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Operator: Good afternoon, and thank you for attending the Snowflake Inc. Q1 Fiscal Year 2026 Earnings Call. My name is Matt, and I’ll be the moderator for today’s call. All lines have been muted during the presentation portion of that call with opportunity for questions-and-answers at the end. [Operator Instructions] I’d now like to pass the conference over to our host, Jimmy Sexton, Head of Investor Relations. Jimmy, please go ahead.

Jimmy Sexton: Good afternoon, and thank you for joining us on Snowflake’s Q1 Fiscal 2026 Earnings Call. Joining me on the call today are Sridhar Ramaswamy, our Chief Executive Officer; Mike Scarpelli, our Chief Financial Officer; and Christian Kleinerman, our Executive Vice President of Product, who will participate in the Q&A. During today’s call, we will review our financial results for the first quarter of fiscal 2026 and discuss our guidance for the second 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 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.

Sridhar Ramaswamy: Thanks, Jimmy. And hi, everyone. Thank you all for joining us today. We are off to a strong start to the year and I couldn’t be more proud of our team. Our core business is very strong, our product delivery remains on overdrive, and our go-to-market engine continues to get stronger and stronger. We are in the zone and there’s still an enormous opportunity ahead. At Snowflake, our mission is to empower every enterprise to achieve its full potential through data and AI. Our AI data cloud helps customers get more value out of their data, innovate faster and remove friction from their business operations. And as I have shared in the past few quarters, we are extending that value throughout the data life cycle.

We remain disciplined in driving operational rigor across our business, gaining greater efficiency even as we continue to invest aggressively in growth. We are building our strength in executing with urgency and focus to capture the opportunities ahead and sustain durable momentum. Product revenue for Q1 was $997 million, up a strong 26% year-over-year. Excluding the impact of leap year, product revenue grew 28% year-over-year. Our growth rate was stable quarter-over-quarter, showing no deceleration. Remaining performance obligations totaled $6.7 billion with year-over-year growth of 34%. Our net revenue retention was a very healthy 124%. As you can see, we have started the year with strong revenue growth and overall very healthy results, and we are increasing our growth expectations for the year.

As I’ve shared in the past few quarters, Snowflake is obsessed with creating product cohesion to make it easier and easier for our customers to innovate faster and unlock more value from their data from ingestion to insight. Enterprise leaders like Canva and JPMorgan Chase bet their business on Snowflake because our platform is easy to use, connected to enable fluid access to data wherever it sits, and trusted by companies of all sizes and industries. And we are continuing to deliver on our vision of being the end-to-end technology provider for our customers’ data journey. We’ve made important progress in delivering an extensible and flexible connectivity platform, both unstructured as well as structured data. Snowflake connectors, which leverages the technology from our acquisition of Datavolo enables customers with seamless connectivity and data integration with key platforms like Google Drive, Workday, Slack, SharePoint and more to tap into critical data across the business.

Global pharmaceutical leader, AstraZeneca, for example, can now analyze critical business data from systems like SAP and Workday with ease. And customers like CloudZero leverage hundreds of powerful, active data sharing connections to securely exchange data with their partners and customers, driving value across our ecosystem. As our data engineering business continues to show strength, we are helping our customers streamline and scale their data pipeline with less and less friction and realize meaningful cost savings. By consolidating data in Snowflake, Dentsu, a global marketing agency managing data for numerous Fortune 500 clients, reduced costs by 30% from simplified data architecture and reduced dependence on third-party tools. They now use Snowflake Data Clean Room to help global brands securely combine customer data without compromising privacy, enabling more personalized marketing campaigns while reducing risk.

On the analytics front, our world-class solutions continue to power mission-critical operations for our customers. Global technology leader, Siemens, is collaborating with Snowflake to help manufacturers unlock new levels of operational efficiency and scale. This enables customers to unify their information technology data such as supply chain management and financial data with operational technology data like data from client [Indiscernible] systems and industrial equipment, leveraging Siemens’ industrial edge and Snowflake AI data cloud to gain better insight, improve machine performance and optimize production processes across their business. And as AI reshapes the enterprise, Snowflake is helping our customers lead the way with AI-ready data.

Take Samsung Ads a leader in connected TV advertising. They leveraged Snowflake to connect advertisers with millions of Samsung consumers while upholding strict privacy standards. By unifying their data on Snowflake, Samsung Ads drives innovation in personalized customer experiences and accelerates the development of new AI and ML-powered advertising features, enabling advertisers to deliver more relevant content and enhance the advertising experience. We have an incredible product momentum, and we are continuing to innovate at lightning speed. In fact, this quarter alone, we have brought over 125 product capabilities to market, a 100% increase over what we delivered in Q1 of last year. We continue to see strong adoption of open data formats, especially truly open modern table formats like Apache Iceberg.

We recently announced that our customers can now leverage many of Snowflake’s core capabilities including data sharing, security and performance optimization using Apache Iceberg, giving them even more flexibility to manage and query data at scale. When it comes to AI, pretty amazing to see our progress. A year ago, we were just getting started. Now we have over 5,200 accounts using our AI and machine learning on a weekly basis. Cortex AI has gone from a nascent product area to a foundational pillar of enterprise AI strategies for customers around the world. It’s accelerating clinical research for health care companies with unified access to information and turning automotive customer reviews into actionable insights to help them personalize their service.

