Good afternoon, and thank you for joining the Snowflake Q2 Fiscal Year 2024 Earnings Conference Call. My name is Kate, and I'll be the moderator for today's call. 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 call over to your host, Jimmy Sexton, Head of Investor Relations at Snowflake. You may proceed.

Good afternoon, and thank you for joining us on Snowflake's Q2 fiscal 2024 earnings call. With me in Bozeman, Montana are Frank Slootman, our Chairman and Chief Executive Officer; Mike Scarpelli, our Chief Financial Officer; and Christian Kleinerman, our Senior Vice President of Product, who will join us for the Q&A... [Technical Difficulty]

Excuse me, ladies and gentlemen, it does look like the speakers have disconnected. One moment while we get them reconnected. [Operator Instructions] Ladies and gentlemen, we have the speakers back in the call. You may proceed with the presentation.

Good afternoon, again, and thank you for joining us on Snowflake's Q2 fiscal 2024 earnings call. With me in Bozeman, Montana are Frank Slootman, our Chairman and Chief Executive Officer; Mike Scarpelli, our Chief Financial Officer; and Christian Kleinerman, our Senior Vice President of Product, who will join us for the Q&A session. During today's call, we will review our financial results for the second quarter fiscal 2024 and discuss our guidance for the third quarter and full year fiscal 2024. During today's call, we will make forward-looking statements, including statements related to the expected performance of our business, future financial results, strategy, products and features, long-term growth, our stock repurchase program, and overall future prospects. These statements are subject to risks and uncertainties which could cause them to differ materially from actual results. Information concerning these risks is available in our earnings press release distributed after market close today and in our SEC filings, including our most recently filed Form 10-Q for the fiscal quarter ended April 30, 2023, and the Form 10-Q for the quarter ended July 31, 2023, that we will file with the SEC. We caution you to not place undue reliance on forward-looking statements and undertake no duty or obligation to update any forward-looking statements as a result of new information, future events, or changes in our expectations. We'd also like to point out that on today's call, we will report both GAAP and non-GAAP results. We use these non-GAAP financial measures internally for financial and operational decision-making purposes and as a means to evaluate period-to-period comparisons. Non-GAAP financial measures are presented in addition to and not as a substitute for financial measures calculated in accordance with GAAP. To see the reconciliations of these non-GAAP financial measures, please refer to our earnings press release distributed earlier today and our investor presentation, which are posted 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 Frank.

Thanks, Jimmy. Welcome, and good afternoon. Q2 product revenue grew 37% year-over-year to reach $640 million. Non-GAAP product gross margin expanded to 78%. And non-GAAP adjusted free cash flow was $88 million, representing 50% year-over-year growth. In Q2, we continued to execute in an unsettled macro environment, but with incremental improvement in general sentiments and engagement. Generative AI is at the forefront of customer conversations. However, enterprises are also realizing that they cannot have an AI strategy without a data strategy to base it on. We have a head-start in this race, with the epicenter of highly curated, optimized and trusted enterprise data. We now have a presence was 639 Global 2000 customers. AI reaches beyond enterprise boundaries. Models need external data to answer challenging questions. Data sharing makes Snowflake uniquely positioned to enable AI workloads. As of Q2, 26% of Snowflake customers are data sharing, up from 20% in the same period last year. Approximately 70% of customers with more than $1 million in trailing 12-month product revenue are data sharing with an average of six stable edges. For years, we focused on the programmability of our platform via Snowpark. We are seeing momentum. In Q2, we added more than 400 Snowpark customers and our consumption grew approximately 70% quarter-over-quarter. The 63% of our Global 2000 customers are using Snowpark on a weekly basis. Document AI is now in Private Preview. With Document AI, customers can use natural language to ask questions of unstructured data. Legal contracts or invoices are now available for inquiry and analytics. This is an early example of how language models are expanding our opportunity. With Snowflake Container Services, we are bringing LLM models like Reka and NVIDIA's NeMo into Snowflake. You heard in my conversation with Jensen, Snowflake is sitting on a goldmine of…

