Earnings Call Transcript - SNOW Q1 2025

Operator, Operator

Hello, everyone. Thank you for attending today's Q1 Fiscal Year 2025 Snowflake Earnings Call. My name is Sierra, and I will be your moderator today. All lines will be muted during the presentation portion of the call, with an opportunity for questions and answers at the end. I would now like to pass the conference over to our host, Jimmy Sexton, Head of Investor Relations.

Jimmy Sexton, Head of Investor Relations

Good afternoon, and thanks for joining us on Snowflake's Q1 fiscal 2025 earnings call. Joining me on the call today is 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 session. During today's call, we will review our financial results for the first quarter fiscal 2025 and discuss our guidance for the second quarter and full-year fiscal 2025. 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 from 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'll also discuss certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in today's earnings press release. The earnings press release and an accompanying 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, CEO

Thanks, Jimmy, and good afternoon everyone. Before we get into it, many of you have given me a warm welcome to my new role over the past few months and I just wanted to say thank you. I've been focused on three key priorities in my first quarter as CEO: listening to and learning from our customers, driving execution and alignment within our go-to-market teams, and fueling our innovation and product delivery. I have been really impressed by how the team has responded and by our overall pace of play. We have a lot of opportunity ahead of us, and there's a lot of excitement across our company to go and get it. When I look at the Snowflake growth story, it was first driven by an amazing data product and then by the layers of collaboration and applications that we added on top to make Snowflake a true data cloud. What is exciting about AI is that it can turbocharge our capabilities and growth on all three layers. It also helps democratize access to all the amazing enterprise data in Snowflake, massively increasing our reach. The progress we've made in AI over the last year, culminating in the past quarter is remarkable. We believe AI is going to continue to fuel our platform, helping our customers perform and deliver customer experiences better than ever. As evidence of our Q1 results, our core business is very strong. We're still in the early innings of our plan to bring our world-class data platform to customers around the globe. And in the first quarter alone, we saw some of our largest customers meaningfully increase their usage of our core offering. The combination of our incredibly strong data cloud, now powerfully boosted by AI, is the strength and story of Snowflake. I want to touch on our Q1 results and Mike will get into the details with you. I'm really proud that our team delivered a very strong Q1. Product revenue for the quarter was $790 million, up 34% year-over-year. Remaining performance obligations totaled $5 billion, year-over-year growth accelerated to 46%. And non-GAAP adjusted free cash flow margin was 44%. Given the strong quarter, we are increasing our product revenue outlook for the year. Working through the second quarter and beyond, our priorities remain the same. I've had conversations with over 100 customers over the past several months, and I'm very optimistic. Snowflake is a beloved platform, and the value we bring comes through in every customer conversation I have. We are critical in helping our customers run their businesses. For example, one of the largest US telecommunications companies relies on us to help them close their books every month. We also help a global financial services customer with their counterparty credit risk process. The art of the possible on Snowflake is truly incredible. It's also probably no surprise that AI is top of mind for our customers as well. They want to make all business data in Snowflake available to everyone, not just the business analyst. They want us to help drive clarity, value creation, and reliability as they enter this new frontier. Over the last quarter, my time spent with our go-to-market teams has been focused on driving execution and alignment. Internally, we emphasize consumption and new customer acquisition. And we're developing an end-to-end cadence for both priorities. This includes developing sales motions in specific workloads, such as AI and data engineering. We have more to gain as we standardize our consumption mindset and effectively execute. We expect that this efficiency will contribute to further revenue growth. Those of you who know me know that I have a relentless focus on product innovation and delivery. Teams across the company are building and delivering at an incredible pace. Earlier this month, we announced that Cortex, our AI layer, is generally available. Iceberg, Snowpark Container Services, and Hybrid Tables will all be generally available later this year. We're investing in AI and machine learning, and our pace of progress in a short amount of time has been fantastic. What is resonating most with our customers is that we are bringing differentiation to the market. Snowflake delivers enterprise AI that is easy, efficient, and trusted. We've seen an impressive ramp in Cortex AI customer adoption since going generally available. As of last week, over 750 customers are using these capabilities. Cortex can increase productivity by reducing time-consuming tasks. For example, Sigma Computing uses Cortex language models to summarize and categorize customer communications from their CRM. In the quarter, we also announced Arctic, our own language model. Arctic outperformed leading open models such as LLaMA-2-70B and Mixtral 8x7B in various benchmarks. We developed Arctic in less than three months at one-eighth the training cost of peer models. AI serves as a bridge between structured and unstructured data. We see this with Document AI; customers find value in extracting features on the fly from piles of documents. We're making meaningful progress on Snowpark Container Services being generally available in the second half of the year, and dozens of partners are already building solutions that will leverage container services to serve their end customers. We view Snowpark and other new features as our emerging businesses. These are in the early days of revenue contribution, but we're seeing very healthy demand. More than 50% of customers are using Snowpark as of Q1. Revenue from Snowpark is driven by Spark migrations. In Q1, we began the process of migrating several large Global 2000 customers to Snowpark. Our collaboration capability is also a key competitive advantage for us. Nearly a third of our customers are sharing data products as of Q1 2025, up from 24% one year ago. Collaboration already serves as a vehicle for new customer acquisition. Through a strategic collaboration with Fiserv, Snowflake was chosen by more than 20 Fiserv financial institutions and merchant clients to enable secure direct access to their financial data and insights. We announced support for unstructured data over two years ago. Now about 40% of our customers are processing unstructured data on Snowflake. And we've added more than 1,000 customers in this category over the last six months. Iceberg is enabling us to play offense and address a larger data footprint. Many of our larger customers have indicated that they will now leverage Snowflake for more workloads as a result of this functionality. More than 300 customers are using Iceberg in public preview. Snowflake has a powerful and unique partner ecosystem. Part of our success is that we have many partners that amplify the power of our platform. They range from big organizations like EY and Deloitte, but also firms like LTIMindtree and Next Pathway. S&P Global sees us as a strong collaborator in their cloud distribution model. Companies like Observe, Blue Yonder, RelationalAI, Fivetran, Hex, and Domo have built their software on top of Snowflake. These partners bring entirely new capabilities and unlock new use cases for us and our customers. They also often bring new customers to us. And they really care about how easy it is to build on Snowflake, how reliable Snowflake is, and also about how we can jointly go to customers. Partners bring enormous power to our data cloud vision. Their success creates success for us and our customers. To wrap it up, Snowflake is the world's best enterprise AI data platform. Combined with our collaboration capability and thriving application platform, we are driving powerful network effects that will fuel our growth. AI vastly amplifies this opportunity both in the near and medium terms. Our product philosophy is simple: one platform with all features available. We're turning every analyst and data engineer into a sophisticated AI analyst. The magic of Snowflake is that we make difficult tasks easy. Stay tuned for more to come at the Snowflake Data Cloud Summit coming up in San Francisco, June 3rd through the 6th. I look forward to seeing you all there. Now I'll turn it over to Mike.