As one of the world’s largest food and beverage company, Kraft Heinz, is leveraging Snowflake Cortex to empower its employees with innovative AI tools Lighthouse or Kraft Heinz AI their new internal AI assistant. This initiative is designed to revolutionize internal workflows, enhance efficiency and drive AI adoption across the organization, paving the way for future advancement in agentic AI. Earlier this year, we launched Cortex Agent, which is now helping customers like Luminate Data, a leading provider of entertainment insights, scale how they process and retrieve both unstructured and structured data. That foundation is critical for developing, deploying and orchestrating the data agents driving their AI applications. And we have further solidified our leadership by continuing to integrate cutting-edge models into Cortex, ensuring day one availability of Meta’s Llama 4 model.

As I shared last quarter, we announced an expanded partnership with Microsoft to host OpenAI models on Microsoft Azure. We continue to provide our customers with choice and flexibility to leverage the world’s leading models for their enterprise AI applications. We also launched the first of our AI-powered migration enhancement. Now our customers can use Cortex to test and review issues during their migration journey, making a time-intensive process much more efficient. And this is just the start of what AI can do to make migrations go fast. All of these innovations are focused on driving real value for our customers. We are making it easy to tap into structured data. We are making it easy to tap into unstructured data as well. And we’re helping our customers build a strong foundation to lead in the era of agentic AI.

We’re continuing on this momentum and you’ll see even more from us in just a few weeks. During the first week of June, we’ll be joined by tens of thousands of customers, partners and developers at Snowflake Summit, a four day event, one of our biggest yet, where we will reveal some truly exciting new capabilities we are bringing to market to support our customers at every stage of their data journey. As we innovate, we remain committed to scaling efficiently. Under the leadership of our new Chief Revenue Officer, Mike Gannon, we have renewed focus and rigor across our go-to-market case. We’re growing our go-to-market operations while maintaining our close collaboration across engineering, product, marketing and sales to bring products to market effectively.

This ensures that we are able to deliver greater value to our existing customers while continuing to win new ones. We’re also expanding our addressable market. With the launch of Snowflake Public Sector Inc., and our recent Department of Defense Impact Level provisional authorization, we’re now equipped to deliver mission-critical data and AI solutions to the national security community, including the United States Department of Defense, its military branches and industry partners. We also introduced new automotive solutions as part of our AI data cloud for manufacturing. These solutions empower companies like CarMax and Nissan with advanced data and AI solutions to drive innovation and efficiency. And they are using our own AI internally to boost productivity.

Our go-to-market teams use our sales knowledge assistant powered by Cortex to access insights from our sales knowledge base using natural language, with fast [indiscernible] Streamlit apps like our Customer 360 app, they can tap into rich insight on customer consumption front. I’m proud of the discipline and efficiency we have built across the business. We’ve got a strong operational rhythm, we’re investing strategically for growth, and we are laying the groundwork for scale. Mike, why don’t you take us through more of the financial details?

Mike Scarpelli: Thank you, Sridhar. In Q1, product revenues grew 26% year-over-year to reach $997 million. As Sridhar mentioned, we saw no deceleration in the business when adjusting for leap year. We continued to see meaningful growth from new product offerings. Both Snowpark and Dynamic Tables outperformed expectations in Q1. Other areas of strength included technology and retail sectors. Q1 was a strong quarter for bookings. On our last call, I noted two large customers ran out of capacity in Q4 and elected to delay their larger renewals. As expected, both of these accounts signed $100 million-plus contracts in Q1. We view this variability in bookings as normal for our model. Our focus on new customer acquisitions is yielding positive results.

We added 451 net new customers in Q1, growing 19% year-over-year. Turning to margins. In Q1, our non-GAAP product gross margin was 75.7%. Our non-GAAP operating margin was 9%, up 442 basis points year-over-year. We continue to focus on driving greater efficiency across the entire company, while investing for growth. Non-GAAP adjusted free cash flow margin was 20%. As discussed on our last earnings call, we had several large customers purchase as they consumed in Q4. This booking behavior impacts the seasonality of our free cash flow. We expect this year to be more second half weighted. In Q1, we used $491 million to repurchase 3.2 million shares at an average weighted price per share of $152.63. We still have $1.5 billion remaining on our authorization through March 2027.

We ended the quarter with $4.9 billion in cash, cash equivalents, short-term and long-term investments. Now moving to our outlook. We expect Q2 product revenue between $1.035 billion and $1.04 billion, representing 25% year-over-year growth. We expect Q2 non-GAAP operating margin of 8%. For FY 2026, we are increasing our revenue guidance to $4.325 billion, representing 25% year-over-year growth. As always, we forecast based on observed customer behavior. We expect non-GAAP gross — product gross margin of approximately 75%, non-GAAP operating margin of 8%, non-GAAP adjusted free cash flow margin of 25%. Finally, we will host our Investor Day on June 3 in San Francisco in conjunction with Snowflake Summit. If you are interested in attending, please e-mail ir@snowflake.com.

