering because cannot figure out this computer signed stuff. This other guy, and it's becoming very evident who this other guy is, tries to program on an IBM 360 mainframe punch card gets this syntax wrong in a Fortran program. The first time we ever tried to program and said, "I'm never going to do anything to do with computer science ever in my life."

In the meantime, this other person not only gets a degree in computer science, but it goes on to get a PhD and goes on to run a company, a tech company. I guess by now you know who the 2 are. And so I'm very proud to call you somebody that I did not even know, but I never knew that somebody from the other side of the street.

It's a small world.

It's a small world. And we have common friends. I just found out that we have some really, really good common friends. On that note, personal note out of the way, a warm welcome to Goldman here.

Thank you.

I think it's the first time we're doing this conversation together. So let's talk about what is your vision for Snowflake in the next 4 to 5 years? You've got a rich background, you were off to a quiet the company is rejuvenated. It feels like it's breathing another dimension of life in its relevance and its core and the opportunity. So not to put you on the spot, where are you going with this in the next 4 to 5 years?

Yes. For the past 10 years, data has been at the center of many companies, but mostly in the context of how do we do it more efficiently? CIO is cared about it. And all of a sudden, with the advent of AI. People are increasingly realizing that high-quality data is going to be at the center of how they transform their enterprise. That's our aspiration to help enterprises realize their full potential with data and AI and all companies start with a certain history. We came off history as an analytics platform.

And what we are doing, and it's an ongoing process. is to become an all-encompassing data platform from inception when data is first man to insights, that sort of feedback into how systems should operate. And AI is both a consumption layer. You can get the information faster. It's also a massive accelerant of the value creation cycle. And that's what we aspire to be. It's an exciting time to be at the center of data and AI. But I joke to people that actual mainstream journalists asked me questions about things like Iceberg, [ Microlly, ] [ Open Format ], but I think it is a reflection of the times that we live in, how much AI is changing our work and that all the data is going to play in driving that change.

Got it. I wanted to ask you, you've had a rich banker at IBM. You ran the ads business, you're VP of ads. What about that experience has informed you better to be able to run a company like Snowflake?

I'd say 2 things. One is sort of an intuitive understanding of the power of data when it comes to creating great systems. Google exceptionally lucky that it landed on a business model that at its core, both in search and in ads was a feedback loop. Search, as you all of you know, came up age with the page rank, which you can think of as the feedback loop of popularity. You are a great page. You've a bunch of other great pages pointed to you.

Similarly, with search ads, I would drive our advertisers crazy when they ask me, what should I put in my ad to make sure that people click on it and convert on my site. And my genuine answer would be Well, I don't know. But if you put the right things, you'll make sure you show your rates because the feedback loop would pick that up. To me, that's a -- and everything that we did in aid of that. We built some amazing streaming systems back in 2005 because you needed that to support that kind of scale. And it's very much infrastructure as an enabler of massive business outcomes.

That's the early part of my career. And the latter parts of my career were then about how do you wield an actual incredibly large business through tons of change. The mobile change was terrifying for Google because credit growth on desktop, which was the driver of our revenue increases had pretty much flattened out by 2019 and things like the mobile revolution, we're still a twinkling sort of in our eye, had not really exploded. How we made the transition, what it took to steer companies through very large internal transformations of their business was also a particularly profound lesson.

And it's a combination of these 2: the power of technology to change the course of businesses, combined with what does it take to run a large business and navigate through change moments that feel incredibly daunting. Mobile was certifying because that was the only place where we saw growth and mobile queries made 10 revenue that desktop queries. And -- but it's the confidence that comes out of being able to navigate through changes like that.

And it's not like the thing that I tell our customers, CEOs that I talk to is we want to bring world-class technology in data that can let them compete on an even playing field with the giants, with the Googles and the Metas of the world. And that's how easy we want to make our technology relevant and applicable to our enterprise customers, especially in the era of just massive, massive change.

So the core of Snowflake data analysis, old world investors who would say, that's data warehousing. So this is data warehousing the cloud. I'm sure you have a different view and a different frame with which you view your market opportunity. How different is that frame with which you view the opportunity? And why is it so why is the conventional wisdom that it's a data warehousing company with a limited TAM in the cloud so wrong?

Yes. Because platforms evolve over time. And what used to be what used to be "just air over our housing", became an incredibly scalable analytic platform in the cloud that could also do machine learning so that you could begin to feed the value of that data back into systems. Disney, for example, uses us to optimize guest experience when people are visiting in their park also from your data warehouse.

And part of what we did was turn this data warehouse into a collaboration platform. Companies like Fidelity went away from doing literally hundreds of IT integration, bringing in files via FTP and SFTP as error prone a process as possible. Two, collaboration comes out of the box and can deliver business value like with a couple of screens as opposed to needing to run an IT project. It is the accretion of this functionality. More recently, we have expanded pretty significantly, thanks to Iceberg and Snowpark into data engineering. And all of a sudden, the power of Snowflake's IP, which is a data platform can now be applied to data that is outside Snowflake.

And to me, the value comes from the addition of all of these pieces but we are now beginning to add both data ingestion platforms, but also transactional support for things like [ Unistore ] and [ Postgres ]. And then on the other side, with Snowflake Intelligence, which is our agentic platform, some of the pieces is all of a sudden a whole lot more than the individual pieces. And think about it, a hyperscaler the Kubernetes platform plus cloud storage and beta networking.

And yet these are trillion-dollar companies. To me, it's that it's power with data at the center that we are able to tap our origins, which by the way, we are not ashamed of. We are proud of is that infinitely scalable data warehouse on the cloud. But many things can come out of it if you add the right things into it.

We had Summit, your conference back in June. It felt like it was not a technology company conference in a good way. It felt something bigger. So there was a bit of sensationalism in there, perhaps like an AI conference. You have Sam Altman, you had all these...

My friend, Sarah, who interviewed Sam and me afterwards texted me -- Sara Guo, who runs Conviction. She said, thank you for inviting me to your [ rock party ].

She's amazing. We had her on a panel a couple of years ago, is fantastic. Going to be a superstar, is already a superstar. And her husband is going to be [ pure ] after tomorrow.

Oh, brilliant, a big shareholder.

Yes. Good Okay. Summit, and coming back to Summit, it's been 3 months since Summit. As you reflect upon the products that were announced, as you sleep through the customer conversations, what is coming back as 1 or 2 products that are that we really hit it and that's got a big future? Does anything come -- become apparent to you?

I mean, first of all, we announced lots of things at Summit, but in many, many ways, the mentality that I've put with our product team is it's a culmination moment. It's not a try and frame everything into one point in time moment. I just feel like we're living in a near planning for 1 or 2 days in a year is just like not that smart. And so we are very iterative.

But in terms of products, that show incredible promise. I would put snake intelligence right up on top. It's an agent platform. Our sales force is internally at Snowflake. A good number of them are using it. We are rolling it out to everyone. And in brief, what it does is on my phone, it gives me access to all of the sales information that we have, our customers, our prospects, how much they've been consuming, what kind of use cases they have active and the account hierarchy all of that information, even attainment information is there in one place.

It's all permissions so that I see a view that's very different from what an account exec are and SC can see. To me, it's an indication of what the future world of data access and data manipulation is going to look like. Honestly, I can ask questions off of it that I would not have dreamed up doing even 6 months, 6 months ago, I've had to go to an analyst who would then have to work for a day or 2 to answer these questions. I think it has a remarkable promise. We are in the process of scaling it. So I don't have great revenue numbers to report. But that very much feels like a before and after a moment in terms of what can you do with data that's in Snowflake.

You had Cortex AI SQL that's for the technical user, Snowflake Intelligence for the business users. Can you give us the most resonant use cases for each of these products not within Snowflake. You already talked about that. When you talk to your customers, what are the best examples that you're hearing about how these 2 products are lighting up the account base?

Yes. AI equal for those of you that don't know, essentially introduce some AI primitives into Sequel itself. So when you think about summing up, let's say, revenue numbers by region to come up with an aggregate, you can also think about, let's say, taking customer feedback and organizing it by product category, but summarizing the top feedback using an AI aggregate function, super technical.

But on the other hand, what this lets people do is use the power of AI on huge volumes of data without needing to figure out things like, well, how much capacity do I need? How is that going to be configured, how do I handle failures start doing all of the stuff we take care of all of the data processing for you. customers 100% use that to do a lot of sentiment feedback on feedback that's coming from customers. just make a whole lot of these kinds of U.K. is trivial. It's no longer some complex pipeline or process that you have to set up and run.

Snowflake Intelligence, the kind of solutions that again resonate BlackRock, for example, is creating a customer 360 with it. A lot of customers, BlackRock is one of them, have substantial amount of data sets within Snowflake, some are structured. Some are also unstructured. Something that will surprise you folks that are used to thinking about Snowflake as a structured data company is people routinely store customer feedback customer conversations, AI companies actually store things like model responses, text.

As Snowflake field, we support these columns called variant types that can hold a huge amount of data all of a sudden, you can get a single view with a thinking model deciding. Should I be looking at feedback? Should I be looking at the current account balance what am I as a customer service person what am I allowed to see? What am I not allowed to see? All of that stuff we taken care of. It's use cases like that, that are resonating, can be a health solutions, similar kind of product now built with clinician notes on top of health data. It is that one-stop shop access to a ton of information, context around it, and an agent loop that can decide which tool to call when, that's the resonance.

What I tell people is I'm sure everyone in the room at this point has used things like ChatGPT or Gemini, Deep Research. And what I tell people Snowflake Intelligence is, it is ChatGPT, Deep Research with access to all of the data sets that matter to you, it's the same kind of agent loop thought I would answer to your question.

That drives up consumption. You find more use cases, more applications to use the platform be more consumption?

That's right. That's a consequence. I'm actually very proud of the consumption model in here because it removes a lot of banks from our customers. The first body that, especially with all of the articles lying left and right, about 95% of projects not doing well. This not is what does this mean for how much I'm going to be spending, what I can confidently tell our customers is you don't spend unless people use the product and get value from it. If something that you build gets no consumption, well, then there's no money to pay. I think that's what is helpful. And as a company, we also starting from customer value with consumption as a consequence rather than the other half.

Yes, trying to -- let me see if I can try to ramp up consumption by introducing this particular product, no, I get that. Let's talk about data integration. You said it opens up a massive TAM in the most earnings -- most recent earnings conference call. You also made an acquisition of a company called Datavolo. When I went around the booths at Snowflake Summit, people are buzzing about your newly branded product. So tell us more about why this could be a big opportunity and how you go to market because this is a separate product than the core engine or maybe there is some adjacency?

Well, first of all, part of what Snowflake does is it creates an integrated product. This often ends up taking time for us, which irritates our sales teams and our product managers. We've gotten better at it. but there's only 1 Snowflake SKU that comes with everything. It comes with AI and it comes with open flow. It's an important structural advantage.

What OpenFlow enables is just this ability to be able to connect to different kinds of systems and bring data over to Snowflake or to cloud storage. It's a lot of connectors. We also have partners do this. We don't see this as an either or, but many of our customers end up liking the fact that, again, it's a one-stop shop. There is not a new contract to sign or a new tool to figure out and 100%. I think this makes it much easier to bring data on a periodic basis into Snowflake. And from there, starts data engineering. There can be analytic workloads that are built on top of it and obviously, access via AI. So we think of this as a very good addition.

We also acquired this company called Crunchy. It's a Postgres database. The idea, again, is very simple. Lots of our customers want to build applications that host transactional data within Snowflake. We want to make it super painless for them to create these Postgres instances. Postgres has become the de facto standard for OLTP databases. And we feel these just significantly expand our TAM keeping the product pretty cohesive.

Got it. And I wanted to ask you one more product question then go to market. The Snowpark connector for Spark or codes, you kind of brushed over it on the earnings conference call and tried to get at it in the follow-up. So can you tell us, what can you tell us more about -- it is something this opportunity to run Spark workloads on Snowflake has been there for some time. Did you just formalize it through a hardened connector and so there's a real opportunity ahead of you tell us more about what's ahead on that side?

Yes. I mean we have always aspired to do data engineering workloads. In fact, it is a significant part of snowflakes business, but it has also been very snowflake meaning it was always step 1, bring data into Snowflake, and then do data engineering on top of it. What iceberg, which is the interoperable format, unlocked for us is all of the data that is sitting on cloud storage that can now be acted upon by Snowflake. And the other learning that we have had is that over time, de facto standards form, and Spark is one such standard for data processing.

And what Spark Connect does is it makes it super easy to run Spark jobs without needing to translate anything right inside Snowflake. Snowflake's performance as a data processing engine is the best at that is out there. This just makes it easier. And it is also a little bit of us meeting our customers where they want to be let's face it, people do not want to run like do custom stuff to be able to run Spark code. This just makes it a whole lot easier. It unlocks more for us was very much getting started in this area. I think Open Flow fully rolling out, Spark Connect fully rolling out is what is going to unlock data engineering in a very big way for us.

Is this a different opportunity that has opened up, so you might need a different sales motion to go after these unmanaged Spark workloads, et cetera? The proposition is slightly different on structured data?

Yes, 100%. We have a good formula now for how we take new products to market, which is we hired a small specialist team they go create a set of like the early win marquee use cases that show that we can get great things done. And then we figure out what is the scale motion. AI, for example, we decided to actually have a bigger specialist team for special AI use cases. But on the other hand, we also did enough enablement so that the broad field sales team can do many simple AI use cases.

At the end of the day, it's not rocket science to be able to build a chatbot either on structured or on unstructured information, the simple ones. The more complicated use cases, yes, is going to require the specialist folks. I think we are increasingly getting better at being flexible about what is needed to take a new product to market. It's a little bit of applying this recipe. All of you folks have dealt with enterprise companies know that specialist motions can take a life of their own, and we want to be careful about how we do it. But [ Mike Gannon ] and our new CRO, has a ton of experience for, when do you spend something up and when do you drive it broadly across the field so that you don't end up with like 5 overlays that are as large as your actual sales team. We feel good about the motion.

Got it. On that, so it's a perfect transition to GTM. What have you unlocked on the go-to-market side with the hiring of Gannon as you build your sites towards what at least we think is a $10-plus billion revenue company, how do you see GTM changing? Product engineering is there. I mean you've got all of a sudden in 18 months, a flurry of new products -- what needs to change or be enhanced on the go-to-market side that you can get to the going from $1 billion to $5 is hard where if you do it, and you're there, 5 to 10, it's a different 10 to 20. Even so how do you go to 10? And what are your sights beyond 10, if you do have sites beyond 10?

Yes. I mean, first of all, I think go-to-market has evolved a lot in the last 18 months. Mike's arrival is a welcome addition to the team. But in terms of stuff that we've been working on, I've talked about how we are now a lot more quantitative about the consumption life cycle. We track use cases pretty carefully. There's even more work to standardize what the use case is and how do you measure incremental consumption from it. the core proposition is you can only optimize what you measure. This is something that all of us can relate to. And so we've gotten much better at that. And then to the level of sophistication of what's the difference between a 90th percentile account or sales rep and the 50th from -- like the median.

And another big important change that I fully -- that Mike is also pushing is the role of our solution engineers. We got this amazing person from Microsoft, who has run large portions of their solution engineering team in Azure to run our team. And now...

You can get an angry call from Satya.

Thankfully not. But they -- our solution engineering folks now have much more of -- they are the leaders of consumption. And in fact, part of what we have done is make the role of account execs versus the solution engineers that opco equal ones. We're not going to execs talk about things like deals under earlier stages of the use case life cycle, while the step up and talk about how we are driving consumption, how they are driving go-live. It's a big change forward with an intimate understanding of what does it mean for somebody to be productive week-on-week, month-on-month, quarter-on-quarter. I think it just gives us a lot more flexibility about where we invest.

Similar to ads, my attitude is, I'm just a portfolio manager. I'm just looking for the efficient frontier when it comes to figuring where do I want to put sales head count putting a lot of it. We hired 800 people in the first half of this year just into that function. The second big change that Mike is busy pushing is a rebooted partnership approach. Most of Snowflake gets delivered via solution like system integrator partners. Definitely, they are undergoing a world of change with AI. And we think we have products that can let them demonstrate value a whole lot faster.

We hired a new head of partners as well from AWS. That's a huge focus for Mike. And I think these are the things, combined with the specialist motion for taking new products to market. I think these are the key ingredients that will let us go from the $5 billion over to the $10 billion. Look, we are very, very -- first of all, we are early in the on-prem to cloud migration cycle. And AI has now given a powerful reason for every CIO to now tell their CEO that having great data, having data in Snowflake is what is going to drive transformation for your business. we feel like AI can be a big pool for how data is brought into Snowflake. And that's the thing that's going to drive us, first of all, faster to the $10 billion that we want to get to, but we'll end up creating a much larger TAM as well that we will continue to aspire to.

Got it. One other thing I wanted to ask you was, I know that you spent quite a bit of time a big technology company, and you've had a fascinating chance to watch the foundation model battle what seemed like it was a 2-horse raise and became a 3-horse race and a 4-horse race and 5 and 6. Some people think it's raised to the top. It looks like it's race to the bottom, more competition coming in, equal amounts of not equal, but surprisingly, how quickly it takes for somebody coming from behind the catch-up. Why are these models all doing the same thing? How does it all end if you have a perspective? And where is the next value realization from this model. So where are we going as an industry, we've not seen much business return.

What do you think makes for like a good AI prognosticator? Yes. well, it is to predict early and predict often. It's just this tough, it's just really, really hard. And while it is the case that some folks like [ Grok ] have come from behind and magically caught up. There are plenty of other trillion companies that are trying and not quite making it.

So I think there is absolutely a little bit of [ Genesco ] to who are the great AI companies. It's not all that easy to compete. I think the word is like very much still to be written in terms of how this world is going to transpire. And the other thing that I'll tell people is that, honestly, yes, there's a lot of success with AI. But if you think about what are the 2 super hits with AI, it is coding agents, and it's ChatGPT. It's consumer chat. Everything else is pretty small in inside of the big scheme of things.