Mike Scarpelli, CFO

Thank you, Sridhar. Q1 product revenue grew 34% year-over-year to $790 million. Our largest growth contributors included a media entertainment Global 2000 and a large retail and consumer goods company. Smaller accounts outside of the Global 2000 were an important source of performance. Inter-quarter, we saw strong growth in February and March. Growth moderated in April. We view this variability as a normal component of the business. Excluding the impact of leap year, product revenue grew approximately 32% year-over-year. We continue to see signs of a stable optimization environment. Seven of our top 10 customers grew quarter-over-quarter. Q1 marked the first quarter under our FY '25 sales compensation plan. Our sales reps are executing well against their plan. In Q1, we exceeded our new customer acquisition and consumption quotas. Non-GAAP product gross margin of 76.9% was down slightly year-over-year. As mentioned on our prior call, we have headwinds associated with GPU-related costs as we invest in new AI initiatives. Our non-GAAP operating margin of 4% benefited from revenue outperformance. Our non-GAAP adjusted free cash flow margin was 44%. As a reminder, Q1 and Q4 are seasonally strong quarters for non-GAAP adjusted free cash flow. We ended the quarter with $4.5 billion in cash, cash equivalents, short-term and long-term investments. In Q1, we used $516 million to repurchase 3 million shares at an average price of $173.14. We have $892 million remaining under our original $2 billion authorization. Now let's turn to our outlook. As a reminder, we only forecast product revenue based on observed behavior. This means our FY '25 guidance includes contributions from Snowpark. FY '25 guidance does not include revenue from newer features such as Cortex until we see material consumption. Iceberg will be GA later this year. We have invested in Iceberg because we expect it to increase our future revenue opportunity. However, for the purpose of guidance, we continue to model revenue headwinds associated with the movement of data out of Snowflake and into Iceberg storage. The negative impact is weighted to the back half of the year. For Q2, we expect product revenue between $805 million and $810 million; we are increasing our FY '25 product revenue guidance. We now expect full year product revenue of approximately $3.3 billion, representing 24% year-over-year growth. Turning to margins, we are lowering our full year margin guidance in light of increased GPU-related costs related to our AI initiatives. We are operating in a rapidly evolving market, and we view these investments as key to unlocking additional revenue opportunities in the future. As a reminder, we have GPU-related costs in both cost of revenue and R&D. We announced our intent to acquire certain technology assets and hire key employees from TruEra. TruEra is an AI observability platform that provides capabilities to evaluate and monitor large language model applications and machine learning models in production. We are excited to welcome approximately 35 employees from TruEra to Snowflake; the impact of the transaction is reflected in our outlook. For Q2, we expect 3% non-GAAP operating margin. For FY '25, we expect 75% non-GAAP product gross margin, 3% non-GAAP operating margin and 26% non-GAAP adjusted free cash flow margin. Finally, we will host our Investor Day on June 4 in San Francisco in conjunction with the Snowflake Data Cloud Summit, our annual users conference. If you are interested in attending, please email [email protected]. With that, operator, you can now open up the line for questions.