Before opening up the line for questions, I just want to update you on my transition as mentioned on the Q4 conference call last quarter. We are in the process of interviewing many great candidates, and we will make an announcement in the future when we have more firm details to share. With that, operator, you can now open up the line for questions.

Operator: [Operator Instructions] First question is from the line of Keith Weiss with Morgan Stanley. Your line is now open.

Sanjit Singh: Thank you. This is Sanjit Singh for Keith Weiss. Congrats on an outstanding Q1. Sridhar, I want to talk about some of the trend lines of the business, particularly around consumption as it progressed through the quarter. How was consumption sort of exiting the quarter and through the month of May? That’s my first question, then I have a follow-up.

Sridhar Ramaswamy: As you know, we don’t comment on consumption within a quarter. Overall, Q1 consumption was very strong coming out of the holiday period, and you see that in our results. Of course, Q1 had one less day compared to Q1 last year. But overall, we feel very good about our consumption.

Mike Scarpelli: And I would say, Sanjay, we just gave guidance for the quarter and that’s based upon the customer behaviors we’re seeing through today.

Sanjit Singh: Yes, and that Q2 guide was very strong, so I think there’s a lot to take away from there. And then, Sridhar, on the product front, you mentioned about the adoption of Cortex continuing to build. I was wondering to see what are sort of the monetization trends associated with Cortex. And to what extent are customers making commitments associated with Cortex in mind and driving that into their sort of overall consumption of Snowflake?

Sridhar Ramaswamy: I would split that into a few parts, Sanjay. One is that, it is very, very clear that people invest in Snowflake. People invest in data systems not just for what they used to be able to do before, which is things like analytics and machine learning, but increasingly for what they will be able to do today and in the future. And part of what I tell our customers is that, by working with us, by bringing data into Snowflake, they are making their data, they are making their processes AI-ready as a firm. And we have taken a very measured approach to how we have had our customers use AI. As you know, we don’t sell AI separately. It’s not a SKU. Customers are not signing up for contracts on AI so it’s on their existing spend.

We have focused a lot on use cases that deliver value today. I’ve talked about some of these examples. It’s everything from being able to create chatbots on documents like we’ve created for our own internal enablement or Siemens has created for all of the PDF manuals of their 150,000 devices. Two, putting business data directly into the hands of end users without needing analyst or BI tools in the mix. We are beginning to see compound systems get adopted, where you bring in more than one data source that can disambiguate between the kind of questions that the user has, or multi-step flows where you take data from one source and use that to answer questions or do follow-ups from others. So it’s very graduated from that perspective. But the overall point that I want to make is that, every user of data, every CTO, including our own, now realizes that their data strategy, especially one with Snowflake, is a direct unlock for whatever they’re going to do with AI, both today and several years down the road.

So in that sense, I think the road maps are emerging. It’s not do AI separately on the side. It is more of invest in Snowflake to get your data house in gear and realize value from AI as we go along.

Sanjit Singh: Awesome. Thank you for that.

Operator: Thank you for your question. Next question is from the line of Kirk Materne with Evercore ISI. Your line is now open.

Kirk Materne: Yes. Thanks very much and congrats on a nice start to the year. Sridhar, I was wondering if you could just dive in a little bit on the comment around Snowpark and Dynamic Tables outperforming. I was just curious, and I’m sure it’s a bit of both, how much is just the product maturation and sort of the readiness for customers to take those on versus some of the things you guys have got done on the go-to-market side over the last year in terms of enablement and sales enablement with those products? Thanks.

Sridhar Ramaswamy: Good question. It’s clearly going — clearly, it’s going to be — it’s both. You need great products that drive utilities. And in addition to those features that you mentioned, Snowpark and Dynamic Tables, I would say that our investments in things like Iceberg also vastly increase the scope of the kinds of things that our customers can do with the data. And similarly, Snowflake connectors is then going to make more and more data available for these data engineering tools as well as [Indiscernible]. And this is why Christian and I stress the end-to-end data life cycle a lot. And so our motto often is we want to be there from injection to insight when it comes to data. Having said that, we have hired, in addition to Mike, amazing leaders in sales that are in charge of driving these more specialized motions.

Yes, not everybody in the Snowflake sales team is going to become an expert on our AI product or the latest advancements in Snowflake connectors out of the box. So we have a specialist motion that is very targeted, that identifies the highest-value use cases that our customers have, pioneers implementations for them so that they can be used as a template to be repeated in other places and increasingly with our GSI partners. And so you need to be able to do both. You need to create products that create value, but then a go-to-market team that can enunciate the value and do the hard work of both establishing the flagship customers and then driving sales across the sales teams.

Kirk Materne: Great. Thank you all. See you in a couple of weeks.

Operator: Thank you for your question. Next question is from the line of Raimo Lenschow with Barclays. Your line is now open.