So my offtake is it's still pretty early. I think we will see the impact on our personal lives on enterprises, just it's going to take a few years, it's going to be gradual. And my take is that so much technology has already been invented that if it truly permeates the world, say the way that mobile phones did in terms of the reach that they finally have what 6 billion, 7 billion people in the world, I think it's actually going to be transformational for society. this is without taking into account things like AGI.

So in that sense, I'm very optimistic about how much value can be created with AI. And I think it's still pretty early and my -- if I were to bet, I would bet that it is not a unipolar or a bipolar world, that there are several people with great capabilities. Is that going to get commoditized down to I don't think so either, because it is truly, truly difficult to kind of be at the cutting edge, and it's more than money. I think that's what is going to keep some of these companies unique.

And you folks, again, know this already, open AI has run off with consumer attention. People are not going to change all that quickly over to a new app unless it is significantly better I think there are some things that have absolutely been established that are going to be much harder to break down compared to others.

Got it. Right on the heels of this presentation is going to be venture capital panel. So these folks. I'm going to be asking the same question. And I've been doing the panel with these guys for about 10-plus years. we're going to call it even better than the all-in podcast because that's how high the quality of the venture capital panel is. It really is. If you have a couple of minutes, you should watch it. The other tap that was very interesting was 50% of your new customer wins in the quarter were attributed to the AI.

It had an AI influence, absolutely.

Tell us more about that factor.

I mean, look, every customer that's betting on Snowflake is betting on the next 10 years. and it's already very clear that AI is a big part of whatever it is that's going to show up in the next 5 years. And this is where our ability to make AI is simple. For our reps to be able to say, let me show you what is possible with data on Snowflake become such a big deal. And so it also points to the importance that AI is going to have in the future on other stats that we released as part of earnings was that something like 1/4 of deployed use cases have some element of AI in them. So I think this points to both the ease of use that Snowflake AI has, but it's increasingly important for the entirety of the data life cycle.

Got it. Two minutes. Anybody has any question. Standing room only. This is so cool yes, in the biggest ballroom. Anybody? Okay. Then maybe...

Stun the room with your brilliance.

No. You have a question for me. Let's turn it around.

What is your prediction for software?

Software is not dead, first of all. I think there is a view that maybe tied despite the fact that I could not execute my Fortran program on an IBM 360 mainframe back in college, when you were blazing new trails just a mile away from me. I do believe that we can confuse the user interface and how attractive AI makes it to be to visualize a complete disruption of the software stack.

And I think what's going on is when Netscape went public in 4 years after that, the web browser became the new fascination in the front end. That's right. And the enterprise software industry used the web browser as the front end to revisualize the way in which end users interacted with the software. The back-end logic did not necessarily change. The back end logic, the logic of doing business is the logic of doing business that's expressed in code.

But what it did do was to help you about the user interaction model and the same way, I think AI is the new UI. It does not change the logic. Certain things don't need -- you don't fix things that are not broken, but we know what's broken, that the engagement model, the front end, and I think many of us confused, not me, not you, but many confuse the lack of usability or the complexity of the user interface to be and flow with the software, and I would beg to disagree. So I'm extremely optimistic about how so.

So now there are a lot of cross currents. You guys have emerged from this period of declining NER. Now you finally hit stability and starting to see improvement. The same thing needs to happen to the rest of software cohorts. And if I have to say, the software prints actually all -- most of them looked better than expected and showed some sequential acceleration. So I am very, very bullish.

On that note, let's give a round of applause to Sridhar Ramaswamy. Thank you so much.

Thank you.

Okay. Let's get started. Good afternoon, everyone, and morning to our partners in India. My name is Hwee Bee, and I Lead Partner Marketing for Asia Pacific and Japan. And welcome to SPN Pulse, our quarterly partner update series designed to keep you connected with Snowflake strategy, innovations and our ecosystem. This is where we share what matters most for you, Snowflake's latest business momentum and strategy, our key product innovations, our customer success stories that inspire all of us and what's next for our partner ecosystem in Asia Pacific and Japan.

I'm delighted to have all of you with us today. And in the next hour, you will hear from Sridhar Ramaswamy, our CEO, on Snowflake's latest business performance and what's driving our growth. There will be a fireside chat between Sridhar and Ash Willis, our VP of Partner and Alliance, on how partners are at the center of this momentum. Next, we have Jeff, our Product Director, who will share with us our latest product innovations. And joining us today is also our customer, XLSmart, the largest telco in Southeast Asia, the Chief Analytics and Strategy. He's going to share with us their data transformation journey and how XLSmart is serving over 82.6 million mobile subscribers and capturing 29% of the telco market share.

And finally, we are also going to share with you our latest partner programs, how we can continue to win with you. And throughout this session, we continue to ask for your feedback and your support to give us your questions so that we know more about you, right?

Next, I'm going to pass it over to Ash, right? Ash, are you here with us?

Hi, I'm here, Hwee Bee. Good to see you, and thank you for the introduction. It's always a great pleasure to be on Pulse. And I'm particularly pleased for this one to call out that this is the first time we're doing simultaneous translation for our friends in Japan and Korea. So great to see that feature.

And also awesome to welcome Sridhar Ramaswamy, our CEO, to the call. Welcome, Sridhar.

Ash, excited to be here.

Always love welcoming you to APJ, albeit virtually today but looking forward to seeing you in Japan next week.

That's right. That's right. It's going to be a great trip.

So Sridhar, lots going on, lots to talk about. I have many, many questions for you for our fireside chat. But I think to kick off, just to set the scene a little bit, there's a couple of slides that we're going to get you to run through. So I'll hand over to you for the first 10 minutes, and then I'll jump back in with our fireside chat.

That sounds great. Let's move to the next slide.

Sure.

Good morning, everyone. It's truly an honor to be here today. The journey from data to business transformation represents our single biggest joint opportunity. We are at an inflection point in technology, a moment where the convergence of data and AI, driving transformation is reshaping every industry. I don't think it's going to be an evolution. It's rapid change. It's going to be closer to a revolution. And that's the most critical part of the story, which is that the destination is not AI itself but the business transformation that it enables. And that's where you, our partners, create the ultimate value.

By combining data with your industry expertise and services, you deliver the true outcome, a transformed business. And our shared goal is to change the way business is done through data. This isn't only about transforming our customers. It's about transforming our own businesses and strengthening our partnership with you all to lead in the AI era. At Snowflake, we have a simple but powerful vision to be the engine that powers this new era of transformation with data and AI.

Let's move forward. And as a company, we are at the forefront of the data and AI transformation, the single technology -- biggest technology wave of our time. Every enterprise is thinking and rethinking how it uses data and AI has become the engine of transformation. And together with you all, our partners, Snowflake is uniquely positioned to define the standard for AI-ready data in the enterprise. And our momentum at the center of this enterprise AI revolution is undeniable.

In Q2 fiscal '26, which we just wrapped up, product revenue reached $1.09 billion in the quarter, up 32% year-on-year, accelerating from last quarter. Our net revenue retention, a key metric of how people, customers are leaning into our platform is at 125%, showing that customers are expanding strongly with us. Today, we have 654 customers spending more than $1 million annually. This is a new record for us. And this is proof that Snowflake's momentum is your tailwind. Every new workload, every new AI project creates demand for your services and solutions.

Let's move to the next slide. And the market is doubling from $170 billion in calendar year '24 to probably over $350 billion in calendar year '29. And if anything, AI is accelerating this growth. And this is the opportunity that we must seize helping customers modernize, migrate and build the next generation of AI-powered applications.

Next slide. And we have proof points literally with thousands of customers with over 12,000 customers with over 750 of the top 2,000, the G2000 customers that are a part of the Snowflake ecosystem.

Next slide. And the Snowflake AI Data Cloud. is the foundation for partners to build, to differentiate and to grow. We have a unified platform for data and AI, whether it's analytics, collaboration or applications, all in one governed environment. Snowflake Intelligence, which is in public preview, and Jeff is going to talk about it, it uses natural language to give you data, to give you intelligent agents. We are going earlier in the data cycle. We launched OpenFlow, unifying batch streaming, structured and unstructured data, expanding into a $17 billion integration market.

And you're going to be bringing Postgres inside Snowflake where you're going to have enterprise-grade OLTP, online transaction processing along with, of course, OLAP that we have offered forever. And we also released Spark Connect, enabling you to migrate workloads seamlessly with 1.9x faster performance versus managed Spark. And this is a platform built for partners, open, trusted and designed for scale.

The proof in the pudding. We are delivering features faster than ever, over 250 features shipped in just the first half of this year alone. We are delivering up to 2x faster performance in new optimizations, enabling quicker time to value for customers. And we are enterprise grade. I can't emphasize this enough, whether it's government, whether it's security, whether it's replication, whether it's disaster recovery or compliance, they are at the core of what Snowflake is, enabling partners like you to implement and confidently scale mission-critical solutions.

And our message for you is this, our momentum is your tailwind. Q2 32% growth and $654 million customers create massive pull for your services. And the fact that we have over 6,000 customers using our AI products on a weekly basis is a huge opportunity for you right now. And the AI data cloud that we have created is your foundation for building the next generation of offerings. And we have a differentiated value proposition through all aspects of the data cycle, close to 2x faster than managed spark, Postgres, OpenFlow, AI native capabilities. That means faster migrations, faster deployments and faster customer outcomes for you.

But we value our partnership with you, and we make a strong commitment to you. We put our customers first and being accountable and aligned with customers, with partners like you are not just internal values. They guide how we build with you, how we work with you. Our success is joint success. And what these in turn, what these values ensure is that our growth translates into your growth. And that's the part that is super exciting about this moment.

I frankly feel very fortunate to be right at the center of this massive transformation that is rippling through enterprises. AI is becoming -- AI and data are becoming the new enterprise operating system. And we are proud to be that data platform for you but we are even prouder to be your partners in bringing value from that platform to all of our giant customers.

With that, I think we're going to do a few questions, with Ash?

Yes, absolutely, Sridhar, and really appreciate that context. And I love the message there around joint success. Our success is our partners' success and vice versa. And the role that I get to play working with these partners day in and day out, I think many would attest to the fact that we are seeing a huge amount of momentum across the market. So awesome results, 32% year-on-year product growth, almost 700 customers now at that $1 million mark, great momentum around G2K.

This is kind of a big question to start with. But I guess what really excites you the most about the momentum that we're really seeing across the market and that Snowflake is driving?

The really cool thing, Ash, is that our momentum is broad-based. It's not like we are relying on overspending by a particular segment or a company. Net revenue retention being this strong means that our existing customers are leaning in and investing. We also had something of a record for cap ones in terms of how many cap ones new logos that we acquired last quarter. And that's also really, really exciting for us. But what is cool is the new products, whether it's in data engineering or AI are also accelerating. We mentioned in our earnings, for example, that a full quarter of all deployed use cases have AI in them. That's the magic of data and AI.

And I can tell you, I relate to it personally. Snowflake Intelligence can answer questions for me that I honestly would not even have dreamed of asking a year ago because I know if I wanted answers to questions like that, and I had to go find an analyst and explain what exactly I meant, and then hopefully get an answer. All of that is just a sentence away inside Snowflake Intelligence. To me, that's the momentum of where data and AI come together to create magic. That's the massive opportunity for us whereas like technology vendors working with our partners, we can go to our customers and say, what's going to make a difference for your business? And how do we go about creating it? And the ability to do that super quickly, that's what's magical about this moment.

Yes. Well, I guess, Sridhar, what worries me is when you come to me with a question, I know that you already know the answer most of the time, and you're just testing me to see how well I know it.

Well, no, but that's the part of democratization, which is that we are limited by our imagination. We are limited by our curiosity. We are limited by how much time we are willing to put in. At any given point, I have 3 deep research papers that like I want to read. I just can't find the time and the mental energy to like stick it into my head, and that's become the bottleneck, which honestly, that's a fun place to be.

Yes, it is. It is. And I think the amount of change that we're seeing around the technology is remarkable. 600 -- how many new features I captured that?

250 plus just in H1.

Just in H1, right? And I sort of look at the landscape and I look at the ecosystem and the amount of evolution that's being driven there. You touched a little bit on the concept of new logos, cap ones as we call them. It's quite amazing to see the opportunity that, that represents for our partner ecosystem and just the amount of new demand that's coming on to the platform as well.

Indeed, yes. Please go ahead.

Just to dig into that a little bit more. So we talk about growth, we talk about new logos. We talk about new product features as well and growth at the base but also going deep within accounts. Where do you kind of see the biggest opportunity for our partners? And I guess the flip side of that is where would you like to see them focus as well?

I think you should, first of all, acknowledge that in this moment, a lot of our joint customers, CEOs are aware of the transformative power of AI, but it is also a thread that we can pull, meaning that if you can start with what's the business goal that a particular customer wants to accomplish enabled by AI, you can quickly enroll that into, okay, these are the kinds of end-user products that you need to create. Perhaps it's Snowflake Intelligence, perhaps it's a modern BI platform like a partner like Sigma. But then you can unroll that back to, okay, what are the data sources that we need.

And assisted by AI, Snowflake is also working hard at making migrations go faster. We're introducing a slew of new features into SnowConvert AI, which is our free product to enable migrations to happen. I think that's the part that's exciting, Ash, which is that the entire data life cycle can come alive because great data in Snowflake is AI-ready data and AI-ready data is the data that drives business transformation.

That's a really interesting point, right? I know that everyone wants to talk about AI and everyone wants to kind of talk about the innovation and the business outcomes that AI drives. But at the end of the day, it comes back to the data, right, the quality of the data, the availability of the data. So just to double-click a little bit, you mentioned SnowConvert. Why is that so important to our longer-term strategy? And I know it's an area that you've really been doubling down on with the product team.

Yes. Some of these migrations are really hard. I am part of migrations that have taken 12-plus months, and it is terrifying for people to go through those kinds of migrations. But the same technology that helps people write great code, fresh new code is also one that can help people write tests when you're doing migrations. Migrations have mostly been thought of in a very waterfall traditional kind of sense. You're on this tool that does a conversion, it generates some errors, you go fix the errors and then you move on. And it's only, for example, much, much later that you start loading data into the destination system. And as soon as that happens, you discover a slew of problems.

What AI can do is get into much faster iteration loops in all of these situations so that you can fix problems along the way. And we are busy experimenting by putting engineers to work on migrations directly, what additional tools we need to be built. I see this as the beginnings of a pretty large unlock for Snowflake and for our partners.

Yes. Yes, absolutely. And I think the old saying that saying that you can't have an AI strategy without a data strategy holds.

That is correct. High-quality data is going to matter so much more and things like knowing the semantics behind data. Let's face it, every department, every company defines revenue in its own unique way. And every company, again, defines an active user in its own unique way. How do you capture those semantics? That's also a problem that we are working on. We introduced this concept called the semantic model, where information about data is stored right along with the data in the Snowflake, and we are busy building connectors can help extract some of these semantics that are locked away in other tools, for example, like DI tools without it being easy for AI systems to be using.

We are storing the data closer. We are storing like the semantic information closer to the data so that any tool, by the way, not just Snowflake's own AI tools can use that information to provide great AI answers. And it's another theme that we constantly press on at Snowflake, which is how can we be good citizens in a customer's data ecosystem? How can we make sure that we are interoperable. I was in a conversation earlier with Satya from Microsoft today. He was kind enough to record a video for us. And one of the things that he mentioned was how excited he is that Snowflake Intelligence data agents can be exposed inside Office Copilot.

I think all of these is what makes AI so much more powerful because it becomes a part of how we go about solving problems starting from migration to how do you get value from the data that is created.

Yes. We certainly hear a lot from partners and customers around the importance of openness and flexibility and connected ecosystems. It's very much top of mind. I'm going to keep digging a little bit on product innovation because I think it's just so impressive, the speed of innovation and also the thought process around a lot of that. I know this is probably going to be a bit of a hard question for you in terms of asking you to pick a couple of favorites. But out of those 250 new innovations, what are some of the ones that really excite you?

I mean we have Jeff. So I have to please him. But kidding aside, I would say that Snowflake Intelligence has been a game changer. It still can get better, but just the things that you're able to do. I'll give you folks a simple example. I met the CEO of CLEAR about a month ago at a conference. And I knew that Austin International Airport was a customer of Snowflake. I knew that there were a few others but I didn't exactly remember. So I confidently told her, a number of airports are Snowflake customers. And she promptly goes, really, which one? What do I do? But I promptly type in who are Snowflake's customers in the aviation industry, not only did it bring up airports but it also brought up other customers like United. It brought up transportation authorities. And that was like this aha moment of, wow, I can ask like a total left brain question and still get an answer.

But look, I'm also an engineer. I love so many different aspects. The other day, I was writing a streaming ingestion to see how rapidly we could ingest data while still delivering fresh data. This is a new ingestion platform that we've built, got some amazing numbers. I'm a practitioner of what we preach. I use coding agents left and right to create tools, mostly just for my amusement because I'm not really good enough to create software for other people. But just that ability to use our various features, I think, is, again, something that's pretty magical about this moment.

I think the accessibility of technology and information is really empowering. And I hope you don't mind me sharing with the audience but when I was in Menlo Park a couple of weeks ago, you and I were chatting about an e-mail that you had sent me that had some data. And I'm like, "Hey, Sridhar, that's really cool.

How did you do it?" And you're kind of like, I just vibe coded it. And I'm like, what? But you actually showed me how to produce some code, amazing amount. Like I probably spent a couple of hours a week kind of trolling through some e-mails. And just with some really simple code that took maybe 10 minutes to build, I now just get access to all of this information.

That's the magic of today, Ash. And part of our aspiration and goal with tools like Snowflake Intelligence is to be able to bring that magic on all data for all our customers. I think that's the excitement that we have to look forward to because this is technology that truly makes the complex just go a whole lot easier.

Well, I hope you know like next time I have a problem like that, I'm going to come to you as well for some more tips around vibe coding.

Just going to vibe code it up.

So we are -- I just had my leadership in town this week, and we spent 2 hours going through some AI tools. And I think it's quite remarkable just to see it how much efficiency you can gain as well, like our SEs building demos on the fly for customers.

That's right. That's right.

We're working on a couple of initiatives that we're going to take to our ecosystem to help teach them some of those tips and tricks as well, which is pretty cool.