Thank you, Frank. Consumption came in line with our expectations for the quarter. In May, we saw a return to growth with strength continuing into June and July. From a booking standpoint, we saw promising signs of stabilization with new bookings outperforming our expectations. However, we believe, productivity has room for further improvement. Q2 remaining performance obligations grew 30% year-over-year, totaling $3.5 billion. Of the $3.5 billion in RPO, we expect approximately 57% to be recognized as revenue in the next 12 months. This represents a 32% increase compared to our estimate as of the same quarter last year. Our net revenue retention rate of 142% includes six new customers with $1 million in trailing 12-month product revenue. We now have 402 customers with trailing 12-month product revenue greater than $1 million. We continue to focus on growth and efficiency. We generated $88 million in non-GAAP adjusted free cash flow, outperforming our Q2 target. Q2 represented another quarter of continued progress on profitability. Our non-GAAP product gross margin was 77.9%, benefiting from a one-time credit from one of our cloud service providers. Non-GAAP operating margin was 8%, benefiting from tight controls on headcount additions and the over-achievement in product gross margin. Our non-GAAP adjusted free cash flow margin was 13%. We continue to have a strong cash position with $4.9 billion in cash, cash equivalents, and short-term and long-term investments. We did not repurchase any shares in the quarter, but plan to opportunistically repurchase shares using our free cash flow. Now let's turn to guidance. Our forecast assumes that our largest customers will continue to be a growth headwind. We are seeing encouraging signs of stabilization, but not recovery. Our forecast calls for these customers to more closely align their consumption with their annual contract value. For the third quarter, we expect product revenues between $670 million and $675 million, representing year-over-year growth between 28% and 29%. Turning to margins, we expect, on a non-GAAP basis, 4% operating margin. And we expect 364 million diluted weighted-average shares outstanding. For the full year fiscal 2024, we expect product revenues of approximately $2.6 billion, representing year-over-year growth of approximately 34%. Turning to profitability. For the full year fiscal 2024, we expect, on a non-GAAP basis, approximately 76% product gross margin, 5% operating margin, and 26% adjusted free cash flow margin. And we expect 362 million diluted weighted-average shares outstanding. We will continue to prioritize hiring in product and engineering. We still expect to add approximately 1,000 employees in fiscal 2024, inclusive of M&A. With that, operator, you can now open up the line for questions.

Absolutely. We will now begin the question-and-answer session. [Operator Instructions] The first question will be from the line of Keith Weiss with Morgan Stanley. Your line is now open.

Excellent. Thank you for taking the question, guys. Mike, I wanted to dig into the comment about kind of a large customer activity. You talked to us about sort of consumption coming more in line with the committed contracts. Can you give us any visibility what's happening on the contract renewals? Because as you go through these big contract renewals, are you seeing any change in their behavior of what the large customers are willing to commit to you? And any impacts that we should be thinking about on how that's going to impact bookings and sort of RPO on a go-forward basis?

Yes. No, we're seeing customers renew. This quarter was a good renewal quarter. We had our largest customer, they renewed under their existing terms. They did a $100 million three-year renewal, even though their revenue run rate is at a higher amount than that. I think we did $9 million or $10 million plus TCV deals this quarter and most of those were renewals. And so, customers are doing that. But remember, that doesn't necessarily equate to consumption, and we do know some of our largest customers are trying to consume at their contract rate rather than going above that.

Got it. So, the dynamic is really on consumption, it is not on contracting as of yet?

No. It's around consumption. As I said, the contracting, actually feel the sentiment really just seems to change in July with customers really re-engaging with us. And so -- and I think we'll have good bookings, but that doesn't equate to consumption. It takes time for the consumption to come in.

Got it. And then, if I can sneak one in for Frank as well. You talked about, you need a good data strategy to have a good AI strategy and that's something we hear a lot when we're talking to customers and people out there in the field. So that really resonates. When it comes to kind of go-to-market and sort of the selling motion, does having to have the gen AI conversation while a long-term positive, does that disrupt the sort of typical kind of data cloud discussion that you guys have been having for the past like five years with these customers? Does it have that risk or has it been elongating the sales cycles in a real way?

No. I would say so, Keith. I think we were actually saying that having highly organized optimized, trusted, sanctioned data is incredibly important for deploying large language models. If you think you can just drop a model on top of a data lake and just see what happens, that's not going to end well and that's what people are realizing. So, they really got to get super serious about their foundations, before -- if you don't have a good foundation, there's not much you can build on top of that. There's tons of governance issues involved as well. We spent literally decades as an industry making data highly governed. In other words, who can have access to what. So that now needs to translate into the world of large language models as well. So, there's tons of questions that are coming up that are really important for the enablement of language models and AI generally. So, being extremely organized on your data is going to become a premium thing. And we're obviously -- that's -- we've been on that, but it has become more important as a function of this.

Got it. Yes, it definitely resonates with the competition we're having as well. So, thank you very much for the time guys.

