Snowflake Aktie

All right. Well, we'll go ahead and get rolling. I am Rob Owens. I head our technology research practice at Piper, and I also cover infrastructure and cybersecurity stocks and very happy to welcome our first company this morning, Snowflake. And with us from the company, I can't forget Katherine McCracken, who's sitting in the front row; and CFO, Brian Robins. So Brian, welcome.

Thank you for having us here today, Rob.

Thank you for coming. Maybe just start with a little bit of perspective. A year ago, you had signed on as a Snowflake CFO, but came here in the capacity of GitLab on kind of going out. I think it was your last week effectively. It's been a hell of a year. It's been a fun year pocalypse, you name it, everything else. So maybe a little perspective about year one at Snowflake for you looking back.

Yes. I guess I would start with what my priorities were and what I've done for a long time and really the focus on go-to-market execution, revenue growth and just operating efficiency in the model. And so one of the reasons why I joined Snowflake, sort of what do you want to do when you grow up type story.

I wanted to be at a company that was really at the center of AI. And I read or heard somewhere you have to have a data strategy, you have an AI strategy. And you love to see what Snowflake was doing and all the customers that were putting our data into Snowflake. That coupled with the fact that Snowflake had just recently had a CEO change. And Sridhar was just really, really well known in the industry for product innovation, product velocity and so forth.

And so for me, those elements were extremely compelling and interesting. And so it's been a year, and we've had great operating leverage in the model. We committed to being GAAP profitable in 4Q of next year. And then in addition to that, we've also reaccelerated growth. We just had a tremendous print this last quarter and doing it in a number of different areas, doing it in the core business as well as in a number of AI products that we're releasing.

And speaking of what you want to do when you grow up, but you're obviously not new to this. You've worked at a scaled enterprise before as CFO in Verisign, high growth in GitLab, both public companies. Maybe speak to walking into Snowflake day one and what it was like there, some of the puts and takes and differences versus prior companies because it had scale and growth.

Yes, absolutely. And so my first 90-day priorities at Snowflake was, one, spend a lot of time with the team. The outgoing CFO has been -- was transitioning out for a period of time. And so I wanted to make sure that I could spend time with all my directs and folks that report to them and so forth. And so spend time with the team was super important.

I also wanted to get out and talk to a lot of customers. I think being a CFO, when I was a financial analyst out of grad school and sitting in a cube, I said, man, I ever make it to the CFO office. I really want to be out in the field talking to customers as I think that really helps you on resource allocation and where to allocate dollars around the business.

And so I sort of made a career working closely with the CRO. And so I met with a lot of customers to understand why they're using Snowflake. But most importantly, I ask them like where can we improve? What can we do to be better and how are we differentiated? And then third was meet with investors. Obviously, we're here due to our customers and our investors and really wanted to understand what was on investors' top of mind, what things were going well, what wasn't going well and if there's anything that could influence and change in my first 90 to 180 days.

So you mentioned you want to be a company that was at the center of AI. And clearly, I think with new leadership, Snowflake's affected a lot of change from a product strategy standpoint and has become that AI leader. In fact, a couple of weeks ago when you reported, it was one of the strongest quarters we saw across our entire coverage universe. So maybe talk about those elements of the business that are going right right now.

Yes. We're super fortunate to have built this database technology about 10 years ago that is super scalable and cost effective for our customers. And so you had to have a really good foundation to do that. That, coupled with just the amazing leadership that we have across the entire company, Sridhar, C.K., Vivek, Denise Persson has been our CMO for, gosh, for about 8 or 9 years. So we've had really -- a lot of the people at the company have been there for a while with really deep leadership. And we had the ability to infuse AI into what we already had.

And what was the most impressive thing about what's happened is I probably talked to 3 to 5 CFOs or CEOs a week now, either existing customers or customer -- potential customers and talk about how I've changed the way I work and how AI has really impacted myself. And so with AI, we talk a lot about CoCo, but we have cowork, we have a number of different things. It's amazing. I interact with my computer. I use Wispr Flow. So I talk to my computer now. And I just prompt my computer most of the day.

Talk back or sometimes.

I talk to my computer most today. And what's super interesting, we acquired a small company called Natoma. And with MCP connectivity now, we now have this harness that I actually work out of, right? And so I can do the orchestration, optimiz -- I don't have to go to all these different applications and it's tied into my calendar, it's tied into Gmail, it's tied into Slack. And so every piece of data around the company because we're Snowflake. So it's a data company. It's role-based access, has security, has governance. And so it just makes it a different way of working than how I worked the previous 30 years.

And relative to Snowflake and the performance, which has been very strong in the last couple of quarters, the Street is all hung up on this 3% mark, which I think your model kind of.

What 3%?

You probably never heard of that before. Coming off to 5% plus beat quarters, what's really driving this underlying inflection in the business? And how durable is this growth rate? And how come -- as you come in as CFO, your predictability has gotten worse because you're beating by more.

Thank you Rob.

Yes, welcome.

That's a I'm super fortunate to have inherited such wonderful people at Snowflake who have worked there for a very long period of time. And so in my FP&A organization, I have a data science team, and they've been forecasting the business. They've been there 7, 8 years, about 8 of them for a long period of time. And when they first start forecasting the business, you could really understand the degree of variability.

But literally, every single night, we run several machine learning models. The pipelines run for about 4 hours. At 5:00 a.m. every morning, Sridhar, myself and a number of people, Katherine, a number of people around the company, get a very detailed forecast e-mail. And so we are 100% consumption business, so not the easiest business to forecast. It's not a waterfall of licenses or what have you. And that forecast is based on customer level as well as product level rolled up to every single region.

And so that part of the business, since the team has been doing it for so long, gets forecasted with a very, very, very tight range of -- in a tight range. So standard deviation is very small. The -- for new products, it's a little bit more difficult, right? So we have years and years of launching new products, and we obviously save all that data and can go back and look at the pattern recognition of what's happened early in. And then we actually try to map that against what's happening.

CoCo being one of the most successful product launches in the company's history has grown way faster than a number of those other products. And so when you have a very short period of data, and so when we always talk about sort of our guidance, one, we say that we haven't changed our guidance philosophy or forecast methodology, you go back and you look at the observed behavior.

And with just a short amount of data with sort of high numbers, I think it would somewhat be reckless to extract that out and just apply that to guidance. And so we take that number and apply a haircut to that, and then that's sort of what comes up with the number. In first quarter, obviously, we had about a quarter's worth of data. Now we have 2 quarters' worth of data. So we're getting closer to what that number is. But we're really happy with how we've had adoption across a number of different accounts.

And so on the call, we talked about roughly 9,100 accounts have adopted CoCo, a little over 5,000 have adopted Cowork and what we're trying to focus on now is get our sales team to go in and talk about the use cases to drive deeper penetration in those accounts, so we can get more users. And so that's what we're in the process of doing. But it's really -- the 5% comes down to -- the core business is doing well and is accelerating. We're also seeing benefit in the core because of AI. And so Sridhar did a post on LinkedIn a little while ago, and I think it was something to the fact that when someone adopts CoCo or AI, we're seeing 11% uplift to their core business that they would do otherwise. And so both of those are sort of contributing to the beat.

And maybe you can drill down on that a little bit more because CoCo is creating a great unlock just in terms of migrations, moving data over. Just how it's benefiting the whole business in this cycle that's benefiting you guys? .

Yes. It's interesting. When I talk to CFOs and CEOs about where we're at on our AI Agentic journey of changing the CFO organization, one of the things I try to stress is like all this is within a year. And so no one is behind. You just got to get started. And so CoCo and then I also talk about this is more of a human transformation than a technology transformation. So the biggest thing is just really driving adoption through the organization and to get people to use it.

And people, depending upon what organization they're in, use it for a number of different things. And so for instance, in our group that does our migrations, they use it to actually help migrations. And so though we've got a whole bunch of skills and agents that can actually take the migration time down. We were around 10 or 11 months, now we're around 6 months. There's some customer dependency there, so we can't get that down to 0, although we'd like it.

But we're currently working as much as possible to bring down the migration time frame. For myself, I use it for, call it, like analytics and business intelligence. And so we've actually canceled all of our BI and analytics contracts. And so I use CoCo to streamline and can spin up dashboards in a heartbeat with whatever I want around the business, whatever time period, they aren't static, they are active because of the MCP stuff, I can then turn that into an e-mail, send that straight out of CoCo.

And so there's 2 different things that we're using that for. One of the things that comes up prior to me getting here, one of the things that the company talked about was optimizations impacting the revenue. And one of the top 10 skills is a cost optimization skill. And so in a consumption business, it's really the last thing we want a customer to do is be surprised about how much consumption they have.

We want the customer to know exactly how much they're consuming, what they're getting out of it, and we really need to drive that sort of positive ROI for the customer. So there isn't this -- Sridhar announced that this unknown time bomb, if you will, in our customer base for them getting billed way more than they otherwise should. And so our account executives and our sales team spends an enormous amount of time with our customers, really trying to work with them on the use cases, the ROI, the savings that they're getting out of it and so forth.

And when you contemplate new customer and new customer growth, how much of that is new products, new projects, excuse me, versus migration of more traditional data warehousing technology. And relative to that more traditional data warehousing technology, how much is left in terms of migration opportunity.

Yes, we are just starting to scratch the surface. One, there's just -- there's a ton of data produced, but there's so much legacy workloads in the legacy solutions. And then there's a lot of sort of solutions today that are AI filled. And so AI is really going to help the ability to access those workflows, understand your data, get more people to access the data and so forth.

And so one of the things I talked about in Investor Day. We're in a market that is roughly going to $300 billion in the next 5 or 6 years. And that doesn't include all the analytics and business intelligence. That doesn't include all the other stuff around it that you can do with CoCo and co-work and so forth. And so that's what's super exciting for us at Snowflake. And that's one of the reasons that was what helped our print. We talked about 50% of the beat was related to our AI products and 50% was related to the core.

And there are these secular tailwinds sort of an industry now sort of pushing towards getting your data in a database such as Snowflake, so you can actually get value out of it. When I talked about -- and I don't mean to harp on this too much, but when I go talk to CFOs and CEOs and then show them what we can do, they're like, I want that now.

And so when I joined Snowflake over a year ago, I wasn't having any of those conversations. I probably spend 25% of my time now talking to customers. I'll be in New Zealand and Australia on a customer trip, starting to hold CFO roundtables to discuss how AI has impacted businesses. And so there's a number of things where the interest level is so high and there's so much curiosity that, that's really driving demand as well.

And I thought one of the big surprises coming out of your Analyst Day, Brian, was the fact that I believe it's 6 quarters from now, but that you're going to achieve GAAP profitability in the fourth quarter of next year. And it was a more contentious comment than I thought because there are some that applauded it and said, this is great, and there's others who said, no, you should be leaning in. Now you're one of the very few AI acceleration stories. Subscription revenue or consumption revenue has been, what, 4 or 5 quarters now. You've seen that acceleration. Why is this the right move at this time to push towards GAAP profitability. Because you've got a pretty big competitor out there that's running at free cash flow breakeven.

Yes. It's -- I'll answer it the reverse way in the sense of say, did I do anything that put constraints on the business to be able to make that comment? And the answer is absolutely not. Like we're spending as much as we want to spend in the business. We go in to these large enterprises and our gross retention rate is really high.

The core of our business really comes from customers 6, 7, 8 years ago. As they get on Snowflake, they move more and more into Snowflake. Now with stuff like CoCo, more people within the companies are actually using it. And we're seeing -- so you can imagine as a customer builds with such -- we have a great net retention rate, one of the best in the software industry.

Because of that, the core of our business is older customers, and we're doing everything we want to do. There's nothing -- it's sort of a misnomer that to grow fast, you can't get operating leverage in the business. I think if you look back at my history at VeriSign.

I've never called you cheap to your face.

Been wisely. VeriSign, when I got there, we're in low single digits. We took the operating margin up very -- I'm not going to say it because I don't want to apply anything, but we tripled, quadrupled the market cap of the company under my CFO tenure. Did the same thing in a number of different companies. And I'm a real big believer that you can drive accountability and execution in a company to get top line growth as well as bottom line leverage.