So I'm going to talk a little bit about competition. And you said it before, like we focus on the customer but I think that we also need to be mindful and aware of sort of what's going on in the broader landscape. So as you engage with customers and I guess, partners, big, small in the middle, how do you kind of describe and position Snowflake's differentiation, competitive advantage? And I guess, how can partners as an extension of Snowflake really help to amplify that message?

I mean one of the things that we have to do is give our partners great messaging to distinguish Snowflake from the competition. We are the best analytics platform that there is on the planet. And the values that we bring, which is simplicity, making complex things easy to do on Snowflake, making sure that everything is connected, whether it is data that sits inside Snowflake that one department or a customer can share with another department or us ensuring that AI features work out of the box with things like governance. This is what we sweat.

And we also sweat trust a lot. We want our customers to trust the results that they get from a platform like Snowflake Intelligence. It is our ability to create this one platform with a single security model based on open standards that uniquely differentiates us. There are some competitors that pay lip service to openness and go and garner the market on "open projects" and then start making proprietary changes to them. We don't do that.

When we bet on open formats like Iceberg, we are happy participants in the process. An open format means collaborating with other people. Some of them might not agree with you. But similar to a democracy, we think it produces great outcomes for the entire industry. And that's what we have consistently pushed.

And what you get from us is AI and analytics and applications on the same governed platform. And it is these qualities, the ease of use, the connectedness, the trust that is at the center of Snowflake that we want to make sure that all of you emphasize. When you do a project on Snowflake, you're doing it on a battle-tested enterprise-ready platform that is going to leave a very happy feeling with all the customers that you implement Snowflake with. And that's the thing that's going to distinguish us.

And remember, we always put customers first. If there's a problem, we will be there with you solving those problems. And thanks to folks like -- amazing folks like Ash and Chris Niederman, who just joined us, we also are genuine in how we are leaning into the partner ecosystem. We want you to succeed because your success creates our success. And this is what distinguishes us very, very foundationally from our competition.

Yes, that's a great answer, Sridhar. And I've just seen Jeff jump on, but I am going to steal a couple more minutes of your time and just make you hold off for a few. But just to recap on that point, the acronym that I like to use to remember what you just said is ECT, easy, connected and trusted.

That's right. That's right.

And I add an O on the back of that for openness. So I think that for our partners, if you remember ECTO, it's a really good way to describe the advantages of Snowflake.

Love it.

So Sridhar, closing question. I'm not going to let you go without this. So you live and breathe this day in and day out. I guess it's a landscape that is moving so quickly. What's kind of your boldest prediction in terms of what this space holds over the next, I'd like to say, 5 years, but I kind of think that, that could be a little bit too far out. But as we look into the future, where do you kind of see the technology? Where do you see Snowflake? And how would you like to see partners grow with us, I guess?

Yes. I think the -- first of all, I agree with you completely. I think people making 5-year predictions in 2025 are either bold are like kind of cookie because this is just a time that is changing so rapidly that is really hard to make any kind of predictions, 5 years is an eternity. And my team comes to me and says, by the way, that they're going to launch something and build on November 5. I go, really, that's like a decade away. What are you going to launch next month? So we need to keep that in mind.

But I think the role that we are looking forward to is one in which every workflow. And remember, workflow is just a fancy way of saying, I'm going to move from this tab, copy some information and put it into this tab. That's what workflow for most of us is. It's a pain in the a***. And but every such business workflow will be AI augmented, AI-enabled. Every application that people will want to use will have a natural conversational interface.

And there is no way to separate out data from AI because data is the fuel that makes AI come alive. And that's why we say there's no AI strategy without a data strategy. And our partners are going to be at the center. All of you are going to be at the center of driving business transformation with all our customers. And by the way, driving massive business transformation in how you operate. What AI is doing right now is sort of redefining the line between software and services. We have to embrace the fact that, that line is going to get a lot more blurry is going to create so much more opportunity for us.

But what I want you to take away is that the Snowflake AI data cloud is that foundation that we feel very confident about and we feel that we can be an incredible ally for all the partners that are here.

Excellent, Sridhar. Listen, that's a great closing message. Super exciting about all the work that's happening at Snowflake and across the industry. And I think it just underscores what an amazing opportunity that represents for our entire partner ecosystem. So thank you so much for being so generous with your time today. I know it's quite late for you, and there's a ton of stuff going on having you here really underscores the importance of our partner ecosystem to Snowflake. So personally, I really appreciate it. And I look forward to seeing you in Tokyo next Wednesday.

That's right. Thank you, Ash. Look forward to it. Thank you all for attending. And by the way, I'm super happy that I went before, Jeff, because it's a really tough act to follow.

Yes. I don't moderate panels with Jeff because I find it so difficult to get into the details but thanks very much, Sridhar. See you next week.

Take it away, Jeff. Take care. Thank you, Ash.

Jeff, welcome to APJ, albeit virtually, as I just said, to Sridhar, I know you do have some time planned out here in the not-too-distant future. I think we're going to see you at a couple of SWT events.

Yes. And had the -- had the chance just a few weeks ago to go down to Sydney and see some folks there as well. So that was great. So looking forward to it.

Yes, of course, I missed you at the Sydney event but I did hear good things and one of our largest SWTs across the region. Unfortunately, the coffee is not as good in Sydney, Jeff, as what it is in Melbourne, so...

So I have been to Melbourne a few times and somebody from Seattle, I've always been impressed Seattle has a strong coffee culture. Melbourne absolutely does as well.

Well, I say the best coffee in Seattle is the coffee that comes from Melbourne. So I've spent a lot of time there over the years also. So I'm sure I'm going to get a lot of timing comments as a result of that statement.

Jeff, thanks for taking the time. You are deep into product and what we're doing from a product perspective, day in and day out. Very, very topical for this audience, and I appreciate you taking the time to come and speak to the APJ partner ecosystem. So I'll pass over to you, and I'll see you at the end of the session.

Perfect. Thank you so much. So yes, I want to take just a few minutes here. It's a great segue with the panel you all just heard with Sridhar, talking about the state of the business and the direction of where we're headed. I just want to spend a few minutes here and talk some about some of the pulse of the product pieces and specifically, I want to spend some time thinking about what we're doing around some of the investments around AI.

Now Sridhar already did a phenomenal job from a high-level overview of what we're trying to accomplish. So that's going to actually save me a bunch of time. But I want to just focus on 3 big areas of investment that are happening in AI right now. The first one being agents and intelligence, Sridhar was able to spend a good amount of time talking through some of that. The other one is around AI SQL. The last one is around ML platform. Now all of these are just 1 part of a slice of our investments. If I think about Snowflake all up, we're making investments in data analytics, data engineering, apps and collaboration and AI. So I'm just kind of focused on 1 slice of it.

But you'll see here in the next few minutes how really Snowflake both directly to customers and through partners, through both partner solutions and partner-assisted deployments is trying to help integrate AI everywhere from ingesting data all the way to getting insights on the data.

So again, Sridhar already set up a little bit around what we're hoping to do with agents and intelligence but I'll just double click and I'll even show you this in action here in just a second. So for us, what it really comes about is how we can bring AI on top of all of that proprietary unique data within an organization, within an industry within an ecosystem and really start to accelerate the business transformation and the business insights that happen as a result.

You likely are using generative AI in your day-to-day life. But once it comes to your work job once it comes to your enterprise context, oftentimes, the AI without the data becomes very, very less useful, like useless almost. And so bringing that data with the AI is where we see a bunch of potential and a bunch of things coming together. So one of the pieces here is we want to make it very easy to create agents, specifically agents that work on top of your enterprise data. So agents is a very exciting term in the industry right now. You can almost think of it as just how you can start to use these leading industry leading LLMs and models to actually go perform more complex tasks, sometimes entirely autonomously all on top of your data.

So I want to give one very critical example when it comes to data agents. And that's with how easy it can be to get access to the data that you need at the right time. If you look at any enterprise and an employee within that enterprise, they're making dozens of decisions every single day. Now sometimes those decisions have huge consequences, potentially millions of dollars are on the line with a different decision and how frequently are those decisions made without access to the right data.

And it's very understandable why that happens. Sometimes finding the right data can be very time-consuming, you're navigating through a bunch of dashboards, you're trying to remember what was that report that had the right data. Maybe you're going to find the right report but then you have to slice and dice it to the right scope. There's so much work. Very often, what I have found in the past is that I would just end up e-mailing my data team and being like, hey, I need this specific slice to the data, and I'm kind of waiting for some human manual effort to go and sort that out. So what we want to provide is a better way with bringing agents on top of your data to provide the solution of Snowflake intelligence.

So let me just show you quickly what that looks like in just a few minutes. Sridhar already mentioned how he uses this and some of his day-to-day interactions because inside of Snowflake, we have about a dozen of these agents that we're using, while we work at Snowflake to help, I do everything from managing our road map and managing our backlog. Sridhar mentions that he can look at customers and what customers are doing.

But here in this demo, I just have a simple agent here, and this is called my Product Insights Agent. So this is an agent that I've connected to a few data sources. This knows a bunch of information about sales that are happening in my organization and also has access to a bunch of data that is unstructured. You could think of like slack conversations, e-mails, calender invites, customer support tickets. Now this is fairly limited, just for the sake of the demo but this list can get as long as you want it to, which says, hey, agent, you're now an expert in all these pieces of the data, this is data that is securely governed and running in Snowflake.

So what this means now is if I have a question, how are sales doing over the last 2 weeks by region compared to forecast. I'm just going to ask Snowflake Intelligence. See what happens right away is this agent is immediately looking at my question, and it's figuring out what's the best way to answer it. Now a few important call-outs here. The first one is all of the interactions that are happening here are all happening on top of my secure data. It knows who I am. It understands the data that I have access to. All of that is being enforced. Even the models themselves, the reasoning, the LLMs that are powering this, these are all running out of Snowflake. So my data is never leaving. It's all in my control with role-based policies enforced automatically.

Now you'll see here in just a few seconds, I get my answer. It's given me some information here. It's even been smart enough to render this as a table in my case or is a chart, apologies. You can see here one more thing I'll call out even too. You can get nice little things like this green shield. Now I love this green shield any time I work with an agent because this sets me not only did this come up with the right query behind the scenes to answer this question but it pulled from a query that had been certified by my data team. This is a verified query. As Sridhar mentioned, you have a specific definition of things like revenue or a customer. Well, this is pulling from it to help me get the insight to the answer.

Now I'll show one more quick thing here before we jump into some of the other product updates. To me, this is great, like this already saved me time from having to dive into or dig through different charts manually. But what I really like and where the power of AI starts to shine is what often happens is I'd look at a chart like this, and I'd quickly say like, hey, what's going on here west, right? All my sales seem to be going about to forecast but something is going on here with the West. And this is where often traditional data exploration tools really struggle. But where Snowflake Intelligence and because these agents are deeply connected to all of the data in Snowflake, instead of sending a slack to my data team, I'm just going to ask this agent tell me why the West is underperforming. That's a very abstract question. There's a bunch of things that could lead to that.

And you can actually even see here, I'll go ahead and expand this because this might take a little bit to run. The agents now thinking, okay, why I have inventory data, I have marketing data, I have my trend data, what's the variance like over time. The agent is now thinking, okay, why I have inventory data, I have marketing data, I have my trend data, what's the variance like over time. The agent can actually now go through and go through a bunch of things. You're saying like here, oh, it's looked at marketing scores. It's looking at inventory levels. There's a number of queries and steps that now my agent is exploring the data in the same way an analyst would in my organization. I have now my own personal virtual analyst right here providing insight on my data, coming up with the right context.

You can even see here, I'll quickly show here. This has even pulled in some snippets from Slack. Like it looks like I have some Slack conversations that might be relevant to sales in the West. This is giving the agents some clues, some additional context across that business data so that now after a few seconds, I can have some contributing factors, not just on what happened but why it happened and what I should do about it. This is incredibly helpful. I get recommendations, I get root cause analysis, all of this happening empowered through my data inside of Snowflake.

So this is one huge area of investment that we're making, which is how we can bring these AI agents on top of your data in Snowflake seamlessly, connected to the business semantics, connected to the role-based access control, all running securely with your data. So agents and intelligence is a big piece of investment.

The other one I want to quickly shine a light on in the last few minutes here is AI SQL. Now Snowflake Intelligence is a great way to access insights for anyone in the business, whether you're the CEO, a support engineer, a salesperson, you name it, Snowflake Intelligence is for you. AI SQL is great for the builder. And to me, this is a powerful set of AI tools that you can bring directly into your data workload. I'll just show a very quick example here. So the other week, I was working with a large data set of Snowflake data. It was actually survey results. And as part of the survey that we ran with some of our customers, we had a bunch of verbatim responses.

What would you like to see Snowflake doing better? We had thousands of these responses. And I wanted to understand, well, what are the big trends across of all these thousands of responses? Like what are people asking Snowflake to do more of? Now if I wanted to, I could have tried to copy and paste all of those thousands of responses into an LLM and asked it to summarize but it would blow up. It would not work. LLMs can only deal with a certain amount of context at one time. But what I could do and what I did do is I wrote a single line of SQL code that looks very similar to the code here, which said, "Hey, Snowflake, you know how to query huge sets of data, right?" Almost infinite sizes of data Snowflake knows how to query.

It now is also powered with these AI functions like AI summarize or AI filter or AI classify. And I said, "Hey, Snowflake, for all of these rows of feedback, I want you to use an LLM to summarize all of the findings and give me a 2-page report of what everybody said for this question." and Then I clicked Run, a single line of SQL that had some AI summarized just like what I've showed. It spanned for about 30 seconds. Snowflake behind the scenes was scaling out my query the same way it normally does. But as it scaled it out, it was weaving in these LLM calls to generate and summarize the components. So after about 30 seconds, I got back a simple 2-page report that said, "Hey, Jeff, for the survey, the thousands of responses, these are the big trends." It was incredible. It was mind-blowing. This truthfully just happened about a month ago. The power of being able to weave in AI to do more complex type of operations is a big area of focus. And we want to make sure that as you're doing it, you're doing it in an easy, efficient way.

The last one I'll just mention here quickly before I wrap up is around just general ML platform as well. So you might be using generative AI either to do data processing, as I mentioned, or data insights with intelligence. But sometimes you might want to build more custom models, custom forecasting models, custom next best action models, where we have a full-fledged platform to do this inside of Snowflake or alongside our amazing set of partners.

In general, from a product standpoint, how we focus on things is we want to make sure we have a general platform that can take care of the basis to make it easy, connected and secure. But we want to make sure that we integrate phenomenally with partner solutions because we know partners are incredibly good at finding industry-specific, business-specific differentiated solutions. So from ML to AI, you will see us always working to make sure that we're providing a path so that we can continue to work with partners to provide those specialized solutions.

So to kind of wrap up on this pulse of the product, I know this is just a small taste, a small pulse. Across the board, you heard Sridhar say the same thing. We want to make sure that this is easy, fully managed infrastructure. You don't have to worry about spinning up the complex pieces needed to run either agents, generative AI or models. All of this is connected across the data in your organization, across the data in your ecosystem, across the data in the marketplace and trusted at its core.

Now if you're interested on how you can get started and learning more about these AI features, just a few next steps I'll share is my last slide, which is start today, think about how you can bring everything from unstructured data, slot conversations, e-mail, support tickets, survey results to that structured data. We have use case evaluation workshops where we can help pinpoint some of the high ROA use cases, things that we're seeing across the industry are moving the needle. Then you can even join some of our prototyping workshops where we can go not just talking, actually building, like what would it take to get your very first agent similar to the sales agent that Sridhar uses or the product agent that I just showed.

We're excited to work with you, continue to part with you and figure out how we can deliver this goodness and this exciting potential of AI into the hands of our shared customers. So thank you so much for letting me spend some time with you this afternoon or this evening, and that's what I want to share with you all today.

Excellent. Thanks so much, Jeff. So much amazing work going on. I love those demos around Snowflake Intelligence and Sema4 agents. But I also love the fact that ETC, easy trusted connected, or easy connected trusted depending on which way you want to position it. I had no idea you had that slide in there. So pleased to see that, particularly following on from the discussion with Sridhar.

Thanks for jumping on. Always a really valuable session around the pulse of the product. So I appreciate you doing that.

Just a quick reminder to all of our participants, if you do have any questions, please use the Q&A function at the bottom of the screen. We have a bunch of people sitting here answering those questions. So keep them busy.

Okay. For our next session, I am going to be speaking to one of our fantastic customers from Indonesia. But before I do, Hwee Bee, I think there's a short video that you would like to play.

Yes.

[Presentation]

Great. Thanks, very much, Hwee Bee. And Sami, welcome to SPN Pulse.

Thank you, Ash. Happy to be here.

Now I was wondering where we had met before, and I just figured it out. Every time I walk into the office in Singapore, I see that video playing. And that's why your face was so recognizable when we connected the other day. So thanks for joining us, and thanks for being an amazing customer of Snowflake.

Happy to be here, Ash, and happy to be a customer for Snowflake as well.

And I should also say congratulations. You were just recognized as one of our Data Driver of the Year awards, Data Executive, I believe.

Thank you so much for that.

So I joke with you the other day that, that means that we use you for all of these presentations moving forward. So you've got to commit to multiple hours every week to do these things.

100%. And like I said, the learning is mutual, right? So I always learn a lot from these sessions. So looking forward to that.

Okay. Good stuff. So Sami, just to get started, I think there's a couple of things that we should clear up. So in that video, we just saw a company called XL Axiata but the title of this session is all about XLSmart. What's going on there?

Yes, yes. So basically, last year, we were at XL Axiata, and earlier this year, just very, very recently in April, we actually completed our merger with the fourth operator here. So that's a premerger used to be called SmartFren. So we merged XL Axiata and SmartFren and produced a new company, which is called XLSmart. So we are now much bigger, much well positioned to fight in the market.

So we need to get Mr. James Butler from our marketing team to come and film a new video with you.

Correct. Correct. Exactly.

Good stuff. So the other thing that I just noticed on there is it said XL Axiata had 57 million subscribers but the notes I have here are a much larger number.