Operator, Operator

Our first question today comes from Keith Weiss with Morgan Stanley. Please proceed.

Keith Weiss, Analyst

Excellent. Very nice quarter, guys. And thank you for taking the question. Looking at the front page of the investor relations page, 5 billion queries. It looks like your query volume is actually accelerating now again. Can you walk us through some of the drivers of that acceleration? Is it new products that are driving the acceleration? Or is it the relief of optimization or just a better data center? So just a little bit more clarity on what's driving that acceleration. And then on the other side of that equation, it looks like there's still pressures on the price per query. Any indications on whether that pressure on the price per query is coming more from the compute side of the equation or the storage side of the equation? Any color there would be super helpful.

Sridhar Ramaswamy, CEO

Thank you. Overall, as both Mike and I said, our core business is very strong, and growth is coming from both new customers as well as expansion from existing customers. As we gain more and different kinds of workloads, for example, AI and data engineering, they are increasing quite nicely and are all contributing to additional credit growth. The relationship between credit growth and cost per query is not a simple straightforward one. We look for broad growth across the different categories of workloads that we handle, and they've all been doing really well.

Operator, Operator

Our next question today comes from Mark Murphy with JPMorgan. Please proceed.

Mark Murphy, Analyst

Great, thank you very much. I’ll add my congratulations. Sridhar, you trained Arctic LLM with a pretty amazing efficiency. Could you walk us through the architectural difference in the product that might allow it to run more efficiently than other products out there in the market? And, Mike, is there any directional change to the $50 million target for GPU spend this year, just considering the launch of Cortex and Arctic LLM and it sounds like some Snowpark traction. Should we think of that trending a little higher?

Sridhar Ramaswamy, CEO

Thank you. So absolutely, we did train Arctic in a remarkably short period of time, a little over three months, on a remarkably small amount of GPU compute. A lot of the training efficiency of these models comes from architectures. We had a rather unique mixture of experts architecture. These are increasingly the architectures that are driving impressive gains for all of the other leading AI companies. But what also went into it was just an amazing amount of pre-experimentation in order to figure out things like what are the right data sets, what orders should they be fed in, and how do we make sure that they're actually optimizing for enterprise metrics, the kind of things our customers care about, such as whether these models are good at creating SQL queries, for example, so they can talk to data. We are taking very much the view of how do we make AI much better in an enterprise context because that’s where we have the most value to add, and our AI budgets are modest in the scheme of things. Being creative in how we develop these models is something that the team comes to naturally expect. I think that kind of discipline and scarcity, to be honest, produces a lot of innovation. I think that's what you're seeing. And then in terms of investments, I'll hand it over to Mike in a second. But I'm comfortable with the amount of investments that we are making. Part of what we gain as Snowflake is the ability to fast follow on a number of fronts, as well as the ability to optimize against metrics that we care about, not producing the latest, greatest, biggest model, let's say, for image generation. Having that kind of focus lets us operate on a relatively modest budget pretty efficiently. The focus very much now is on how do we take all of the products that we have released into production. We have over 750 customers that are busy developing against our AI platform. This is a fast-moving space, but we are very comfortable with both the pace, the investments, and the choices that we are making to make AI effective for Snowflake. Mike?

