Good afternoon. Thank you for attending today's Snowflake Q3, fiscal year 2026, earnings call. My name is Jayla, and I'll be your moderator for today. All lines will be muted during the presentation portion of the call, with an opportunity for questions and answers at the end. 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 Robbins, 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 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 throughout their end-to-end data lifecycle and effectively harness AI's potential every step of the way. Our continued focus on operational rigor and close-knit product 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, TS Imagine, a global SaaS platform for financial services, used Snowflake Intelligence to build an AI agent that now handles tasks equal to eight and a half 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 platforms for more than 100 million fans worldwide. This unified data foundation helps Fanatics better understand its customers, boost sales, and grow its advertising business, powering the launch of new Fanatics advertising audience networks this year. This momentum has enabled us to achieve a major milestone: $100 million in AI revenue run rate, achieved one 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 lifecycle. 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. 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 capabilities 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 that 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 integrations, enable secure and seamless data access across the systems our customers use every day, and unblock 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 turnout representing a more than 40% year-on-year increase in participation from last year. More recently, our annual Build Developer Summit saw a 43% increase in attendance year-over-year, underscoring the growing excitement and engagement across our global audiences. 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 Bobsled 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 ice for a medal-worthy performance. 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 industries. Even as we supercharge the data lifecycle with AI, we remain committed to strengthening our core data foundation to ensure that Snowflake will continue to deliver the trusted, performant, 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. Take EVgo, which is using OpenFlow 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 the Telemetry 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 insights. 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 have 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, it 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 industries. I'm incredibly proud of our teams for the 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'm pleased to introduce Brian Robbins 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 2,000 customers now total 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 four nine-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 were 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 one 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 and $1.2 billion, representing a 27% year-over-year growth. We expect non-GAAP operating margin of 7%. We're raising our FY26 product revenue guidance. We now expect product revenue of approximately $4.446 billion, representing 28% year-over-year growth. We are reiterating our FY26 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 ready to support our next phase of growth. Second, I prioritized spending 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 placing 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 seeing the 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 discipline execution. With that, I'll now pass the call to the operator for Q&A. At this time, if you would like to ask a question, it is star followed by one, or your telephone keypad. If for any reason you would like to remove that question, it is star followed by two. Again, to ask a question, it is star one. As a reminder, if you're using a speakerphone, please remember to pick up your headset before asking a question. All questions are limited to just one question each. Our first question comes from Sanjit Singh with the company Morgan Stanley. Sanjit, your line is now open. Yeah, thank you for taking the questions. 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 versus the endpoint of guidance. But at the same time, when I look at your Q4 guide, it's 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 Sridhar, really impressive in terms of getting to that $100 million AI revenue run rate. You mentioned on the press release that Snowflake Intelligence is one of the fastest adopting products. So why don't you 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? Thank you for the time. Thanks, Sanjit. 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 was really only one surprise in the quarter, and that was the hyperscaler outage, which impacted our revenue approximately $1-$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 to $4.446 billion, and the FY guide is really the most meaningful signal, and I think the guide really reflects the underlying behavior that we've seen in our customer base going into the fourth quarter. Sridhar, over to you. Yeah. Snowflake 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 for my sales team, whether it is consumption information or the Workday hierarchy itself of who is managing whom, 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 dimensions, analyze data in ways that previously were simply not possible before. And so we have a slew of customers, whether it is the USA Bobsled team or Fanatics or folks like ServiceNow or TSImagine 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 whenever I have dinners 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 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 for us. Appreciate that thought, Sridhar. Thanks. The next question comes from Kirk Materne with the company Evercore ISI. Kirk, your line is now open. Yeah, thanks very much. And thanks for taking the question, guys. 