Exactly. Because now we have completed the merger process in this Q2, we had our first quarterly report out as well as a merged entity. So we have now a lot more customers.

82.6 million mobile subscribers if the data I've been given is accurate.

Exactly. That's the correct number.

So considering you serve so many customers and so many people in Indonesia really trust your company, what role does data play both in terms of your growth strategy but just running the business on a day-to-day basis?

Exactly. So basically, data is at the center point of everything we do. And I'm not just saying it as a clichéd word but practically, every day, every moment, whenever we are making a decision from a very strategic decisions all the way to very tactical day-to-day stuff, data is all across -- is used widely all across the organization.

I will give you some examples. So Indonesia is a complex geography. So it's a lot of islands and the access to those islands is also difficult as well as we really need to be sure where are we deploying our CapEx because, I mean, telecom itself is a very CapEx-intensive business. And it's a $3 ARPU market. So we need to be very sure that wherever we are deploying our CapEx, the market demand is there. So we use a lot of internal and external data together to build -- and we have built our models that helps us in identifying the geographies where we should expand our network.

But not just from a coverage perspective but also a customer experience perspective. So what is the profile of the customers, what kind of devices are they using? And what is the right experience to build because it's very easy to build, for example, 100 Mbps network all across the country but then the key is the profitability as well. And when the ARPUs are like very small, it's the volume gains, right? So what is the best network dimensioning to serve these millions of customers serving their mobile needs. So we do a lot of those decisioning on using data.

Then the other very big part is the personalization. So how do we reach out to our customers in a meaningful way where they feel connected, where they feel that we know we understand their needs and what we are presenting to them is something which best suits their needs, not only just a product perspective but in the journeys, in the channels we are reaching out to them, we have more than 50% of our revenues coming from the digital channels as well. So digital plays a huge role, and that also opens up a huge opportunity for us to know our customers better.

But all of that is only possible when you have the data in the right format, in the right context in a trusted way, which then can be activated and used for these variety of use cases, Ash.

That's a great overview. And I heard you say customer a lot but it's both in terms of using data to provide better customer experience. But also you sort of take the circular approach there where it informs business decisions and investment decisions that ultimately results in better customer experience as well.

Exactly.

So large company, we hear time and time again that many large organizations or many organizations, large and small, really struggle to scale data initiatives. They look good on a whiteboard but getting them into practice can be pretty hard. Maybe you could share a little bit of background in terms of why you decided to modernize your data platform and some of the things that you were really looking to solve as part of that project.

Yes, yes. No, 100%. And I would say that we were one of those organizations as well where we were literally struggling to get the analytics out of the of large data that we had before the migration on-prem. So much so than whenever -- most of the times, whenever we are doing a complex analysis, I had to wait for weeks for my analysts because usually the response I get that, we have to schedule it over the weekend because during the weeks, oh, we don't have enough window to run this analysis. And some of the examples that I said, you can't wait for weeks because you have, let's say, the Board meetings upcoming, you have to get the approvals from your shareholders of the business and you have to prepare a business case. And just this -- this is just one example.

So it was really big pain for all of us to be able to get those numbers out. And as I mentioned, I mean, XL has always been a very data-driven company. We try to -- and we aim to gain -- get all our decisions based on the data and facts. So the scalability was significantly hurting our speed of execution and speed of decisioning.

Then the other thing was the reliability of the numbers of the data itself because just like many other companies, our legacy platforms had built over the years, and there were silos. There were different numbers of different versions of the numbers. Security was also another thing of concern. So ease of use, scalability, reliability, all of those channels -- all of those were the reasons that actually pushed us to actually making this transformation as a must-to-have, must-win battle rather than have something, which is like we are doing it just for the sake because everyone is talking about AI and transformation. Let's do that. No. We had very clear reasons why we want to do it, and we pushed all the way on the execution.

Okay. So that's a really good overview in terms of sort of the what or the why. If we dig into the how a little bit, like how are we partnering together to really drive this transformation?

Yes. I think that's a great question because that how becomes extremely important when you have to have an excellent execution of an idea. So all the ingredients were there. We were very much convinced that we need a modernized analytics platform. So we ran a whole process to select the best technology that we -- that can potentially serve our needs but then how do we execute? And that's where I think the word there is a trusted partner. We are not talking about the cheap best. We are not talking about somebody who just says yes to all of our needs and desires and then fail later in the project but a trusted partner where we can actually have a dialogue, who can understand what is our business challenges, what are we trying to achieve and also help us in shaping the program in the best way possible, right?

So that's what we started to look out for that who can help us, and why I'm saying trusted because one of the challenges we are facing is because of the legacy, almost nothing was documented, and I will be open on that. So we wanted somebody who actually gets their hands dirty with us, go into the scripts, look at the logic of how the data is being processed over the last 10, 15 -- I mean, XL was 29 years old, 28 years old organization. So how those things have been developed and work backwards.

So it's a lot of reverse engineering that was involved. So we wanted somebody who can actually work very closely as one team with us. And that's what exactly happened that helped us in delivering this project on time.

Okay. So I know that I love the way that you said that. It's not necessarily about the cheapest but it's really about driving the outcome. And I know that you guys leverage partners heavily as part of your project. But one that I want to call out, in particular, is the partnership between Snowflake and AWS. How did that collaboration kind of fuel the success of this project? And you've spoken about this publicly on quite a few occasions as well.

Yes, yes. No, exactly. Because, I mean, when we were starting this journey, and I think moving from on-prem to cloud was already giving us a bit of anxiety in terms of build shocks and how all of this work and then working with 2 separate partners at that time and even before starting the project, we were actually mindful of how all of this tri-party kind of a thing will work because AWS is there hosting the platform and then Snowflake is the AI data platform that will be there. We will need expertise from AWS in specific areas but we'll also need expertise from Snowflake's team in the product specifically. So how that will work.

But just from the beginning, when we were actually evaluating all the way through the tender process and when we were scoping the project, what worked very well is AWS and Snowflake actually, those teams come together as one team. And when our teams joined, when I was going on the floors, when the actual action was happening, you couldn't actually tell that who is coming from Snowflake, who is from AWS, who is our own team and then there were other partners involved. All of that blended very well as one team.

And I think that was the key success of the project that it wasn't, no, no, no, AWS -- there's no problem with the AWS, everything is working. and it's the Snowflake, which is not working. Many times, they both supported the cost and make sure -- I mean, in the initial beginning, we had issues. There were performance issues. There were quality issues. But when everyone worked together on this, they were able to solve those problems. That's the heart of the success -- the key reason why we were able to be successful.

Sami, that's -- I couldn't have asked for a better way to finish this conversation. When you said that you couldn't tell who was from AWS, who was from Snowflake and who was from XL. For me, that's the testament of an amazing partnership. So we spoke offline just around some things that are kind of really important and some key messages for partners. So we had lead with data value, show customers how to put data at the center of decision-making, prove quick wins. So faster analytics and double-digit cost savings resonate across every industry, position security and governance.

So if we look at the T in ECT or ETC, depending on which way you want to describe it, trust is a massive thing for customers as well. And then this concept of ecosystem enabler. And I think this is where for our data cloud services, SI partners on this call, helping to bring the whole ecosystem together plays a really critical role.

So Sami, thank you very much for taking the time. I truly appreciate you coming and sharing your journey with our partner ecosystem. Thanks for being an amazing customer of Snowflake, and congratulations again on being recognized as a -- with a Data Driver of the Year Award. I really appreciate it.

Thank you, Ash. Thanks for having me here and looking forward to our continued partnership and driving even more value from this.

And I'm looking forward to seeing the new video release. So when I walk into the office, I'm seeing XLSmart.

Let's do that.

Good stuff. Thanks, Okay, Sami.

Okay. Thank you, Ash.

Thank you. Okay, Hwee Bee, am I doing a quick close?

Yes, you have a quick wrap up, and we have a few exciting updates on our partner programs as well, right? A quick...

Yes, absolutely. So firstly, a big thank you to everyone for joining us today to our presenters. And yes, really, really, really appreciate you guys taking the time to jump on.

A couple of quick program highlights that I just wanted to cover if we can jump to the next slide, Hwee Bee. We have a lot of work going on in the background at the moment to ensure that we keep pace and we continue to provide the right benefits, the right incentives, the right programs, investments into the right areas. So Sridhar touched briefly. We have a new Head of Worldwide Partner and Alliances, Chris Niederman. Chris is going to be joining us on the next SPN Pulse. So he's committed to come on, do an introduction and really frame up some of the thought process around how we think about partnering. But there are a couple of changes that we are rolling out now. So if we can jump to the next slide, Hwee Bee?

Yes.

We can just build all these out. Okay. There we go. So firstly, you're going to see some new metrics come. So in order to qualify at different levels within our program, we wanted to provide some more flexibility. We wanted to be able to recognize partners that are more focused on customer acquisition versus expanding within our existing customer base. So stay tuned for some announcements that are going to be coming there. Very, very relevant to the APJ market. We've got some new country-specific tiering goals. So not all markets are created equally. Many markets are at a different level of maturity and a different level of market size. So we've been able to tier some of those program requirements.

We're also enhancing our services registration process. So what we want is to make it easy for you to tell us Snowflake projects that you're working on. It was a little bit of a convoluted process before. So we've really streamlined that as much as possible. And off the back of that, we're rolling out a new incentive to reward you for registering those services projects with us. So stay tuned. There are going to be some announcements coming around each of these, and we're updating SPN portal as we go as well.

Next slide, Hwee Bee Tan. So look out for these enhancements, these e-mails. There are some enrollment steps required, particularly for our new services registration incentive that's going to be coming. Keep getting those in. And as I mentioned, jump on to the portal and you can see all of the program updates there.

So with that, thank you very much for joining us. I hope you enjoy this Pulse. We had a lot of fun delivering it.

Yes. And Ash, I think thank you so much for hosting the whole session. And now I'm opening up to the partners in terms of some feedback from you, right? So let us know what's your feedback, any interesting topics you would like us to cover. We are more than happy to accommodate. We have come to the end of today but I'm going to stay on screen for a couple of minutes to wait for your feedback. So let's take 3 minutes to give your feedback so that we want to hear from you. Yes.

Excellent. Good wrap up, Hwee Bee. Really important that we get this feedback folks. We want to make sure that this is a valuable use of your time. Tell us what you want to hear more about moving forward. Thanks, folks.

Thank you, Ash. Team, I'm going to stay on screen 1:10. So in about 3 minutes, and then we'll close this webinar. So 60 more seconds. So any feedback is really, really welcome. Thank you so much. And we will have our next Q4 SPN Pulse coming soon in November, yes. We'll keep all of you informed.

So more 30 seconds, I'm going to close out. Thank you all for joining us today. Really, really appreciate it.

Good afternoon, thank you for attending the Snowflake Inc. Second Quarter Fiscal Year '26 Earnings Call. My name is Cameron, and I'll be your moderator for today. [Operator Instructions]. And I would now like to pass over to your Jimmy Sexton, the Head of Investor Relations. You may proceed.

Good afternoon, and thank you for joining us on Snowflake's Q2 fiscal 2026 Earnings Call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; Mike Scarpelli, our Chief Financial Officer; and Christian Kleinerman, our Executive Vice President of Product, who will participate in the Q&A.

During today's call, we will review our financial results for the second quarter of fiscal 2026 and discuss our guidance for the third quarter and full year fiscal 2026. During today's call, we will make forward-looking statements, including statements related to our business operations and financial performance. These statements are subject to risks and uncertainties, which could cause them to differ materially from our actual results. Information concerning these risks and uncertainties is available in our earnings press release, our most recent Form 10-K and 10-Q and our other SEC reports.

All our statements are made as of today based on information currently available to us. Except as required by law, we assume no obligation to update any sets. During today's call, we will also discuss certain non-GAAP financial measures. See us the presentation for a reconciliation of GAAP to non-GAAP measures and business metric definitions, including adoption.

The earnings press release and investor presentation are available on our website at investors.snowflake.com. A replay of today's call will be posted on the website. With that, I would now like to turn the call over to Sridhar.

Thanks, Jimmy, and hi, everyone. Thank you all for joining us today. Snowflake has delivered yet another strong quarter. And I'm proud of the incredible work across our team and the deep partnerships with our customers to deliver these renewals. Our core business remains very strong. And we continue to deliver product innovation to market at a rapid pace while strengthening our go-to-market and for growth. We're executing with intensity and alignment and continue to see an enormous opportunity ahead. Snowflake remains laser focused on our mission to empower every enterprise to achieve its full potential through data and AI.

We're delivering our more than 12,000 customers tremendous value throughout their entire data life with an AI data cloud that's designed to enable faster innovation and remote friction from business operations. We remain disciplined in driving operational rigor across our business, gaining greater efficiency even as we continue to invest aggressively in growth. We continue to execute with urgency and focus to capture the opportunities ahead and sustain durable momentum.

Product revenue for Q2 was $1.09 billion, up strong 32% year-over-year, demonstrating an acceleration in growth from last quarter. Remaining performance obligations totaled $6.9 billion with year-over-year growth of 33%. Our net revenue retention rate was a very healthy 125%. Our non-GAAP operating margin increased to 11%, reflecting our focus on efficiency and operational rates.

As you can see, we have delivered strong revenue growth and healthy result this quarter. And as a result, we are increasing our growth expectations for the year. At Snowflake, we believe the great technologies defined by the experience of making something complex, feel effortless. And we put simplicity at the center of not just our product design, but our entire customer. We are committed to delivering a cohesive product with fast time to value and it's a differentiator that leads customers to choose Snowflake again and again.

It's why enterprise leaders like Booking.com on the Intercontinental Exchange used Snowflake. Our platform is easy to use, connected to enable fluid access to data wherever it sits and trusted by company. of all sizes and industries. And global hospitality icon Hyatt Hotel uses Snowflake to simplify data management and ensure unified go.

By consolidating enterprise data into a single environment, it empowers its teams with fast, secure access to information, enabling them to make informed decisions that enhance customer experiences. and drive operational efficiency. This quarter, we delivered on our product strategy, introducing incredible new innovations to drive value at each stage of our customers' data journey.

Of course, AI is front and center. We are continuing to advance our leadership in enterprise AI with Snowflake Intelligence now in public preview. This platform enables every user to talk to their enterprise data turning structured and unstructured data into actionable insights through natural language, and it empowers the creation of intelligent agents directly on enterprise state. Early adoption is underway with customers like Cambia Health Solutions, which serves 2.6 million members in the Pacific Northwest. They leverage Snowflake's Intelligence to create its first intelligence agent to assist their teams in improving health outcomes for its Medicare members. This intelligence agent helps KB Medicare teams quickly analyze vast amounts of both point in time and longitudinal data, enabling them to scale their ability to deliver differentiated, personalized health care experiences and ensure members receive the right care at the right time.

Then there's Duck Creek Technologies, a leader in insurance core systems and analytics, who is leveraging Snowflake to drive innovation with AI and agent workflow. They're using Snowflake intelligence to power internal teams and increase efficiency across finance, sales and HR, ultimately setting the standard for the insurance industry. Alongside Snowflake Intelligence, we introduced Cortex AICL, bringing AI natively into SQL. Customers can now invoke AI models directly within Snowflake, eliminating data movement and unifying analytics and AI in a single step.

We have also made great strides to deliver faster, more seamless performance with the launch of Gen 2 warehouse. Already, they're helping our customers deliver up to 2x faster performance and greater efficiency, automatically optimizing resources to accelerate insights and simplify data management without increasing costs, strengthening the value that our customers see from so. Without introduction of Snowflake -- we have reinforced our commitment to developers, enabling our customers with enterprise-grade Portal to build and run their most critical AI-powered applications on post-script right, inside the Snowflake AI data clock. And we have extended our connectivity platform with Snowflake open flow, making it easy to bring in structured, unstructured, batch or streaming data.

Built on our acquisition of Data Volo, OpenFlow provides seamless access to all enterprise data and now supports change data capture from our through strategic partnerships. When customers already using open floor to unlock new value from their data architectures OpenFlow expand our reach into the $17 billion data integration market. It's also now easier to bring no work close into Snowflake with Snowflake Connect -- public preview. This enables our customers to bring our Spark workloads directly into Snowflake, eliminating the burden of managing and tuning separate spark environment Customers can now run Spark data frame and Spark SQL natively on Snowflake's high-performance engines, simplifying operations and accelerating time to value. Overall, it was an amazing quarter for product innovation.

In the first half of year alone, we launched approximately 250 capabilities to general availability, demonstrating both the pace of our innovation under breadth of our platform expansion. But we are not stopping there as we innovate. We are continuing to strengthen our platform and help our customers do more with their data to deliver great business out.

As more companies face a challenge of data spread across different places, we are helping them effectively share data and collaborate. As of this quarter, 40% of our customers are now data sharing on soft way. driving powerful network effects that strengthen our ecosystem and expand customer value. We're continuing to see strong adoption of Open Data format, especially truly open modern table pharma like -- expert. We now have over 1,200 accounts using experts ongoing our leadership in bringing truly open standards to the enterprise.

Our progress with AI has been remarkable. Today, AI is a core reason why customers are choosing Snowflake influencing nearly 3% of new logos won in Q2. And once they are on our platform, AI becomes a cornerstone of their strategy, powering [ 15% ], all-deployed use cases with over [ 610 ] accounts using Snowflakes AI -- we have embedded AI across the data life cycle to accelerate analytics, transform workflows and even power migration. For example, Snowflake convert AI uses AI-driven automation to speed up large-scale migration, minimize manual recording and reduce risk, helping customers move faster and with greater content. Cortex AI continues to play a foundational role in enterprise AI strategy. For example, Thomson Reuters is transforming how its business users easily access information by deploying AI-powered agents built on Snowflake Cortex search and LLM observability. This enables -- insight, seamlessly handle drag and text to SQL and significantly reduces time to insight and costs across functions like finance and -- then there's BlackRock, which is leveraging Cortex AI, Snowflake Cortex to help its team serve their clients more effectively and at a much larger scale. Our technology allows them to pull together every piece of information they have on a client, from their past portfolio not from a recent call and get instant insights. It's like a super power that helps them understand exactly what each client needs so they can provide the best possible service.