Mike Scarpelli, CFO

And I will add that, yes, we think we may be spending a little bit more on GPUs, but it's also about the people that we're hiring, specifically in AI. We talked about the acquisition of TruEra. Those people all fall into that organization. As I mentioned, the world of AI is rapidly evolving, and we are investing in that because we believe there’s a massive opportunity for Snowflake to play there, which will have a meaningful impact on future revenues.

Mark Murphy, Analyst

Thank you very much.

Operator, Operator

Our next question today comes from Kirk Materne with Evercore. Please proceed.

Kirk Materne, Analyst

Yeah, thanks very much and congrats on the quarter. Sridhar, can you just talk a little bit about how we should think about your customers' time to value with Cortex? How long do you think it takes them to start using the technology before it can start to translate into a little bit faster consumption patterns? And then just one for Mike. Mike, can you just talk a little bit about deferred? This quarter was down perhaps a little bit more sequentially than we've seen in prior years. I don't know if there's anything on-time in nature there, but if you could just touch upon that, that would be great. Thank you all.

Sridhar Ramaswamy, CEO

Thank you. One of the cool things about Cortex AI and our AI products in general, in the context of the consumption model, is that our customers don't have to make big investments to see what value they'll get because they don't have to make commitments to how many GPUs they're going to be renting, for example. They just use Cortex AI, for example, from SQL, which is very easy to do without a pre-commitment. This means that they can focus very much on value creation. The structure of Cortex AI is also such that anybody that can write SQL can now begin to do interesting things, like looking at how often a particular product was mentioned in an earnings transcript or being able to go from other kinds of unstructured information, whether it is text or images, to structured information, which Document AI, our AI product there does. We very much want to structure all of these efforts so that our customers can iterate very quickly, take things to production, get value out of it, and then make bigger commitments on top. That's one of the benefits that come from making the technology super easy to adopt. There's no massive learning curve, nor is there a GPU commitment or other kinds of software engineering required to use AI with Snowflake.

Mike Scarpelli, CFO

Yeah. On your question about deferred, Kirk, if you're referring to January to today, the end of the year, Q4 is always a very big billing quarter. Q1 is not as big of a billing quarter, so you have that flowing through on the deferred revenue. However, RPO, as Sridhar mentioned, is up 46% year-over-year. For instance, we signed a $100 million deal this quarter with a customer who pays us monthly in arrears, so it doesn't show up in deferred revenue. We've signed several deals with big companies that pay us monthly in arrears that don't show up in deferred revenue, but they're in RPO.

Kirk Materne, Analyst

That’s helpful. Thanks, Mike. Thanks, Sridhar. Appreciate it.

Operator, Operator

Our next question comes from Karl Keirstead with UBS. Please proceed. Karl, your line is now open.

Karl Keirstead, Analyst

I’m sorry. Mike, could you elaborate on the comment that usage growth moderated in April? Maybe you could unpack that and explain why it usually does. And then also, when I look at your Q2 and fiscal '25 revenue guidance, it's actually pretty solid. So that would lead one to believe that whatever moderation there might be in April doesn't feel like it according to your guidance rolled into May. Just curious if that's the correct interpretation. Thank you.

Mike Scarpelli, CFO

What I would say is February and March were very strong. April was more muted. Just as a reminder, there are holidays like Ascension Day or Easter in Europe, where companies take a long time off, which impacts consumption. Remember, this is a daily consumption model. The guidance we gave is based upon what we're seeing through our customers as of this week.

Kirk Materne, Analyst

Okay, and Mike, if I could ask a follow-up. You had mentioned previously, including, I think, at a conference in March that your efforts around the tiered storage side, whereby we could see some roll-off on the storage revenues could begin to impact the P&L in the April quarter. Was that the case? And would you be able to approximate the impact the roll-off on the storage revenues had? Thank you.