And what's great about Sridhar is being a business partner, he's in a 100% agreement. And so -- and I think this really is starting to play out. We talked about it a little bit at Investor Day. It's a little bit too early to see it. But on this earnings call, I talked about we just quarter -- or sorry, when we reported the call, the number of heads that we hired this year compared to the number of heads we hired last year really shows us.

So we're reaccelerating revenue. We increased our full year guide from 31% year-over-year to 36% year-over-year. So increased about 500 basis points in just 1 quarter. And at the same time, year-to-date, this year, we've added roughly about 330 people, of which 170 to 180 of them came from Observe acquisition. So 150 compared to the same period last year, it was roughly about 940 people. And so we're doing that with a lot less people as well.

And you were one of the first execs, at least that I interact with to talk about the now disconnect between revenue growth and bodies that you are driving massive efficiencies within your business. So maybe you can double-click on that a bit relative to where you're actually finding those efficiencies and able to drive them.

Yes. So the good news is you find them all -- it's all around the business completely. And so we, as a company, will constantly get economies of scale and stuff that we do with the hyperscalers and all that. But we're using technology, primarily Snowflake to actually get more out of the existing workforce. And then part of our plan to be GAAP profitable was really in engineering, you don't have to hire senior leaders that require large stock option packages.

You can hire people with 1 to 2 years college experience or AI built that can actually prompt and buy code and be more productive at a fraction of the cost. So all around the business, like to me today, I got this thing I talked about called the good morning CFO skill. I run it. It pulls data from about 40 different sources and tells me everything I need to know about the business in the morning. Then if I have a question, I can then prompt that data to actually give me more and more information all the way down to like a SKU level or customer level within a region.

I can look at it over whatever time period I want. So if you think about that, typically, that would require someone in FP&A a lot of time to go back and forth and get reports and so forth. And you don't have to do that as you don't have to do that anymore. And so -- that's one area. We got something called Snowflux. And so we're doing our monthly close flux analysis through AI as well. It's almost built out. We do our BVA analysis. We talked about on our earnings call, we're doing our long-term planning through a lot of agents and less people.

And so it's all over. We have an agent in our sales force called Raven. And basically, you can go in and ask Raven anything. So the amount that it would time that would take for an SDR or BDR to actually go prep an AE before a call is now done automatically through our sales agent. And so these things just provide a wealth of information. And what's really important is that you don't stop here, that you continue on to actually get more benefit. And so like for that sales agent, then to tie enablement materials into it, you then tie that into like Salesforce and you can actually put a probability to something and it can help be a check on the forecast.

And so we're trying to drive those changes all across the business. One of the things I stress to my team is when we first started using AI, what I noticed was people was actually using AI -- sometimes they called it AI, it wasn't AI, but they're using AI-like things to just redo the work that they were doing. And part of the process, and it's -- you have to get into the details to do it.

But part of the process I've gone through with each one of my directs and I spent a big chunk of my -- other part of my time doing this is redefining how work gets done. And so work by default was created based on limitations in technology and certain handoffs, but you can actually break a lot of that down and change the ability of people to process work to just make decisions. And so we're in the process of doing that, which is super fun.

Great. And last quick one for me. You made the comment that more and more customers are pushing for fourth quarter renewals. And obviously, that's coming up. So how big is that pipeline shaping up to be? And how do you manage some of the inherent risk that that comes with a big lump of customers in a fourth quarter renewal conversation?

Yes. So not uncommon to really any software business, right? Fourth quarter is always big. It aligns with the budget and when they're budgeting for next year. And so we've signed seasonality-wise, we've got the majority of our contracts in fourth quarter, and that's happening. It's -- when you're a consumption business and you're a rep and that's your livelihood, you're talking to those customers all the time, right, talking to them about how to use AI, how to use our AI gateway, our AI functions, Co-work, CoCo, all of our different AI stuff about what migrations you can actually bring in, savings that you can get on migrating off legacy software, BI and analytical tools that you can replace.

And so our reps are in with those customers literally on a weekly basis. And so the timing of renewal is just -- it's not like a major event. It's just a normal event for us, and they're already working on that as we get up to that renewal. So I don't have any concerns with that.

Okay. So we're going to have a breakout across the hall with Snowflake. Unsurprisingly, it was one of the more requested names. So please join us there, and we'll continue the conversation. Appreciate it.

Thank you so much.

Okay. Thanks, everyone. I know people are piling in after the keynote. So, I've got a couple of seats upfront, but I'm sure this will be a standing-room session. Excited to kick off the afternoon with Snowflake. We have Brian Robins, the CFO.

Brian, we had a great dinner last night, and it's hard to believe it's only been a year or so since you've taken over. A lot has changed at Snowflake. Maybe just give us your top observations from the past year and how do you think about the opportunity ahead?

Yes, absolutely. First and foremost, thanks for having us here today, and thanks for everyone coming to see us. My two primary goals when I came into the company was really twofold: one, focus on growth and margin expansion and two, focus on the go-to-market execution. And so in my career, I've spent most of my time with the CROs of the companies and have really focused on that go-to-market function. And so -- and I'm happy to say both of those have really played out throughout the year.

On the growth and margin expansion, I'm really happy with the guidance that we gave, the quarter that we delivered, even the previous quarter, and we're doing that with increased operating leverage in the model. And so excited to see that Sridhar and I are very well aligned there, and the whole executive team is. And so, you're starting to see the investments that we made pay off in the go-to-market and also on the growth side. And on the go-to-market side, J.B., Jonathan is our new CRO. He's doing an awesome job.

He's been at the company for over 10 years, really highly regarded. It's been awesome to work with him because he knows the -- he grew up with Snowflake basically and knows the product really well. And so he's really pushing the discipline and changing the way the sales force is selling into our customers. So historically, when I got at the company, it was really a consultative sales approach, and they would go and sort of talk about, what's your problem? How can we help you? And so forth.

And now reps are actually using AI with synthetic data and actually driving outcomes for the customers. So they're actually showing up with agents and models and so forth that actually help drive that use case and that outcome. And so really super happy that those 2 things have played out, and two of my focuses I'll continue to focus on.

Okay. Awesome. And certainly, the recent results have been very positive surprises to the market. Back-to-back reacceleration is very rare in software at this scale, 5% plus beats, but it's also rare by Snowflake history. Clearly, investors are excited about it. We can see the bend in the growth curve. But I'm curious, as you kind of look at the business from probably an unlimited amount of metrics, based on how much you can cut the data at Snowflake, what are some things that surprised you internally, whether it's verticals, new personas, different types of consumption patterns?

That's one advantage of being a data company. We actually measure everything within the company. And so just to comment on the results, and we'll talk about the question, it's awesome to see what the Snowflakes have done and what they've delivered. A lot of hard work has gone into that throughout the entire employee base, and then also really appreciative of our customers.

When we -- Sridhar and I host all-hands after every earnings call, and it was one of these things like, hey, this is great, let's celebrate today. But like after today, like we have to worry about tomorrow and this quarter. And so there really is a sense of urgency within the company and a culture around customers first, delivery, product innovation, and so forth. On the second part of the question, remind me, it was about...

Just as you look at all the different metrics and cuts of the data, like obviously, investors can see this. But anything that you would flag and just stands out to you as you look at it?

I guess, it's the -- it would be -- of course, the metrics are awesome to look at and see what's happening. One, I guess, would be really the durability of the business itself. And so what we're seeing with all the AI initiatives that we have. We talk a lot about CoCo, but there's CoWork, there's AI Functions, there's model training, there's AI Gateway. It's the adoption of those and what people are doing with those tools to impact their businesses.

And so the way that all of us are working today will dramatically change in the next year, 2 years, 3 years and trying to actually embrace that agentic workforce and what that looks like and to see that in our customers and see how they're doing it has just been awesome. I guess the other thing that I'd touch on that's really been amazing is when I got to Snowflake about a year ago, I personally did not use Snowflake that much. I don't know -- I took SQL, I think, in college, it was a required class.

And -- but I didn't really have the ability to access the data, like I have the ability to access the data now. And so I spend a lot of my time in Snowflake, primarily CoCo, and can access like any of the data. So historically, the way it would work is if I would see data and one of our traits that everyone here in the room has -- is we can look at reams and reams of data and find one data point that doesn't make sense.

And then typically, we would go to someone and say, can you give me more data on that? Now you can just talk to basically your computer through CoCo and basically get into the data and understand what's going on. And then it's integrated with our acquisition that we did with MCP connectivity into Gmail, Calendar, Slack, all the other applications they use. So I use that as a harness to work out of.

And the fact that I'm using it so much and we've implemented so much Snowflake on Snow, I talk to probably 3 to 5 CFOs a week, either existing customers or potential customers and the sense of urgency around what you can do with your data at different personas has been really amazing.

Yes. Yes. No, that's great to hear. And I know you've talked about the vast, vast majority of the finance org on CoCo and just all the efficiency that, that's gained. And one of the questions we often get from investors, and I know you've only been at Snowflake a year, so it might be a little unfair of a question. But if we go back to past cycles, right? Where we did see larger beats and growth rates were elevated, there was this wave of optimization.

And I know that's probably very recent memory for a lot of folks that are still at Snowflake. What gives you the confidence that this acceleration is durable, and we're not going to face this huge wave of optimization like we saw maybe 5 years ago as the digital natives kind of lost some momentum?

Yes. It's a good question. And you can ask any question. I have got my colleague, Katherine here, and so anything I get in trouble with, she helps me out. But from the -- when I look at the data and sort of understand the past and sort of what's going forward, we're well past the experimental phase with CoCo and AI, and really seeing the power of what this does within our customer base gives me the confidence in forecasting and guidance. When you have sort of a new tool and a lot of people adopt it and use it, that's great.

But what you really have to ensure is that you're driving positive business outcomes and there's an ROI on that. One of the things with a consumption model is you get to charge like every day when it gets consumed. But if you're consuming for the wrong things or they aren't seeing ROI, that's like bad for the business, like that's like not sustainable. One of the things that we actually have -- one of the things that we have that is awesome is we have a skill within CoCo to do cost optimization, and it's one of the top 10 skills that's used.

And so we actually encourage and want our customers to do optimization. We encourage and want our customers to use CoCo, but we don't want them to do it in an unhealthy way. And so part of the sales execution and excellence that I talked about with JB and the sales team is really developing those use cases and selling those outcomes. And I think that's critically important for stability in the business, and that helps us put that into guidance with durability and understanding.

Yes. And maybe we can stick on guidance for a bit because I know there's a lot of factors in your favor, right? A lot of factors, a lot of time, effort, heart that goes into that, right? But how do you approach it? I mean you're seeing kind of unprecedented levels of adoption, new use cases every day, and new products launch, right? I mean, the innovation velocity. So what -- in simplest terms as possible, like what are you assuming in the guide now that maybe you didn't 3 months ago, whether it's new products, level of consumption, just to kind of help frame how you're guiding the things.

Yes. Let's -- before we jump in, let's talk a little bit just about guidance itself. And so I just want to make sure there's no change in guidance philosophy; we do it the same way. When we look at guidance, we look at sort of the core, which we have a ton of information on and we actually forecast that literally every night. We have -- there's an awesome team back at Snowflake. The team has been there roughly about 8 years.

There are a bunch of data scientists, and they forecast by customer and by product line, literally every night. So the pipeline is run for 4 hours at 5:00 a.m. every morning, I get an extremely detailed forecast of what's happening within the business. And so with the core, we have tons of data. We've done that for a while. Our degree of accuracy is very high there. And with the -- with AI and some of the new products, we don't have as much observed data, and so we're going off of what we know sort of at the time.

That same team, as well as another team, builds detailed product launch models for us. And so we have all the products over the course of Snowflake. We have all the curves and the trajectory of what those products have done. And then we apply the pattern recognition behaviors to those based on what we're observing. And so with CoCo only being out 2 quarters, the first quarter that we did it, we didn't include it because we didn't have any observed behavior.

Now, we're able to -- the next where we're able to, but we don't want to put in the initial sort of small numbers, but really, really high growth percentages. And so we took a haircut to that. But now that we're 2 quarters in, we have more data, we feel like we have a better accuracy in forecasting that. Then we have a whole bunch of new products that obviously are new. So, we don't have a lot of observed data, and so we don't put a lot behind those right now.