We have furthered our AI leadership by integrating the world's leading model in Cortex, ensuring day 1 availability of Open AI's new open source as well as advanced D5 model. providing our customers with choice and flexibility to leverage their model of choice for their enterprise AI application. Beyond what's possible with AI today, we are also making Snowflake, the destination for building the next generation of cutting-edge applications such as 74.AI Agentic AI platform, which helps customers automate workflow for tasks like supply chain and regulatory compliance. As we strengthen our platform and introduce new capabilities, we remain limited to scaling efficiently. Our go-to-market teams are demonstrating renewed focus and rigor as evidenced by our healthy retention rate and our addition of 533 customers including 15 Global 2000 companies this quarter.

This year, Snowflake summit was a clear marker of our momentum. The event, our biggest yet, do record numbers of over 22,000 customers, partners and developers from around the world and underscore the scale of our community and the excitement around the AI data. We're also investing in our partnership. Today, more than 12,000 global partners, including leading cloud providers, technology innovators and system integrators are part of our ecosystem. We are scaling our go-to-market engine, while tightly aligned across engineering, product, marketing and sales. This collaboration enables us to deliver greater value to existing customers, but also win new ones with speed and precision. It's certainly exciting time at Snowflake, and I'm proud of the discipline, efficiency and innovation we've built across the business. We've got a strong operational rhythm. We're investing strategically for growth, and we're in the groundwork for continued scale.

Mike, why don't you take us through some of the financial details?

Thank you, Sridhar. In Q2, product revenue growth accelerated to 32% year-over-year product revenue benefited from strength in our core business. At Investor Day, you heard us outline our 4 key product categories: analytics, data engineering, AI applications and collaboration. In Q2, new features across all 4 product categories outperformed our expectations. With net new customer adds in the quarter, up 21% year-over-year, it is clear that our new customer acquisition motion is yielding positive results. And in the last quarter, 50 customers crossed the $1 million in trailing 12-month revenue, a record for the company, $1 million-plus customers now total 654.

Shifting to margins. Q2 non-GAAP product gross margin was 76.4%. Non-GAAP operating margin was 11% and Operating margin benefited from revenue performance in the quarter. We are focused on delivering margin expansion while investing in our business. In Q2, we added 529 heads including 364 sales and marketing heads. As a reminder, our sales and marketing hiring is weighted to the first half of the year.

In Q2, non-GAAP adjusted free cash flow margin was 6%. As discussed on our prior calls, we expect free cash flow to be weighted to the second half of the year. This expectation is supported by contracted billings, a large renewal base and large deal volume in the pipeline. We did not utilize our versus program in Q2. We have $1.5 billion remaining on our authorization through March 2027. We ended the quarter with $4.6 billion in cash, cash equivalents, short-term and long-term investments.

Moving to our outlook. For Q3, we expect product revenue between $1.125 billion and $1.13 billion, representing 25% to 26% year-over-year growth. We expect non-GAAP operating margin of 9%. We are increasing our product revenue guidance for FY '26. We now expect product revenue of $4.395 billion, representing 27% year-over-year growth. We expect non-GAAP product gross margin of 75% and non-GAAP operating margin of 9% and non-GAAP adjusted free cash flow margin of 25%.

Finally, I'd like to provide an update on our CFO transition. We are making progress on our search and we will make an announcement once we have more firm details to share.

With that, operator, you can now open up the line for questions.

[Operator Instructions]. The first question is from the line of Sanjit Singh with Morgan Stanley.

Congrats on the accelerating product revenue growth this quarter. You guys have been executing quite well. And it seems like multiple parts of the equation came to work in the question for you is that it seems like modernizing the data infrastructure is a real priority among the Fortune 500, the Global 2000, I want to get a sense of like, as we go through this modernization efforts, on the other side of that, do you see kind of durable growth? Or is this customers addressing their legacy data infrastructure, maybe you guys benefiting from that migration, if you will. But what is -- what do you -- how do you feel about the durability of growth on the other side of these data transformation efforts?

Well, I think data modernization is just the beginning of the journey is primarily driven by the fact that legacy systems have trouble scaling, whether it's workloads or our data. And bringing those systems on to Snowflake is step one in value realization. In fact, the feedback that I get from our customers is that this data monetization journey is even more important than before because they realized that AI transformation of workflows of how they interact with their customers is critically dependent on getting their data in a place that's AI ready. And that's where Snowflake comes in, data that is in Snowflake is increasingly AI ready, both for access by consumptive layers like Cortex analyst or Cortex search but also by agency players like Snowflake Intelligence, where you can both ask nontrivial questions. But we fully foresee things like applications coming on top of that data. So we feel very good that we are very much in the beginning of the journey, where data indeed does more for our customers.

The next question comes from the line of Raimo Lenschow with Barclays.

I wanted to focus on the new customer at Obviously, great progress there. I remember last year, the U.S. organization kind of got split into hundreds of farmers and that started to contribute. I think this year you did it for Europe. Is there already a contribution from the European side? Like can you speak to that kind of momentum that you have there on that part of the business?

Yes. What I would say is Europe is still developing, but it's contributing. We are laying the groundwork there. Obviously, we set up this new motion in the U.S. first in the bulk of those new customers are coming from the U.S. where we've been replicating that setup in EMEA as well as APJ. And we think that will yield as well there. But they're performing.

The next question is from the line of Karl Keirstead with UBS.

Sridhar, and Mike Sachiodella at Microsoft on the last call went out of his way to highlight an acceleration in Snowflake on Azure. I'm just curious, as I think through what may have driven the outstanding results this quarter, was there anything unique that you did with Microsoft or with customers that are running on Azure worth calling out? Or did it feel like your outperformance was fairly even across the different cloud providers.

I would say actually, Azure was our fastest-growing cloud. It actually grew 40% year-over-year. Our customers running on Azure. And I would say a lot of that is attributable to better alignment between our field and Microsoft. We've been spending a lot of time in the last 6 months. There, I would also say too that Microsoft is very strong in EMEA, and we're seeing some good uptick in EMEA in our business as well with some large accounts that's contributing to that as well. But clearly, the Azure cloud is the fastest growing, but it's off a lower base. AWS is still the biggest, but Microsoft is moving up.

The only thing that I'll add on top of that is that I think we have both depth and breadth of collaboration. We work very closely with the Azure team at an infrastructure level at the level of Snowflake but also at the level of the end user products like Office copilot and RVI and the go-to-market partnerships that Mike referenced just now are an additional exert on top of that. We see these as long-term benefits for both the companies, and you'll see more and more results come out of it in the future.

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

Mike, it sounds like there's a number of drivers of the upside in 2Q, especially around some of the newer products that came to market across those sorts of growth. I was just wondering, how did you sort of contemplate some of these newer products in the guide for 3Q? I know you guys tend to want to get a little bit of a trend line going before you want to make any kind of bet on them. So I was just wondering how the kind of how that played out in 2Q and how you're thinking about for 3Q?

Well, as I said, they outperformed our expectations. We did have a modest amount in our forecast for those because it didn't just come out this quarter. We talked about it some, but we've been working on these for a while. And when we set our forecast for the next quarter, it's always based on consumption patterns we're seeing today. I would say, yes, Q2 surprised us on the upside, we knew it was going to be a strong quarter, but not as strong as it was, and that's just the nature of a consumption model.

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

I guess to the prior question, maybe if I think about the acceleration in consumption that you guys are now seeing, is this something where this is a normalization of like the demand environment, your customers feeling better about spending again or is there and/or is there something more happening where you're getting increasingly included in these AI initiatives as AI budgets, these new products are unlocking incremental budget spend. And if it's the latter, to what extent is...

We were just cut off in the middle of Alex's question.

[Operator Instructions]. Perfect. Alexksandr, your line is open.

Again, sorry, I don't know where I got cut off, but to what extent do you feel as though the outperformance was kind of a normalization of the demand environment? And kind of improved execution from the field versus getting included in more of these AI-centric budgets and seeing some of these products really initiatives come to fruition. And if you think about it more of as the latter, how do we think about that as we progress through the year is starting to drive really meaningful incremental upside on top of previous consumption trends in your customers.

Mike and I have talked to this before, Alex, which is that our core business in analytics continues to be strong. It's the foundation of the company. And you can see this also in things like NRR, net revenue retention, which was a very solid 125%.

What is happening is that there is more and more recognition that the AI components of our data platform can deliver enormous value. And we're seeing budgets get allocated from large customers for AI projects. And typically, that also happens when the data is on Snowflake because our customers can realize but the things that they love about Snowflake, which is the ease of use, the work that they have put into governance to make sure that only the right people can see the right data, a lot of work that we put in to make sure that AI is trustworthy all of these play into these large customers using us for AI projects.

And for example, of the use cases that were deployed in Q2, close to [ 25 ] close to 1/4 of them involved AI in some form or the other. So this is definitely a trend that will continue. But again, I'll stress something that Mike has said, which is we forecast as well as we can, meaning that as these workloads become more and more mainstream, our prediction models are going to pick that up and increasingly rolled that into our forecast. But we feel very good about our ability to create business value with AI and our customers, and that is a trend that we expect to see both continue and accelerate.

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

I have a question for you. We have seen AI in the consumer ramp just get better and better. So I would argue the rate of improvement of these models, appears to have stalled a little bit, which is disputable. But at what point are we going to see the AI magic that has taken over the consumer world make its way into the enterprise? I mean, certainly, there seem to be some indicators of that happening at the platform layer. But what gives you the direction today more than perhaps a summit that AI and the enterprise is about to work through tangible business cases. And also, I was intrigued by your comment on supporting Spark, I mean that confidence in supporting Spark on Snowflake seems to be a new thing that I picked up. Can you talk more about that as well.

Yes. On the first one, I definitely say that AI is emerging and increasingly powerful force. I can speak to it with personal experience. the kind of questions that we can ask of a sales agent that we developed on Snowflake intelligence has become pretty remarkable. Obviously, I wanted to answer questions like get an update about our customers that I'm about to meet so that my AE does not have to write that particular brief forming. But being able to do a cross-cutting analysis, for example, of the most popular use cases up trends in use cases, questions that I would normally need to go to an analyst for, Snowflake intelligence can figure out how do I pretty complicated plans for these and deliver this.

I think that's where you are seeing the magic happen. And thanks for our partnerships with OpenAI. We launched GPV 5, the same day that they launched it. We launched GPV 5 on Snowflake. And similarly, with Anthropic, gives our customers the best of both worlds, the world's best models combined with the data about their business that they have often painstakingly put into Snowflake. And that's where we are seeing massive value get realized. And that's a little bit of an Aha moment for us, for our customers. I'm happy to show off, for example, Snowflake intelligence to our customers in every conversation that they have and the -- reaction is that they want such functionality directly on top of their data as well.

So Christian, do you want to take the part question, please?

Yes, certainly. So we've talked about snow part for many years and how it has been performing well for us we outperformed all Spark distributions managed products out there. And what we heard from our customers is they will want to simplify the migration effort or cost to be able to get those benefits from Snow Park. Spark itself has introduced something called Spark Connect. And that is what we've done. We've adopted the Spark APIs, but the processing happens by Snowflake, specifically by Snowpark, so now you get the benefit of it is a familiar set of APIs and proven models, but with the performance and cost benefits of Snowpark.

Next question comes from the line of Brent Thill with Jefferies.

You raised the guide more than the beat this quarter. I'm just curious the visibility in what you're seeing in the second half?

I would just say we've consistently been raising by the beat us more for the last 6 quarters. And that's based upon consumption trends we're seeing through literally today, and consumption is strong within our customers. You see that in the net revenue retention, and we're seeing a number of our new products with a lot of uptick in those. And as Sridhar mentioned, we just went GA this year with 250 new features. All these features drive new revenue for Snowflake, and we anticipate continuing to have that type of delivery of new features going into the future. That's one of the things Sridhar's really focused on the last 1.5 years with engineering and product.

The next question comes from the line of Mark Murphy with JPMorgan.

The sales and marketing new hires are again just an enormous number for the second consecutive quarter. I think it's the biggest 6 months of hiring that you've ever had. Can you walk us through the underlying dynamic? Does that reflect pipeline growth stepping up proportionately? And where is that going to place Snowflake in terms of the growth of your quota-carrying sales capacity by the time all of that ramps in 6 to 12 months or however long it takes?

Yes. I would say we've actually hired more sales and marketing people in the first 6 months of this year on a net basis than we did in the prior 2 years combined. But I want to remind you that in Q3 and Q4 of last year, we went through a pretty extensive performance management within our sales organization, in particular, we've pretty much worked through most of that. But we really look at productivity of reps, and we're really focused on getting reps and SEs, by the way, too, we've added a lot more SEs into the organization, we have more specialty sales people within the organization. And we will continue to add as long as we see that we're yielding the productivity. And it's not just bookings is also activity and stuff of what they're doing with customers. And that's strong. But we've always anticipated that the first half of the year was going to be a much higher number than the second half.

The next question comes from the line of Brent Bracelin with Piper Sandler.

Mike, I wanted to go back to the drivers of upside the quarter. If I just take a step back, I sequential growth in product revenue 2.5 years. a pretty sharp year-over-year acceleration in a number of million dollar customer adds. How much of the acceleration here in Q2 and surprise was driven by higher consumption in the core versus an incremental uptake on these new products in AI.

Well, we had some large customers that were doing some migrations of new workloads that drove outperformance some very big customers. I would say we saw a little bit of contribution from crunch that acquisition we did with Postgres. But the newer workloads we're seeing meaningful contribution as well, too. But it's really the core of our business is what's driving the significant upside.

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

Sridhar, one of the questions we often get from investors just in terms of framing the competitive environment. Obviously, Snowflake, Databricks, hyperscalers, including Microsoft fabric despite your recent partnership palentir with others. I'm just curious if you are having these conversations with an increasing number of million-dollar customers. Just sort of how are they bucketing and thinking about the different swim lanes of these various technologies? And do you think like the there's sort of less confusion maybe among the larger players such as yourself that that's helping sort of unlock higher deal flow and velocity for you?

I think, first of all, Snowflake is the best AI data platform that is -- and this is widely recognized by many of our customers and new customers. And we stand out in that respect and the product quality that we have always strived to create, whether it is ease of use and simplicity our connectedness where we don't let silos develop where data is shared as it should be, autumn being a trustworthy platform. that we spend a lot of time on making sure that we reduce hallucinations, work with our customers and having the right governance in place. Increasingly, these quality are apparent to our customers.

Yes, there are some areas in which customers might prefer some of the platforms that you mentioned. But we feel very good both about our strength in the core, which is around analytics, but increasingly in our ability to bring new products, whether it is our Postgres offering or open flow, which is our cloud injection platform or variance supporting Spark or machine learning or AI. We feel very good and confident about our position and the value props that we bring resonate in all the customer conversations that we have, and that's the reason why you're seeing acceleration across the board both in new customers, but also in things like consumption from existing customers or how AI is getting adopted.

The next question comes from the line of Brad Sills with Bank of America.

Great. I see that Professional services had a real nice ramp this quarter. I think it's up 20% quarter-on-quarter. What's going on there? Is that just an indication of customers looking to select for more consultative kind of strategic deals as you get into all these different types of workloads. We just would love to get your thoughts on what's driving that and what that might mean as a leading indicator for the business?

Yes. I just want to remind you that the -- if you look at the total amount of professional services done in the Snowflake ecosystem, we, ourselves, do a very small fraction of that. Most of that is being done the -- we typically want to be more the expert services to help other partners, do things. And for some customers that insist that we are the ones doing the work. And what drove that upside in the TS this quarter was 1 large customer where there were some milestones that had to be that we were deferring that revenue that we recognized this quarter because those milestones were hit. If you took that out, it was a normal growth quarter for services. But our goal is not to do all the services. Our goal is for our partners to be doing those things.

The next question comes from the line of Michael Turrin with Wells Fargo.

Maybe the expansion rate, good to see the improvement there. I'm curious if you think that metric is at all turning a corner with optimizations, data background and consumption trends improving or anything else you'd add around the improvement we're seeing in Q2 whether that's from here and how maybe some of the newer product traction you're seeing informs your perspective on that metric going forward.

Well, I would say, first of all, we never guide to net revenue retention is really a product of our revenue growth, and we grew -- we outpaced our revenue growth this quarter. So you'd expect that net revenue retention to have ticked back up slightly. What I will say is what's driving that is actually, and I mentioned this a couple of questions, we had a number of our large customers that have been existing customers for a while that migrated new workloads that caused an uptick.

And as a reminder, when people migrate new workloads. It typically causes an uptick in consumption and then it normalizes thereafter. This has always been the case. And I would say optimizations actually have nothing that caused anything unusual. We've talked about optimizations before. Customers are always optimizing on Snowflake. If anything, we're trying to get in front of these things with customers, so customers don't use Snowflake unwisely so they don't have to deal with optimizations. And I'm not aware of any customer that's not in an unhealthy place right now in terms of their consumption, where a number of years ago, we were well aware of one.

Your next question comes from the line of Brad Reback with Stifel.

Mike, just picking up on that migration point. I know last quarter, you talked about having good line of sight into that level of activity. Does it look similar for the second half or maybe even bigger?

Yes. We've identified a number of new workload use cases to go into production. And think about this as a number of -- some of these are on-prem migrations. Others are from first-generation cloud infrastructure from raw, S3 or Azure. So yes, we're getting much better than that, I would say, as our SEs, I think, are doing a phenomenal job of really identifying those use case go-lives and migrations.

The next question comes from the line of Patrick Colville with Scotiabank.

This is Joe on for Patrick Colville. You guys had a nice quarter landing incremental GK customers. Can you talk a bit more about the opportunity that you see in these accounts specifically? And I know you have 654 customers spending over $1 million. Have you guys talked about what percentage of those customers are G2K? And then lastly, I guess, how are your sales reps communicating the value proposition to these very large customers to drive spending higher?

Well, what I would say is a Global 2000 customer, there's no reason why the average Global 2000 customer can't spend $10 million a year on Snowflake, just looking at all the different things, and it can be higher than that. And I would say don't quote me on this, but it gets roughly 50% of those 1 million plus customers or Global 2000.

The next question comes from the line of Matt Hedberg -- my apologies.