Thank you. The next question will be from the line of Mark Murphy with JPMorgan. Your line is now open.

Thank you very much. Frank, I'm wondering if you can speak to the expanded Microsoft relationship. I believe you referred to it as nearly a doubling of the commitment. And I'm wondering if you're optimistic on seeing that alignment in the field coming together and perhaps unlocking some new opportunities relating to Azure or even some of the OpenAI workloads that you wouldn't have seen previously. Then I have a quick follow-up.

Yes. Look, the bottom line of working with the cloud vendors is not what gets set at the top levels, the good intentions. What matters is what are the incentives when you get 14 layer down at street level, how do get -- people get paid, that determines whether they're either going to fight you and double and triple down on that or they're going to partner with you. And we see models where we've worked out really, really well, of course, with AWS. But Microsoft, we were not in the place that we wanted to be at street level in terms of the incentives. So, we really took this opportunity when we were renegotiating our relationship with Microsoft to say, "Hey, we have to tackle this, right?" And Microsoft very much wanted to be a bigger percentage of our business and because they are not punching at their weight at all. They're not as a bigger percentage of our business as they should be based on their market share, and they want to be. And this is the way to do it. It's really you need to bring alignment to the field organizations, then you're going to get partnerships and then you're going to get joint selling and then you're going to get your fair share.

Okay, thank you for that. And Mike, as a follow-up. In Q2, did you observe any customers adjusting their indexing or reducing data retention timelines? It looks like the quarter went well overall. But I'm curious if you sense any more or less of that optimization activity heading into the second half here.

In terms of customers changing their retention policy, we really didn't notice any of that. If anything, we saw growth in the amount of storage in the quarter. And I just want to call it, there was really one large customer that changed the retention from five to three years, that was pretty unusual. And in terms of optimizations, we continually work with customers on their own optimizations, but we're continuing to optimize our software as well too, because we are really dedicated to delivering price performance for our customers. And I want to stress, we've been talking about optimizations with investors since we went public. These will always continue optimizations, whether it's customer or us doing the optimizations, because history has shown, when we improve price performance, more workloads come to us.

Thank you very much.

Thank you. The next question will be from the line of Kirk Materne with Evercore ISI. Your line is now open.

Yes, thanks very much. Mike, I was wondering if you could just talk a little bit more about the comment you're seeing stabilization in consumption, but not recovery. I'm just kind of curious when you say that, is that the pressure from the top-down on practitioners starting to ease a little bit and they're starting to feel better about what they can consume and then there's waiting for budgets to kind of get refreshed to sort of get going on the recovery side. I'm just kind of curious if it's sort of the top-downs easing or it's more that they're taking a little while to sort of ramp back up on projects that perhaps they've slowed down six months ago, three months ago.

I actually think it's both, but I definitely think the fact that we kind of saw customers more reengaging with us in July on contracts and that continues into this quarter, I think it is easing a little bit at the top level in terms of approvals for customers and they're willing to commit. But it takes time to convert that to consumption. With that said, consumption is good. It was really good today as an example. But it's only one data point. We want to see more days of that before we think the -- we're into a real recovery. I think stabilization is the right term. We're not seeing customers reduce their consumption right now.

Okay. And then Frank, just as you spoke to a lot of executives at Summit, do they recognize the fact that the road to AI does require perhaps a heavier level of investment than they were thinking 12 months ago? How do you think that factors into sort of their thinking on budgets as we go into '24? Thanks.

Yes. The reality is, they don't really know yet, in any real definitive terms, what this is going to take. I mean I think a lot of people, I think this is correct, they have characterized their foray into language models as experimental exploratory and sort of trying to get their arms around how big a breadth box is this. So it's going to take a while before we get a real read of what the level of investment is. There are people who are going to -- stomach to do this. I mean one of the challenges -- one of the great things about search historically has been that search, also had a very potent business model that go with it to pay for it and we cannot sort of unleash AI and have no business model that pay for, and people will get tired of that really, really quick. So these are -- GPUs from NVIDIA, they aren't cheap, as powerful as they are. So, we all have to bring that into alignment and into focus and have a sensible go-forward strategy. So, a lot of the use cases we'll focus on, what are we getting for this, right? This is not just fun and games and planning your next trip to Yellowstone. People are going to be asking very, very hard-hitting questions, "What is this doing for us?"

Thank you, all.

Thank you. The next question will be from the line of Raimo Lenschow with Barclays. Your line is now open.