Raimo Lenschow: Perfect. Sridhar, if you — looking at Snowpark and adoption there, how do you see this playing out at the moment and maybe more in the future in terms of like going wall to wall with one vendor versus kind of having different pockets of data that are sitting in your system versus kind of other systems? How do you — what are you seeing there at the moment and how do you think that will evolve?

Sridhar Ramaswamy: First of all, I think one of the things that is making us successful, Snowflake as a company is our acknowledgment and willingness to work with customers that have complex data ecosystems. It’s always going to be just actually true that there are on-prem legacy systems in most large customers, or that there are large data estates that are sitting in cloud storage. But I think what is unique about this moment is that customers are a little unhappy about needing to stitch together many different tools in order to achieve even relatively simple things. If you think about it, if you wanted — if you and your company wanted to build a chatbot on a corporate sitting in SharePoint, it’s rather painful if you have to use four different tools in order to produce that.

And at the end of it, you won’t even have your governance right because you have to go to [Indiscernible]. Part of what we want to be able to do for cases like that is have Snowflake connectors point to the SharePoint repository. And if there is any augmentation or transformation of the data that is needed, you get that done, for example, with Snowpark, and then you create an index with Cortex Search and hook it up to a chatbot. I think we will continue to see specialist players that exist, and we partner with them and we value our partnerships with them. But there are also a number of use cases that are ripe for effectively like ease of use and consolidation. And that’s the thing that we are leaning in to. Anything to add, Christian?

Christian Kleinerman: Maybe super quickly, the other investment that we made is around Iceberg, which also creates opportunity for customers who have an open architecture and be able to mix and match technologies as they see fit.

Raimo Lenschow: Yes. Okay, perfect. And then one quick one for Mike. Obviously, it was very good opportunistic on the share buyback side this quarter. How do you think about that traction there for the rest of the year now that shares are starting to look better? Thank you.

Mike Scarpelli: We will continue to evaluate share buyback on a quarterly basis, and we have no plans right now. We’ve been more opportunistic in terms of the buyback, but we do fully anticipate between now and 2027, we will utilize that.

Raimo Lenschow: Okay. Thank you.

Operator: Thank you for question. Next question is from the line of Karl Keirstead with UBS. Your line is now open.

Karl Keirstead: Okay, great. Thanks. Mike, in the comments from you and Sridhar, there was really no mention of macro per se and no evidence in the numbers that you guys really saw much pressure. You certainly did back in 2022, 2023. And I’m just curious, Mike, how you would draw a contrast. Is it that a lot of the post-COVID optimization efforts are now largely behind you, Snowflake just in a better place in terms of the product portfolio? Or maybe just the degree of macro pressure, the wobbliness we’ve all seen in the last couple of months is just not as severe as you had to deal with in that post-COVID downturn? Some contrast might be helpful. Thank you.

Mike Scarpelli: I would say coming out of COVID, I think it was very different. In that environment, we had a lot of digitally-native well-funded start-ups that were spending crazy and weren’t really focused on costs as much. Our customer base has really evolved into the — some of the largest companies in the world that are much more mature, that are much more cost-focused. And I am not seeing any big optimizations plan within our customers like what we saw coming out of COVID with those. But I will remind you, our customers are constantly optimizing. That may be a little bit, but they’re always looking to do things more efficiently and that will continue. I would say in terms of the macro right now, we really have not seen the impact of anything with the current — the news on tariffs and other things today.

I think if we would have seen that, we would have saw in the new — number of new customers. We have a great new customer add and we had great additions to RPO with this confidence, and that shows the confidence our customers have on making big bets with Snowflake.

Sridhar Ramaswamy: The only small comment I might add is that this is something that our sales team practices as well, which is to make sure that whenever a use case gets implemented, that they actually take the trouble to tidy things up and make sure that things are optimized, because our sales team has learned, thanks to 2022 and 2023, that inefficient spend from customers inevitably leads to a contraction later anyway. And we are better off making sure that it is always efficient spend.

Karl Keirstead: Okay. Thanks. And maybe as a follow-up to Mike. Mike, did Snowflake have any exposure to any of the larger AI natives that on the margin might have given you a little extra oomph this quarter?

Mike Scarpelli: Nothing extraordinary. We do have a number of the AI companies, our customers, but none of them that are all less than 1% of our revenue.

Karl Keirstead: Okay. Thank you.

Operator: Thank you for your question. Next question is from the line of Mark Murphy with JPMorgan. Your line is now open.

Mark Murphy: Yes. Congrats on the great execution, Mike. Even if we were expecting a strong start to the year in terms of the hiring in sales and marketing, I don’t think we would have pictured this many hires. It’s a very big number. Can you speak to that dynamic and just whether — are you hiring into a pipeline that is strengthening around Cortex or Snowpark or some other opportunity that you see opening up?

Mike Scarpelli: What I would say, Mark, is just our softness we have in the business, and it’s not just AI, it’s everything that we’re seeing in Snowflake. And as you know, Q1 is always our biggest hiring from a sales and marketing perspective because we try to get those people on board at the beginning of the year to deal — so they can be part of our sales kick-off and all of our sales and enablement that we do with the employees in the beginning of the year call the new features and stuff. So I wouldn’t read that much other than the confidence we see in our business. But as you know and we talked about, we’re still really looking for operational excellence, and we are constantly looking at productivity of people and we will add people and we will stop adding if we don’t see productivity pay off with those people.