Yes. And on those new products, I mean, what are kind of the top 2 or 3 new products that are now out there that you're most excited about or customers are seeing kind of initial traction with?

Absolutely. CoCo's sibling is called CoWork. And so CoWork has over 5,000 accounts. CoWork is what I'll call data with rails around it. It's like certified by the data scientists. And so when you go into CoWork and ask questions, there's no hallucination, the data is correct, you can actually roll that out to a lot of people. So I love that. The AI Functions we're seeing, AI Gateway is super early.

But one of the things that we're seeing with our customer base is their commitment to some of the large foundational models because new models are coming out on a pretty frequent basis. They want to be able to have model choice. And so, one of the things that we're able to do is people make a Snowflake commitment for CoCo and consumption, and be able to use the right model for sort of the right question, or the right activity, the right query, to basically optimize the cost they're paying for that. And so, you don't want to use the most advanced, most technical, most expensive model for a very, very basic question. And so you'll use maybe an open-source model or a model that costs much less.

Right, right. Makes sense. One of the other questions we get just in terms of some of the recent performance is this sort of distinction between AI-driven and the core, right? And I think it gets a little confusing because AI can benefit the core, like new use cases, you need more data. But can you just sort of talk about those 2 dynamics? And how do you kind of see the growth of the core business trending just given these new use cases that are coming on top of it?

Yes, absolutely. When we put our revenue together, there's no -- and I know a number of companies do attribution to saying AI revenue is this, so maybe they're selling a bundle and they have to -- for accounting purposes, they have to break it out. Ours is down to the SKU level. And so we have a series of products that are AI related. And so when we talk about AI and the performance, it's based on those SKUs.

And then the core is the core business that data warehousing, storage, analytics, ingestion, what you know is Snowflake as a whole. And so, one of the things that we're seeing that is extremely encouraging, and really goes to the durability of the business, is we're seeing that when people adopt our AI products, primarily CoCo because we have the most data around CoCo, they're actually consuming more of the core. And so it may be more personas. And so you're reaching more people within the business.

So typically, pre the CoCo and all the AI functions, you would basically interact with Snowflake through your data team, who would actually write the SQL queries. Now anybody within the business can get access. And we don't charge like CoCo is not -- we don't charge for it. You just get it being a Snowflake customer and then the charge is the consumption that you actually draw in using the product. And so we see more personas and then we're seeing migrations go faster. And so we're seeing more data come in relative to migrations. So there's a whole bunch of different things that AI is doing that's driving strength and acceleration in the core.

Yes, got it. And presumably, with the acceleration implied in the guide, you're kind of seeing those trends continuing on a go-forward basis. Another topic that you led off with in terms of your priorities was margins, right? And I guess, how do you think about sort of trade-offs with higher AI revenue, right? Gross margins were lowered a bit last quarter in terms of the full-year target. Just give us a sense of how you think about that going forward as Snowflake becomes a greater AI business.

Absolutely. I guess the first thing I'll say is we are in such an awesome market today. There's so much opportunity, and it's changing so rapidly, and there's just so much opportunity out there, and the TAM because of these things that we're doing, is expanding very, very rapidly. And so with that, Sridhar is very committed to revenue growth and meeting our customers where they're at, giving them tools, helping them get value out of their data through AI and other methods.

And so the #1 thing from a company perspective is we want to capture that market share. And so growth is extremely important. You see in our guidance for the full year. We raised it from 31% year-over-year growth to 36% year-over-year growth. And so that's a pretty significant step-up off of a prior quarter where we had another big increase. But we've done that with increasing operating leverage in the business. And so -- what we are very committed to is increasing operating leverage in the business.

For those of you who weren't at Investor Day, we committed to being GAAP profitable in fourth quarter of next year. And so slightly over a year out. And so we're seeing a really big -- we put a plan around stock-based compensation. You've seen a really big decline there. You're seeing us hold gross margins with the increased revenue that you see. We guide 74% non-GAAP product gross margins, and you're seeing the strong cash flow generation. And so we're doing that with less people around the enterprise.

One of the things I talked about on earnings, if you look at sort of year-to-date hiring for first and second quarter this year versus last year, we added roughly 330 people this year. Last year, we added about 950 people. Of the 330 this year that we added, 180-ish of those more or less came from the Observe acquisition. And so the amount of people that we're adding with the acceleration of revenue, I think, is really a testament of how times are changing.

On gross margin specifically, the company has done a really good job over the years of basically getting leverage and scale with our providers once we sort of reach a certain threshold. And so as we launch all these new AI products, AI products naturally have a lower gross margin than our core business. The good news is it does drive our core business, so that's helping out. But we feel that there are things that we can do over time to maintain a strong gross margin.

Yes. And you talked about on the operating margin line and particularly the people side of the business, right? Having to hire significantly less despite this growth acceleration and certainly it was evident to us at Investor Day, just how much internally Snowflake has kind of transformed on this AI organization, certainly across product velocity, your finance organization as well. What are like some of the biggest efficiencies that you've seen through that transformation? And do you kind of feel like this is the new normal of like very, very minimal headcount growth even if growth were to continue to accelerate from here?

Yes. Efficiencies are happening all over the business. If you start at the top of the P&L on revenue, we're seeing efficiencies in the sales organization. We actually have a model called Raven, and people can go in and speak to Raven really about anything. And so for instance, if I got the user list of who's here attending in this, I could easily download it into Raven and say, how much ARR is sitting in the room for financial services as a big vertical for us.

And so -- and it would tell me basically by customer, what everybody is spending and who's not customers. And so the power to do that is like I could never do that, right? Like it would basically -- I'd have to get the list. I have to send it to an FP&A analyst, it would take a while, they would go do all this stuff. But with these models, you can actually connect all this different data structured, unstructured and literally get it at your fingertips.

And going back to the CFOs that I talked to and the CEOs that Street talks to, that's why they're so passionate about like I need to get this yesterday and get it in my hands. And so from the sales side, we're seeing it on prospecting, how they're putting deals together, and so forth. And so we have not hired as much in sales, and seen the productivity per rep go up dramatically this year, and that's within the sales organization.

And so I know folks like yourself would go and scrape the Internet for open reqs and try to see how much people are hiring and that would be an indication of growth, like unfortunately, you can no longer do that. On the R&D side, we're getting with relatively flat to declining headcount, we're getting increased productivity on lines of code produced at an astonishing rate. And so what we're doing is we're -- one of the things that we're doing to get GAAP profitable was a big component of that was stock-based compensation.

And what we're doing is we're hiring a different type of person that can actually be an engineer versus what we would historically hire. So historically, you'd have to hire someone with 8 to 10 years of experience, really senior, maybe had a management role in the past. And now you can hire someone 1 to 2 years out of college who really is AI-pilled, that has vibe coded, can use CoCo, and so forth. And so we're seeing a dramatic difference in the 2 of them on the productivity. If you go into the finance organization, we have probably 150 different use cases, I say probably because they're developing them every day.

So it's more than that on how we're using Snowflake on Snow on doing automation. And so it's sales and marketing, the same thing. HR, we have an HR skill that can give you the health of your organization and can tell you everything you want to know about your organization, people to check on, people stats and how much they're using AI tools, like a whole bunch of different stuff. And so the point of all this is we're seeing efficiencies and you can tell by headcount, but I really think we're just getting started. I think the efficiencies that I think of the curve of adoption and proving out the efficiencies, we're in the early beginning stages of that.

I talk a lot about AI being a human transformation more than a technology transformation. And so really getting individuals to understand what the art of the possible is, is really, really important. And so I meet every week one-on-one with my direct and someone from my data science team, a senior data analyst manager to talk about what they're doing to become AI-native and how they're changing their organization. It's just literally every week, a one-on-one just on AI in their function, internal audit, investor relations, accounting, FP&A, tax, deal desk because it's that important.

And it's amazing to see the light bulbs go off when you talk about the art of possible. And so you have to give them this North Star of what they need to shoot for on what they can get to from the art of possible. So, we're building a lot today. We're seeing efficiencies, but I don't think we're seeing as much efficiency that you'll see over time once we get some of this stuff fully deployed.

Yes. Wow, that sounds really exciting and certainly kind of a new lead-generation opportunity for you if you're meeting with CFOs every day and showing them what can be accomplished. I guess, as you think about -- you mentioned the North Star and how important that is. Like where do you -- at a high level, where do you and Sridhar view the potential of this business? I mean there's certainly been targets that have been put out there years ago, growth is on the upswing. But how do you kind of characterize that North Star goal to your group of Snowflake, your Snowflake employees every day to keep them motivated?

The market is, like I said, rapidly changing. And one of the thing -- I love many things about Snowflake, but one of the things I love is we are spending millions and millions of dollars, tens of millions of dollars, re-educating and retraining our employee base on what it is like to work in an AI environment, and we're really driving that. And so I'm into a number of people, and they'll come to me and talk to me about careers and maybe taking a CFO role and so forth. And if you're in the job market today, like it has to be one of the most terrifying things because there's a lot of companies that just won't be here in 2, 3, 4 years.

And so what I tell them, the most important thing is to get with a company that's AI forward, they want to make sure you're AI built, and they're going to invest a lot of money in it. And the way I used -- I've been doing this for about 30 years. And the way I used to work won't work going forward. It just -- it won't work. And so I'm really bought into it and really spent a lot of time on it because I think it's critically important. And so from a Snowflake perspective, people are extremely excited because all the customer stories that they're hearing, the impact they're making around the world when AstraZeneca comes and talks and they've done case studies and press releases with us and so forth and they'll come talk at Summit or whatever and they're like, you Snowflake are saving people's lives.

We've been able to do X, Y, and Z because we've implemented Snowflake and are far along on that. They're getting drugs out to trial faster. They're doing it more economically. And it's just awesome to see those results. And that's in life sciences. And then you go to the supply chain, you see the same thing. You go to government and pub sector, and you see the same thing. And so it's super exciting. And within my organization, there's 5 or 6 people who talk to customers on a regular basis, and that happens around the company.

And then people bring those stories back and they talk about that. Obviously, the earnings that we're posting is really awesome. But for Sridhar and I, we want to basically create a set of tools and the ability to access your data to be able to drive your business more efficiently, so you know more about your business. And we're at the center of AI and doing just an awesome job at that. And the key for us to continue to be successful is product velocity. What you did yesterday doesn't matter for tomorrow. And so we need to develop the next CoCo, the next CoCo, get deeper penetration within the accounts, and also sales execution.

Yes. That makes sense. Brian, as the CFO, you probably are involved with the renewals of a lot of third-party software contracts at Snowflake. And certainly, there's been a lot of concern in the market around traditional SaaS businesses. And we've seen them kind of make moves to kind of look more like databases in some ways with UI layers going to AI labs and everything. How do you kind of see this playing out, both from what you're doing with your third-party software contracts, putting more stuff into Snowflake and then the opportunity for you to kind of be more of an operational database in some of those use cases for your customers?

Yes. Great question. So let me unpack that. From -- so procurement sits under me as well. And so I get to see all the deals that sort of come through the pipeline. And because things are changing so rapidly, I am very hesitant to do 3-year deals. And so I won't save money, but I won't get locked into a long-term commitment. And so I'll basically sign 1-year deals. Not saying I won't sign 3-year deals.

There's some software that is critical to what we're doing that will be there and you can tell it will be there in 2 to 3 years or we've talked to them about their road map, they're AI pilled, they're changing the way they're working. And so some -- we will do some 3-year deals. But the standard is a 1-year deal, where that's changed a little because historically, you want to commit as much as you can for as long as you can to get the biggest discount you can.

And I'm just not -- because things are changing so rapidly, I'm just not -- I can't -- in my mind, I just have a hard time wrapping my mind around that. So, we're actually doing lower deals. From a data coming into Snowflake and then other companies trying to be a database or be a database layer. The thing about Snowflake that is incredible is we are an open system. We have the Iceberg tables where we're encouraging you to -- you can query your data from anywhere, anyhow, or whatever. It's our -- we have to create great products like CoCo to incentivize you to use us.