Thank you. Great to see the stabilization as well from my end, congrats there. Frank, one for you more -- as you -- as we all realize that data is kind of the new fuel, we do see more [indiscernible] like, oh, no, I have the data, I have to data, which kind of in a way like kind of puts them on a slightly different course than it used to be compared to your partnership. How do you see that competitive landscape evolving for you, since you, in theory, are the natural kind of holding source for data that is used in AI? How do you see this playing out for you guys? Thank you.

Well, we see it play out really well. I think we agree with you. I mean, we think that data is becoming infinitely valuable and that's for all kinds of reasons, because we can no longer run enterprises and institutions based on what we call anecdotal observation because the world is too dynamic, too disruptive and we have this massive disintermediation happening. We no longer have all these intermediaries between us and the end customer. You can only run direct-to-consumer businesses with data. I mean, you see that, for example, in insurance like auto industry, companies like GEICO and Progressive and Liberty Mutual, I mean, you can only run these businesses on top of data, telemetry data being really important. And we think every business, every institution is going to develop a complete and total dependency not just on data, but the ability to harness that data. So, this is a full-on transformation really of how industry and institutions have operated and worked at the leading edge of it, but we're also very much at the beginning of it.

Okay. Thank you.

Thank you. The next question will be from the line of Karl Keirstead with UBS. Your line is now open.

Thank you. So, Mike, just wanted -- maybe two quickies. You mentioned there was a one-time credit from one of the CSPs. Are you able to define how material that was? It sounds like it might have certainly hit gross margins. But just wanted to clarify.

That was about $4 million that hit in the quarter.

Okay, got it. Thanks for that. And then maybe just a follow-up. Mike, what I'm hearing you describe is an effort, you mentioned, certainly, your largest customer, but perhaps others taking their usage a little bit closer to their ACV. So, one would think that that would result in a decent amount of headwind, yet your guidance for the third quarter, in my judgment anyway, is relatively solid. So, what's the offset? What customer segment might be ramping nicely to offset to some extent the headwinds from your large customers? Thanks so much.

Well, we're now at 639 Global 2000 that are only consuming on average on a trailing 12 months at around $1.5 million, $1.6 million. A lot of those are still doing their migrations and we don't see that stopping. It's the larger customers. They just are not forecast to grow as quickly. They're still growing, but at a slower pace. Now, that -- once again, this is a consumption model, that could turn around tomorrow.

Yeah. I get it. Okay. Thank you, Mike.

Thank you. The next question will be from the line of Kash Rangan with Goldman Sachs. Your line is now open.

Thank you so much. Frank, I think your fireside chat with Jensen was absolutely illuminating. He was looking at the opportunity set with structured data in the Snowflake ecosystem and almost salivating, and yet you seemed a little moderated and that you need to have a business case. So, when are we likely to reach a point where Generative AI coupled with Snowpark could really lead to a tangible increase in consumption outside of the core data cloud business? And one for you, Mike. If we are to read your comments, three months of stability, four months of stability, it looks like including August, does that mean that net expansion rates reach a bottom and could potentially start to stabilize and rebound as we head into the later part of the year? Thank you so much.

Well, it's Frank, Kash. I mean, in the really short term, I'm only talking days, weeks, and months here, where you're going to see language model begin to immediately impact the business is that SQL generation. I mean, in other words, the analyst job is going to be up leveled so much. I think people are going to be able to drive queries into the data much better, much faster with far less skill requirement than they ever have before, and we're showing that off every day. So, I mean, these days, you don't even have to be literate in order to be able to have interactions with your data. So that really is an expansion vector that is just enormously and it's very close to home, that gives us really how you use data and how you use a platform like Snowflake. The other area where you're going to see drivers of workloads is that people get to search for data related to what their general angle of inquiry is in a much more effective manner than they have been able before. And this is also where it's very important that you can search beyond enterprise boundaries, because the context of data is not limited by your enterprise boundaries. We can go on and on and on about our use cases, there is a million of them. As you get further down, you can start asking really, really hard questions that in prior periods, prior eras, we really needed to launch whole analyst teams to go research and investigate topics, where now the data will be able to -- the systems will be able to generate queries and the type of data that will immediately, very, very quickly begin to generate insights. And that's by the way, that's going to become the leading edge for structured proprietary data, which is, of course, the center of our universe.

And on your question on net revenue retention, I just want to remind you I'm not going to guide to net revenue retention. But I do think over time it is going to continue to converge closer to our growth rate. I do think it will stabilize, but I do expect it's going to come down slightly from where it's at right now just in what we're seeing today.