Mike Scarpelli, CFO

Sure. We did roll out to all of our customers the tiered storage pricing, which started at the end of last year. Depending on the amount of commitment you're making on an annual basis, you get tiered storage pricing. Essentially, you get your storage discounted from the list price of $23 per terabyte. In this quarter, that impacted us somewhere between $6 million and $8 million. I don't remember the exact figure, but that’s pure margin that was impacted. That’s not to say there are other big customers where we've always discounted their storage given their size. That is just the pure impact due to the tiered storage that was rolled out to everyone. That will continue to impact as people renew their contracts. However, storage mix as a percent of revenue has remained pretty much consistent at 11%. That did not change. We're actually seeing growth in storage with Snowflake.

Kirk Materne, Analyst

Got it. Okay. Thank you for both answers. Super helpful.

Operator, Operator

Next question comes from Raimo Lenschow with Barclays. Please proceed.

Raimo Lenschow, Analyst

Thank you. Sridhar, thank you for all your comments around the AI evolution for you guys. Is there a kind of a vision for you where is the demarcation line in a way where you want to play versus where you don't want to play in this new AI world? Obviously, there's a lot of LLMs out there. Do you need to do observability? Or is that more for people to hire or kinds of knowledge? Can you just clarify how your thinking is evolving? Thank you.

Sridhar Ramaswamy, CEO

This is a fabulous question. First and foremost, it is important for all of us to acknowledge that AI language models are going to impact multiple levels of what you can think of as a data stack. For instance, the way in which people are migrating from an old system, an on-prem system to something like Snowflake will be facilitated by the presence of a Copilot that can do much of the translation. We already have such a translation product and we think AI is going to make that go even faster. In other areas, such as data cleansing, data engineering, which may not be as flashy but require huge investments to ensure that data is enterprise-grade, we think AI will play a significant role in both the creation of those pipelines and in ensuring data cleanliness. For example, if PII inadvertently enters a table or a distribution becomes skewed, language models can help detect deviations from patterns. Looking up the stack, we have a powerful product for writing SQL, our Copilot within our user interface, that can significantly accelerate an analyst's ability to understand a data set and be productive with it. Ultimately, this leads to a data API which puts enterprise data into the hands of a business user, but with a high degree of reliability. My point is there is a broad impact. Automating some of the work that an analyst has to do, such as troubleshooting problems, will be things that a language model can accomplish. For a variety of problems, small models, like those we are capable of developing from scratch, like we did for Document AI, or a midsized model like Arctic, can effectively suffice for most applications. There are academic benchmarks, such as MMLU, which is notoriously difficult and heavily relies on model size and resources spent on training those models. We can accomplish a great deal with a small team under a modest investment without needing to compete at the level of companies that are spending billions of dollars. We don't need to be there. Being focused on what we need to deliver for our customers will take us far, with the current level of investment. I'll add that we have excellent partnerships with many entities. Today, I wrote about how we're collaborating with various AI companies, but we also have partnerships with Mistral and Reika, among others. The field of AI is so vast that I don't think any single company will produce every model out there. We excel at developing the models needed in our core business and actively collaborate with a broad range of players for other kinds of models. They see value in our 10,000 customers and strive to go to market together, which I believe will trend positively into the indefinite future regarding our strategy.

Raimo Lenschow, Analyst

Okay, perfect. Thank you.

Operator, Operator

Our next question today comes from Brent Thill with Jefferies. Please proceed.

Brent Thill, Analyst

Mike, on the acceleration of RPO up 46%. I know you mentioned the $100 million deal. But was there anything else that was surprising to you in the quarter that helped in this reacceleration? Any other notable trends that maybe you haven't seen or you're starting to see now?

Mike Scarpelli, CFO

Remember that 46% is year-over-year. The year-ago comparison didn't include the $250 million deal we signed in Q4 that went into there. There was another $100 million deal signed subsequent to that as well. Overall, we are very pleased with the number of CAP 1s in our bookings in Q1 and the several $100 million deals. We are very pleased with our business as our customers deepen their commitment to Snowflake.

Brent Thill, Analyst

And quickly for Sridhar, I know you mentioned the priorities are the same, but you are the new CEO. From your perspective, where are your top priorities for the rest of 2024?