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 new customer wins. And I realize you all land small and then grow with your customers. But with AI coming on and 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. Thanks very much. Hey, Kirk, thanks for the question. I think things like Snowflake Intelligence now play a key role in making the power of data come alive every single time we are pitching a new logo. One 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, say, 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 a helping hand. It's not the dominant thing, but definitely having a helping hand in close to 50% of the new logos that we acquired. I would say it 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 of the data lifecycle more efficient. I've used OpenFlow. It's pretty magical to be able to sync data, whether it's from an Oracle OLTP system or from Google Drive, onto Snowflake. I think shrewd 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 Snowpark, 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. Brent, your line is now open. Thanks. Sridhar, good to hear the news on AI bookings inflection. 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 reinfluence even more excitement on the platform? How do you think about the trajectory? And does it have a bigger ramification back after 2026 then as those deals go live? 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 predict 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 forecasts 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, great to see. Is that in backlog, or what goes into backlog from that relationship? The 200 million is a buy-side that we're buying from Anthropic. And in some ways, it's obviously our confidence 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. Brad, your line is now open. Hey, guys. 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 circumstances last quarter where some positive things came together to drive a pretty strong result. Just in general, I think across all of the cloud names, we've seen pretty strong momentum this year. 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. 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? Thanks. I'll start. We are super early with migrations. I think you folks heard Matt Garman say today that he thinks maybe we are 15%-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 pull 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 Datometry, 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 lifecycle. 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. Yeah, just real quickly on the 4Q guidance. All I would say is that 4Q is a little tricky in the sense that you're given the 4Q guidance and the 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. The next question comes from Raimo Lenschow with the company Barclays. Raimo, your line is now open. Thank you. One question to stick to that one question. Sridhar, Zero Copy comes up a lot in the conversation. And every vendor is now talking about, "Oh, we're doing Zero Copy. That helps to kind of help us play better with everyone else in the ecosystem," etc. 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? Thank you. Yeah. Zero Copy generally comes up 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 a 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 these 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 that single pane of glass sort of view on everything that matters to them. And obviously, with agentic AI and agentic 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. The rest of the stuff works as though it is magic. 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. Okay. Perfect. Thank you, Bracelin. Our next question comes from Mark Murphy with the company JPMorgan. Mark, your line is now open. Hey, this is Arti on for Mark Murphy. Thanks for taking my question. Congrats on the strong quarter and continued momentum. I know you've touched on this theme 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 influencing their 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? Thanks. Yeah. The strongest pattern that we have had to work hard and earn 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 products that produce the magic. And so building on earlier products like Cortex Analyst as well as Cortex Search, Snowflake Intelligence, the agentic platform that can use these different subproducts flexibly, is the big unlock for us. What you're also seeing is a number of these customers have tried to string together agentic systems by, let's say, creating MCP servers on tables and sticking them into a foundation model. 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 fail-safe, that they're reliable and can actually answer the questions they're 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 are clearly valuable and useful for every business user, I think that's the narrative shift that you are seeing in a number of these companies, and Agentic AI is still evolving. 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. Thanks so much. Very insightful. Narrative shift, I think, is a great way to describe it. Our next question comes from Kash Rangan with the company Goldman Sachs. Kash, your line is now open. Hey, this is Matt Martino on for Kash. Sridhar, I want to stick with the AI topic here. The number of customers leveraging Snowflake AI is accelerating very, very quickly within your installed base, and you're able 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? Thanks a lot. Yeah. 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 Snowflake, the company itself, has had to use to have an effective data strategy. And with things like Snowflake Intelligence and Streamlit, which we are very heavy users of, 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 able to tackle as the data platform. Thanks, Sridhar. Our next question comes from Alex Zukin with the company Wolfe Research. Alex, your line is now open. Yeah. Hey, thanks, guys, for taking the question. 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 multi-product portfolio more broadly, and then, Brian, any timing elements. Last quarter, it seemed like there was a little bit more of a one-time bump or boost to product revenues from consumption from some very large deals in the quarter, but then this quarter, you also had four 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 a 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 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 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 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, 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 two and a half to two and a half years-ish on average. It's not really directly tied to consumption within a quarter, and I would not read too much into timing constraints like that. Brian? Yeah. 