Thank you. The next question will be from the line of Brad Zelnick with Deutsche Bank. Your line is now open.

Great, thank you so much. I've got one for Frank and one for Mike. Frank, stable edges continue to tick up, which is great to see, and I know it's an important metric for the company and its strategic vision. Anything else perhaps qualitative that you can share in terms of how data sharing is progressing? And for you Mike, great to see the margin upside, everybody is happy about it. But with such a huge opportunity, how can you be sure you're striking the right balance of investment, especially when you're up against such well-capitalized competitors? Thanks, guys.

On the topic of data sharing, we instrument that whole side of the business very, very carefully and we drive it on a quarterly basis. But sometimes data edges are very enterprise-specific. In other words, they just have things, the use cases, that just pertain to their business and these are bilateral relationships between Snowflake accounts and different institutions. But where it gets really interesting, where you get real network effect kicking in, is when you have industries or sub-industries where data sharing just makes sense. And obviously, in financial institutions, because financial institutions inherently have been pumping data around in massive, massive volumes for -- literally for generations. This is an absolute no-brainer. And we do the vast majority, historically data edges have been in the financial services sectors become almost a standard. This is how we move data from A to B to C. Asset management particularly has a really big need for that. But the other area, and again, this is an industry, in supply chain management. I mean, in the supply chain, there are multiple entities to the degree that they all have Snowflake accounts, it's very easy to get visibility and supply chain across entities and being able to flag supply chain events much earlier and get visibility to that. So once you're in a supply chain, there need to be on Snowflake and share data with your supply chain partners is going to become very, very compelling. And we announced at Summit and even earlier our relationship with Blue Yonder, for example, which is really the largest software company in the world in supply chain management, that they are re-platforming on Snowflake. So, we think that's another sort industry/sub-industry where every manufacturer, every retailer is going to become an opportunity for us. So it's a little bit of color on how these things develop from our perspective.

On your question on investments in the business, Brad, given the opportunity, we are investing as fast as we think we need to invest. You did see the guided margins to 4% for operating margin where we just did 5% this quarter and that's because we're investing. As an example, we have a 1,000 H1100 GPUs reserved, that's an extra $1 million a month as we're working on AI. I'm not getting requests that people need more headcount in the engineering organization and the sales and marketing. Until we see an increase in productivity, we're going to be very methodical about how we add resources into those areas. So, we're definitely not under-investing in the business, at least I'm not getting the feedback from any of the executive team with regards to that.

Thanks for the additional color.

Thank you. The next question will be from the line of Tyler Radke with Citi. Your line is now open.

Yes, thanks for taking the question. First question just on the commentary around some of the projects you are starting to see better momentum there, particularly in July. I was wondering if you could just comment on the nature of those projects. Are they larger deals than you typically see or maybe they include more Generative AI or data science given all the new products that you released? If you could just kind of contrast the pickup and kind of where that's coming from?

Yeah, just -- you want to get going?

You, go ahead, Frank.

So, you shouldn't equate projects with deals, okay, because there is tons and tons of projects going on and projects relate to use cases and workloads and applications. But what we said in the prepared remarks is, we've really seen a sort of a sentiment change from the earlier quarters where people were sort of trying to cut off their limbs to fit within budgetary constraints and all these kinds of stuff. And then where do -- and that's why you see unnatural acts to save money. That has really subsided considerably and the conversation is really going back to where it historically has been, as you know, we want to do these applications, these workloads, these migrations. And of course, we're pushing the boundaries on much more sophisticated use cases in machine learning. And obviously, people want to understand how do I deploy large language models on the Snowflake platform. And we have outlined that an excruciating detail and demonstrated, showcased how we are doing that and we're super excited about how that's unfolding for us and our customers.

Great. And then follow-up, just in terms of the Snowpark revenue. Any update on kind of where you're expecting that to track as you exit this year? And then these related services, whether it's the Native App Store or Container Services, would that all fall under Snowpark theoretically when that goes GA next year? Thank you.

In terms of Snowpark, as we said, whereas Frank talked about seeing 70% growth in Snowpark consumption, still relatively small, but meaningful. We have a number of customers that are in the process of doing their migrations, a few quite large ones. I do think next year, it will be more meaningful to revenue. But on $2.6 billion in revenue, it's a couple of percent of our revenue this year.

Once that container services become primetime and that is part of Snowpark, obviously, that means any workload becomes a fair game to be deployed on Snowflake. This is obviously running close to the data, inside our governance perimeter, it's essentially virtualization for the cloud. So, we think there is enormous upside for us once those services become generally available across all our cloud platforms.