Sridhar Ramaswamy, CEO

I touched on them. Driving product innovation faster is definitely way up there in the list. You see this coming to fruition with things like how rapidly our AI platform, Cortex AI, came to market, or what we did with Arctic. Yet, I want to stress again that we see incredible potential across our AI data cloud. AI-related initiatives are one part of this, but support for Iceberg is actually an exciting new chapter for all players in data. We had an announcement yesterday and today at the Build Conference. The general theme is we are able to leverage Snowflake for more of the data that is sitting in data layers, in addition to new technologies such as Hybrid Tables, which expand the types of applications running on Snowflake. Product innovation is one focus. Just as equally importantly, helping our go-to-market teams take these products to market, having specialization to zone in on the applications delivering the most value for our customers, ramping up on just enabling Snowflake internally and also doing a great job of enablement with our many partners. That broad suite of bringing products to market, I would say is my other top priority. I also spend significant time on the road talking to customers, roughly every other week. That's how I get to meet over 100 customers in approximately 70 days. This gives you a rough breakdown of my priorities: making sure I’m present with customers and field personnel, focusing on product execution, and improving go-to-market efficiency.

Brent Thill, Analyst

Thank you.

Operator, Operator

Our next question today comes from Matt Hedberg with RBC. Please proceed.

Matt Hedberg, Analyst

Sridhar, we spend a lot of time focused on the investments you're making in R&D and GPUs. However, I'm wondering about your sales and marketing forecast and maybe what you've learned from your time there, especially when you noted expanding your reach. Specifically, does your sales motion need to change or evolve when talking to, say, data scientists, for instance?

Sridhar Ramaswamy, CEO

This is a great question, and I touched on this in the answer to my previous question. Absolutely. The kind of product offerings needed to effectively engage a data science team differs from those needed for a team running warehouses. What’s exciting, and I can tell you from many conversations with customers, is that applications written on top of Snowflake, referred to as managed applications, allow our customers to utilize their data and share it actively with their clients, placing us in direct conversations with business leaders in these companies as we become part of their revenue generation strategies. Yes, different product strategies are needed for different products and the varying people who can benefit from them. We created a specialized partner organization, for example, that focuses explicitly on data providers who can bring additional data to Snowflake and drive revenue opportunities. Similarly, with AI, we need people who feel comfortable navigating the world of language models. Our strength lies in making AI accessible to all analysts. That is a major benefit. Thus, change is occurring in our go-to-market strategy, though it's a gradual process. We constantly consider the best ways to take specific products to market or solve individual customer challenges, which you can see reflected in how our field organizations are structured and managed.

Matt Hedberg, Analyst

That’s great. And maybe just a quick one for Mike. I appreciate the color on consumption trends. That’s super helpful. I know you said you based your guidance on what you've seen this week. I guess my question on May is whether you've seen May bounce back a bit versus the seasonally slow April.

Mike Scarpelli, CFO

As I said, our guidance is based upon consumption patterns we're observing in the quarter, and that’s reflected in our projections.

Operator, Operator

Our next question comes from Brent Bracelin with Piper Sandler. Please proceed.

Brent Bracelin, Analyst

Thank you. Good afternoon. Sridhar, you flagged Iceberg as a potential unlock that could accelerate growth. Maybe that's a longer-term view. Could you walk through how or why spending could actually go up for Snowflake in an environment where customers move to Iceberg?

Sridhar Ramaswamy, CEO

Iceberg is a capability to read and write files in a structured, interoperable format. Yes, some customers may move a portion of their data from Snowflake into Iceberg format because they have applications to run on their data. The fact is, however, that data lakes or cloud storage usually holds data that is often 100 to 200 times the amount of data stored inside Snowflake. Now, with Iceberg as a format under our support, you can run workloads with Snowflake directly on this data without having to wait for an uncertain future to pitch and win these use cases, whether in data engineering or otherwise. Iceberg becomes a seamless conduit into all this information that existing customers already possess, and that's the unlock I’m talking about.

Christian Kleinerman, Executive Vice President of Product

I would just add to what Sridhar said. We have many existing customers who echo what Sridhar described. They possess vast amounts of data ready for analysis that may not necessarily need to be copied or ingested into Snowflake; they want to combine data in Snowflake with that existing data. The opportunity here is very real. Our recent announcement with Microsoft is entirely about making data available through Iceberg, by which we aim to facilitate access and broader integration with Snowflake capabilities. Thus, the opportunity is not distant and does not require prolonged waiting periods.