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 was 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 Colville with the company Scotiabank. Patrick, your line is now open. Thank you for having me on. I guess, Sridhar, Brian, one for both of you, please. Past 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? Thank you. The $100 million is primarily the product suite, but it's the whole stack. It is Cortex AI and AI SQL. It's accessible from SQL also as a REST API. 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. And then Snowflake Intelligence, which builds on these building blocks to provide an agentic 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 dataset that is in Snowflake be AI-ready. You're already seeing this play out in the collaboration space, where instead of sharing a dataset, you can, in fact, share an agent on top of that dataset so that the recipient on the other side can straight out just start asking business questions off 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. My point is making all data in Snowflake AI consumable and making the act of making that AI consumable is something that we will honestly be spending a lot of time on. 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 OpenFlow, or 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 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. Brad, your line is now open. Great. Thanks very much. Sridhar, the results are very impressive. The booking is super great. The operating margin obviously downticked 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? Thanks. 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 two quarters because we saw tremendous opportunity. And what we are 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 or solution engineers. We have rolled out coding agents for those folks. I talked earlier about how we want to make it super easy for every single rep, every single solution engineer to be able to do custom demos, custom POCs for our customers. Obviously, we have a big services team as well. Making them AI native is a big transformation. So the way we look at next year is, yes, we will continue to invest in the business, but I think there are 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. Yeah. 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. Mike, your line is now open. Great. Thanks for taking the questions, guys. And Brian, congratulations again on the new role of CFO over at 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 at 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 attributed it, or I thought that you guys positioned it as 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, we'd love to get your perspective on whether the guidance philosophy has changed at all at the margin. Thanks again from my side. We appreciate the questions. Yeah. Let's 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 recalibrate ourselves back to the 3% beat. And there is also 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 as actually still a very solid beat at some 2.5%. Yes, the Q2 beat, and we are upfront with you about it, had some large migrations that also had one-time activities. But we also have cautioned you that large migrations are lumpy 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? Yeah. 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. Basically, we snap the chalk line and where they're at in 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, where 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 I did when I first joined, but one of the things I did was spent a lot of time with the team that wrote all the AI models that does the forecasting on a daily business of our revenue. Super impressive team, very detailed, and I can assure you that there'll be no change to the guidance philosophy. Our next question comes from Matt Hedberg with the company RBC. Matt, your line is now open. Great. Thanks for taking my question. 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 are thinking about that long-term balance of OLTP and OLAP within Snowflake. Yeah. 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 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'll take us a couple more months to get the product into GA. But all of the early conversations that we have is that customers are very welcoming of Postgres 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 both of these. But I think bringing Postgres to market will be an important step forward for us, especially for things like agentic solutions that need an OLTP store to function effectively. 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. Tyler, your line is now open. Thanks very much for squeezing me in. Really impressive to see roughly $1 billion of RPO bookings in the quarter. I was hoping you could talk a little bit about the three nine-figure deals that you had in the quarter. How are those sort of structured from a duration perspective? And are you expected to see significant growth in those deals? In other words, were they large expansions? And then just a follow-up for you, Brian. Anything we should be thinking about as it relates to FY27, whether it's headwinds or tailwinds on the model? I know you're not giving guidance, but just as we think about new products, optimization headwinds, anything you'd call out. Thank you very much. Well, as a matter of fact, Tyler, we had four nine-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, you want to take the last question? Yeah. Tyler, as you mentioned, we'll guide to FY27 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 we at Snowflake are focused on empowering our customers throughout the end-to-end lifecycle 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 evidenced by our product revenue growth and strong outlook for the remainder of fiscal 2026. We see a long runway of durable high growth and continued margin expansion ahead. Thank you all. That will conclude today's conference call. Thanks for your participation and enjoy the rest of your day.