And you were asking about how do our salespeople articulate the business value. We spend a lot of time with sales enablement and educating these people, and we really wanted to be in a discussion about not as what is the cost of Snowflake, what is the value you're getting. And I would say some reps and teams are very good at it. Others are developing. But that's really the way we go to market is what is the business value you're going to get out of using Snowflake.

The next question comes from the line of Matt Hedberg with RBC.

I wanted to circle back on fun sheet. Mike, you noted it contributed a little bit to the quarter. Just kind of curious about how that's progressing? How is that integration working? And when you're thinking about addressing OLTP and OLAP opportunities, where are we in that sort of evolution curve because it feels like it's certainly a long-term opportunity. There's obviously some increased competition there. But just kind of give us an update on kind of how that's progressing.

I'll let Christian answer this one.

Yes, the integration from crunchy to what we're calling Snowflake post-progressing extremely well. The part that we highlight are most excited is that it's not just opposed to service, but it is posts with enterprise readiness and enterprise capabilities, customer manage keys, replication, business continuity. All of that is making great progress, and we will be in preview in the next couple of months very soon. And the customer interest that we're seeing is very, very strong.

Next question comes from the line of Patrick Walravens with Citizens.

Sridhar, can I have sort of a big picture question here, which is, do you agree with people who are observing that the frontier models are converging in their performance? And if so, what are the implications of that? Like where do those companies -- where do the opens and drops or do they go next? And what are the patients that for Snowflake...

First of all, I think every prediction that we have made about various kinds of motor has not really turned out to be true, some 6-odd months later. So I don't think it's quite the case, not these models have plateaued along every dimension. If you think about the increase in quality that these models have been able to demonstrate, even over the last 6 months, it's been a pretty remarkable transformation.

And when it comes to the enterprise, obviously, there's no products that have been adopted at quite the same scale, let's say, a consumer ChatGPT with is nearly 1 billion customers. But these kinds of experiences become useful only the data that matters to the enterprise, all of the PDFs that are sitting in SharePoint are the various other data sources that they are also become accessible to these models. And that's what we have created with Snowflake intelligence. So I think there is ample this ample runway. But I think the remarkable progress is also being made in Agentic AI in these models, learning to use tools of different kinds. I dabble a bit in core generation models and their ability to get work done has gone up by a pretty remarkable amount again over the past 6 odd months. And I think you're going to see situations in which every complicated as that humans are involved in is going to have Agentic solutions that are assisted, where the model you do some of the work. under the humans the model to be able to be a lot more effective. So I think from that perspective, it's still very, very early innings. Think of all of that happens in an enterprise, whether it is insurance claims processing our regulatory reporting or anomaly detection of variants or even going through the due diligence process for an M&A or a complicated legal thing that you have to do. All of those are areas where application of data and AI is very much in its infancy. So I think honestly, years of work ahead in terms of the value that we can get from the models have advanced so much that I think just effectively using them in all of the workflows that matter to enterprises is going to create enormous value for all of them.

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

I just wanted to come back the impressive stat that we heard earlier regarding the volume of accounts which are adopting your AI products and features, I think we're going full mid- to high teens sequentially here. And I just wanted to -- together, so we have the adoption with -- but can you discuss what's the monetization strategy that you're putting in place behind that adoption curve? Is the larger sales effort required on Snowflake's part to ramp the revenue behind that usage? Or how does that play out from your seat?

Yes. We were very deliberate about how we brought AI to Snowflake. We wanted it to feel natural to be a natural extension of how people access data. I've talked previously about primates around search and structured data that was the foundation of how we began to introduce AI. They, themselves, were useful in that people could create chatbots on structured data or be able to talk to your SQL as it were to get -- to get a structured data. We've then introduced higher-level constructs that sit on top of it, like Snowflake Intelligence.

But one thing that we were very, very deliberate about was the need for these capabilities, we truly be easy to use and for our customers to be able to get value very quickly from them or at least experiment with them very quickly. And this is what has led to the broad adoption, to be honest, without us investing in a massive sales play.

Yes, we have a specialist team but compared to the size of our regular sales team, it's actually quite small. And so from that perspective, that brand, 6,000-odd number is very active. We are now beginning to see situations in which a product like Snowflake Intelligence is rolled out very, very broadly to the entirety of a workforce. For example, with the sales data assistance, I want to make sure that it is rolled out to every salesperson and the beauty of that is all of the permissioning, all of the complexities of making sure the right person has access to the right information is the thing that we make very, very easy to implement. These kinds of use cases are the ones that are going to be driving meaningful revenue for us. And yes, we are having our specialists focused on these use cases because they're going to drive more revenue but this is all part of a very deliberate strategy of creating world-class products, getting very broad adoption and demystifying AI and then working on use cases that generate massive value for our customers. and in turn, revenue for us. And that's the beauty of the consumption model in that our customers don't have to make some very large commitment to a project that's not yet delivered value. Yes, they have to go implement a project which we make easy but we may be -- like we make revenue only our customers -- when our customers recognize value. So we feel good about where we are. I think this is the right way to have taken AI to the Snowflake data cloud

Next question comes from the line of Gregg Moskowitz with Mizuho.

Great. Maybe another question on AI, if I may. We've recently begun to hear, Sridhar, meaningful customer commitments to Cortex AI. In other words, not just some customers exploring it, but a real uptick in usage. I know you called out some interesting wins in your script, but more broadly, is this consistent with what you're seeing? And if so, it would be helpful to hear a bit more on the primary use cases that you're seeing for -- par.

The primary use case is inevitably centered around a combination of bringing structured and unstructured information together in a custom repository, for example, as a data agent. I talked about BlackRock in the script, I think, where I said they're creating a little bit of a Customer 360. All of the information that is relevant to our customers available in a single chatbot. And I use this kind of functionality very frequently, but if I'm going to have a meeting with a customer. I want to know everything about them that we know I'm able to get at what kind of relationship we have with them, how much we are spending, what the open use cases are, any other recent notes, Workday information about -- hierarchy here that manages them. it is that pattern. It is a flexible access to data that repeats itself over and over again. And people just apply that in very different ways. Thomson Reuters uses Cortex to create a set of products for their internal views. They have products for HR teams, finance teams and so on. I think that it gets really interesting is now in having -- in being able to take a whole set of actions using Agentic AI, where in addition to getting information that you want, you're also then able to say, okay, and now take -- like do this update, send an e-mail or update a record in sales force or some other actions. I think that's where we are seeing easily get created.

Next question is from the line of John DiFucci with Guggenheim.

My question is, I guess it's sort of a high-level question. Listen, these results are really good. Everybody has noted that. And you've been pretty clear that you're focused on AI for the future, but these results are primarily driven by our core data warehouse and analytics business. And then we understand why focused on AI and I guess why everybody is asking lots of questions on AI. But I'm more curious about that core market. There's still a huge amount of this market that's still on-premise. And you have the pole position in cloud-based data warehouse, you're the pioneer and people love your products. But can you talk about the sustainability of that market as a growth driver? And are there any other solutions on the horizon that keep you up at night that could disrupt that market, the data warehouse cloud-based data warehouse and analytics market, like you disrupted the massive on-prem data warehouse market.

I mean, first of all, I should be clear that we have been consistent in saying that it's not an either or -- our core business continues to be very strong, and we have talked to you folks about a number of metrics, including net revenue retention, which is measured over a 2-year time frame that supports that. And other things that we are doing, especially in the areas of AI are important because that is where utility is going to be delivered in a massive way, both today and tomorrow. So it's not the case that we can say we should just focus on our core business because people that bet on Snowflake are bidding on Snowflake for the next 5 years. And we need to invest in both.

But to your point about the sustainability of the core analytics market, 100%. I think there is a lot of business to be had. There are a lot of on-prem systems. And part of what we are figuring out in this moment is AI is going to disrupt potentially everybody, including us. This is the reason why the trust product innovation so much. This is the reason why, in addition to creating products like Snowflake intelligence, which are cutting edge, we also obsessed about how to make sure that our migration technology is the latest and greatest that there is because being able to migrate legacy systems faster is going to be a -- benefit to Hoover that can do that fast and that would be a final thing. Yes, there is a big market in legacy systems that are going to be migrating over. all the cloud players, including us, are benefiting from that on-prem to cloud migration. But you really need to innovate on both fronts to be successful in the long term.

Due to the interest of time, that was our last question. I would now like to pass the conference back over to Sridhar Ramaswamy for closing remarks.

Thank you. In closing, Snowflake is at the center of today's enterprise AI revolution, delivering tremendous value throughout the end-to-end data life cycle. Snowflake is EDU, connected for seamless collaboration and trusted by enterprise-grade performance, driving customers to choose and expand with us. We continue to execute our scale as evidenced by our product revenue growth and strong outlook for the remainder of fiscal '26. And we see a long runway of durable high growth and continued margin expansion. It's an exciting time for Snowflake, and I look forward to sharing more of our progress in the quarters ahead. Thank you all for joining us.

That concludes today's call. Thank you for your participation, and enjoy the rest of your day.

Hi, everyone, a warm welcome to SPN Pulse. Let me start over, right? I forgot to unmute myself today. I'm Hwee Bee Tan, I'm the Head of Partner Marketing for APJ at Snowflake and Partners, thanks for your time. It's been an incredible week following our summit in San Francisco, where we welcomed over 20,000 attendees doubling our growth this year.

We're excited to bring that momentum back and continue our journey with all of you today. Today's session is all about what's new, what's next and how we can continue to grow and win together this year. You'll hear directly from our leadership, Mike Gannon, our CRO from Snowflake, and learn from the inspiring journey from our customer, Spark New Zealand as well as RelationalAI, right, how they're transforming and leading the way with us. Today, we also have our latest product innovations that is brought to you at Summit. We're having Amanda Kelly to share that with you later. So stay tuned to the very end of this session. To kick off all the things, I'm delighted to hand over to Ash Willis, our VP of Partner and Alliances for APJ today. Over to you, Ash.

Great. Thanks, Hwee Bee. Good to see everyone. Hey, Hwee Bee what are -- what shirts are you wearing? Is that the L.A. Olympics, right? Good stuff. So I missed out on grabbing one of those. But for everyone's benefit, we announced last week that Snowflake were a sponsor for the LA28 Olympics, which is super exciting. And I'm waiting for the announcement around Brisbane '32 as well just to get ahead of things. So good stuff. Thanks, Hwee Bee. And Mike, welcome to our APJ SPN Pulse. Good to have you here.

It's great to be here. Thank you for having me. Exciting.

And I know you're joining from the East Coast of the U.S., so quite late, and we appreciate you jumping on board.

Glad to do it. I'm getting used to these late nights to talk to all my Asia Pacific leadership team and customers. So glad to be here.

The joys of being the CRO of an international company. We're super excited to have you on board, Mike. Just over 3 months at Snowflake now. I know you've been super busy with a whole bunch of activity. I do want to talk about last week at Summit in a moment. But let's kick off with our latest earnings announcement. So great time to come into Snowflake.

I know you had the opportunity to do the earnings call from the New York Stock Exchange. So maybe share a couple of observations in terms of Q1 results. A quick intro to yourself, your background and why you chose to come to Snowflake.

Yes, sure. So for those that have not looked at my LinkedIn profile or had the chance to look at me virtually. So I joined Snowflake 90 days ago as Chief Revenue Officer. I've spent the better part of the last 29 years, really in 2 -- really 2 companies. The first 15 years of my career was with EMC Corporation. And I started my career just as the third industrial revolution was kind of taking shape in the Internet, for the most part, was changing the way that we are operating business to business as well as business consumer. So I had a lot of great experience and fun driving transformation and building out infrastructure for companies that were starting to deal with more e-commerce applications and transforming the way they operated from a technology perspective.

And I then took a shift over to VMware, and I just wrapped up the General Manager of the Americas at VMware and I went over there because I started to see that software was starting to eat the world and people were delivering more value through software-defined data centers. So obviously, as the leader of virtualization, we started to draw a much greater expansion of virtualization beyond compute into storage and networking and really helping customers build private clouds. And inevitably, we built out partnerships with all the major hyperscalers because we recognize customers want to move their applications to the hyperscalers. So we created this.

VMware private cloud in each one of these hypers is giving customers the ability to vMotion applications between on-prem and the hyperscalers. And certainly got a lot of experience watching customers trying to modernize their applications to move it to modern cloud. And I think that was certainly a very difficult challenge. But after 12 years of VMware, it became apparent to me that customers started spending more time and effort, modernizing their data because they recognize they're going to get much more faster path to value by driving a new data architecture as opposed to trying to lift the legacy application and moving it to the cloud, which wound up being much more difficult than people had ever anticipated.

So as I start to look at really the next 10, 15 years of my career, I knew I had to get into the data space. I was afforded the opportunity to interview for this role, which fortunately enough, I was able to get. So I'm 90 days into the role. We kicked off the year with some tremendous earnings. We had our first $1 billion revenue quarter, which represented 26% growth year-on-year, which was a fabulous way to start the year. Our net revenue retention is 124%, and that metric basically suggests that customers are not just doing a static renewal. They're expanding their contracts with Snowflake.

So demand continues to expand with all of our customers. What's resonating well is I'd say our remaining revenue obligation, which represents really the backlog is $6.7 billion, and that's a 34% year-on-year increase. So customers are signing up for longer-term contracts. That backlog obviously is an addressable market for everyone on this phone call to go help figure out how we can drive migrations, analytics, AI, that's really the attack surface that we've left open for our partner community to come in and help customers drive consumption and get value out of it.

So our remaining revenue obligation continues to be very strong. And of the 11,200 customers that we have total, we added about 451 in Q1. And approximately half of those customers today, so about 5,200 customers, so about half are actively using our AI and ML products. And that to me is a really strong leading indicator of how customers are looking at Snowflake, not just from a data perspective, but really trying to simplify their AI initiatives. So we're seeing great adoption around the AI. So great way to kick off the year, a great way for me to learn what customers love about VMware.

And again, of all the customer meetings I've been in, I consistently hear we're easy, we're trusted and we have this great connected capability of allowing zero-copy data shares between 2 different Snowflake instances. So we're starting to create this market network effect around Snowflake. So really just great, great opportunity for me to see the company on display in Q1, and see some fantastic results. But I think, as I've always said, the market votes with their wallets every day. And right now, they're voting on Snowflake and it's an exciting time to be here.

Yes. It's an incredibly exciting time to join, Mike. I think anytime a company sort of celebrates their first $1 billion quarter is amazing. But as you touched on, this momentum that's building around data, and having been at Snowflake now for just some 3 years, when I first joined, Snowflake was doing some amazing stuff but it was very much pitched kind of in this technology space.

But I think this trend around sort of driving business impact and the value that we can add to business is really impressive and really becoming very front of mind. So one thing I just want to unpack a little bit more that you spoke about there. So $6.7 billion RPO, maybe just dig into that a little bit more and what that means in terms of opportunity for partners because I harp on this all the time. But I think it's good for this audience to hear from you in terms of what that opportunity actually looks like?

Yes. So we have a unique compensation structure at Snowflake. We don't get paid when we book a contract. Our sales teams only get paid when we drive consumption. So that means, we've convinced the customer that Snowflake is an essential data and AI platform. They buy into a multiyear contract with us, which is what we're seeing trending wise, as customers are moving to multiyear contracts.

And then we start looking for use cases, what data sets are we going to help them modernize. The low-hanging fruit from us continues to be legacy traditional warehousing technologies like Teradata, Netezza, Oracle Exadata, SAP HANA, legacy warehousing that frankly isn't scaling to the needs of the company. So we're doing a lot to drive migration. So there's an unbelievable opportunity for us to partner with this ecosystem to help drive and accelerate migrations onto the platform.

So when I say that we've got this large backlog pending, you're going to find a very receptive audience at Snowflake that wants to partner with organizations that can help accelerate migrations onto the platform because that's going to drive consumption, and that's when our sales team gets paid. So it's a very unique structure from that perspective.

But the thing that's really landing on me when we talk about AI, most customers look at us bringing a legacy transactional, very structured data set into Snowflake. And I'll give you a great use case example of where we did this for a very large construction supplier in the U.S. We had already brought a very structured data set into Snowflake, and we're helping them running some advanced analytics around typical structured data. But one of the things that they had was -- the challenge was they've got several people that run -- their task is they build bids.

So people will send them a blueprint, an architectural blueprint and you will have a person accept that blueprint. And over the next 3 days, they will count how many windows and doors and trusses and 2x4s and they build out a bill of materials and they submit their bid, and they have about a 20% success rate where people will contract them for supplying the builder for that architectural blueprint.

Well, we were able to showcase in a presales motion when we were able to bring that unstructured data that blueprint into the Snowflake environment and map it up to their structured data, we were able to basically use an AI model to go through and analyze 100 blueprints per day as opposed to 1 every 3 days. So when a customer sees the unlocking that value and now that person can submit 300 bids a day versus 1 over 3 days just by bringing an unstructured data set and a structured data set together and using a basic AI SQL query, it became a very powerful unlocking mechanism.

So what I think most customers are really starting to recognize is when they bring these structured and unstructured data sets together, they get a much richer look at their data ecosystem and they're leveraging us to really simplify the business outcome. So that to me was really an unlocking moment where the customer wasn't sure we could do it.

So most customers lack the awareness that this -- the powerful platform we can deliver. So that to me, became an eye-opening moment for me to say, this is where customers are struggling as they don't think this stuff is capable, but we can really deliver some pretty significant outcomes just if we ask the right questions. And I think what I'm asking my team to do is let's assess the business outcome first and then figure out how the technology will deliver the outcome. So that to me is a lot of -- that's what I'm working on with my team is making sure we identify the business opportunities and the business outcomes. The technology is the easy part, really.

Yes. I think it's a trend that we see out here as well, helping sort of customers imagine the art of the possible. Everyone wants to talk about AI. Everyone kind of recognizes that technology can add a bunch of value to their business. We're really trying to understand what those key use cases are. And I love that one that you just described because not only is it improving productivity, but I would imagine that it's more accurate than somebody trying to count out windows by hand and all that sort of stuff as well.