Brent Bracelin, Analyst

Quick clarification for Mike here. We’re knocking down some big deals, including another $100 million deal in Q1. It sounds like another one in Q2. Last I checked, the macro environment is quite tough. What is driving that? Is the AI roadmap helping?

Mike Scarpelli, CFO

These are all existing customers and large customers. It remains core data warehousing, but they are all interested in and want to discuss our AI initiatives. Many of these deals, including the one mentioned by Mike, have us positioned at the core of their business.

Sridhar Ramaswamy, CEO

Several of these large deals, not the one Mike mentioned, but others in particular, are structured so that Snowflake becomes the key avenue for large customers to monetize their data by allowing their customers access. This serves as a catalyst for bigger commitments. AI certainly helps in all of these scenarios, and these large investments reflect a strong belief in Snowflake as the AI data platform. Shall we go to the next question?

Jimmy Sexton, Head of Investor Relations

Operator, next question. I think we have audio issues.

Sridhar Ramaswamy, CEO

Yeah, we have a little audio glitch. Please be patient.

Jimmy Sexton, Head of Investor Relations

We can’t hear the operator.

Operator, Operator

Apologies, can you hear me now?

Jimmy Sexton, Head of Investor Relations

We hear you now.

Operator, Operator

Okay, I am so sorry about that. Our next question today comes from Patrick Colville. Your line is actually open. I apologize.

Unidentified Analyst, Analyst

This is Joe Vandrick on for Patrick Colville. Sridhar, I know you joined Snowflake about a year ago, but you've now been CEO for about three months. Just wondering if there's anything that surprised you or that's worth calling out that you've learned since stepping into the CEO role? Also curious about your view on a few other products, Streamlit and Unistore. If you could talk a bit about the customer engagement you're seeing there. Thanks.

Sridhar Ramaswamy, CEO

Yeah. I've been here at Snowflake close to a year. As I said, I've had many customer conversations. The amount of love and respect that our customers have for the core product, its usability, efficiency, and maintenance-free qualities that dramatically lower total cost of ownership continues to pleasantly surprise me. It's crucial that we maintain these qualities while releasing new products, and we take care to ensure that. Uniformly, the feedback we receive on Cortex, which is our AI layer, from informed tech reviewers, is that we truly make the hard things easy. Anyone that can write SQL can now accomplish some pretty impressive tasks using AI. I think that combination of simplicity and ease of use is an incredibly powerful asset for Snowflake. In terms of Streamlit, Streamlit is a rapid prototyping environment that allows you to write an application and have it run on Snowflake without additional setup. There are multiple applications inside Snowflake, whether it's our compensation information, finance information, forecast updates, or even chatbots I've personally created; they all run on Streamlit with incredible operational efficiency as part of the already existing Snowflake instance running in the customer deployment. Many users have adopted it extensively, showcasing Snowflake's functionality. We see this as a huge, hugely positive application and the team is also focused on developing notebooks, which will be an important priority going forward. So, lots of positive things on that side. Regarding Unistore or Hybrid Tables, these are meant to address workloads that are more transactional compared to the analytics workload traditionally associated with Snowflake. It is currently in public preview and will be generally available later this year. I think it opens new classes of applications that can run efficiently on top of Snowflake. The same Snowflake magic applies here: you don't need to deploy servers or manage Kubernetes clusters for operational tasks. We currently have roughly 300 customers actively using hybrid tables. We can expect significant growth in that number in the near future.

Christian Kleinerman, Executive Vice President of Product

Streamlit is now available on all three clouds, which has driven strong adoption. Regarding Hybrid Tables, many of our customers are awaiting general availability later this year.

Operator, Operator

Our next question today comes from Brad Reback with Stifel. Please proceed.

Unidentified Analyst, Analyst

Hi, this is Rob on for Brad. Thanks for taking the question. For Christian or Sridhar, over the past few months, including yesterday, Snowflake Ventures is investing in a few observability and logging companies. I'm wondering what the underlying strategy is with these visibility-type investments, and whether there is a big opportunity you are trying to address.