One of the things around -- that we talk a lot about internally is its customers first. If you don't make a great experience for your customer, we talk about it's really easier to migrate into Snowflake because of all these tools. It's just as easy to migrate out of Snowflake. And so let's say the reverse of it, too, right? And so we have to create those great customer experiences to keep them within Snowflake and to give them the ROI of doing that.

And since we don't run -- we aren't an HR application, we aren't a CRM, we don't run all these various components, like a lot of people want to put all their data in Snowflake so then they can use tools to actually be able to understand more about their business. And so I'm very encouraged with how things are going and what we're seeing from a business momentum perspective with people putting data into Snowflake.

Yes. One of the questions we often get just on the competitive landscape is, what do we make of the Frontier Labs, right? Obviously, you have a co-opetition dynamic with the hyperscalers that you're used to, both partners and competitors. How do you see the Frontier Labs? Are they in a similar boat as the hyperscalers? Or do you view them differently?

Yes. I think it's -- I've talked a lot today about sort of the changing business environment. We have great partnerships with the hyperscalers. We also compete with them in some way as well. And so -- but I talk to the CFO of AWS, John, on a very frequent basis. I talk to the folks at Microsoft and the folks at GCP on a regular basis as well. And so we have a very good relationship there. And they're using us as well to sell more workloads. And so they're -- in some cases, they're given quota relief to their team to do that and so forth.

And so, we have a good working relationship there. And same thing with the large foundational models. We've just did our Summit that we recently had. We had the Co-Founder of Anthropic, up on stage with Sridhar, talking about the partnership, now we're working together. We work very closely with OpenAI. Christian talked a little bit about SpaceX and Grok. And so, we're working with all of them. And so I think you said it right. There's a very healthy, stable partnership, but there's also some form of competition as well.

And maybe in the closing minute or 2 here, Brian, if you're sitting back up on the stage, which you're certainly welcome back next year and growth rates have meaningfully accelerated once again beyond where anyone is thinking of, what would you say are kind of the 2 or 3 main reasons why that happened? And anything else you just want to leave the audience with before we wrap up?

Yes. I think it's -- Katherine and I are doing our jobs if we aren't communicating to you on a regular basis about the business and the driving factors of where we're going. And so there shouldn't be any surprises. And I think it really is the durability of the business as it relates to AI, the ability to do migrations quicker, to get more personas, to basically get deeper within these accounts and to really show them what can happen.

So, I don't view from now to next year, it's giving a forecast in these markets is just absolutely crazy. And so we're trying to execute every single quarter. But I think you'll see more of the same. We're developing a lot of AI products and features really with the goal of driving consumption and for getting people to put more data within Snowflake. And so that's really the goal of the company in this AI era that we're in.

Awesome. Well, Brian, really appreciate you coming to the Citi conference. Thanks, everyone, for packing the room. It's a great discussion. Thanks, everyone. Thanks so much.

All right. Fantastic. We will go ahead and kick off the Snowflake session at the Goldman Sachs Communacopia Conference. I'm Gabriela Borges. I lead our software franchise. Delighted to have with me on stage, Sridhar Ramaswamy, CEO; Brian Robins, CFO. Thank you both for being here.

Thank you.

Thank you. Pleasure to be here.

Sridhar, I want to start with some of your conversations in the field. Tell us a little bit about what you're hearing. If we look at the modern data tech stack today versus 2024, 2025, the pace of innovation has changed. So tell us a little bit about what you're seeing in those conversations and hearing in those conversations about the customer journey to go from old to actually new, meaning AI-enabled.

There's always been a lot of demand for data modernization. But the word migration strikes terror in the heart of pretty much every CIO or CDO, honestly, like everyone in engineering. It just takes forever, highly uncertain outcome and so on. I think AI is having a pretty profound impact on how quickly you can get those done and expectations, not just from me for my team, I've talked before about how I want migrations to be mostly automated, but even customers are expecting it. We have a very large manufacturing client, for example, doing a Teradata migration, planning aggressively to finish it in less than 3 quarters is not something you would have heard of in 2024. That's like, that's part of why a lot more is possible. Let's go get it done.

But what is equally interesting is now the ability to have conversations at a data platform level, at the level of Snowflake, which has stayed out generally of a lot of these kinds of conversations is having them about how we can deliver business value. It is everything from like how can we automate invoice processing at a really large energy manufacturer because processes like this are always super manual, super spotty. They would do like spot checks here and there. They estimate that on $10 billion that they pay out every year, they'll be a percentage point more efficient, except that that's an astronomical amount of money. It's talking about that or talking about supply chain optimization or talking about how do you implement a custom CDP a whole lot faster.

It's having conversations like that, that I think are very, very distinctly 2026 compared to the previous one where honestly, most CEOs won't even bother to talk to me. It's like, oh, a data vendor, who cares? I think that change is what is remarkable about this moment.

I want to come back to custom CDP, but let's stay on the migrations thread for a moment. When we came to your conference in June, a lot of the system integrators we were talking to spoke about how migrations can now be fixed cost instead of variable cost because of coding tools. Tell us a little bit more about the shift from variable cost migrations to fixed cost migrations and how we think about the impact that coding tools and then that segues nicely into CoCo specifically can have on that pace of migrations.

I would say this is a larger trend. AI fundamentally is making software industrialized. And I won't underestimate even now the threat that it poses to every tech company, every software company. It's a profound, profound shift. What it has also done is it has obliterated the distance between what a data platform like Snowflake is and what applications running on top of data can be. Now stuff that people build on top of Snowflake will not look like your standard packaged SaaS app. It will have its own look and feel. We can talk about that. But the other thing that it's done is it has also squished the distance between a data platform and actually what used to be called services because you can begin to automate a lot of things.

And in as much as software, represents like what we, humanity call intelligence. That's what they do. They put workflows. They put data structures into place. They organize our thinking. AI accelerates that massively, which is why a number of folks because they can now bring the power of coding agents are basically saying, wait, I can compress the time of something like a migration massively and also feel very confident that the weird problems that will come up during any real migration, the little odds and ends can also be fixed equally quickly. And they're sensing an opportunity because the majority of the industry is still operating on time and material, a lot of time and a lot of materials and a lot of money.

And so the progressive system integrators are going, I can guarantee outcomes. This is what we do as well, where we are saying we can deliver outcomes for our customers, simply because the ability to get things done fast is much better now than before, but also the ability to deal with unknown things is just also a whole lot better. It's the combination of these that I think will drive a massive change through the services industry. Not that I think services will go away. It's just going to look dramatically different, a lot smaller than what it did before, but one that is much more tied to what are the outcomes that customers want.

Let me ask about CoCo specifically because Brian, then we can bring you into the conversation as it pertains to how you think about guidance. So Sridhar, we started talking about, look, it's not just the number of customers in the installed base that are using CoCo, but it's actually the depth of usage and the net new use cases that you're also solving for. So I guess part one would be tell us a little bit about how you as an executive team push to deepen and strengthen the usage of CoCo within any given customer.

Yes. So a lot of it is what we have learned ourselves. I think I've talked about this previously, part of a huge unlock for Snowflake, the company was having a coding agent available to every single person within the company. It was not a specialized tool. And so we saw bursts of creativity and innovation in every department. And that's been very helpful for us just to understand what is possible with AI. And the nice thing about harnesses in general, and I think the reason they're going to have a profound impact on everyone, including all of you, is the work that you do now is visible, observable within one system, which also means that it is optimizable, it is automatable.

And so we have a lot of insight into what is a customer doing? What are we doing with CoCo? Are we doing repeat things? For example, we'll now make recommendations for here's a skill you should be building because you seem to be doing this very often. And it's a quick hop from there to here are a set of skills that your colleagues are using. This is something you can use to make yourself more effective at work. And so we can understand things like the depth of usage and then tie it back to things that we can do. At the end of the day, life is about what's an action that you can take that can produce an outcome that you want. And so we have things like hands-on labs that have proven to be highly, highly effective.

This is basically a 2- to 3-hour tutorial run by one of our more technical people with the customer. And because of that, the customer gets more effective in what they do. Their data teams are happier. They get more work done, simpler to debug annoying problems that are a part and parcel of their life. But it also gives us a clear road map for this is what it takes to drive truly deep adoption with each of the customers that matters to us.

I'll just add on to that. One of the things that CoCo has done is it's opened up the aperture to who we sell to from a persona perspective. And so a year ago, when I joined Snowflake, I hardly had any customer conversations. Today, every week, I'm meeting with 3 to 5 CFOs and talking to them about what we're doing internally on CoCo and what the art of the possible is. And I think there's no better way to actually expand CoCo or CoWork adoption by showing people how you're using it internally.

And as Sridhar said, these skills that we're making can be applied to vast sets of data that our customers to actually get them started to use that. And so I think once you show them what you do internally, the art of the possible and how quickly you can speed up things, they're extremely interested.

And Brian, Sridhar used the word burst there. So look, it's a new product cycle. And you're going to have customers that are experimenting. You're going to have questions around gross retention durability. I think you've already said gross retention has held stable even as CoCo has scaled. My question for you is as an analyst, we try to model CoCo. And we also try to model the impact that migrations and the speed of migrations is having on your business.

What advice would you give us as we try to think about some of the blue sky scenarios over the next 18 months? And how do you derisk the forecast from customers getting really excited about CoCo, but ultimately, it's still very competitive and you've got experimentation and could that usage pattern actually fade over time?

Absolutely. It's something that we struggle with internally as well when you launch a new product. If you ask anybody how to model that, it's -- we don't have that much data. It's difficult. Fortunately, we have an amazing team internally that has been doing this for a very long period of time and have built very sophisticated models to understand what new product adoption will look like and then compares that to historically what new products have done. We'll then take that and then basically model up what a scenario is. And then we have a very wide and deep group that discuss what we'll put in guidance from that perspective.

And so when we do come up with guidance for the core platform like the migrations, it's based on observed behavior. And we have years and years of data with that and can get pretty close. For the new products, we try to be a bit conservative. So we don't take a month's worth of data and extract that out and say this is what it's going to be like all for this year and next year. But now that we've had 2 quarters of data, we feel more confident in what we can extract from that observed behavior.

When you and I first met, we started talking about the customer cohorts and how -- look, it sometimes takes year 1 is the initial ramp and then year 2 is really when a customer gets into full swing Snowflake adoption. Has anything changed as you look at the speed at which these customers are ramping?

I think from a cohort perspective, if you look at all the verticals across the company, we still have the same sort of vertical penetration, if you will, in financial services, manufacturing, government and so forth. As far as ramping with the use of CoCo and AI, we're seeing customers ramp much quicker than what they've ramped historically. And so whether it's our partner network or what we're doing internally, what Sridhar talked about is outcome-based pricing has really built the credibility with our customers. If someone comes to me and says, I can guarantee you x for this set price. And I know historically, that took a lot of time and a lot of materials and so forth, like I'm all in, and we're seeing that from a customer perspective.

We do track internally how long it takes for them to get up to their consumption run rate, and we're seeing those curves get steeper and steeper and steeper. And so customers are deploying quicker. They're consuming quicker. They're using partners, us and themselves are using our agents to actually do that. And so it's really exciting to see.

And that's a lot of where our go-to-market teams have to go. Our CRO often talks about shifting right towards outcomes, which just means that Snowflake as a company and their team, in particular, has to focus a lot more on how do we get use cases live with customers? How do we get it to scale within customers? And what do we have to do and it's increasingly a result of many things that used to occupy lots of time, getting ready for a meeting, doing research about what does the customer have, what's their data estate, how do you maintain it? All of that getting easier, faster.

And similarly, for solution engineers, they spend a lot of time building a little demo that would take forever. Now they can be conjured up kind of on demand. And so there's this big shift. We have even created basically new job functions. One of them is called an activation engineer, an activation solution engineer that are expressly charged with what Brian said, which is how do you get a new logo to go live on Snowflake much faster than what they would otherwise. And so that is a trend that we want to keep leaning into and pushing.

I would also say selling into or actually describing what we do to all these personas. If you go to a CFO and show them what the possible is, they're immediately saying, how do I get that up and running like yesterday. When Sridhar goes and talks to a number of CEOs, they want it yesterday. And so the sense of urgency around getting these results are also super fun.