Yes. But I think to your point, it's not a job elimination. It's not AI taking over the role. It's making that 1 person far more productive. And I think that's really the unlocking moment right now as everyone scared AI is replacing jobs. And listen, eventually, it may. But the first iterations of what we're seeing is we're really just unlocking productivity and allowing customers to accelerate revenue streams and potential.

So the first unlocking moment, I will say, is really leveraging AI to drive productivity. And I think what's going to happen is you're going to see a skills shift, right? So what might be a radiologist is doing today, maybe we don't need to hire 30 new radiologists in the health care market, I can scale my business more with the existing head count I have. And that to me is, I think, the power of AI, at least within its first iteration.

I was chatting to a radiologist at a forum earlier this week, actually, and they were saying, always, hey, what's AI doing? And they were talking about in terms of improving accuracy and to diagnosis, not reducing the number of roles but providing better outcomes at the end of the day. Mike, I'm conscious of time, and I've got so many more questions.

Let's get a couple of more to speed round.

Let's switch to Summit. So last week, we were joined by more than 20,000 of our great friends in San Francisco, what were some of your initial thoughts other than [indiscernible].

It was eye-opening for me on a few -- again, I'd shared I just left VMware after 12 years. And I think VMware started their user conferences in about 2003. And at our peak, just before the Broadcom acquisition, we had about 18,000 people there. And to see Snowflake only after the sixth year of having our conference, right, we doubled our capacity from last year at 11,000 to 20,000 this year. So unbelievable amount of people. I was not expecting that big of a show. So I was a bit blown away by that. But I'd say the quality of people that we're showing up to the event was really eye-opening to me.

I mean we had Chief Data Officers, we had Presidents of AI. We had people that were truly trying to lean in and understand how Snowflake can unlock value for them and deliver better outcomes. So obviously, we had a large degree of analysts and architects and AI engineers. But I also saw a pretty good concentration of business minds coming to Snowflake and trying to look for the art of the possible talking to other customers about what they were doing with Snowflake. So there was an unbelievable network effect of customers learning from customers.

And what's really interesting is 70% of the content that was delivered at Summit last week was delivered by our customers, right? So it's one thing to hear a very biased opinion coming from us. But when you hear how customers are leveraging the technology, that to me was an incredibly powerful statement. So I was super impressed by the content. I thought the logistics was fabulous. The energy level was high, but to see how our customers were contributing to the Summit, I thought was just awesome.

Yes. So a couple of customer highlights like seeing [ Camber ], like a very big customer that we're incredibly proud of out here in APJ to be featured as part of a keynote was amazing. But I'm not sure if you're aware, Mike, like we had so many submissions from customers and partners that wanted to join us to advocate for Snowflake at Summit to present that we could only take such a small portion of those submissions. So...

It's an unfortunate byproduct of having a popular show as you can't have everybody. There's only so many hours and sessions we can hold. But I do think next year, we're going to have a bit of a problem. If we get to 30,000 people, the Moscone Center is going to get really crowded. So -- but we are excited to take our world tour. So for those that could not travel in to San Francisco, we've got a world tour going all around the world. So we're going to bring Summit to you I'm going to try and hit a few of them as well. So we're excited to bring the content for you to Asia for those Asia Pacific that can travel in. We're coming to a market near you soon. So look at our registration sites and get yourself registered. We're going to have some great events on the road.

Yes. We're excited to welcome [indiscernible] for a few of those events. Last point on Summit, we were chatting at the start of this call in the green room, and you were saying you had an opportunity to spend some time with the Snowflake investor community. I think there's always some really interesting insights that come out of those conversations. Are there any kind of key things that you could share with this audience?

They had quite a bit of -- they had a lot of questions of -- so I was on the panel. So we had 200 investors in the room. It was myself, Sridhar and Christian, our Head of Products. There was a lot of questions that were certainly around product and strategy in the future. But the questions I was receiving was really relative to, Mike, how are you looking at the go-to-market function? And as I explained to the investor community, there's really 2 things I'm focused on, which is, one, is how do we scale our business efficiently. And number two, is velocity. How do I remove friction from our selling motion?

And what I shared with the investor community was my desire, a very strong desire to build an incredibly strong partner ecosystem, right? So our global alliance and channel is going to be a big part of our future go-to-market motion. In order for me to scale this business, I can't keep hiring direct sales reps. We're going to be making a major investment in our channel and distribution. So we've got wonderful relationships with the hyperscalers. There's some emerging needs for sovereign clouds in Europe. So we have to make some investments and some unique investments in Europe.

And there's probably some of that even in Asia Pacific, where we have to build sovereign clouds. We talk a lot about the ISV marketplace, right? These are people that are building businesses on top of Snowflake. It's an incredibly rich channel for us as well. And then we've got incredibly strong systems integrator market where companies are leaning on systems integrators to help them drive modern data architectures and platforms.

But the fourth and what I'd say, frankly, is an underinvested leg of our channel is our distribution network with traditional resellers. So I'm spending a lot of time what I'd say, ramping or rebuilding out what I think is going to be a very rich partner resell program. So coming soon, you're going to see a big investment from us in resell. We're excited to launch this. This, to me, is coming from EMC, coming from VMware. I saw the power of the channel. I believe in the channel. And I'm going to be making some big investments there.

So that was a really big part of my presentation to the analyst community is how we're going to scale this business. We're doing quite well already. We're going to grow the business 26% as we've mentioned to Wall Street. We raised our guidance at 26%. We should be doing well north of 35% if I activate this channel. I shouldn't say if, I'll say when I activate the channel.

So the investors, obviously, like that message.

The stock went up that day. It was good.

Stock went up and ratings went up as well. But Mike, just to close out, in the last 3 months, you've already made some pretty decisive moves to make us a more partner-friendly organizations and things around comp neutrality for marketplace. And as you said, there's a bunch of other really exciting stuff to come. I could keep asking questions for a long time, but Hwee Bee is going to boot us out. So thank you so much for joining us. Thanks for investing in this ecosystem. And we hope to see you out here very soon and get you back on Pulse, maybe in 6 months' time and get a few more updated reflections once you've had a bit more time in roll as well.

Yes, I would love to do it. Thank you so much. It's an honor and privilege to be on the show with you all tonight. I look forward to getting into the market soon and making my trip through Asia Pac and seeing as many of you as possible face-to-face. So thank you so much. Pleasure meeting you all virtually.

We're a great host, Mike. So you have a good time.

Very good.

See you later. Thanks.

All right, take care. Bye. Bye.

Hwee Bee, you're on mute, if you're trying to do an introduction.

No, I'm just saying a good one for Mike, and we have our customer and partner Anshuman and Marco is here.

Good stuff. Hey, guys, thanks for joining us.

Thank you for having us.

Great to be here.

Good to see you both. And I know that there's some really good work going on between Snowflake, Spark New Zealand and RelationalAI. But before we jump into that, Anshuman, why don't you give the audience a quick introduction to Spark New Zealand. It's obviously a brand and a company that's very familiar to me, but maybe our -- not all of our audience are familiar with you guys.

Certainly Ash. So Spark New Zealand is the largest telco in New Zealand. New Zealand is not a huge country, but...

It is a little country just off the side of Australia, right?

It is, right? You can miss it easily, but we should...

Beyond the -- now I'm going to get a lot of group about that statement from my Kiwi colleagues.

Yes. So yes, the largest telco, we have the highest share on mobile in New Zealand, but we are also significantly into the business and small and medium enterprises in NZ. Small and medium enterprises make up 97% of New Zealand's businesses, and we play a significant role in there. And 45% approximately of our revenue comes from enterprise and small and medium enterprises. We are an agile organization overall. And we went agile quite some time back about 10 years back as an entire organization. And we are really, really agile in the sense that we are very open to using technology, trying out things, experimenting and interested to innovate and try new things out. So I'll probably stop there.

It's good. That's a super helpful overview. Marco over to you may tell us a little bit about RelationalAI and your role.

Yes. Thank you, and hi, everybody. So look, RelationalAI is a technology company headquartered originally actually in Palo Alto, and we've been partnering with Snowflake since very, very early on because there are a lot of actually ex-Snowflakers part of the team. The goal of the company is really, in my opinion, very simple, is to enable organizations, partners and customers of Snowflake to empowering them to make better decisions on Snowflake, okay? And there is a true -- it is an inner belief that organizations pretty much live and die by the quality of the decisions that they make.

And what we want to do is to make the process of making decisions within Snowflake across a variety of different use cases and questions as easy as possible to the end user. We are approximately 200 people globally. So still quite small but growing. We are fully remote, and so we are spread across the globe, I think 50-50 between the U.S. and the rest of the world. And my role is really to lead the go-to market team globally and that really encompass like sales, delivery, marketing and partnership. And so it's a real pleasure to be.

Good. And we've -- as you said, we've got some really good work going on together. So good stuff. Anshuman, like, let's -- you mentioned Spark is an agile company that's been my experience working with you guys as well. But I know that you guys also have a strong vision for AI and leveraging AI is kind of a strategic lever to drive business growth. Could you tell us a little bit more about that and what that looks like?

Okay. I think when most organizations start doing AI or start getting into AI, it is generally for incremental benefits. The way we look at it is more at an organizational level, right? Can you look at the potential of AI to change the way the organization works? Am I reimagining the organization in a world with AI? Am I taking the current processes incrementally or taking a more holistic approach. You're not building for the future. Now when we build our architecture and we build for our use cases, we are generally taking a lens, which is a longer-term lens.

A problem statement, can we take the problem statement? Let's say an example of it is provisioning a complex product, right? And there are multiple steps to it. That problem statement might come to us from a provisioning team. but we extend it out to a bit further out to say, at some point, can we enable customers, right, to come in via self-service and actually provision a complex product themselves, right, which is extending it beyond the immediate customer who's asking for it and taking it to a level where AI can go and make it really, really streamlined for customers.

Now there might be multiple steps to that journey, right? Step 1 might be that we build it for provisioning because the stakeholders are from provisioning team. Step 2 might be taking that same thing and making it easier for our sales teams to sell the product. And step 3 might be actual customer enablement and self-service of that particular outcome. But when we build the architecture and we build that use case, we take a lens as to where can AI go, right? What's the potential of this use case and we build for that use case so we can extend it over a period of time in the next 6, 8, 10, 12 months.

At the same time, the other thing that we also consider that to be able to do it, we need to have skills of the future. And as we can see what's happening in the world of AI is a lot of skills, which are about coding, right, a lot of skills which are about functional knowledge on a particular topic. They are becoming more transient. You can acquire those skills easily. Core skills are tending to go more towards creativity, problem solving, et cetera, right?

Are we bringing in those people into Spark, the best of those people in with us on this journey? And at the same time, are we also providing training to everyone else in the organization in terms of how to use AI, how to survive in the world of AI? Now, we are on a journey on all of this. We are not the best or the perfect in this, but that's the lens that we take when we look at the potential in a world which is where AI will play a significant role in the life of each one of us going forward.

Okay. I'm going to jump around a little bit because I think that you're touching on a few interesting topics there, but can you share maybe 1 or 2 examples of where Spark NZ have already adopted or already built certain AI-powered tools or initiatives?

So I think one of the examples which I'm really proud of is something which is about call summarization. And I'll give you the context of this problem statement, which is, we get x million calls in our contact centers. And every call used to require an agent to spend 2 minutes to summarize the call at the end of the call before they log it into CRM.

You mean a real agent in this instance?

Yes. Now it's to classify very carefully, like when you're talking about humans versus robots, robotic agents. So yes -- and I think taking the call is important because that's the value-add providing customer experience is important. But logging a call, right, with human -- with your interpretation of it is not the most value-added thing, and then we had an industry of compliance around it, right, to say, our agents doing it, et cetera. So what we did was we brought AI in the life part of the call, right, where as soon as the call ends, AI summarizes the call. And my personal idea was summarize the call and log it, but we have to work with stakeholders in all of these areas.

So our ask from contact centers was you cannot log it, you have to play it back to the agents for them to validate if it's correct or not, right? And we had to play it back on a live call path and they update it and then log it into CRM. What we observe is the number of times people actually updated is nearly 0. And I'm waiting for a day when we'll flick the switch and will get automatically logged into CRM. But that's a real example, live example of AI in a live call part, which we have implemented, which is generating business value.

The other impact, benefit of such things, which you don't anticipate is that the quality of data that we are generating about each of these calls is much -- of a much higher quality compared to what was getting logged earlier, which is downstream benefits in terms of root cause analysis and then reducing the actual calls or issues that customers face.

That's a really interesting example. And it sounds like it's returning some pretty impressive business results. Marco, talk to us a little bit because I know you have a great partnership with the team at Spark. And talk to us a little bit about the work that you're doing there, and I know there's some stuff around relational knowledge graphs that are involved in this project as well. Maybe you could share a little bit of context.

Yes, absolutely. And I'll start maybe by briefly telling you the people here, while we think it's really, really crucial actually this concept of capturing knowledge, which really links to what Anshuman was saying here, capturing really data and knowledge. And look at the core of it, again, there is a belief that every organization is different and knowledge is captured in a variety of different ways within organizations, right? You find knowledge across spreadsheets, you find knowledge into our databases, you find knowledge into application code, you find knowledge in documents, you find knowledge in people's head.

And structured and unstructured.

And structured and unstructured. And as we said like in [indiscernible] like knowledge is spread really, really everywhere. And that knowledge fragmentation generates a lot of pain actually because it's really difficult to provide a unified a cohesive view of your enterprise. And so sadly, when somebody asked a very simple question what are my -- what were my sales last week? Now teams crumble, right? Because they're like, okay, it's a week 5 days, Monday to Friday? Is it a week 6 days?

And do we include all the products here? Is this flag should be true, should be false? How should we interpret that question. And by the way, that's a problem that is true for people within the organization, but it's also true for the newcomers into the organization, right? They need to learn how the organization operates. And in the era of agents, agents are little -- I call the agents, little interns, little employees in your organization. It's true also for agents, right? They need to learn. They need to understand how enterprises operate.

And so really, the point of a relational knowledge graph is to solve this challenge of this knowledge silos. And it's interesting back in the days, remember, Snowflake we said like, hey, our common enemy are the data silos. I think now I make the statement, our joint common enemy beyond the data silos are the knowledge, the knowledge silos, right? And so what that does then, it enables organization to describe to store, to share, to discover and then infer a new knowledge.

So now why is that important, right? And maybe -- and this now links to some of the work we're starting to do with -- of course, with our friends at Spark here. Well, it's -- in just a few -- just to give you an example here, right, in just a few weeks, the team from Anshuman here leverage RelationalAI knowledge graph to build a reliable digital twin of their network to simulate then complex users and network behaviors, okay?

And that now implies that actually you get LLMs that now can become creatively smart, okay? They can be actually fine-tuned to their knowledge graph, so they can understand the business domain. And then thanks to a set of what we call reasoners in Snowflake, fully embedded within Snowflake by the SQL reasoner, a predictive reasoner to reason about the future, a prescriptive reasoner to reason about what to do about the future. Now this system can really answer all sorts of questions, okay? They can answer questions about the past. They can ask -- they answer questions about the present, the future and then what to do about it. And so that is why such a technology and reasoners together are becoming important and really, really central to, I think every organization will become more and more important moving forward.

Yes. It's an incredibly interesting space and like this idea of knowledge-centric and really understanding those different perspectives, yield some pretty powerful results. Marco, just a little bit around like how do you guys integrate with Snowflake to help make some of these outcomes a reality for customers such as Spark NZ.

Yes. Look, again, the belief here is that we need to meet the users like end organizations where they are, right, and really be able to complement their past, existing and future investments, right? And so where users are nowadays and where enterprises are, they're on Snowflake. They are actually storing all sorts of data, on relational systems, right? And so it is important to be able to then provide support for users and enterprise within those relational system to build and deploy AI at scale and being able to actually build intelligent applications.

And so what I'm talking here is really being able together, like with Snowflake and with partners like ourselves or with more partners, thinking through what I call a unified relational AI infrastructure. And apologies here for the play in words, but you see what I mean when I say relation because it's built on a relational paradigm. That has a set of key characteristics really as a data centricity, so you get all the data types being able at your disposal, right? You get as you said, actually get structured data, you get structured data, you get images, right? You get documents, you get graphs, you get tables, but it's all there for you.

Then you get a set of reasoners available for you, again, baked in into the infrastructure that helps you to actually reason on that data. And get your secret reasoners, you get your vector search reasoner, you get your graph reasoner, you get your predictive reasoner, prescriptive reasoner. But these are ways for you to interact with your data. And then you get a semantic layer, okay, on top of it, which is, in our case, is a relational knowledge graph that enables you to model the business and then express rich semantics that go beyond the type of things you would do with BI or dimensional semantics.

And then last but not least, because nowadays everybody is talking about the ability to just question -- ask a questions to your data, a post trained large language model that is really fine-tuned to that domain so that now it understands how the business operates and can use those reasoners to ask questions to the data. And this is really a paradigm shift as you called it out, Ash, because in the past, we will be pretty much application-centric, right? We will take data and ship data to the application. That's what we'll be doing.

And what we are seeing here, if you have now a unified AI infrastructure, you are much more knowledge in data centric, okay, where you are actually sending -- not sending, but really bringing compute to the data and the infrastructure is now providing you with the capabilities that historically you would either buy via point solutions or you need to build and maintain yourself. And then this approach really dramatically simplifies infrastructure, accelerate innovation and unlocks the full value of really data and knowledge together.

That's a really comprehensive answer, Marco. But I think that you touched on a key point there right at the end, which is -- there are lots of great points, by the way. But you spoke about accelerating innovation. So Anshuman, to close out, how has this partnership between Spark, RelationalAI and Snowflake helped you guys accelerate innovation?

I think I'll just probably touch upon a few of the points which Marco mentioned, which I really like. And it's the fact that at the end of it, I mean, for any AI or any of the things that we are doing, data is the oil, and whatever AI we are doing on top of it, that is, again, creating more data and more information. And for us, moving into the journey of moving our data into the cloud, right, with Snowflake was the starting point of a lot of the AI and innovation that we have been able to do.