Mark Murphy: Okay, understood. And then Sridhar, can you speak to the federal government opportunity because you had touched on it in your comments? Do you think that DOGE is going to run through this initial process of eliminating wasteful spend and then maybe pivot back towards issuing some new RFPs? And then you’ve got the right certifications. I’m just curious if you think some agencies might be moving off-legacy on-prem data warehouses and maybe moving some of that on the Snowflake a little later in the year.

Sridhar Ramaswamy: This is an active topic of conversation with many departments in the government. I think — I was in DC a few weeks ago, met with a number of folks, and there is both an increasing awareness of what Snowflake can do. The fact that we have very low operational overhead figures prominently. And there’s also a little bit of an astute change where they’re very much focused on how do we make sure that our data infrastructure is run efficiently. There’s also a desire for things like cross-department sharing of data because that just leads to more efficiency. We’ll have more to say on this topic. And Mike, our new CFO, is certainly actively looking at this area as well. We are optimistic and hopefully, we’ll have more to say about this in the coming quarters.

Mark Murphy: Thank you.

Operator: Thank you for your question. Next question is from the line of Kash Rangan with Goldman Sachs. Your line is now open.

Kasthuri Rangan: Hi. Thank you very much. It’s really heartening to see the positive shift in the narrative strategic positioning and the execution and handling of everything, so good to see that. Two things still, regardless of, although you’re approaching $4 billion run rate and growing 26%, 27%, which is remarkable. If I could take the liberty of poking at the NER, so 124% is good. Not many companies even reach that number at your scale if they reach your scale. But you have had 137 new products launched in the most recent quarter, and Scarpelli will be quick to point out that the NER metric is more of a 24-month trailing metric, which I completely appreciate. But why wouldn’t and why shouldn’t NER be better, given the tail effect of new product introduction, you’re landing customers at a record pace.

And to Mike’s point, the yield of enterprise customers is higher quality. You’ve got AI which didn’t exist in 2022. Volatility, which should play to your advantage. So anything on that front? And anything that your new CRO could do to land that number, as good as it is, even higher? And I have a very quick follow-up question. Thank you so much.

Mike Scarpelli: What I would say on that is a number of our newer customers that are not in that cohort are contributing to our growth beyond what the NRR is. And there was one of our large customers that grew so much last year. In this year, they’re still doing very well but they didn’t grow as much this year. And that’s really the dynamics. It’s those newer customers coupled with that. But once again, over time, NER and revenue growth rate will converge as we become a more mature company.

Kasthuri Rangan: Got it. And maybe one for you — Thank you, Mike. Good to hear your voice. One for you, Sridhar. When you look at what’s happening with the hyperscalers, Microsoft certainly talked about fabric yesterday, on and build, certainly seem to be making a lot of progress in the idea of data fabric at scale, truly open to enable AI agentic architecture. It’s a thing that’s not lost on the big guys. Where does that lead Snowflake? And what is going to be the one or two or three things that the Snowflake’s platform would do better so you can build your vision towards being a $10-plus billion revenue company? That’s it from me. Thank you.

Sridhar Ramaswamy: Just like the hyperscalers are formidable, they are amazing both from an engineering execution and business perspective, but they also work with Anthropic and OpenAI because they are the best — among the best model makers in the world. Similarly, we are very uniquely positioned in terms of being the excellent data platform that is. And we’ve also learned how cooperating really leads to a better outcome, whether it is with AWS, which is our biggest partner, or more and more with Azure. There are many customers that Azure not play is just a better outcome for everybody that is involved. And we have deep partnerships between the team. I think it was six, seven months ago that we announced, for example, that from Snowflake, you could read tables that are in 1 lake, and we are also actively talking to them about 1 lake being the data layer for Snowflake that’s at the bottom.

We also collaborate with them at the top, where we have things like Cortex analysts and Cortex agents be available as components in Office Copilot, for example. So we very much take this approach of finding customers, for example, who are on a modernization routine or who want to get value, AI value from data and figure out how we can work together. Yes, there is competition, but I think there are more cases than not where we are very, very effectively working together, and it’s on the uptick, especially with Azure.

Kasthuri Rangan: That will be huge. Thank you so much.

Operator: Thank you for your question. Next question is from the line of Matthew Hedberg with RBC. Your line is now open.

Michael Richards: Hey, guys. This is Mike Richards on for Matt. Thank for taking the question. Congrats on the results here. You’ve clearly made great progress on the products front here, but I’m just curious how you feel about the maturity of the go-to-market motion to support your AI developments. That’s it for me. Thanks.