Let's talk a little bit about custom apps. So Sridhar, you started talking about custom CDP. And certainly, there's been a debate on the application level on what is the future of applications? How do we think about packaged apps versus headless architectures versus some of the interesting things that customers are building on top of Snowflake. Some of the things that you announced at Summit, workflow orchestration, agents and on the application layer as well, tell us about how you see the app layer evolving.

I mean, first of all, I think this is a time of just a lot of innovation with what is possible because let's face it, building any kind of meaningful application before, again, was just a hard thing to do. Now pretty much any of us can pick up a coding agent and say, "Hey, I want not just a web app like an Android or an iOS app." And dealing with the app stores is the most time-consuming part of doing something like that now. The apps themselves are easy. So we are playing around with lots of different formulations, including things like should the notion of an application be rethought as a handful of self-evolving skills running on top of a data substrate. What I mean -- let me give you a specific example.

Let's say like, oh, you want an internal survey application, easy enough to imagine. What do you want to do in a survey application, especially if it's just internal? You want to know who your employees are. You want to know like how you target, let's say, a particular group. You want to manage visibility into the results that are coming. It's not that complicated. Now if you have a Snowflake deployment like we do, absolutely, our Workday hierarchy is mirrored in Snowflake. So that's where you get that from. You set up a couple of tables to store surveys, to store results and figure out notifications, and admin does some things and then an ops person says, these people are allowed to send out surveys and off people go to send out actual surveys.

Now I didn't really talk about UI. You can conjure that up on demand by saying, "Oh, if somebody clicks on this link, bring up this React app for them to respond to the survey." And so what would be an actual SaaS procurement is a handful of skills that can be installed on top of data that is already sitting in Snowflake. It's governed. You don't have to worry about, hey, does everyone have access to this data? You can manage the visibility. You can also do follow-up analysis on it. If it's freeform text and you want to run AI on it, that's not an issue. So you see where this is going. I think it just makes many, many more things possible.

This is not to say that this is the end-all be-all solution for everything. But to the extent that our vision consistently for multiple years has been we want to be the place where we can bring together all of your data and get a 360 view, analytic view of the data, it sets us up very nicely to rethink what is an application of the future. For what it's worth, we sync our Salesforce data also into Snowflake. And so if I want to create an annotation application that's not part of Salesforce, but acts partly on Salesforce data and can push it back, that too, is possible. It's a very different way of thinking about what's -- the first time I tell people an application can be reduced to a handful of skills, you get a blank stare like really, what does that mean?

And similarly, these skills don't have to be static. As you look at how people are using them, they can get additional functionality over time. They can self-evolve. That's how a lot of support systems are like my team's SRE systems are evolving. They built the first version. They built some skills, put some data. They looked at how they were analyzing it. Then they created newer skills. And they said, oh, half of these things can be automated by agents looking at stuff first as opposed to having a human look at it. And now that's a very different notion of what an application is. It is something that is evolving as it goes along.

So I think the world is rich with possibility, and there's just going to be a lot of innovation everywhere. And as a platform, we focus on what are like these little nuggets that we can lay out there that is going to convince some right person in one of our customers to say, here's something else that I can build with it. We learn from them and then figure out how to make it more broadly available to other customers. You see this feedback loop and where it's going.

Let me ask you on the skills side. So this is a little bit of an orthogonal question, but it's so top of mind in the last 3 months. Do you have a view on how the ecosystem will evolve between open source, open weight, and frontier? And does that impact how you think about your business strategy and dynamics like skills?

I mean for a company like Snowflake, the more competition between our suppliers, the better. I mean, let's say, it's the same for you. If like Anthropic is the only one making great models, you're in trouble, I'm in trouble. We're all in trouble. And the fact that OpenAI is creating amazing models is good for the world. It's good for them. It's good for us as well. I look at open source the same way. I think innovation here presses the foundation labs to innovate even more. And I think as a phenomenon, this is great for us. It also feeds really well into the -- like the Snowflake narrative of we are truly a cross-platform solution.

We are one of the few people that can tell you, you can run on AWS and you can effectively run exactly the same deployment on Azure with not a whole lot of work. And if you want to do disaster recovery between these instances because some regulators on top of you saying, you can't go down if AWS goes down, that too is possible with Snowflake. We look at models the same way. It offers up lots of options for choice for optimization. And the thing that I think is unique about this moment, and it's not a good or bad. It's just a strange happenstance of the moment is that none of the model makers so far other than something like a ChatGPT, which does have true consumer lock-in, have been able to create that kind of lock-in well into a pretty massive investment cycle.

Every smart engineer knows that they can move instantly from a cloud code to a codex, not a problem or vice versa. My team moved over from being heavy cursor users to CoCo users on the solution engineering side without me having to harangue them, which is normally how like things work with situations like this. I think that's also pretty unique, which just means that something that can interoperate between these models is a pretty cool thing to have.

And I think skills themselves are the great equalizer because they're English. It means that every model is immensely capable of taking a skill that perhaps was written for a different harness for a different model and figuring out how to tweak it to work in a new situation. I think this all makes up for a robust choice for all of us.

I think this next question is for both of you all. So strategic value of selling inference pass-through into your installed base versus potentially a lower gross margin. How do you think about that trade-off?

I mean, first of all, it depends -- I hate to start like this, but it depends on what inference is. If it is merely reselling undifferentiated capacity from a large supplier, you're not creating any value. That's like fake news on the part of people that are doing this trying to pretend that they have a business. On the other hand, if you say, I have a gateway that actually can do a meaningful job of helping my customers optimize spend. In other words, I am creating value on top of my suppliers, and it has some amount of stickiness and redeeming value, that's a meaningful new category.

And so we look at inference, for example, in the context of can I offer my customers choice because we buy capacity in -- both from OpenAI and from Anthropic, and we have the capacity to do things like run open weight models ourselves. In that context, inference becomes a little more interesting. We also generally take the lens of -- it's important for us to understand what our strengths are. Our strength is as a data platform and inference as a component of a modern data platform absolutely makes sense to us. And we also like to sell at the highest value-creating point. In other words, my order of preference is always, if I can convince a customer to use CoCo or CoWork directly, that's what I want them to be using.

If they say, no, all I want is model capacity from you, I'm going to run my own harness, yes, we will do it. If they say, I want neither of those, I just want the data platform and you can be a back end to cloud, we will do that as well, somewhat more reluctantly. It's important that you have your Maslow's hierarchy of where you're creating value and acting according to that. And Brian and I are super aligned on what are the business outcomes that we want to drive. Let's say, if CoCo adoption were to go up massively, and it has an impact on our gross margins. I'm happy to come and explain that to you all day long. That's not an issue because it will drive a meaningful acceleration in our overall business.

And I can also tell you, as we grow in scale, as we do these things, we get better at optimizing. We get better at running open source models, which will have much better margins. We obviously buy a bigger quantity from the suppliers like we do with AWS and the $6 billion contract, which gives us better economics. There are good answers to things like gross margin, but it needs to make strategic sense. What I have little appetite for is being a blind reseller of someone else's intelligence.

100%, I think the analogy is what we do with the hyperscalers where we put our software on top of them and basically deliver a value-add service that has good ROI for our customers. And that's really key. From a gross margin perspective, we have a lot of control over that. The hyperscalers is another -- we announced, I believe it was last quarter, a big deal with AWS. We constantly work with our hyperscalers to do stuff better, faster, cheaper, and we'll continue to do that as it relates to inference. When we launch new products, the #1 thing we want to do is make incredible products that people will adopt, get value out of and will drive revenue.

And then we have demonstrated that we can actually show leverage in that once we get economies of scale and a number of customers on that. And so that's on the gross margin side. Regardless of that, we're very committed to getting operating leverage in the model overall, and that's what we guided to for the year. And so the framework that we do our gross margin and we have very accurate models internally is based on the uplift that we've seen in our AI products, which is phenomenal, and we like that, and that's what we guide to for the rest of the year, but we're confident that we can actually continue to get leverage in the overall model and do things around gross margin.

I think this is an interesting thread to pull on just for 2 more minutes here. So let's fast forward and imagine that this time this year, CoCo, we think, is a home run. And Brian is coming on the earnings call and gross margin is not where the Street modeled because CoCo was fantastic. Talk to us a little bit about the guardrail on CoCo gross margin, and Sridhar, you sort of alluded to it there. You can explain this holistically as part of a much larger value proposition.

Well, I would say first is we are a consumption business. And so it does take time to ramp. We also know, although we're bringing that down, and we also build models to understand what this consumption is going to be. And so it's really Sridhar and I don't want to surprise folks. And so if we were seeing that massive CoCo adoption beyond what we're already seeing, we would have the ability to communicate that to you within a quarter or 2 to manage that.

I want to ask a technical question on agents. So Sridhar, there is a school of thought that the systems and architectures of today are not going to scale for real-time agents because agents have so much more volume, for lack of a better word. Talk to us a little bit about how you would address that concern if investors say, well, Snowflake was founded x number of years ago and is designed to scale for humans, not for agent queries.

There's a little bit of a meaning and attempted category creation by the folks that say things like this, but 100%. There is richer data that comes from agents and things like trajectory analysis for all kinds of purposes. The good part is how do you make your team more efficient? The bad part is, is there someone in the company that's actually doing research on bioweapons like as a CEO, you really want to stop that very quickly. And so there's the good and bad aspects of just needing to make sure that you do better with that. But in all of this, the overall criticism that we have not addressed super low latency data well is very legit. I'm a big fan of like laying it bluntly to my teams and accepting things when we need to do better.

I said this to you folks, I think it was 2 years ago at what we had done in machine learning and notebooks. It was not up to par. Fast forward to now, you're not going to hear that from any of our customers. Not only is the offering really good, we have also, thanks to technology like CoCo, massively accelerated the process of migrating, let's say, from whatever set of notebooks that you have on to Snowflake or being able to create not one, but dozens of machine learning models as part of an experiment that you are running. Making sure that we deal with data that, say, has 500 milliseconds or less of freshness requirement is not something that we do really well right now. There's a team that's actively at work on this. It basically comes down to things like what are the trade-offs that you want to make in terms of cost and efficiency and also just like the querying speed that you want.

You've been putting into place things like interactive tables for much lower latency serving. And our streaming solution has brought things like the freshness down to the 2 to 3-second range. And there's active work underway to bring that further down to the 500-odd milliseconds at which point it stops being an issue. It's an opportunity. It's a threat. We are well aware. We absolutely are working on it.

I want to ask the switching cost question both ways, which is we've talked already about speed of migrations accelerating to Snowflake. We've also talked in the last year about things like standardization of data tables and data graphs becoming less grave, for lack of a better word. How do you think about the longer-term implications from switching costs potentially going down in the data infrastructure world?

I mean, just bluntly, I tell my teams, I'll admit this to you. If migration into Snowflake can be made a whole lot faster, migration out of Snowflake can be made a whole lot faster. That's the world we live in. And it applies to data platforms. It applies potentially to consumer software. It applies everywhere. And that is something that we all have to understand. And so you have to -- and then there are also broader industry trends like a lot of CDOs and CIOs simply saying, I don't want my data to be held hostage by anyone. You don't crush them. You can't crush them for saying that. So we support open format.

We want to increasingly make it painless for our customers to deal with open format. So we have now something called Snowflake managed Iceberg tables, which is a fancy way of saying you kind of have your cake and eat it too, which is you can store data with Snowflake, but have it be queryable in Iceberg format by any other engine. And the place where we create value has to be further upstream. It has to be in do we provide better governance? Do we provide better disaster recovery? Is it easier to create and run agents on top of Snowflake? Do we provide a better observability solution. And so there's this whole stack of things on top of the data platform that is open that we have to be providing. And to a large extent, for a lot of these, my attitude is bring it on. It's not that easy to create the platform that Snowflake is.

If it were, the hyperscalers would have eaten our lunch like 10 years ago, it is hard to do. And I think AI actually accelerates what's possible with Snowflake. But I think the Snowflake of old, which used to hang on to a set of what it thought were inviolable that could never change. I think that company has also changed. This is a company that's much more attuned to where is the world of data going? Where do we create value? What is strategic value that we could be creating in a way that's still faithful to what our customers want. We feel good about how we are positioned. Absolutely, what can migrate in, can migrate out. You need to both be paranoid about that, but also seize the opportunity while you can.

