Snowflake Inc. (SNOW) Earnings Call Transcript & Summary

Please welcome, Head of Investor Relations, Jimmy Sexton.

Good afternoon, everyone. Thank you for joining us here at Snowflake Summit in Las Vegas. And for those tuning in, thank you for joining the live stream. I've heard it's been a nightmare getting here from New York. So I hope all of you had a chance to attend and/or listen into the kickoff keynote last night with Frank and Jensen from NVIDIA. I think it laid out our road map in partnership with NVIDIA very cleanly. And we're going to dive a little bit into that today. It is also now available for a replay on our website. So if you didn't get to see it last night, please give it a listen. We also had an incredible main-stage keynote this morning. I think we're going to double click on a lot of the product announcements that we made, but that is also available. Outside of today, we also have a week full of jam-packed customer sessions that we encourage all of you to attend. We can tell you one thing, but I think hearing it from our customers is more important. So before I dive into the agenda, I want to acknowledge our safe harbor. And so here is our lineup for today. So first, you're going to hear Frank Slootman, our Chairman and Chief Executive Officer, talk about really our North Star in mobilizing the world's data. You've heard us talk about this dating back to 2020 during the road show. And I think today, it is even more relevant with everything you're hearing around the different generative AI use cases. Next, we're really excited to show a prerecorded video with Frank and Satya Nadella from Microsoft talking about the next phase of our partnership coming off the heels of that press release you saw yesterday. Next, we're going to invite Christian Kleinerman, our Senior Vice President of Product, to talk about kind of our pillars of innovation and kind of how generative AI plays into this that's top of mind. Then Christian is going to invite Sridhar Ramaswamy, up from Neeva. That is the deal that we announced last month, the co-founder of that company and talking about how this next phase of Snowflake and Neeva can work well together and how you engage with that data. Next, we're going to have Chris Degnan, our Chief Revenue Officer, come up with Rob Smedley, the Vice President of Data and Technology from Disney Parks. They're going to talk about kind of the foundation that Disney is building on Snowflake and kind of the road map for them in the future. And then lastly, we're going to have Mike Scarpelli come on stage, talk about our long-term opportunity, how we get to our long-term model and hopefully answer questions with some new metrics and disclosures to help you better understand our comfort around that model. And lastly, Mike is going to invite Frank and Christian up for audience Q&A. And at that time, we will have mic runner. So you'll be able to answer your question on the broadcast. So with that, I will invite Frank Slootman, our Chairman and Chief Executive Officer.

Hello, everybody. How are we doing? My most important thing today is to tell you that I'm still here. And I just stepped up on the podium, also stepping down. So I just want to make sure you heard that loud and clear. It's always amazing how these things seem to air on the first day of our conference when our Board member, Mark McLaughlin and myself and the company sort of [indiscernible] denied the rumors. That's the world we live in. I hope you had an opportunity to hear our session with Jensen last night, which I think was super cool. Partnership is very meaningful to us. And obviously, this morning, if that didn't convince you that we have absolutely a ton going on in a world of AI, you can take a shot now, if you'd like, because there's a drinking game going on, then not much will. But if you need more conviction, we're here today, and I'll also recommend that you sort of wander around to the partner pavilion, because you can see large language models running in Snowflake containers all over the place, and you'll see a bunch of our partners literally running large language models on Snowflake already. So sort of the trains left the station, and we're running really, really hard to fill in behind that. I think Christian's session this morning is -- this afternoon is really important, because he's going to really lay out the whole framework, all the different ways that we are going to enable these language models. This is really important because we cannot really predict how customers are going to do this. There's mostly a couple tightly coupled. We see a lot of people using Microsoft's hosting of open AI, Azure-to-Azure type of model. So that's sort of in the Microsoft world is one model. And then there's other models that are being attempted at the same time. So it's very early going. The great thing is we just have this wonderful architecture and a lot of other things that we just delivered are really intersecting with all the possible options and configurations that people are going to want to entertain. It's not quite as simple as Jensen said last night, and you just go put this AI factoring on top of your data and poof, you just get pelt with insights and intelligence. I like his vision, by the way, I just absolutely love it, but it's going to take a little other work to make that all go. So Jimmy just said, when we went public, which is now almost 3 years ago, it feels like dog years to me. But our tack line at