We started off with the general idea of moving data into the cloud from why people move into it from a cost, scalability, et cetera, perspective. But that has kind of put us on the path of this journey on exploring and experimenting with this data and creating new things. The things which Snowflake is bringing to the table in the form of partners and partnerships is really, really interesting and important for us because I had a feeling that the world of the future will be a world of partnerships because there's so much innovation, which is possible now because -- and again, AI is making innovation possible to quite an extent. And I see us having a lot more partnerships going forward than we have now.

And the ability to onboard such partners, right, using the Snowflake AI and data cloud easily, seamlessly and bring them where the data is and then use that in a meaningful way, I think, is of immense value to us.

The other thing I wanted to also mention is I think we've gotten an awesome account team from Snowflake, which works with us because we are very demanding sometimes, right? We ask for -- we have asked for like the Cortex functionality 3 years back because we thought this was possible. And I keep pushing Richard and the team to put us in touch with your product team and partners, and they're extremely helpful, and they bring the teams in which I really, really appreciate as well, and it's a great working relationship for us. So I see it's a very strong relationship, and I see it growing with new partners coming in, and the ability to work with partners like RelationalAI is great for us and my teams.

That's good. We talk a lot about accelerating time to value. And I think hearing those stories is really valuable. And I'm so pleased to hear that you pushed Richard and the team hard down there and the results are definitely showing. So we're at time, Hwee Bee is going to boot us out in a moment, but Anshuman, Marco, thank you both for joining SPN Pulse. Lot of great insights and really enjoyed it. There we go, some photos from last week.

It's great.

Amazing. I'm the most handsome in this group. No doubt.

Well, it's good to see some of those shots and it looks like you got to do a couple of videos there as well. Fantastic. Good stuff. Thanks, folks, and over to you, [ Chris ].

Amanda. Amanda will be joining us. Yes. See, I think, Amanda, you need to click the button on the right hand, Yes. Yes. Cool. Hi, Amanda, how are you?

Hello. I'm doing well. Excited to be here.

Okay. Awesome. So Marco and Anshuman, you can, no, on the back -- you can enter the backstage, and then we can have Amanda share with us. Over to you Amanda to share with us the latest innovation of Summit.

Yes. I'm so excited to be here. I was actually just in Korea and Japan a few weeks ago. I know I need to make it out of Singapore, too. But then I was at Snowflake Summit, we announced so many amazing things for Snowflake and for you as partners and customers. And so I'm really excited to talk to you about really the future of AI-driven data platforms, right, which is what we're building at Snowflake. And we truly are evolving from a data cloud to a fully integrated AI application platform that's redefining, right, how products, applications and intelligent systems are being built, right?

And we're giving you just the download today of some of the top, top things, but really, you should go, you should watch a bunch of the videos online, so many cool announcements, so many new quick starts, things in our solutions center that you can get started with and try today.

So I wanted to talk to you just a little bit about our mission, just kind of fit in where we're investing and how these things are working, right? Obviously, we talked about we're becoming the AI data cloud. And we are still focused on giving you that 1 unified managed platform that's going to make it easy, right, for you to connect, bring all your data together to trust it, right, to make sure it's governed it's secure and then to do some pretty cool things with it, which I'll show you about in a second.

So next slide. This is kind of a simplified view of a lot of the things that we're investing in, especially on the data side, right? You've got all these different data sources. Many of them you've already brought together in Snowflake, but there's probably a lot more that you want as well. right? I often think about as a product lead, I often only see a slice, right, of what we're doing. And it's frustrating because you don't have the full picture of what your customers are doing, what's happening in the market, where you can only get that by bringing in more data into more applications that help you join these views together, right?

And then, of course, right, you have -- the hard part. You've got to get all that data process. You got to get it into a place that you can actually do things with. And then once you have that there, right, we can use AI in so many interesting ways on the BI layer for data science, for apps, for sharing that makes it really easy for you to actually drive decisions and outcomes in your company.

Next slide. So as we look at kind of the overall Snowflake platform architecture, right? It always starts at the bottom, right? It starts on the cross-cloud, right? No matter what cloud you're in, what region, right, we are able to support you. We're able -- that's why we're able to support so many of the largest companies, right, in the world globally, right? We are a unified AI-ready data platform that starts at the data layer, right, unstructured data, semi-structured, structured, it doesn't matter, right, what data type it is. It doesn't matter what format it is, iceberg, right? Is it a hybrid? Is it OLTP, right? We're going to support it, right?

And then we have data lakehouse, we have data warehouse. We have ways to work with process that then to interact with it, right, to transform is it's equal. Is it pipeline? It really doesn't matter, right? And then that layer on top of it, right? We're democratizing AI across the stack, right? And that goes everything from the SQL embedded AI to fully agentic AI experiences that were helping you make accessible, right, to not just the builders in your org, right, but to all the users as well.

And ultimately, that's what's going to help you, not only drive efficiency, right, on your data side and through your stack, but also have that business impact, right? We're making sure that everything, right, top to bottom is designed for that better economics faster development for you and overall total -- lower total cost of ownership. All right. So let's go into some of the amazing announcements that we made. And again, this is just a slice, right, of what we're doing.

Next slide. Okay. So on that kind of base layer, right, how are we making sure when you bring your data in, right, that you feel confident, right, that we are giving that lower total cost of ownership. So here, we're making advancements that are really going to help you, as partners, not only simplify, right, your operations and governance, but free up those resources so that you can focus on more better value-added things, figuring out what the heck is going on with AI right now.

So the very kind of basic level bullet point to. I'll start there, right? We've unveiled the standard warehouse generation 2, and that's going to give you 2.1x faster analytics performance. It's going to accelerate the insights that you can bring to customers, right? We all love faster, faster is better, it's awesome. But we all know right managing warehouse is not always the most fun thing to do, rightsizing that correctly, making sure you have that right-sized compute to scale.

And that's why we're also introducing adaptive compute, right? This makes warehouses even easier for you to use. It's taking that burden of platform management that nobody really loves off of your hands, so that you can deliver faster results also at a lower cost. Really exciting. It's in private preview, right? You should go and you should check that out, right?

Similarly, simplified ingest pricing. A lot of you are using Snowpipe to bring that data in. Thank you, do more of it, right? And we have more coming up about how you can even bring more data in and we're making lower pricing for you, right? It's going to give you faster data onboarding again, better price performance. And so you can be confident in that overall total cost of ownership.

And then, you've got this data, right? We've got you the good price performance. How do you make sure, it's secure, it's governed, right? The right people are having access to the right things, right? And that's where our total horizon, right? What we're doing on the catalog, but also with the governments is going to be so helpful, right? We're doing enhanced interoperability, AI-powered security and governance in our Horizon catalog. That includes a copilot for Horizon catalog as well as AI-led monitoring, right?

So we're taking, again, across the board, more of this off of your plate, right? So you don't have to focus and you don't have to worry as much about kind of the performance and the compute in the warehouse layer, right, and even the governance. We're helping bring AI. We're helping bring that lower cost right? So you can focus on, right, the next stuff? How do you bring in more interesting data, your unstructured data? How do you put it to use? All right.

Next slide. All right. That's what we have, smarter infrastructure and governance.

Next slide. Turning the slides back up. We've got more to talk about.

All right. Next one. Okay, here we go. Okay. Accelerating development. All right. Tools for builders. This is -- I was one of the co-founders of Streamlit. This is where I get really excited about. How do we make it easier for you to bring that data in and do interesting things with it. So we're investing in so many new development tools. And that's really going to speed up your project delivery, help you build more differentiated offerings and drive innovation.

Look back there. Don't give away the good stuff on AI. Okay.

So first, right, you've got a lot of data, right, that's sitting in SaaS tools, it's sitting in lots of other places. You want to bring it together, right? You want to get that great price performance that I was talking about. But you're going to want to do interesting things with the 2 that we're about to show you with the AI side. So getting that connectivity, managing things in, open flow is really going to help you with that, right? Really, it facilitates this open interoperable architecture, right, moving your data of choice around your data lakes and lake houses, it makes it really, really easy for you to adapt to all these new industry standards, like iceberg, right, and bring that data in or write back, right?

So unifying structured, unstructured, backed streaming data into a single platform, all with these kind of connectors out of the box, you can get all of this data in. And then we have workspaces and built on top of workspace we have DBT projects. This is 2 exciting announcements. Workspace is a modern development environment. We'll show you that in a minute, but it makes it really easy for you to edit things. Starting with SQL files and then we're adding in notebooks and streamlits that will all be together kind of 1 central command system for you to be working with your data in your code. And the DBT projects, you can run DBT now natively inside of Snowflake, right?

So if you're already using DBT, this is great. You should go you should try it out running your pipelines there. If you're not, you should try it. DBT is an amazing open source tool. And speaking of open source, right, in Postgres, we announced a major acquisition at some at the Crunchy data acquisition, go and check it out. This is going to make it easy for you to do transactional workloads inside of Snowflake. So go take a look at that. We won't demo it today here, but you should check out the recordings on that.

And then what I will give you demos up in just a second, Cortex AI SQL and semantic use, right? So these are 2 important building blocks that are going to help you build even more interesting things. So I'm not going to say too much about them now because we'll jump into a demo in just a second.

All right. Let's round out some of the big announcements, and then we'll go over to the demos. Last slide. All right. So -- you've got your data in, you've got this solid foundation, right? You transform it, you're ready to put it to use, right? How do we help you put it to use?

Well, first, we have an agentic framework, right, with Cortex agents, and we have Snowflake Intelligence, which is an interface that you can be giving to your business, right, to surface those agents, right, and put them to use. -- right? So this is really exciting for all of you that are really embracing right, these new AI capabilities who want to go to this next wave of intelligent applications.

These are 2 really powerful things both to build the agents, right, and to surface them to your customers. Then back to the builders, right? We're giving you a lot more tools in terms of co-pilot. We'll show you in-line copilot and workspace, which helps you find data, edit it, give suggestions, fix, explain your queries, again, really awesome ways that we're helping make you more productive. And then core technology extensions are a really cool thing that we're introducing in the Snowflake marketplace that allows you to share your Cortex search services, right, it's private listings organizational listing. So that's going to make things like RAG, bringing in your articles, market research, right, into your agents more possible and easy, right?

Again, what we want to do is, where we're bringing in all these models or bringing all these AI capabilities so what makes it really easy for you to build agents and apps that are going to deliver the insights that you have from all that unstructured data, right, that structured data that you're bringing together, right, for that next level of insights for your company.

All right. Let's switch over and I'm going to talk even faster as we go through some demos. And again, we won't get through all of it. So you're going to have to go check out, right? Some of those videos online. All right. So let's go here super quickly. This is open flow.

What does open flow do? Well, there's tons of different connectors, right? Just look at all of the connectors that we have here. If you don't see a connector that you want, this isn't even showing all of them, you can go and you can make your own with NiFi, right? It's a great open source project. And so when you have them, then it's really easy to set up these run times in these deployments.

So we've got run times here. We're showing 1 for Kafka, Polestar SharePoint, rights go to bring in structured, unstructured, batch streaming again, right? It can do it all. And then you can have this running, you can have it inside your VPC, right? It could be on AWS or you could be running it yourself, right, in Snowflake, on Snowpark container services.

We're giving you a lot of flexibility. I won't go into the full wave that you make these, but it's a lot of great things that you can do and it'll help you bring your data into Snowflake. All right. So you brought your data into Snowflake, right now, what can you do with it? Well, one thing is, right, you've got your DBT pipeline. That's going to help you, right? Makes sense of a lot of this transformation. So you can see here, I'm in a workspace, little one, we call Summit Fest here. You can see I have a DBT project. It's actually connected to [ Git ]. You can see some of the changes. I can push them if we wanted to take a look at them right now. So here are some of the dips right here in terms of the side-by-side, right? But if I go back, oops, oh, no, accidentally clicked something. This is how you know demos are live folks. Okay.

But going back in here, we can see we've got my models, we've got [ YAML ] files, right? I can run this -- it's going to compile, right? We can see the DAG from the last time I compiled it, right, all here, right, inside of Snowflake, right, inside of a workspace, making it easier -- even easier than ever, right, for you to manage your data. All right. What else do we have? All right, AI SQL. So I skipped over this later -- earlier, but this is really, really cool. So I'm running this in a notebook here.

And what I want to show you, right, is how you can use Cortex AI SQL to embed generative AI directly into your queries, right? That's going to help you analyze all types of data with just the familiar SQL syntax, right? So here, we're going to show -- we're trying to find some customer issues across text, across image, across audio data. That would have been really, really impossible, very hard in SQL before.

But here, right, using AI complete, right? We are able to do this with some multimodal prompts, very little amount of code that we're doing, that's going to allow us to consolidate that data across all of these different formats and start to put it to use. And then as we have that, we're going to also be able to not only consolidate it right across the text, the image, the auto, but we're going to use the power of AI to semantically join those customer complaints to the solutions, right?

Again, we're just using AI here in SQL. It's allowing us to work with this data. It's allowing us to new amazing things so that we can get these aggregated insights across all of these different types of data, right? And we're doing all of this in just a few lines of SQL on the Snowflake platform, getting direct access to the best frontier models that we have, right? This is an amazing kind of ability that we're providing you out of the box. It's going to give you amazing productivity gains. And if you don't want to take my word for it, check out this amazing benefits that we're measuring here for you, in terms of performance and cost benefits, right? Sometimes 3 to 7x performance benefits. It's really amazing, and you should go and you should try that out now.

All right. What else do we have? Well, let's say, you're saying, well, you know what, that's great. But I actually want to be doing my own ML. Well, notebooks now, we have them, they're GA, you can use them on containers. You can be using them with GPUs. Here I am, I'm running something in the background here. We're predicting diamond prices. I've been running this at GPU for a while. You can be managing your CPU, right? You can be looking at your memory, right? You can do a lot of really sophisticated things now, right, both with our ML platform, right, and with our notebooks, right? You can do a lot of these things too with Copilot, which if we have time in a minute, I'll show you as well. But supercool ways we can share these now, getting so much you can do.

All right, semantic models. I promised we would talk about that, too. All right. So hopefully, you know what a semantic model is, and you know why it's important, but semantic layers help you unlock these AI-powered analyst experiences, right? You can create consistency across your AI and BI. It's really important as you start to build out these agents in these models. And we can also take the semantic models out of the multiple BI tools that you use right now and that every customer has and you move it into a single day or later, right? So we can see here, we've got our tables and schema. And now, we can just very quickly create a semantic view. So here's our DDL for the semantic view. We're just doing this in notebook, we're doing it in SQL. Very easy stuff that you're familiar with. And then here, we can describe it, right?

So since this is stored as a native object, we can describe it just like we do a table or a view, right? Is that easy? So now we can go over to Cortex Analyst, and we can ask you a question, like, I'll copy this question here. What are the top 10 brands for the books category in the state of Texas. So if I go over here and I ask that, right? Now we're going to use that semantic view. We're going to use Cortex Analyst, it's going to help us interpret that question, generate the SQL, run the SQL for us, right, and get an answer.

So this is really cool. I'm starting to talk faster because of all of the things I still want to show you, right? So here's the semantic query. We can look at the physical query. We can go back here. If we ran it here, right, in SQL, you'd get the exact same result, right? So this is a new and very powerful thing that all of you are going to want to explore. It's going to give you a lot of power, especially as, right, you move into more of these AI things.

Okay. A couple more things, right? This is a workspace again. Again, amazing things that you can do, we can pull charts in for you, you can filter them in, you can do these fast. You can look at things side by side. Let's do a new query here. It can find things for you. Look at this, I can say, fine, my COVID-19 data set and give me a sample, right? It's going to go. It's going to use our search under the hood, look across the data sets that I have available. It's going to pull that for me, right? Give me the answer. I can go ahead. I can run that, right? It all works.

Again, we're just weaving all of this AI things in for you. So whether you're a builder, whether you're somebody building those semantic views, whether you're trying to govern your data, it's all there for you.

And last but not least, right Snowflake Intelligence. This is how we help you bring these agents together. Those semantic layers, everything is coming together here, right? So we can ask a question here. We can ask directly in chat. These are things that you can be giving to the business users in your company, and it's going to do everything that we just showed you. It's building all of that together, right, the agents, the semantic layer, the search, all of that is coming together in order for it to reason about it, to go into your data to actually provide you these direct answers for.

So I don't know if I'm out of time yet. We'll keep it on this while I kind of close as it's doing the reasoning and thinking about it. But really, I hope you've gotten from this that Snowflake is no longer, right, just where your data lives, right? It's where your AI applications are born. With these innovations, you now have that toolkit at your fingertips to deliver the smarter, more personalized and more scalable customer solutions, right?

And the opportunity is really just wide open for you as partners, right? Those who move the fastest with these new innovations are going to have so many more things that they can bring to their business, to deliver more value to customers and to deepen those strategic relationships. So please go online, check out the Summit replays. There's even more better exciting things, go to our solution center, see how some of these things are coming, turn on those previews in your accounts, we're really excited to see what you build.

Thanks, Amanda. Really very, very cool demos. Yes. We have a lot of questions that comes at the Q&A. So can you just keep coming -- keep asking, we have SA that's helping with the solutions, and the questions. So go ahead, and thank you so much, Amanda, for all your sharing today.

Thank you. It was exciting. I look forward to coming back to Asia soon. I always love everything that's being generated here. But I will say good night since I'm in the U.S.A.

Yes. Thank you so much, Amanda. Bye. Hi team, we love to have your feedback. So give us your poll in about 2 more minutes, we will close. So we also have up and coming our Snowflake World Tour. So happy to get your polls. And if you are interested to join our sponsorship of our world tour that's coming to our region, do check it out and share your interest as well. And next up our poll, which is what you guys are feeling. Really, really appreciate all the feedback that you have given us to make our show even better the next one. So we'll come back definitely in quarter 3 and quarter 4. So on a quarterly basis, stay tuned for all the updates that we will share with you guys.

So I'll be online for another 2 more minutes for every one of you to fill in your polls, and we really, really appreciate any feedback from you. Hope, all of you had an amazing time. And all the partners, I really appreciate you taking your time to spend this morning with us or afternoon. Yes, I really appreciate it. We will close webinar in about 1 minute's time.

Team, thank you so much, and we will end house. Thank you.