Sridhar Ramaswamy: I’m actually even more pleased with how we’ve been able to seize the AI opportunity. I’ve spoken to you, folks, previously about how we created what we then called the AI ninjas, which were a group of solution engineers that were deeply versed with our AI products, that could be very close to our sales teams around the globe and just the excitement that our sales team feels about AI, but more importantly, the ability to drive AI use cases at scale to both pitch the vision, but also run POCs for them, win them and get them into production. That’s been a pretty remarkable transformation for us. And we are now in the process of making this kind of specialized knowledge available to more and more of the sales team.

I think it is this combination of specialist teams that know more about a sophisticated area like AI, again doing the initial work, but having more of the team participate in it. That’s been hugely positive for us. And we apply similar techniques in data engineering, though with data engineering, I would say it’s much closer to the knowledge and skill set of more of our sellers. In some sense, it’s more natural to them. But AI is doing exceptionally well as well. And we have assembled a team of both specialist sellers and AI but also specialist technical experts that are driving change across the whole sales organization. That, combined with an increasing understanding of what it takes to drive great use cases in general, not just in AI and data engineering but also across other areas like analytics, really heralds a new era of data-driven go-to-market, which I’m very, very happy about.

Operator: Thank you for your question. Next question is from the line of Brad Zelnick with Deutsche Bank. Your line is now open.

Brad Zelnick: Great. Thanks so much and congrats on a good start to the year. Sridhar, as we think — I just want to follow up on Kirk’s question. As we think about Snowpark adoption from here, beyond capturing maybe the Spark jobs where data was moving off platform, can you talk about success that you’re seeing in penetrating, where are the media or data science use cases and any anecdotal evidence that you’re winning over the data science crowd and maybe the impact that notebooks are having would be great? Thanks.

Sridhar Ramaswamy: Yes. I’ll start the answer. Christian can add on. Our notebooks are doing very well. Several thousand customers are actively using them. And there is increasing ability, for example, to train larger and larger machine learning mark. As you folks know, like the world has made enormous amount of progress on the basis of machine learning, even though AI is all the hotness these days. But when it comes to many, many interesting use cases, for example, next best action prediction, which the likes of Hilton do, are how to route guests to the next ride, which customers like Disney do. These are all things that we have gained increasing market share around. Notebooks continue to expand. We continue to add product capabilities for training bigger, better, faster models on machines running and container services.

These tend to be more technical in terms of the kind of people that are involved, the implementations that happen but definitely appealing to the developers, the data scientists, that sort of product-led motion is something that is going on pretty well. Christian?

Christian Kleinerman: Yes. One book addition is Snowpark is a collection of libraries and capabilities that help customers do a variety of activities. We see lots of people leveraging it for unstructured data processing, which is a core part of what we’re doing. As Sridhar said, we’re making more unstructured data available to customers. So Snowpark for extracting structure and signal and doing traditional ML on structured common use case we’re seeing.

Brad Zelnick: Thank you. Maybe just a quick follow-up, Mike. Guidance implies a robust ramp through the remainder of the year. And I think we all see the pace of innovation. Excited for what’s to come at Summit. But what, if anything, would you call out that underpins your confidence things we might not be thinking about or any key assumptions worth calling out? Thank you.

Mike Scarpelli: I would just say, as I said, our guidance is based upon the observed behavior we see within our customers, coupled with we spend a lot of time than we have for the last five quarters now, in really identifying new workloads going to production. We have a pretty good visibility of those, and we’re very close with our customers, and we know what we’re doing. Migrations are moving nicely. We announced where we’ve made Snow Convert available to all of our customers and partners, and we’re seeing an uptick in the amount of usage around that, and that’s what gives us the confidence in the guidance that we gave.

Brad Zelnick: Great to hear. Thanks again.

Operator: Thank you for your question. Next question is from the line of Brent Thill with Jefferies. Your line is now open.

Bo Yin: This is Bo Yin on for Brent Thill. Thanks for taking the question. With features like Cortex AI analyst and Cortex agents that can help users to write more efficient queries, like are you seeing more query optimization as Cortex AI adoption picks up? And what’s been a net impact on usage so far in terms of net new queries and query optimization? Thanks.

Sridhar Ramaswamy: With things like Cortex analyst, if anything, the end users are a step removed from writing the SQL query. The user semantic model to aid in figuring out the intent of the user query and then auto-generate the SQL from it. Certainly, we’ve made available Copilot-like, both for what we call worksheets, which is where people write SQL or Python, but also inside notebook. To be honest with you, I think there is a huge amount of innovation that is coming there, some of which we will show at Summit. The bar for people being able to write code now are modern dollar per platforms like Cursor, which — and get a Copilot and others, which can massively increase productivity in terms of the volume of queries that can be written as well as the amount of work that can be done.

While we don’t have concrete measurements of this leads to X percent more query, we are very happy about being able to help our customers write queries faster or write code faster and be able to debug as faster as well.

Christian Kleinerman: Yes. We’re saying in these calls and other forums that our preference, our goal is to make sure that customers are optimized all the time. I think none of us like to go and spend money and then optimize and go up and down. So we put a lot of effort in technology, some of the core examples that you have are part of that, but query insights, cost insights, governance insights all over this product, how we help customers be in a better optimized state all along.

Operator: Thank you for your question. Next question is from the line of Patrick Colville with Scotiabank. Your line is now open.