the time was mobilizing the world's data. And that is as relevant or more relevant today than it even was done. And when you see this avalanche of AI interest coming at us, it's like this is exactly what we wanted to do, what we're trying to do, and we're just getting a huge help from the world of technology to do things that were just unimaginable not too long ago. As most of you know, I've been around a while, and I told the story about learning Cobalt several decades ago and people then referred to Cobalt as a common business-oriented language, it's like you got it kidding me, because it was just not very business-oriented and it certainly was very hard to learn even in those days. But compared to assembler and machine card, it was readable syntax, and there was a step-up. And then the '80s, I was certainly very much alive in the '80s, the movement towards structured data, structured clear language. I mean, that was a huge step up, but people then said hey, this late people are relatively -- unsophisticated people can now [ create ] these relational databases. Well, obviously, CECL became much more complicated over time. The fact that we're now seeing that we're going the last mile where we're completely obliterating these limitations that we've lived with since the early days of computing is just incredible to me. And you don't even need to be literate these days, you can just talk to a database and come back with a very meaningful feedback and insight. So data, you'll hear about that a lot is the foundation of AI, and we're sitting on a ton of it, and we're sitting on proprietary data. We're sitting on public data. Every combination thereof, every different data type structure to semistructured to unstructured you heard about it this morning. So this is just a great place to be. Now we're just racing to enable every possible configuration to derive these benefits. Good time to be alive. We said over and over again that if you want to have an AI strategy, you need to have a data strategy. If you saw Mihir, the CTO, I had a bigger title than that, but I always call them the CTO at Fidelity, talked this morning, I think that was actually very important. I don't know whether everybody picked up on that. But Fidelity has a textbook strategy and implementation, not just of snowflake, but in general, how you run data strategy in large institutions, large enterprises. I mean it's incredibly curated. It is incredibly consolidated. It's trusted, it's sanctioned, and they have full control. And I cannot begin to tell you that these guys are just primed for AI, and they know it, right? That's why this is so important. You cannot just take a data lake, which might sometimes refer to data lake as landfills, because it's just full of [ files. ] If you think you're going to just stick a model on top of there and flip the switch and everything will be okay, I think you'll have another thing coming. So it's very, very important to get your data strategy in order to position yourself for a lot of the benefits that we're envisioning that we're going to get from AI. Machine learning is well underway. That's a whole separate chapter that we're absolutely fully, fully engaged in. I said this morning, I mean, we have 70% of our large consuming customers, which are customers that consume more than $1 million a year. They are using Snowflake for machine learning. And I see it and hear it all the time that people are running dozens and dozens of models now. Years ago, in the early days of Snowflake when I was there, and we needed still [indiscernible]. We are mostly doing 24-hour cycle, big batch analytical processes. I mean now this kind of data science is really becoming mainstream. So the data strategy part, this is why we talk about it so much is such a key enabler to AI strategy, but it's an enabler to everything. A lot of the conversations I have with customers. I mean there's just immense frustration with people not trusting data. And this is sort of one of the key benefits that data warehousing brought to the world, right? We kind of [ pop ] now because data warehousing was incredibly capacity constrained. We always have the running joke. It was sort of begging for 2:30 a.m. time slot, 3 months from now. It just impossible to get anything done. But what they did bring is this sense of curation and trust and sanctioned data. I will tell you that there's one large financial institution, a customer. They have a very large data lake, which is not Snowflake and -- but they have Snowflake as well, and they have trouble getting people to use their data lake. They're all going to Snowflake. Why? It's trusted, it's sanctioned, right? Understood. The data lake is like anybody's guess. And that's really, really important. We go forward to these higher levels of function. We have to have a data strategy that is working. So I think the Fidelity presentation is more as like a poster child for how to do that. So we're sitting on a ton of data. Data has gravitational pull. It's coming. This is an avalanche just coming towards us. In the beginning, people are going to be looking for simple stuff. I call it augmented query. I just want to be able to ask some natural language questions, whether you saw the Forbes article in -- about State Street. They have added to their dashboards questions like why's my portfolio down today, and it just gives you a nice intelligent textual answer to that question. That's low-hanging fruit. Everybody is going to