We really, really drive internally this customer-first obsession. And so people can come in and leave easily. But if you have a customer-first obsession and really focus on the business cases, outcomes and ROI, one of the top 10 skills that we have is around cost optimization. And so we want people to be fully optimized. We want people to use the product.

We want them to get value out of it. And so we're constantly going back. If we see an anomaly with a customer, we'll actually go alert them and say, "Hey, your bill is running higher than it's been before. Were these jobs that you meant to kick off or not." And so really being obsessed with the customer, I think, really is a key priority for us.

Let's stay on the pricing implication here. So I think about architectural enhancements in Snowflake like the Gen 2 instances, for instance. You've got this natural pricing deflation in your business like any good technology business. And yet your role is to also abstract value and price one level above the core components of that so you can capture gross margin. Maybe, Brian, just tell us a little bit about how that philosophy is coming into play in 2026 with things like Gen 2.

Yes. I mean part of the business is you got to be better from a price performance perspective than the last generation was. And you always have to give your customers the ability to get more out of your product. And so we price that in, but we're seeing the offset of that with volume and new jobs coming into the company. And so we carefully -- as we go and price stuff, we carefully take that into consideration. So there's not big step downs in revenue. But we also want to get our customers to get the benefit and the value of these performance enhancements that we're doing on the platform.

I'll perhaps add on a quote from one of my previous bosses and mentors, revenue solves all known problems.

Fantastic. Let's leave it there. Please join me in thanking Sridhar and Brian for their time. Thank you.

Good day, and welcome to the Second Quarter FY '27 Snowflake Earnings Presentation. Today's conference is being recorded.

At this time, I would like to turn the conference over to Katherine McCracken. Please go ahead.

Good afternoon, and thank you for joining us on Snowflake's Second 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'll review our financial results for the second quarter fiscal 2027 and discuss our guidance for the third 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. We are in the midst of a once-in-a-lifetime technology shift and Snowflake remains at the center of the enterprise AI revolution. AI is fundamentally changing how enterprises build, operate, and make decisions. To stay competitive, every organization faces a new imperative, become an Agentic Enterprise and do it quickly, safely and cost efficiently.

Snowflake is making this transformation a reality. We bring together the core elements of an Agentic Enterprise, a governed data foundation, access to leading AI models, deep application workflows and the unifying agentic control plane that orchestrates across these elements to turn intent into governed action. By putting intelligence work at scale, our customers are building faster, executing more efficiently and reimagining their businesses in ways that weren't possible before. Put simply, the Agentic Enterprise runs on Snowflake. And the traction is translating into strong business performance as evidenced by our Q2 results.

Product revenue came in at $1.49 billion, with growth accelerating to 37% year-over-year, marking our second consecutive quarter of record sequential dollar growth. After exiting Q4 of last fiscal year, at 30% year-over-year growth, we have now added 7 points of acceleration in just 2 quarters. And with our continued focus on executing the discipline and operational rigor, our Q2 non-GAAP operating margin expanded by more than 400 basis points year-over-year to 15%. Thank you to all of our Snowflakes for the hard work and dedication that made this performance possible.

As these results convincingly demonstrate, AI is compounding Snowflakes advantage across 3 reinforcing dynamics. First, AI is bringing new workloads onto the platform. To power their AI initiatives, enterprises need a governed unified foundation for data in context and companies across industries are turning to Snowflake to power that foundation. Second, our first-party AI products, CoCo and CoWork continue to see rapid adoption. As customers build and deploy agents on Snowflake, we are expanding our role into the agentic control plane and creating new opportunities for growth.

Third, AI activation continues to lift overall platform consumption. Customers using AI on Snowflake consume more across the data platform, creating a structural multiplier for our business. Together, these dynamics show how the Agentic Enterprise has created a powerful flywheel across our business. And that flywheel is accelerating.

At the heart of this moment is the continued strength of our core business. Snowflake now provides the data and AI foundation for 14,554 customers around the world. Customers continue to turn to Snowflake because our AI data cloud is easy to use, seamlessly connected for collaboration and trusted with enterprise-grade governance and security. This quarter, we added 692 net new customers, including 14 from the Global 2000, representing a 32% increase in net new customer additions year-over-year.

At the same time, some of the world's most recognizable enterprises are deepening their relationships with Snowflake. Companies like BlackRock and Block are running more of their mission-critical work on Snowflake and in several cases, adopting CoCo to move faster. The pattern is consistent. The more our customers build on Snowflake, the more they lean in. In fact, 65 customers have now crossed $10 million in trailing 12-month product revenue, demonstrating how our largest customers continue to go all in on Snowflake.

Part of our strength is in extending our customers reach to the critical data that sits outside of their organization. Currently, 43% of our customers share data on Snowflake with at least one stable edge, demonstrating Snowflake's role as the circulatory system of the modern enterprise. We enable data, applications and AI agents to move securely and seamlessly not just within, but across organizations. In fact, credit chose snowflake for our data sharing capabilities, which now facilitate privacy-safe ads measurement.

And as customers move quickly to modernize their data estates and establish a strong contact player for AI, more and more customers are migrating workloads to our platform, a process now massively accelerated with AI. For example, one of the largest Australian banks migrated its financial crime platform to Snowflake, processing 17 billion transactions and delivering 10x faster credit performance. Now they're building AI agents on Snowflake to accelerate the migration of the rest of their data estate and automate legacy data discovery and mapping.

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. Because Snowflake sits at the center of our customers' data, business context, AI models and workflows, we are uniquely positioned to become the governed control plane for the Agentic Enterprise. Our breakout AI products, CoWork and CoCo bring that vision to life. They provide a government layer users across the business from knowledge workers to builders can put the full power of their enterprise context to work, all with simple conversational language. With CoWork and CoCo, customers are reimagining some of their most critical business processes from supply chain operations to enterprise-wide sales motion.

[ Saari ], whose risk intelligence supports Fortune 100 enterprises and national security agencies chose Snowflake to rebuild its global data infrastructure and cut costs by more than half. Its engineers are now using CoCo to accelerate the migration of 12 billion records into an AI-ready foundation. And as more customers see what's possible that this technology, adoption continues to build. CoWork expanded to 5,800 accounts, up nearly 11% quarter-over-quarter. Meanwhile, CoCo continues to see rapid adoption surpassing 9,100 accounts and adding more than 2,000 net new accounts in this quarter alone.

We have customers like 1Password, the security company trusted by more than 200,000 business, which choose Snowflake for our CoCo capabilities. CoCo enables their team to move key data pipelines into Snowflake quickly, playing foundation for their data and AI work. And the world's #1 job site, Indeed, has rolled out CoWork and CoCo across its data teams and integrated Snowflake into core data architecture, citing lower cost and greater efficiency, which compounds at the scale that they operate in over 60 countries and 28 languages.

But the opportunity goes beyond adoption. By making it possible to build, collaborate and interact with enterprise data through conversational language, CoWork and CoCo are bringing in entirely new users to Snowflake. Within accounts adopting these products, we see a step change in user growth as Snowflake reaches new lines of business and expands its footprint within existing teams. As we continue to develop CoWork and CoCo as agentic control plans, we are also building the broader platform enterprises need to put AI to work at scale. Model choice gives customers the flexibility to select from leading frontier and open models and evolve their approach as the market changes.

Post training lets them adopt models to their specific data and business context and agent observability analytics give customers full visibility into what their AI is doing, how it's performing and what it costs. And to help our customers optimize cost, performance and speed, we've introduced Cortex AI Gateway, which dynamically route each task to model based on customer defined policies and real-world performance data with cost and governance controls built in. As those economics improve, customers can deploy AI more broadly and with greater confidence, creating another catalyst for adoption and consumption on Snowflake.

Cortex AI Gateway also extends AI from insight to action through its integration of Natoma. Users can now send e-mails, summarize slot conversations, open [ Gira ] tickets and act across their business, all without leaving CoWork or CoCo. We've also continued to advance how our agents understand the unique context of a business. At Snowflake Summit, we introduced Cortex Sense, which captures the business definition and institutional knowledge and AI agent needs and provides that context at the moment it answers the question. This means Snowflake is giving AI both the context to understand the business and the ability to act on its behalf with enterprise security, governance and observability built-in.

As we drive this AI transformation for our customers, we are leading from the front using CoCo and CoWork throughout our own business to accelerate productivity and efficiency. For example, in our marketing organization, CoCo has helped bring search optimization in-house, eliminating $400,000 in annual agency spend, reducing keyword research from approximately 10 hours to 20 minutes and content production from an estimated 24 hours down to just 2. In finance, our long-range planning used to require a 3-person team and more than 50 spreadsheets. It now runs with 1 analyst and a series of models that reflect our pricing structure and consumption dynamics.

Within our sales teams, we have automated prospecting for over 125,000 contacts and leads, with 70% of initial outreach e-mails for inbound leads now being generated automatically before SDR involvement. We are bringing these proven use cases directly to market, while applying our operational learnings to continuously upgrade our platform, moving with speed to capture the AI opportunity in front of us. In the first half of this year alone, we launched over 330 product capabilities to general availability, 35% more than we did in the first half of last year, underscoring both the pace of our innovation and the breadth of platform expansion underway across Snowflake.

Our go-to-market organization also continues to execute as reflected in strong new customer growth. We have deployed CoCo and CoWork across the sales team to analyze pipelines, prepare for customer conversations and accelerate the onboarding of new reps. Our teams are using these products every day, learning firsthand what they can do and taking those insights directly to our customers. We're seeing the results in how quickly customers are putting Snowflake to work. The number of use cases, individual customer projects deployed on Snowflake increased 89% year-over-year as customers move more workloads into production. At the same time, use cases on per account executive increased 43% year-over-year, demonstrating both growing customer demand and strong sales productivity.

And we are pairing this investment in growth with continued operational discipline. We remain on track for GAAP profitability in Q4 fiscal '28 and the operating leverage we built along the way strengthens the durability of statute. Taken together, our rapid pace of innovation, fiber go-to-market execution and operational discipline positions us well to capture the huge opportunity ahead. This quarter demonstrated that the transition to the Agentic Enterprise is accelerating and Snowflake is at the center of it. AI agents are only as powerful as the data and business context there reason from under government's soundings. Snowflake provides that trusted foundation while bringing together model choice and flexibility, access to critical applications on the agentic-controlled plane that connects intelligence to action across the enterprise.

CoWork and CoCo demonstrate what governed architecture makes possible, enabling business users and builders to work with greater speed and intelligence while Snowflake manages the complexity underneath. And importantly, our customers' success with AI translates directly into growth for Snowflake. AI brings new workloads to the platform, extending our reach to new users and drive greater consumption across the business. We're into the second half of fiscal '27 with strong product momentum and we see a long runway for durable high-growth and continued margin extension. The Agentic Enterprise runs on Snowflake, and we're just getting started.

With that, I'll pass it to Brian to go through the financial details.

Thank you, Sridhar. In Q2, product revenue once again accelerated to reach 37% year-over-year growth. This marks our third straight quarter of acceleration. Q2 benefited from continued strength in our core data platform business and a meaningful step-up in AI revenue.

Our AI revenue reflects a broadening portfolio of AI capabilities. CoCo delivered another standout quarter. Consumption of CoWork is scaling and driving revenue contribution alongside a diverse set of AI tools from AI functions and document assessing to machine learning and notebooks. Our go-to-market teams continue to execute well against a strong demand environment. As Sridhar mentioned, net new customer additions increased 32% year-over-year. We added 14 net new Global 2000 customers, bringing a total to 829. Our AI data cloud now supports over 41% of the Global 2000.

Within our existing base, customer expansion is healthy, as evidenced by our net revenue retention rate of 126%. This expansion is underpinned by growth in both migrations and AI use cases. In Q2, 48 net new customers surpassed $1 million in trailing 12-month spend. We now have 828 customers spending above the $1 million threshold. Remaining performance obligations grew 30% year-over-year, totaling $9 billion. As a reminder, we continue to see customers favor Q4 renewals. As a result, we expect bookings to be increasingly weighted towards the fourth quarter. Of the $9 billion RPO, we expect approximately 54% to be recognized revenue in the next 12 months. This represents an approximately 42% year-over-year growth compared to our estimate in the same quarter last year.