Which will be next year.

Thank you.

Thank you. The next question will be from the line of Brent Thill with Jefferies. Your line is now open.

Mike, you mentioned at the Analyst Day you are idling back quota-carrying sales capacity on the new hire front. Have you seen any difference to lean back into hiring quota reps in '23?

It really depends upon the territory and the opportunity. There are some territories where kind of regions we're shrinking where it's overcapacity, and we're re-shifting those heads to other more productive territories as we -- I'm not planning on adding net a lot of new ones for the balance of this year. But as we're starting to plan for next year, there are -- there is additional headcount going into the quota carrying rep area.

Great. And for Frank, on the vertical side, any verticals that are showing more excitement that perhaps weren't online that weren't firing up? Are you seeing anything change here?

Actually, that's a great question, because we had massive outperformance by our healthcare vertical this quarter, and healthcare usually runs fourth or fifth in the lineup of verticals and a massive way to [Technical Difficulty] really excited about, it feels like that healthcare is really getting a move on, if you will. They have not traditionally been an aggressive adopter of technology. But in the world of data, they are moving. They're moving hard and you see it on the provider side, you see it on the payer side, you see it on the pharma side. So I think that's going to become a great contributing segment for us.

Yes. Healthcare and life sciences grew 61% year-over-year in revenue for us.

Thanks for the color.

Very good, too.

The next question will be from the line of Michael Turrin with Wells Fargo. Your line is now open.

Hey, great. Thanks. Appreciate taking the question. I think one of the comments mentioned new bookings outperformed expectations. I appreciate you're still seeing room for improvement, but anything you can add around what drove the improvement versus last quarter? It sounded like healthcare from the prior commentary, but wondering if some of that or certain product releases maybe also contributed there.

Yes. Hard to say whether -- I don't think it was a product release. I would say we saw some nice renewals from customers with growth. We also saw two very large Cap Ones, one large one in Europe, which was -- Cap One is an initial deal, it was a $22 million TCV deal in an insurance industry, and we saw a large gaming company in Korea commit to $9.5 million as a Cap One. So clearly, our message is getting across to these customers, and they see what we're doing and a lot of these want to do more in the area of AI. But first, they need to get their data into Snowflake and it's going to be a journey for these people. It's not going to happen overnight AI for our customers.

I like and appreciate those large deal stats. Maybe just quickly on the back half. If you can just help level set what's embedded in the rest of the year outlook? You've seen multiple comments around stabilization. Is that fairly consistent with what informs the outlook and maybe just any refresh on second half seasonality as expected? Thank you.

Well, Q4 is usually one of our largest bookings quarter and it's shaping up, but that's not necessarily consumption. And -- but the sentiment within our sales team has definitely shifted from where it was in the first half of the year.

Thank you.

Thank you. The next question will be from the line of Patrick Colville with Scotiabank. Your line is now open.

All right. Thank you so much for taking my question. I just want to double-click on your comments. I think you said customers are reengaging in July. You said that in the prepared remarks. I mean, I guess, what do you mean by that? Is it new customers, existing customers, consumption, contract negotiations? And then, any color you can give us on thus far in August would be helpful.

All three of the above. We have new customers. I just pointed out those two large Cap Ones. We usually don't do Cap Ones that big, and it was a very good quarter with some large new customers. We're seeing our existing customers. We saw some nice early renewals with customers where they were consuming faster. And we're seeing customers willing to do larger deals rather than just do a co-term to bridge them through to another period. So that's what I mean by the sentiment is changing with our customers.

All right. I guess my kind of follow-up is -- I mean, NVIDIA reported results tonight. I think looking at the numbers, the data center revenue rose by 150% sequentially. But clearly, like AI spend is hitting the silicon layer. I mean a question I get from investors is when will AI spend more clearly hit the software layer? I mean, do you have any thoughts on that?

I think it's going to be next year. As I said, it's going to take some time for AI. And people are still struggling to get GPUs and there is a time lag between when a chip manufacturer sells their chips to it gets built into the hardware that actually gets deployed in a rack in a data center, and it gets deployed to customers.

I think you will see the leading actually happening in months to come. But the material impact, I think most analysts out there are seeing in 2024 and we tend to agree with that.

And I would say in my prior life, when we were buying racks of servers, there's a six-month delay between when we bought them and when they were actually going into production. And I don't see that any different with GPUs.