do that, right? We just changed our query method from whatever method we had before to natural language, and it's great to go to your Board of Directors show that stuff, showed that you're part of the in-crowd you got it down and all this kind of stuff. But we don't -- that's just far and far from the end game that people have in mind. When we talk to the large enterprises out there, I mean they're talking about collapsing their call centers. They're talking about collapsing their sales and infrastructure. They're looking for massive redefinitions of the economics of their businesses, pricing optimizations in retail, right? So they're looking for -- a lot of it is cost and economics oriented. And it's -- this is the demands that are going to come at our data, because if you heard Jensen last night, the data holds the key. It holds the key to the intelligence, right? We just need AI, the large language models to be able to extract what is in that data. That is really the key thing that we have to do, but it starts with having the data. The chat I had with [indiscernible] last week, we talked a lot about this, and they are keenly aware that data is where the whole game starts. So we have a healthy flow of data. We're doing over 3 billion queries a day. Those are Google-esque type of volumes and those numbers are growing robustly. Our footprint in the Global 2000 is expanding quarter-to-quarter. And we are campaigning all over the world, hard to continue to invest, because we think that is the game is to really establish ourselves. Once we establish ourselves, we grow, you can see that from our net revenue retention rates. So that's really the two selling motions that we have. One is the acquisition of new accounts. The other one is growing in the ones that we have. So that will continue unabated. And that sort of opens up the future opportunity to AI in addition to the current one that we have. So one thing that we talk about when we talk about the data cloud is like fundamental philosophy, the fundamental conviction that underlies is when you have a data cloud, you bring the work to the data. And historically, I mean we lose our conviction very easily because we are -- we have -- from since the beginning of computing, it's been so easy through FTP and APIs. Why don't we just send the data over here, over there? And it's very difficult to maintain that conviction. So our whole data cloud strategy, right, that's sort of the big cell to the world. It's like, hey, all the work that we're doing in terms of workload enablement is aimed at that, that is full alignment with our business model, right? So that's why we're investing so many resources in not just being able to do data warehousing, but expanding through Iceberg OpenTable formats. It really opens up the data lake opportunity, transaction processing, global search, cybersecurity, all the different categories of workloads is incredibly important through this strategy succeeding. We have historically never had data platforms that were so multicapable as Snowflake already is and what it is to become, and it's working. You see the workloads growing and growing on in leaps and bounds, and that's what we have to see, because that means that the strategy is working. The other part is because we come from an on-premise world, we have always kind of looked at the enterprise perimeter as sort of our playground. That's our scope. That's sort of what we live with, and anything outside of our enterprise parameter, it's really difficult and scary and trust and all these kinds of things. Having data connections outside of enterprise parameter was like almost impossible to get that done. We've really redefined databases and database ecosystems. I refer to them as orbits and really data universes. And they're really defined by their business models. They are defined by your business relationships. They're defined by your ecosystems, and they're dynamic. You will grow connections, you will terminate connections. It will be different from a daily standpoint in terms of what you have, but also how much data is flowing in this direction, in that direction, both directions, all this kind of stuff. There's much more fluidity to data. And for AI, this is really, really important because you want to train, you're not just going to train on your enterprise data. You're going to train on relevant data, right? I mean, the hardest thing that I -- in my conversation with Jensen, we have got several chats with them over the last couple of weeks is like, look, we're going to come up against harder and harder questions that people want to ask of their proprietary data. And it's like how we enable that. That's really what the partnership is about, because we can't possibly know. We're super optimistic and everybody is high fiving and all this kind of stuff. But the reality of software, the way we've lived it over the last 30 years as things are always harder than what they -- what we think it will be, and they take longer and all these kinds of things like building a house. So having a lot of the right resources that we know we're going to have very demanding requirements to be able to ask hard questions. Last night, I brought up the example that I've seen in the business of DoorDash and Instacart and so on. People that spend enormous marketing resources to drive their top line. And then they have churn, which is kind of -- churn a sort of the thing that it's like inflation. It just evaporates the