Christian Kleinerman, Executive Vice President of Product

Observability is very important for our customers. One aspect is data observability, which is crucial for understanding data quality and variations. Additionally, as we evolve Snowflake to host business logic and function as an application platform, there's a need for observability concerning code as well. For instance, how do I know what my Snowpark Container Service is doing? Or how do I troubleshoot and monitor Snowpark processes? Observability, therefore, is a significant priority for us, encompassing both data and code, and we'll continue to partner with various organizations to enhance our understanding of what's happening to data and code.

Sridhar Ramaswamy, CEO

In general, I can say that Snowflake is an excellent platform for developing applications. We often collaborate, and sometimes invest in companies that build compelling applications on Snowflake. Observability is one area of focus, but we maintain many partnerships with several customer data platforms, and our list continues to expand because we want to foster a vibrant ecosystem on Snowflake.

Operator, Operator

Our final question today comes from Tyler Radke with Citi. Please proceed.

Tyler Radke, Analyst

Thank you very much. Mike, you talked about some upside from smaller customers during the quarter. Could you discuss the nature of those smaller customers? Are they startups, maybe GenAI companies? Was this performance more of a one-off, or do you expect this strength to persist throughout the remainder of the year?

Mike Scarpelli, CFO

It was very much broad-based, and it's across all industries; I'm referring to the non-G2K companies. Some of these are quite large. There are many private companies involved, and this trend is widespread.

Tyler Radke, Analyst

Got it. And just a quick follow-up on the sales and marketing side. Both expenses and headcount have increased significantly. Is that primarily due to quota-carrying hires? Are they marketing personnel? Could you clarify what's driving this higher investment?

Mike Scarpelli, CFO

On the expense side, we mentioned previously that due to our change in the compensation plan, we would see more commission expenses recognized immediately rather than deferred and amortized. As I said, this does not affect cash flow, but it has added to our expenses. We're hiring a number of sales reps, primarily in the acquisition team within the commercial space, along with business development and sales development representative roles. We're adding personnel throughout the sales organization, including solutions engineers. We feel positive about our business performance; we've met our targets in Q1, and we're continuously evaluating headcount, planning to invest in the sales organization when we see the opportunity.

Tyler Radke, Analyst

Thank you.

Operator, Operator

Our final question comes from Alex Zukin with Wolfe Research. Please proceed.

Alex Zukin, Analyst

Hey, guys. Apologies for any background noise and congrats on a great quarter. First for Sridhar, you mentioned some interesting Cortex use cases from Sigma in your prepared remarks. Could you dig in a bit more and share some of the vision on how some of your larger customers are thinking about deploying Cortex and maybe Arctic? Additionally, how can this impact their experience when moving into more production-grade use cases?

Sridhar Ramaswamy, CEO

I believe I understand your question. I'll address it now. What Snowflake makes easy is the ability to analyze unstructured text information for sentiment or category feedback using techniques like vector embedding and soon the Cortex index. This allows for efficient identification of related support cases for new inquiries and automated response generation. We can consider this a prototype where a central repository contains previously answered questions alongside a language model that processes new queries. The language model automates this process, dispatching truly novel inquiries to a customer service representative while addressing previously resolved cases. The magic of language models lies in automation. Moreover, we aim to simplify access to structured data stored in Snowflake, enabling our customers to interact with this data without complicated procedures. We've developed a product that permits users to converse with Snowflake data, which will soon be in public preview. Our goal is to provide trusting, actionable insights based on this data to a larger user base. There are numerous other use cases. This is a subject I'm profoundly passionate about and could discuss extensively. Hopefully, you have a clear understanding of the types of applications I’m referring to. The first category focuses on unstructured data, while the second emphasizes structured data. Our vision is to integrate all these features into a single enterprise solution where users can ask a question and receive answers.

Alex Zukin, Analyst

Makes sense. Mike, you discussed consumption exceeding expectations and quotas. I want to explore whether there were specific verticals or geographies that particularly stood out or if Snowpark momentum contributed to that strength. Can you provide additional insights?

Mike Scarpelli, CFO

It was really strength in our core business and was broad-based across all verticals. Financial services continues to be our largest sector. However, we observed a notable upswing in technology and healthcare sectors. Their growth outperformed several other segments within the company, but overall, it was widespread.

Alex Zukin, Analyst

Perfect. Thank you, guys.

Mike Scarpelli, CFO

Okay. Thank you, everyone.

Operator, Operator

That will conclude today's conference call. Thank you all for your participation. You may now disconnect your lines.