Our Q2 results reinforce our commitment to delivering both growth and margin expansion. In Q2, non-GAAP operating margin expanded over 400 basis points year-over-year to reach 15%. Our outperformance was driven by strong revenue growth and disciplined head count management. Year-to-date, we've added 334 employees, which includes 173 from our Observe acquisition. This compares to 935 added in the year ago period. We ended the quarter of $4.3 billion in cash, cash equivalents, short-term and long-term investments.

Moving to our outlook. As always, our forecast is based on observed consumption patterns. There are no changes to our forecast methodology or our guidance philosophy. Given the strength we've observed both in our core data platform business and AI business, we are raising our product revenue guidance for the year. For FY '27, we now expect product revenue of $6.07 billion, representing 36% year-over-year growth. This includes approximately 1 percentage point of growth from Observe, consistent with our previous outlook. In Q3, we expect product revenue between $1.588 billion and $1.593 billion, representing 37% to 38% year-over-year growth.

Turning to margins. For FY '27, we now expect 74% non-GAAP product gross margin. This revised outlook includes a higher revenue mix from fast-growing AI workloads, which carry a lower contribution margin today. We're delivering continued operating margin expansion as we offset growing cloud costs with slowing headcount expense. We are increasing our FY '27 non-GAAP operating margin guidance from 13.5% to 14.5%. For Q3, we expect non-GAAP operating margin of 15.5%. We're reiterating our full year non-GAAP adjusted free cash flow margin guide of 23%.

I'd like to close with my 2 key goals for the year: first, help the business to deliver growth and margin expansion; second, support ongoing excellence in our go-to-market motion. AI is fundamental to our progress against both goals. As we help our customers modernize their data and business operations, AI is becoming a powerful growth driver. Internally, AI is locking greater productivity. Across the organization, from sales to engineering to finance, our use of AI is transforming our daily work. AI is driving greater efficiency and reducing our reliance on head count growth. Our progress against both priorities is evident in the strength of our Q2 results.

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

[Operator Instructions] We will take our first question from Sanjit Singh with Morgan Stanley.

Congrats on the second quarter of a pretty material acceleration. The spirit of my question is around the quality of the acceleration that you're seeing and just sort of as a backdrop around when the time the company went public, growth was being driven by a lot of investment in cloud, cloud native companies that may have been unprofitable. And so I wanted to ask a question on the quality of the acceleration on bit of 2 levels.

First, on the right to win, in the script, you guys mentioned supply chain use cases and finance use cases. The question here is why is CoCo along with the platform, the right mousetrap for these use cases that kind of extend beyond classic kind of business analytics use cases? And then on sort of the durability of the growth, like, are you seeing any sort of irrational behavior or poor operational hygiene when it comes to consuming both CoCo and CoWork? So really sort of a question on the quality of the acceleration you're seeing.

This is Sridhar. Let me take a first cut at this, other folks can add on since it's a pretty broad question. First, I think we see the acceleration come from a very broad swath of customers. It is not concentrated, for example, with, let's say, AI native companies. They continue to be a small and a small part of our overall revenue stream. And I think the thing that's also materially different this time around with folks that are investing is that products like CoCo make optimization far, far easier than before. You can point CoCo at a credit that's taking too long to run or you can basically have it debug the top 10 longest running queries are the most idle warehouses, things like that are a lot easier to do.

And in fact, our cost management, our cost management skill in CoCo is a top 10 skill. And it's also the case that as a company, we have learned the lessons of the pandemic and things that we stress with each and every 1 of our customers is the need to drive spend in an efficient way. And this is also a mantra that our sales team itself adopts pretty aggressively because they know that every such case where they go to a customer and point out things that they could be doing better is a trust-building exercise that is going to more than pay for itself in new projects that customers will implement on Snowflake.

So overall, I'm pretty happy with both the fact that our growth is coming from a very broad swath of our customers without a whole lot of concentration in any one particular sector. And also about the fact that the very tools that make it possible to do things quickly also come with a set of functions that make it pretty easy to optimize. And the final point, as I said, others will add on to it. The final point about our right to win for the kind of business use cases that perhaps we previously were not there in the conversation for, AI has massively shrunk the distance between data and value. I'm sure all of you have it in your day-to-day life. But certainly, I, as the CEO can get a whole lot of value out of data a lot faster because of tools like CoCo and CoWork.

And the agentic harness is need a very powerful weapon for solving many different kinds of problems. And it is our ability to take these powerful tools and drive our own transformation, whether it is in making SDRs more efficient or in making account planning work much more effectively at scale or in letting our sales leaders inspect and run their businesses a lot more effectively or our finance team under Brian to be a lot more effective with what they do. We are able to go to our customers and not just reach, but also demonstrate what we have shown for ourselves internally. That just gives us a lot of credibility going into these conversations about transformation.

I'll add just a little onto what Sridhar said. From a durability perspective, we give our guidance based Observe behavior. So we've seen couple of quarters of this behavior. Our sales team is doing a great job with proving the business value of the use cases, and we're continuing to see great new logo additions. CoCo , when we look at CoCo, the accounts that are using CoCo are consuming more of the core as well. And so there's a flywheel effect that we talk about. We had 100 CoCo accounts this quarter. That's up significantly from last quarter. And the gross retention rate has been relatively flat across the last several quarters.

And then just want to emphasize what Sridhar has said as well is, we're actually selling into way more personas today. So in a given week, I have 3 to 5 conversations with CFOs of existing customers of ours or customers that want to be. And so the CFOs are now making the purchase decision, the CRO, CMO, CEOs. And so there's a lot more percentage that we're selling into this broader portfolio of products.

And we will go to next question from Stewart Materne with Evercore ISI.

Congrats on a great start to the year. I was wondering if you guys could try to separate out a little bit or give us a little bit of color on how we should think about what portion of the acceleration is coming from these newer products that are obviously getting really rapid adoption versus sort of the flywheel of those new products on the core? I assume just given the size of the core, it's the core growing faster is probably the bigger factor, but I was wondering if there's any way for us to sort of distill down what these new products are having maybe on their own account.

I would roughly call it even. Our AI products, this is a pretty broad swath at this point. Absolutely, it's CoCo and CoWork. But it's also things like AI functions that make data operations proceed at an impressive scale or even newer products like the AI Gateway, they contributed approximately half of the acceleration that we are seeing.

But there are a lot of other products that are also demonstrating robust growth, and Brian touched on some of them. Whether it's Notebooks or applications written in Streamlit or React that are deployed into Snowflake. And of course, migrations themselves going faster. I have talked pretty much in every single earnings call over the past 6 quarters about migrations. And that is an area where we continue to get faster and faster. And some of the recent advances, both in models and harnesses are letting us run long-duration tasks of a scale and complexity that we haven't been able to do before.

And the rate at which workloads are coming on to Snowflake is also an important factor. And one anecdotal example, that a big network manufacturer is doing a Teradata migration in less than 3 quarters this year. And this is something that would have taken probably 2 to 3 years in any previous time. So these are some of the things that are contributing to our acceleration and [indiscernible]

And we will take our next question from Karl Keirstead with UBS.

Okay. Great. Maybe I'll direct this to Sridhar and Christian. I'd love to ask about model neutrality and model choice. I'm guessing the bulk of tasks completed by CoCo are being directed to frontier labs. But I'm just curious, during the quarter, did you detect any interesting behavioral shift, let's say, a mix shift from open class models to sonet class models? And if that happens, Brian, is there any effect potentially positive on gross margins to Snowflake's financials? And Sridhar, is being model neutral, is that becoming a competitive advantage in cases where Snowflake competes directly with the prospect of a customer using one of the frontier labs stand-alone?

I'll start. Christian will add on. As models have gotten more powerful, cost has absolutely become a concern. And all of you know this, at least as far as the frontier labs go, there used to be somewhat of a dichotomy where Anthropic was available extensively on AWS, while the OpenAI models tended to be more on Azure. The material change that's happened is that both the companies are deploying substantial capacity of their own, but it's also the case that they are available in other clouds than the ones that they started with.

And we're absolutely seeing a lot of interest in being able to switch between different models and also to optimize cost. And this is also where open source models come in. There's obviously been several generations of these open source models, and we support many of them within Snowflake. And yes, we have pretty different economics when it comes to open source models since we run the inference ourselves. So that offers a lot of potential for future optimization.

And within our harnesses, many of the requests that we get from customers come in this mode that we call auto, where we can pair up the task with the model that is most appropriate for that particular task. And that gives us a lot of leeway in being able to optimize task for our customers.

Yes, Karl, in addition to what Sridhar said, another interesting trend that I would call it early, but we're hearing from a number of customers is the desire to post-train open models, which the training itself is an opportunity for us, and we're starting to see a lot of interest.

And to your question on whether neutrality is a competitive advantage, absolutely, it is. We have heard from many, many customers that they made large commitments to one specific model company and later on are saying, oh, I should have wanted to do a different model, whereas the commitment to Snowflake gives them that flexibility. And as Sridhar said, automatic routing into what is the right model for the right task. So definitely a very strong advantage for us.

This is a theme that clearly, Christian and early Snowflake pioneered in terms of being able to offer really great capability across the cloud service providers. To quote [ Yogibera ], it feels like the [indiscernible] all over again when it comes to model neutrality.

Karl, just real quickly, I just wanted to hit on the margin aspect to your question. Going back to -- when we develop products, the #1 thing is we want to develop a great product. That is the key thing that we want to do. Secondly, we want to make sure that we have massive adoption through use cases and driving benefit to then, in turn, drive revenue. And then we'll work on sort of the margin implication of that. Sridhar and I are very committed to driving overall operating margin leverage in the business.

And so you saw our non-GAAP product gross margin go down to 74% because we've increased our guidance so much. And so the mix between our AI products and the course change a little, but we're still committed as we guided to increasing our overall operating margin. And so as we go through and do model choice and use different models, the best thing for us right now is to give our customers the best answer with the best business outcome, and then we'll continue to work on margins as we go forward. But we're committed to driving operating leverage in the model.

And one more thing on this one, Karl, which is even the frontier models have been revising prices down on a regular basis and have been introducing additional models to our families, which have kept cost somewhat intact relative to the usage of organizations.

We will take our next question from Raimo Lenschow with Barclays.

Congrats from me as well. If I look at the organization and if I look at where revenue is coming from at the moment, you're very -- you're still relatively index towards U.S. North America. And can you talk a little bit about what you're seeing in other regions like Europe, Asia? Because it does seems there's like a big opportunity to expand the footprint there?

Yes, absolutely. I think there's -- this isn't region-specific. When set the sales QBR just a month ago and looked at sort of the performance and all regions are performing, and the outlook for our regions are factored into our guidance, but all regions are operating very well.

We will take our next question from Ryan MacWilliams with Wells Fargo.

This really seems like the AI moment for the data space. What would you say is the biggest change on why AI is accelerating Snowflake revenues now? Is it Cortex Code helping users get activated on AI faster? Has it been some of your other product improvements in conjunction with better ad models now making AI as more attractive or customers just more ready for AI? What do you think has led to this AI moment for Snowflake?

I spoke earlier about the flywheel. It's a lot of things coming together. What products like CoWork firmly demonstrated was the ability to get really flexible and quick value from data. The demo that I have unfailingly showed every CEO that I've met is the one in which I look up their company as a customer on Snowflake. It really brings a life the power of data in ways that abstract expressions never can. And there's this growing realization that AI is a massive unlock for getting the data to the right person.

And most data teams are embracing this moment because they see this as a way to get past the unending backlogs that they have had pretty much since time in memorial. That's a little bit of effect number one. And what CoCo has done for us in a super native way is it's made the entirety of Snowflake, Absolutely, our sales team AI native. They feel a lot more confident about being able to support any use case on Snowflake because the answer to most problems that a customer or you run into is to simply ask CoCo how you solve the problem. And in most cases, it can solve it by itself.

And so we see a lot of customers, a lot of partners take on migrations, get projects done that honestly, we would not even have conceived of when we originally wrote Cortex Code. That's the magic of these coding agents. And in a funny kind of way, CoCo also makes it far easier to create agents and get value from data itself. And this is the combination that makes Snowflake so attractive. And it's not just acquiring customers. We track this metric called like time to 80% of purchased consumption for new logos that we acquired. And it's -- and we measure it cohort by cohort.