value. I onboard a customer, and they place an order and then they don't come back or they come back a month later and then they're gone for 3 months, and that drives them insane, because their models are not working. The marketing investments are not making sense. It's like, how do I grow my business if I can't get to a model and not understanding why churn happens, is just maddening. Being on the Board of Instacart myself, I hear about it every quarter. And it's one of those things like where does data begin to start answering that question? And we know that it can, right? So you're getting to the hardest questions that people have in their business in the world of auto insurance, and we have a lot of other insurance customers between GEICO and Progressive, Liberty Mutual and so on. I mean they live on telemetry data, right, because auto insurance, I mean, they're driving their costs down, they're driving their prices down, but it's all about pricing risk or the way they price risk is by understanding what the claims -- risk and the claims profiles are for people's driving histories. And they don't even -- they barely need any other data and that kind of data to be able to run their businesses. So some of these insurance companies are doing really well competitively, driving their prices down and get the profitability is going up. That's all data, right? So you cannot even conceive of running these businesses anymore without having that kind of data and the analytics and intelligence to be able to extract the value from it. So we see this happening all over the place. Data used to be an assist to running business. Now it is really the core of running businesses. And as we get more and more sophisticated with things like large language models, it will be inconceivable. I mean we will be running everything through these models. That's really where we're headed. So I'm going to switch gears on you, because we're going to talk a lot more AI, so opportunities for taking another shot here with the rest of the afternoon. We -- as you know, we signed a contract with Microsoft and yesterday, we went public with that. I think Mark, we more or less doubled the size of the contract that we previously had, which was done 2.5 years ago. So there's a good philosophy in that relationship. The conversation I have with the CEO of Microsoft, is like, hey, you're about half the size in the Snowflake business that you should be based on your market share, and I know why it is. And the reason is we don't have incentive alignments in the field. It doesn't matter what you think or what I think. What matters is who gets paid on what, right? That directs and drives behavior. And they fully agree with that. And I said, a, we have to change it, number one; and b, we have to codify that in the agreement. Otherwise, this won't change, right? And we will not sign up for big numbers unless we have really strong assurances that we're going to get alignment in the field. That's really how our Amazon relationship has become so productive, because the behaviors and the dynamics in the field really dictate how these things work and there's very little to do, whether the CEOs like each other or not. So they've been very forthcoming on a number of fronts. Maybe Mike will talk about it later more details if he wants. But I also asked such as says, hey, I want to record a clip because I get asked about the Microsoft relationship all the time, and they hear me talk about it, but I want them to hear you talk about it, okay? And he -- to his credit, he readily agreed to that. And so we recorded that clip. So we're going to play that for you now, and I'll let it speak for itself. [Presentation]

Great to be with you today, having the opportunity to talk about our partnership. We've been working together for quite a number of years, but this is a new chapter, and we thought it was a good time to create a little bit of context and clarity for all our respective stakeholders. I'll quickly rattle off with what we think are sort of the major dimensions and then you can sort of weigh in with your unique perspective. in terms of what it means to Microsoft, what it means to you, what it means to our shared customers. We've been running on Azure for years now, we share thousands of customers already, Azure is really a big part of our world. But this is a dramatically extended relationship. I mean we did our last contract just a few years ago, and now we're back and we're doubling up the [ anti ] here. So the velocity is tremendous. So it's very exciting. Azure has also been the fastest-growing part for Snowflake and as a share of our business, and I think you agree with that, we just have room up and we're really looking at what does it take to do that? The second aspect is greater field alignment. And this is really an important part to partnership. You and I can have great alignment, but you get 14 layers down. It's a whole different thing. So I'm very excited about the progress we're making in that regard. And then finally, I think this is really great, working on joint solutions, the ability to co-sell joint solutions, especially in the areas of data science, Power BI, obviously, with Snowflake, machine learning and AI, Azure ML, as your AI. So we're really excited about how the relationship is coming together, certainly from the Snowflake perspective. And I think our audiences are going to be super interested in hearing your perspective as well. So go ahead.