Patrick Colville: All right. Thank you so much taking my question. I guess my one is for Sridhar. Last year, the Arctic LLM was launched. My question is how important are first-party foundation models to Snowflake’s strategy as of today? Or is there like a slight pivot more to kind of partnering with third-party foundation models?

Sridhar Ramaswamy: I’ve said this before, I think the business of training a truly large foundation model has gotten to be a very expensive proposition. We have an amazing team of AI researchers, but they tend to focus more on things like training. We have always blogged, for example, about how we can be much more efficient, like much more correct at generating SQL queries by using post training techniques. This makes Cortex analyst better. I think at least for now, the era of us training, let’s call it, frontier foundation models is not something that we are actively looking at. But the research team continues to do amazing work, as I said, in post training but also in areas like inference optimization, which has a huge impact on latency, it has a huge impact on margins in AI.

So we continue to have a robust presence in the area but we work with partners. Meta is a big partner. We were a day one launch partner for the Llama 4 model that came on. We actively collaborate with Anthropic, with OpenAI. Mistral lots of model providers. The one fun thing I’ll add is that in the area of embedding models, these are small unsung heroes, but they are the models that essentially produce fingerprints of documents that you want to index for a chatbot, for example. We have robust embedding models that we have open source. We have to be opportunistic about where we can create value because can’t afford to spend the billions of dollars that it takes to be a part of research today.

Patrick Colville: Crystal clear. Thank you, Sridhar. And can I just squeeze in a follow-on for Mike? I mean, the bottom line is clearly less of a focus when you’re growing the top line 26% with the possibility to reaccelerate in the back half of the fiscal year. But one key operating margin was strong. Nonetheless, the fiscal year op margin was left unchanged, as was the fiscal year free cash flow margin target. So I guess, what were the puts and takes there as to why leave those targets unchanged?

Mike Scarpelli: Well, what I would say is in Q2 is when we have a big user event, and that’s a very expensive event, Summit that we operate, and that typically has an impact on our operating margin in Q2. And that’s factored in. And we’ll just continue to revise our forecast for the year on a quarterly basis going forward.

Sridhar Ramaswamy: I actually I think that we are being pretty thoughtful when it comes to expanding our operating margin. It was 4% in Q1 last year. It’s 9% Q1 this year. And this is part of the benefit of practicing what we preach around AI. We spend a lot of time figuring out how engineers can be more productive with AI, how we can get small work done. Similarly in the sales team, we want to automate many of the tasks that our sales team doesn’t like to do anyway so that they can be more productive in front of sellers. We feel that we are in quite a bit of a Goldilocks moment where we can continue to grow revenue very strongly while continuing to be very efficient when it comes to operating margin and free cash flow.

Operator: Thank you for your question. Next question is from the line of Alex Zukin with Wolfe Research. Your line is now open.

Alex Zukin: Hey, guys. Thanks for taking my question. Maybe just a high-level one first for you, Sridhar or Mike. I guess again, it seems like what we’re seeing — what we’re hearing from you is the demand environment is really unchanged, untouched by all the macro headlines. You’re seeing new product adoption ahead of expectations. So maybe just the first one, are you seeing a change? Like is this being driven by any kind of identifiable AI tailwinds? You’re seeing a change with how customers are either investing in their AI stack with Snowflake or building agents? Specifically, they’re building more on data rather than just focusing on the models. Like maybe just help us understand and conceptualize the AI tailwind or you’re placed within these AI budgets that a lot of your large customers clearly are making those bets.

Sridhar Ramaswamy: As I was saying earlier, I think more and more people have internalized that to be good at AI, your data needs to be in here. And what we have done on our side is to create a product, whether it’s a semantic model that is very close to the data, usable by anyone, mind you, not just by Snowflake, but also products like Cortex Analyst that can actually unlock the value of that data, both by immediate use, like a chatbot on a specific data set but much more importantly for use in an agentic workflow. So more and more of our conversations can now focus on what creates value from a business perspective. So AI for Snowflake, rather than being this additional thing that we do, in some ways, becomes the natural end state for what investing well in data means.

And of course, we are using AI ourselves both within the company but also in different aspects of the product. We talked about code generation and notebooks being accelerated by Cursor-like experiences. On the other hand, we make Snow Convert, our conversion tool free, so that anyone could use them. And we’re bringing agentic workflows into Snow Convert so that people can do things like testing with synthetic data far sooner than what they would have done in a traditional waterfall-style migration. I’d say it’s a combination of all of these trends that are driving Snowflake forward.

Alex Zukin: Excellent. And maybe just as a follow-up, maybe Sridhar for you or for Christian. There’s been a lot of excitement that we’ve sensed around Gen2 and particularly the performance improvements that your customers are seeing. I guess maybe just touch on, is this potentially leading to unlocking new use cases around the capabilities introduced? Or how should we think about the potential for some of these new functions as they percolate in the platform?