Basically, of the customers that you acquired, let's say, in January, what fraction of them are consuming more than 80% of the purchased capacity, call it, 3 months after their purchase month. And this metric has very, very visibly improved for the newest cohorts of customers that we are acquiring. That's the part of AI. It's faster to get projects done. It's faster to get value from data. And that's the flywheel that we think is really driving the acceleration in our overall business. And as models continue to get smarter as our ability to run more long-duration things, agents in the cloud continue to mature. We expect this flywheel to accelerate even more.

We will take our next question from Matt Hedberg with RBC Capital Markets.

Congrats from me as well. I wanted to piggyback on the CoCo, CoWork line of questioning. It just seems increasingly that both products are really well positioned to identify the modern enterprise. And Sridhar, you mentioned you use it every day, your sales team is using it every day. I'm just kind of curious, how deep within your knowledge worker base is CoCo being used like things like procurement as an example. And is the right way to think about CoCo being more of a sandbox, if some of these use cases become more repeatable, that these can be brought over to CoWork as more turnkey use cases of agents?

This is Christian's favorite question, so I'll let him answer it.

Absolutely. Like the pattern that we're seeing is we're leveraging CoCo and CoWork throughout pretty much every function and every key business process throughout Snowflake. And we're leveraging that not only to inform the quality and completeness of our products, but also go and engage with our customer, tell them, this is how you become AI native, this is how you go and drive efficiencies. And that continues to accelerate and inform one another.

And you're talking about sort of how deep it's used by knowledge workers like just in my organization, we're using it in deal desk and tax and accounting, internal audit, SG&A, treasury. So we have over 150 Snowflake on Snow within the organization, where people are using CoCo to fundamentally change the way that they do work. And so the adoption within the finance organization is almost at 100%.

And it's true across functions.

We will take our next question from Koji Ikeda with Bank of America.

So you described AI as a structural multiplier because customers using AI consume more across the broader Snowflake platform. And so what is the consumption uplift for AI adopters relative to comparable nonadopters? How has that developed across the earliest cohorts? And what evidence are you seeing? Or maybe what is giving you the confidence that all of this reflects higher lifetime consumption rather than projects just being pulled forward?

Yes, I'll take a first cut, and Brian will add on. At this time, we aren't ready to share the exact uplift numbers, but we do measure cohort behavior. And as CoCo adoption gets deeper, more users within an account adopting and more accounts and more customers themselves adopting. The effect is pretty noticeable for all the different cohorts that we have worked with.

And what gives us confidence that this is not merely projects being pulled forward, is both the breadth and depth of use cases that are coming our way in terms of what people are doing with CoCo and CoWork. It is allowing people to do fairly sophisticated actions that previously would have required things like applications. Our own sales leadership teams, for example, have been experimenting a lot with their inspection process, how they can drive their business forward.

And something like that would have required a specialized piece of software, a multi-quarter implementation cycle and then a staged rollout, things like that are literally now a matter of a pretty smart sales leader saying things in English and having CoWork translate that into what looks like a product. This, combined with the fact that we are now having conversations with our customers about a set of use cases that honestly, we would not have been considered before. This is everything from supply chain optimization are much better support systems in the case of Sanofi, our much better fraud and risk detection systems. This is what gives us confidence that there is both breadth and depth in what AI is able to do for Snowflake.

We will take our next question from Brent Thill with Jefferies.

Sridhar, on CoCo, good to see 2,000 accounts added. I guess when you start to see now quarter-over-quarter, is there a difference you're seeing in adoption? Are you getting bigger lands, more users, bigger consumption right out of the gate? Anything that you're seeing that's a trend line since the product is shipped?

Yes. I work with the team that basically does go-to-market. This is the sales team, especially on the solution engineering side, our specialist team, but also the product team. And we have a pretty sophisticated methodology for measuring CoCo penetration from -- we need to get through legal terms all the way to there are a set of daily users of the product that are living inside CoCo. We have our own pipeline for what does this -- for the different stages of this penetration.

But more importantly, we also now have a suite of tools ranging from in-product guidance within Snowsight to hands-on labs that we run for 3 hours with our customers. And obviously, we have a lot of customers we can do hands-on labs with each and every one of them, but we are getting much better at matching our actions to the things that are going to drive outcomes. We are also doing a good job of sharing best practices across the different years in the globe.

All of this is driving just really positive momentum. And more importantly, this feels like a problem that is ours to solve and drive at scale for the simple reason that CoCo makes every single thing that a customer does with Snowflake go faster and better. So it's among the easiest sales that we have done to our customers. But I'm also pretty happy with how methodical and thorough we are being in driving CoCo adoption.

We will take our next question from Brad Zelnick with Deutsche Bank.

This is Dan on for Brad. Congrats on a great quarter. I wanted to maybe go back to an earlier question on kind of model neutrality or optionality with open in frontier models now being offered. Maybe there's a third lag around models of your own like Arctic that might be specifically tuned for the Snowflake platform. I'd just be curious what the latest is in terms of your ambitions here and kind of fold into the overarching model strategy for CoCo and CoWork?

Yes. So Christian here, Brad. We have not changed the direction we've been on, which is we're not training models to go get into a frontier type of model. But we have continued developing models in the Arctic family for tasks that are more specifically, more constrained, that we can provide higher accuracy and more efficiency. We do that in some of the AI functions. We do that for some of the document processing. We do that for embedding, et cetera.

So we will continue doing that type of activity. And as you know, the mix and matching of Frontier, close models, open wave models and our own models with fine-tune models will continue to be part of how we help customers at the end of the day, deliver -- achieve what they want, which is what is the right model for the right task that gives the correct results at the best efficiency.

We will take our next question from Alex Zukin with Wolfe Research.

Congrats on an exceptional quarter. I guess maybe Sridhar, it feels like we're still very early in the Agentic Enterprise experience. And yet you guys are already seeing a pretty meaningful inflection. And I appreciate that it's too maybe early to share the kind of ARPU expansion at some of these early adopters. But you talked about accessing larger kind of strategic priorities, maybe larger budgets. So maybe can you just talk about how much -- what is the embed of opportunity that you are now able to access and see in terms of budget dollars? And maybe we then -- we've heard some really exciting tails of your FTE program and some of the exceptional tracking that's getting out there in the marketplace, particularly on the outcome-based selling. So maybe just give us a sneak preview of that as well.

Yes. As I was remarking earlier, AI has dramatically lowered the distance between business value that somebody sees -- I mean, that a company sees and the data estate that's next to it. And often, it's not as complicated as it sounds. Recently, I was talking to an asset manager that manages tens of billions of dollars of assets, and they have this problem where they get a very large number of data sets delivered to them every single day. They have a large pool of assets that they have and the set of decisions that they are in the process of making about new moves that they could be taking.

Obviously, this is distributed across hundreds, if not thousands of people. That act of distributing information effectively is basically manual is place. It's a sheets being passed on, someone has to download a spreadsheet and update a model that's probably sitting on their local PC. And we are talking to them about how do we construct effectively like a multiplex or demultiplexer for the most important information that is coming and that can meaningfully lower both their return and reduce their exposure because models just do a much better job of doing this kind of work.

And that's just one among many, many, many conversations that I end up having, which is pretty remarkable for a person effectively heating a data infrastructure company. We've also hired a set of exceptional folks that have industry expertise that can answer simple questions around what are the top 6 things that are going to make the biggest difference to a company's top line and bottom line? And is there a new perspective that we can offer to these. And this is what the frontier engineering team is doing. It is combining a knowledge of what is possible with the data platform with harnesses like CoCo and CoWork with the industry-specific knowledge needed to drive meaningful outcomes to our customers.

We have talked publicly about working with folks like Sanofi in our frontier engineering program. But this is an area where there is breadth and depth of adoption. We are, for example, helping a big financial institution effectively overhaul their digital and data strategy and bring it to the modern world in a way that is very, very sustainable for them. And the confidence that we have going into these kinds of engagements it's not just that we commit to delivering the outcome. Obviously, we get paid only when we deliver outcomes in situations like this, but it's also in the fact that Snowflake is an open, well-understood platform.

And compared to some pretty proprietary folks out there where you have to go back to them after you get the first outcome, we can confidently tell them that their data team is very, very capable of driving further engagement with the projects that they have done and building on top of it. It's the combination of these things, our ability to truly talk about business outcomes, commit to deliver but deliver it on a clean, open, well-understood architecture that makes the customer looks good and stay good that I'm most excited by.

We will take our next question from Tyler Radke with Citi.

Sridhar, I wanted to ask your take on some of the moves we've seen from traditional SaaS companies partnering with LLM and sort of becoming more of a database themselves if the LLM sort of take the UI layer. How do you see this playing out? Does it make sense for Snowflake to take on more of the system of record data? And how do you sort of anticipate that, that competitive overlap looks over time?

I mean, the way I think about this is that as software gets easier and easier to create, it's the data and semantics that require more and more important. It isn't lost on any of us that our ability to talk about new value with our customers is driven both by the breadth of the data estates that many, many of our customers have on Snowflake combined with the power off the harness, obviously, using the best models. So I've been very, very consistent for now 2-plus years in my conviction, in our conviction that owning the user experience is critical.

And we see CoCo and CoWork as fundamental to our future because they demonstrate to us and to our customers what is possible. But on the other hand, we understand that we live in a world where we have to play nice. Snowflake is only a part of the overall software estate that our customers have. We offer interoperability at multiple levels. But we think our flagship products are very important to our future.

Yes, I'll add maybe the notion of some of these application providers becoming database players is not a new trend. And what we hear consistently from CIOs and CTOs is, if I use 3 applications, I'm not going to copy my data into 3 different platforms. It's easier to consolidate in a single central platform like Snowflake, which is why we have bidirectional zero-copy partnerships with many of them, and we see a lot of customers aligning their data estates with Snowflake.

Yes. And our investments in which Christian has pioneered and spearheaded with the team for a very long time around being able to host applications in Snowflake, small and big also positions us exceptionally well for many applications, not just analytic ones, but also systems of record operational ones that can be built right on top of Snowflake.

And so internally, we have many projects, some of which Christian and I like don't even know of people that are building interesting applications on top of the analytic data and operational stores that they're setting up within Snowflake. You can definitely expect to hear a lot more about things like hybrid tables and progress because they are the foundation, we think, for a new generation of agent applications, some of which will have UI and some of which weren't on top of Snowflake.

We will take our next question from [ Dami Jaffjee ] with JPMorgan.

Congrats from my end on the strong results here. Maybe if I can ask on the full year guide and trying to parse out the increase in the foot year guide between core increases on the core versus AI. I think that last quarter, you had mentioned most of the full year guide increase was on account of CoCo. This quarter, it sounds a lot more balanced between core and AI and your confidence in forecasting acceleration and product revenue growth also seems to be much higher. So just wondering if there's something fundamentally that changed during the quarter in terms of consumption of the core from your customers that's driving that higher visibility nnd raise to the full year? Or is it more just on account of visibility after having got through like half of the year at this point?

Yes. This is Brian. Thanks for the question. We base our guidance based on Observe behavior up until the call that we have. And what we saw is that we talked about sort of CoCo, CoWork and all the AI functions driving additional business, but as well as the people who adopt them, they're also increasing business within the core. So it's a reflection of the strength that we're seeing in our AI products as well as the underlying strength that we're seeing in the core.

Thank you. This concludes today's question-and-answer session. I will now pass the call back to Snowflake for closing remarks.

Thank you, everyone. The Agentic Enterprise runs on Snowflake. We have just achieved 37% year-over-year product revenue growth, marking our third straight quarter of acceleration, while expanding our non-GAAP operating margin 400 basis points year-over-year to 15%. AI has created a powerful flywheel effect across our business, strengthening platform demand, driving adoption of our native AI products, and in turn, fueling greater consumption across the business. And this flywheel is accelerating. Based on this strength, we have increased our fiscal '27 product revenue guidance by over 500 basis points to 36% year-over-year growth. We are executing with discipline and focus and see enormous opportunity ahead. Thank you.

Thank you. This does conclude today's call. Thank you for your participation. You may now disconnect.

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.