Absolutely, Frank. First of all, it's so great to be able to sort of launch this next phase of our partnership. As you said, we've been working together, but I do think that this is one of those points where you are already such a mainstream part of the core enterprise. And so therefore, Microsoft and Snowflake coming together to address what our pressing needs of our mutual customers, so that they can do more is fantastic to see. And I think you captured it well, right? I mean it starts with really both, the Azure platform and the Snowflake platform coming together, because they are two mission-critical things for any enterprise. And so the more we can work across all over the world, across all the different types of configurations for mission-critical applications. And so we are excited about the next leg of work that we will do in bringing the best of Snowflake, the best of Microsoft Azure and at the infrastructure there. But then as you said, at the end of the day, it's about field alignment so that we -- your sort of frontline and our frontline can go in, in an aligned fashion to solve some of these critical problems that customers have and critical challenges customers have, having the shared incentives. So we're really looking forward again in this next phase to really take out some of the friction and more align ourselves at the face of the customer. I think that's fantastic. But perhaps the third dimension is what's most exciting, right? Because in some sense, what's the value add? What's sort of the real coming together of Snowflake and Microsoft represent? I think you said it well. Snowflake is where a lot of the most critical data of an enterprise is. And data is the most critical asset even in this new age of AI. In fact, AI doesn't happen without sort of data. And so therefore, Azure AI and some of the OpenAI models coming together with the data in Snowflake, I think that's a place where there's so much demand. And so I'm very excited about what we can do for our customers. Power BI on the other side, right, as an experience layer on top of this data. We already have, I think, significant amount of Power BI usage on top of Snowflake. Us taking the next step on that will be exciting. Or even per view and some of the data governance capability beautifully integrated into Snowflake, or even some of our ETL, like our data factory. So these are all things, I think, in a very customer-centric way. What excites me about this next phase is every customer in a time like this is looking for what's that edge they can get to get on top of their digital investment. And I think Microsoft and Snowflake coming together to help customers get more out of what they're doing with infrastructure, with their data, with their AI is something that the two of us can, I think, uniquely contribute to, and I'm really looking forward to that.

Yes. Thanks for saying that. We think the AI angle has sort of put additional context on the relationship. And as we talked, it is not the same thing as scheduling your next trip to Yellowstone. This is about asking really hard questions of your own business. And how do we collectively enable that? So I think these are exciting times, and thank you for the support, but also thank you for your leadership in the world of AI, because the whole world is moving forward, and this is a great time to be expanding our partnership.

No, I look forward to it as well, Frank. And I think as you said it well, which is in some sense, we now have a new reasoning engine. With the data engine, when you bring those two things together, you can really fuel the next generation of productivity for every business process in every enterprise. And so really looking forward to this next expanded phase of our partnership.

Terrific. Thanks for taking time today.

Thank you so much, Frank.

Thank you, Frank. And virtually, thank you, Satya. I hope all of you are picking up on this theme that really Snowflake is at the intersection of this revolution within AI and hearing Frank speak with probably two of the most people in the midst of this revolution, we believe, is really powerful. And so to lean into more about what Snowflake is doing with our product, I would like to invite Christian Kleinerman, our SVP of Product.