Thank you. The next question will be from the line of Alex Zukin with Wolfe Research. Your line is now open.

This is Ethan Bruck on for Alex Zukin. I just had a quick kind of numbers question. So, if we calculate the product revenue of cRPO booking growth, it decelerated to 13% from about 30% last quarter. So, just curious, how should we think about this as an indicator of future consumption for future product growth?

So, I guided to full year revenue at $2.6 billion, and we'll guide next year next year.

Okay. And then I guess just to ask the August trends question in a different way. I guess, has some of the stability you called out trended into August kind of the first one for the quarter? And then just on the seasonality, is there any kind of month to note as you think about the rest of the year that seems that either seasonally strong or seasonally weak?

Well, as I said, August is shaping up very good. I called out yesterday, it was actually a very good consumption, but one day doesn't make a trend. Q4 is definitely seasonality with the holidays with Thanksgiving in the U.S. and the Christmas holidays that does impact daily consumption. From a bookings perspective, Q4 though, is clearly our largest bookings.

Got it. Makes sense. Okay. Thank you, and congrats on the quarter.

Thank you. The next question will be from the line of Brent Bracelin with Piper Sandler. Your line is now open.

Thank you. I wanted to go back to the discussion around the increase we're seeing in model training capacity. Clearly, billions of incremental dollars going into NVIDIA GPUs here. You need data to train the models. I appreciate there's going to be a lag relative to when the spend hits the data layer. But are there any technical hurdles that need to be overcome? Or you do you think this cycle is different and that there are other considerations as well? Just thinking through that -- the investment we're seeing right now in infrastructure and thinking through what are the other factors we need to think about before it starts to impact the data layer? Thanks.

I'll start, and then maybe Christian can follow, even think about the question while I'm talking. You can't characterize as AI as one thing, right, because you see the things that people are doing with unstructured data and the whole notion of copilots and the systems and tutors and all that, it's very much focused on contextual data. And we see action with support call records, contact centers and so on. But again, you look at Snowflake, who sits on mountains of structured proprietary enterprise data, that's a different realm for AI than the very text model-oriented type of inquiry. And I have to say that just from all my conversation with customers, I mean people are behind, I would say, the textual side [Technical Difficulty] proprietary data, how we're going to approach that. We view that as our business, and we're driving that very hard, and hence the emphasis on getting your data [indiscernible] in order because you just cannot unleash large language model and hope for the best, because of all the issues that we've mentioned before around governance and just understanding of what kind of data we are generating in the process. That's why I said the early going, you're going to see a lot of upside from [indiscernible] that analysts are going to be able to generate data far quicker, far better than they ever have been before. And we're really massively reducing the skill sophistication requirements to be able to do that. Data in and of itself is going to be a big driver for us.

Yes. Christian here. I would add maybe two areas in addition to what Frank mentioned. The first one is around having the right data to be fed into these models. Frank started the call with no AI strategy without data strategy. And it is very pleasing that the results of traditional ML or Gen AI is a function of having the right data, the right data quality, the right metrics. The technology will be as good as the data that is fed into. All of the investments that we make on data quality and cleansing and pipelines, all of that is very important. The other piece that I think will be a technical imperative for everyone doing AI and Gen AI is around the measurement and feedback how good are the solutions, how do I know if there are potential buyers into the data or are there gaps in their understanding and performance of the model. Those two are inherent parts of the lifecycle and [interestingly, they all run to] (ph) having a great data foundation enabled service.

Helpful color. And then lastly for Mike on consumption, one follow-up. The implied Q4 product growth is, I think, 26% at the midpoint. I know there's a delta between signing growth and consumption. Exiting this year, do you think product growth stabilizes in the mid-20% range, maybe starts to reaccelerate next year, or is it just too early to tell?

Let us finish Q3, and then we'll guide to Q4, and I'll see how next year is looking. But I do anticipate -- there's a lot of new things coming out next year, that we think are going to have a very positive impact on our consumption from. Remember, we have Streamlit goes into GA, Unistore towards end of this year.

Public Preview.

Public Preview. We have Containerized Services next year. There's a number of things that are happening that are all going to have a positive impact on our revenue growth rate next year. So stay tuned for that.

Helpful color. Thanks.

Thank you. The next question will be from the line of Brad Reback with Stifel. Your line is now open.

Great. Thanks very much. Gentlemen, you've talked about changing sentiment a couple of times during the call. How much of that is your sales team being better able to engage with the customers selling value, just looking at the problem from a different perspective given the macro trends versus customers just feeling better about their businesses and the macro unleasing the demand? Thanks.