Christian Kleinerman: Yes, Christian here. The best way to think about Gen2 is our latest and greatest compute environment. What we’ve done is we’ve combined the latest hardware instances that we can get from the cloud providers, which are often faster but also more expensive with a good number of software and improvements that we have, and at the end of the day is part of our eternal ongoing promise to customers to always deliver the best price performance of the market. Some of the benchmarks that we have on Gen2 are completely phenomenal relative to both to Snowflake, say, a year ago, but also to many of the competitive patterns there. So think of it as price performance, which continues to correlate with time to insight and time to value, and it’s a material step forward.

Alex Zukin: Perfect. Thank you, guys. Congrats.

Operator: Thank you for your question. Next question is from the line of Joel Fishbein with Truist. Your line is now open.

Joel Fishbein: Thanks for taking the question. Mike, you mentioned earlier on the call you had the strongest new logo quarter, which was fantastic. Just a question around that. Are you seeing — is this a result of better, stronger execution and strategic focus? Are you seeing a more favorable win rate in competitive environment? And just as a follow-up, too, to that is of those two $100 million deals, can you just tell us which verticals they were in? Thanks.

Mike Scarpelli: So on the $100 million deals, they were both in the financial services vertical. And what I would say on the number of new customers, this is not a result of something we put in place this quarter. We started last year with really breaking out an acquisition team that is just focused on new logos, and we’re seeing the benefits of the groundwork that we put in place last year and we’re pleased with the results. I think we have a very good leader there, and we’re replicating what we’re doing in North America and in EMEA as well, too. So we’re pleased with the number of new logos that we’ve added and it’s a big focus of ours.

Joel Fishbein: Thank you.

Operator: Thank you for your question. Next question is from the line of Brad Reback with Stifel. Your line is now open.

Brad Reback: Great. Thanks very much. Last quarter, Mike, you talked about some changes to the sales force comp plan as related to bookings and commits, not just consumption. Maybe an update on how that’s tracking and if that had any impact on the strong bookings in the quarter.

Mike Scarpelli: Yes. Obviously, I think it helped, but the real strong bookings for those two big deals that we knew were going to come in,. I would say, I think in general, salespeople are happy with having a bookings component, but still the principal driver is consumption revenue. And as a reminder, we paid on bookings [indiscernible] last year. So it’s not like we weren’t doing it last year. We just are giving them a quote for bookings as well, too. And I think it’s going to take some time to see the real roots of that change, whether that worked or not, but I’m pleased for Q1. I think they had a very solid Q1 and it definitely helped.

Brad Reback: That’s great. And then just a quick one, getting into the weeds a little bit. CapEx was up a bunch to a fairly high number. Is there onetime items there or is this the new level?

Mike Scarpelli: No. The CapEx was really associated with our new headquarters in San Mateo. As I spoke about previously, we signed a new lease in a Menlo Park office and there’s a fair bit of CapEx that went into that as well as in Bellevue. I talked about that before, too. That really — that just opened this week, that office, and there was a fair bit of CapEx that went into that as well, too. I’m not expecting any major office buildouts the next couple of years actually now.

Brad Reback: Perfect. Thank you.

Operator: Thank you for your question. Next question is from the line of Tyler Radke with Citi. Your line is now open.

Tyler Radke: Yes. Thanks for taking the question here. Mike, you talked about some strength in technology customers in the quarter. I was wondering if you could double-click on that. And what are you seeing specifically among kind of larger AI native customers in terms of their consumption?

Mike Scarpelli: It’s good. But as I called it before, we have a number of AI companies and they’re still less than 1% of our [indiscernible]

Tyler Radke: Okay, great. And the follow-up question I had was for Sridhar. We recently saw Databricks acquire Neon, which was a company that Snowflake Ventures had invested in. And I’m just curious if we can get an update on your strategy around Unistore. And just sort of your view on the positioning or some of these serverless databases that are out there in the market.

Sridhar Ramaswamy: We believed in transactional systems for This is why we got to work on Unistore about five years ago. Unistore, the product, is doing very well. as a standard is nothing to be scoffed at and it’s adopted widely. But we are very happy with what we have invested in terms of transactional stores so far. And we will continue to invest in the area because it’s a very natural addition to what we do.

Tyler Radke: Okay. Thank you.

Operator: Thank you for your question. There are no additional questions waiting at this time, so I’ll pass the call back to Sridhar for any closing remarks.

Sridhar Ramaswamy: Thank you. In closing, Snowflake is at the center of today’s enterprise AI evolution. Our focus on making Snowflake easy to use, connected to enable fluid access to data wherever it sits and trusted for enterprise-grade performance is what makes us differentiated and beloved by our customers. And we are committed to supporting them through their end-to-end data journey from inception to insights. Our product revenue growth and strong outlook for FY 2026 demonstrates our continued ability to execute at scale. Our pace of innovation coupled with our ability to bring products to market quickly is driving high growth, and we are committed to maintaining that momentum. We believe Snowflake’s long-term profile is one that showcases durable, high growth and continued margin expansion. It’s an exciting time for our company. I look forward to sharing more of our progress in the quarters ahead. Thank you all for joining us.

Operator: That concludes the call. Thank you for joining. You may now disconnect your lines.