Hi, everyone. Good to see some familiar faces. I'm sure I have not met all of you. Hopefully, many of you were able to see Frank and Jensen last night. Hopefully, many of you got to see the keynote this morning. I will recap a subset of the announcement from this morning and contextualize on why it matters for U.S. investors, but very important to say, I am not covering everything we covered this morning. And this morning, we didn't cover everything we had at the conference. So the innovation runs broad and deep, and this is a great opportunity for all of us, for you that are here in person to talk to our customers, talk to our partners. And hopefully, you can sense the excitement that we have at Snowflake, but most important, that the ecosystem around us has. With that, the talk this morning framed our innovations in 3 different themes. One is the concept about a single platform. And we will not get tired of emphasizing that Snowflake, Snowflake, Snowflake, a single product. Once an enterprise integrates with their security system, their identity system, their overall enterprise infrastructure, all of the additional capability we built fits right in there. And that's a big part of the value prop that we have. We're also simplifying the cognitive load. We like asking our customers, can you even tell us how many services your favorite cloud provider launched last week or last year? Now think about if you want to embrace a multi-cloud world, it is really, really difficult to just keep up with the number of products and the complexity that comes from the cloud providers, whereas we're very focused on taking a lot of the efforts, complexity on our side and simplifying, simplifying, simplifying, so that it is easier to adopt Snowflake. So a single platform we'll talk more about. Chapter #2 or theme #2 was around how do we help our partners and customers, both distribute, deploy, monetize data products. It could be a data set, it could be an app. We'll talk about it. And a lot of what you heard Frank talk about, a lot of what you've heard us talk about for the last 2, 3 years is how do we do away with trade-off and dilemma that exist in most organizations today, which is I want to do more with data, but I also want to be able to govern it and secure it, and go and ask CIOs, are you at odds? Do you have tension with your data science team? And invariably, there is some tension, sometimes it get resolved towards data scientists can do less, sometimes it get resolved towards, we are taking some risk on security, and our whole value path of bringing competition to the data is there should be no trade-offs. Go get amazing value out of the data, but don't give up on security or privacy or the type of analytics you can build. So let's dive into these three themes in a little bit more detail. The first one, I already alluded a bit, for us, very important, it's a single platform. And you wouldn't believe it, like it's 12 years -- 13 almost, since Snowflake came out to the market, and our architecture continues to be a massive differentiator relative to most of the solutions out there. Architecture has 3 tiers, core storage, as much compute as we want, a global services or cloud services layer that coordinates all of this. And there's a fourth element of our architecture, which is what enables us to provide cross-region and cross-cloud experiences for our customers. I've mentioned it in prior conversations with some of you. A lot of people can easily say, oh, I am cross-region and cross cloud, because they took some VM or some container and made it run. But we are truly cross cloud where we can give our customers and partners an experience that transcends any one region, any one cloud. Some of the stats you see here, Frank alluded the number of queries we're running on a daily basis is quite large at this point, 3.1 billion queries. We included the number of rows that one of the largest customer table that we found a single table with 50-plus trillion rows. And we took our 5 largest customers by data volume and compressed. They have 170 petabytes in Snowflake. I cannot emphasize compressed enough because our compression ratio could be 5x, all the way to, say, 20x. So this could be 5 customers have more than an exabyte of data inside of Snowflake. Going to the specific announcements that we shared this morning. I'm going to keep it at a high level without going into the details. But the way I think all of you are going to see at the largest organizations have the data platform wars, for lack of a better name, play out is, what are the engines that control the rights and the governance of what data? All of you understand as it's under management really, really well, better than I do. Think of the concept of data under management or bytes under management. That's how we'll see at least for the largest organizations where there is a concept of open file formats, open table formats that anyone can query, but who is the custodian of that data? And the announcement this morning is the introduction of a single unified iceberg table type, which will let customers choose, do you want Snowflake to just be a read agent of the data, or do you want Snowflake to be the custodian of that data? The words that we use are unmanaged and managed. And the most important thing, the reason why we shifted our plans from a year ago a little bit is to make sure that there are no trade-offs on performance. We will give customers that choose to use open file formats and open table formats performance comparable to what they see with Snowflake internal tables. We announced, it was October last year, someone should fact check me, the acquisition of Applica. And at the time, Gen AIs and LLMs were not this singular topic that we discuss all day long. But we had already seen a glimpse of the value that this type of technology can bring to certain problem spaces. And in particular, we said: This is pretty amazing. You can ask a natural language question of a document and be able to get answers. Those answers become structured data, which then you can use for applications or pipelines or maybe