It's Frank. Look, it's not the sales team. There's really a change in how customers engage. A couple of quarters ago, like I said earlier, people were doing unnatural acts to force that themselves into spending envelope and they were doing it almost regardless of consequence, that's fixation on that reset, we obviously felt that. The change in sentiment is that, that has passed. We are now sort of, okay, we're comfortable with the path that we're on, now we're talking again of our projects and migrations and use cases. We're trying to basically get a grip on deploying large language models. What do we have to do with data, with the infrastructure, answering governance questions and so on. So in other words, the sentiment is very constructive and engaging on core data strategy, that's a big change from where we were a couple of quarters ago. Obviously, the salespeople are perceiving that as very positive, that's where you want to be.

That's great. Thanks very much.

Thank you. The next question will be from the line of Derrick Wood with TD Cowen. Your line is now open.

Thanks. Mike this is the strongest sequential growth quarter you've had in three quarters, I think you were at 6% and 6% and this was 8.5%. Your guidance for Q3, there's kind of 5.5% sequential and further some level of conservatism in there. But just in terms of the Q2, are there any kind of one-time consumption dynamics to call out? Or does that just really kind of inform us that the optimization headwinds that you saw in Q4 and Q1 kind of dissipated in Q2?

Yes. Q2 has more days in it. Remember where consumption model was actually 5% quarter-over-quarter, working days adjusted was the growth rate. And actually Q3, it goes up when you look at the working days in Q3. Remember on a consumption model where really kind of 70% is the scheduled work, there is a big piece that is tied to work days that does have an impact. So, there is growth next quarter in that guide on a days adjusted basis, working days.

Okay. Frank or Christian, I just -- I was hoping to double-click on the new Container Services. It seems like it enables you to deploy different types of third-party engines, apps, code bases directly in the platform. You can streamline a lot more workflows which I think when it comes to building AI models seems pretty interesting. So, I was curious, what are you most excited about in terms of this new capability and opening up new consumption especially when it comes to AI?

Container Services was the absolute hit, star performer at our Snowflake Summit conference. I mean, customers were just mesmerized by the possibilities that a platform capability has because we essentially eliminated any limitation on deployment on Snowflake. And why do you care? I mean the thing is, first of all, you wanted to deploy close to the data for all the reasons that we talk about. This enabled us, you get a fully trusted sanction platform where you can deploy applications without any further questions. And one of the challenges that you have in cloud computing is, who's managing this, right? I mean what is the safe space to deploy into and whose is really guaranteeing the high trust enterprise-grade capabilities of the platform? So, we are bringing that. So, we're going to see a lot of services, a lot of them could be on-premise legacy engines that are going to be containerized and re-service as a cloud service, right? So, a lot of things that were old will be new again. So, it's virtualization for the cloud and having secure, safe, high-performance, very, very efficient spaces to run services and applications. And so, the sky is the limit on this capability, and we and our customers and our partners could not be more excited about the potentials and the possibilities here. But specific to AI, this matters a whole lot because the containers, our vehicle, our vessel, if you will, to deploy large language models, there is no limits on which models and how many models and for what segments of the business we can deploy. And we can shift gears very, very quickly. And we have incredible flexibility in terms of deploying these capabilities, because you're going to see a lot of change and a lot of movements. We've already seen an enormous amount. That's going to continue. So, we're very, very well-positioned architecturally platform-wise to enable the AI revolution with Container Services.

Awesome. Thank you.

Thank you. And the final question will be from the line of Sterling Auty with MoffettNathanson. Your line is now open.

Yes, thanks. Hi, guys. Just one question from my side. You mentioned sales productivity a couple of times. I'm curious, how would you grade your go-to-market sales execution in quarter? And are there any specific changes that you're making to further optimize given the environment for the back half?

I think in general, our execution in Q2 on the sales side was actually quite good, it improved. But there is still pockets though, where there is room for improvement when you look in certain territories or geos. We have a new leader in EMEA. EMEA -- certain markets in EMEA doing good. Others, there's a lot of room for improvement. We have a new leader in EMEA. There's certain pockets in Asia that are doing good. But there is others that have a lot of room for improvement. So -- but in general, overall, as I talked about, you can see through our bookings, it was a good execution from bookings perspective in the aggregate last quarter.

Understood. Thank you.

Thank you. That will conclude today's Q&A session and today's conference call. Thank you all for your participation. And you may now disconnect your lines.