even store in a traditional table. What we have announced this morning is something that has been in preview for a few weeks. So we have some positive customer feedback already. But is the ability to have documents that are stored in Snowflake, you can use this document AI technology and ask questions in natural language and extract values for those questions. Very important, it's not a language model, it's an image and text model. And if you haven't seen the demo from this morning, there was image handwriting recognition involved, where if a document has some parts of it that are text and parts of it are image, and the image can be run through OCR, optical character recognition, we will be able to extract those value out of that. Think of the use cases of legal department that wants to ask questions on how many contracts have this type of terms or limitation of liability above some number, those are the use cases. And we're quite excited about where this is at, which is in product preview. One of the early debates that we had at Snowflake when we were saying we want to do more in AI and ML was: Are we catering to people that know ML, data scientists that understand which algorithm we're going to use and which function and training and loss function, or do we cater to analysts? And over the last couple of years, our answer has been very simple. We're going to appeal to both, because both are personas that are really keen and really close to the value prop of Snowflake. What we announced this morning, and these are functions that are now in public preview, our ML, machine learning power functions but targeted to analysts. So if a SQL analyst who wants to run a forecast and say, sales data or any time series, they can do so without having to know the underlying machine learning technology. We announced forecasting, we announced anomaly detection. And the third one is a personal favorite of mine. We call it Contribution Explorer, which -- what it does, it helps you answer: What are the conditions that may have contributed to a metric changing? Typical example, maybe my sales -- same-store sales quarter-over-quarter are down. Why is it? And then this can run through a number of dimensions and say, maybe it's this product line or maybe it's this specific location, something like that. Important for us, this is all a driver of consumption. It's SQL functions. They can be called from within SQL, from within Snowpark, but they drive quite a bit of CPU consumption. Unistore was covered in Frank's section of the keynote this morning. I'll be the first one to say, relative to what we shared with you last year, it has taken a little bit longer. I think I said, this is a holy grail, and this is really hard. And yes, it is still the holy grail, and it's still really hard, but the progress is amazing. You see here some of the logos of companies that are leveraging or using the preview of Unistore. And the most interesting thing is we have 5 customers send us a note and said, we are putting this thing in production. We know that you're not condoning it. We know that you have production rules. We know that this is covered by previous terms. It doesn't matter. They liked it enough that they've gone live in this. So we continue making progress. The next milestone here is for us to be in public preview likely towards the end of the year. In reality, all of these milestone transitions are a function of customer feedback and what the technology is, but it's making really, really strong progress. And we have people waiting in line to leverage it. Category or chapter #2 or innovation theme #2 is how to distribute, monetize and deploy data products. We framed in a lot of applications, which is where the heavy lifting happens, but everything here is applicable to data products. The other interesting thing about data products is many of our data providers have started to do, here's a bit of product with a streamed UI. And I don't know anymore if you call that a data set or you call it an application, which is why in our mind, it's all one and the same. One of the announcements that I think are most meaningful to the adoption of our Snowflake marketplace or products in the Snowflake marketplace is the ability for our customers to purchase products from the marketplace by drawing down from the commitment that they've made to us. If any of you are thinking, oh, yes, this is what you do with the cloud provider marketplaces. Yes, same concept. But the beauty of this is that this is now a cross-cloud ability to draw down. So if a customer commits to us, say, $100,000, they could draw down for some compute on Azure, maybe some compute on AWS, maybe an application is going to run a different deployment. So we think that we're going to dramatically lower the friction that it takes to be able to leverage the amazing data products that are in our marketplace. This was announced today as generally available in the U.S. with some exclusions that you see in the footnote there. The largest announcement we made last year was the native app framework, which is the mechanism by which we bring rich applications to run close to the data. And the whole reason why we did this is to accelerate the time to value to all parties involved, particularly if there's someone building an app, today, they spend 80% of their go-to-market cycles going through legal, procurement and security reviews. And that same cycle is experienced in the consumer. I like an app, I like some machine learning technology, I want to use it, and yet, I'm spending a lot of time just making sure that, that vendor can conform with my security, privacy and compliance needs. The Snowflake native app framework just turns that on his head, brings the competition to the data. And as long as an application can vouch for, the data is not getting copied out. Hopefully, that whole cycle of going to market gets accelerated dramatically. Probably the single biggest announcement today is that the native app framework is as of this week now in public preview. And we showcased 25-plus providers, 40-plus applications, already published in the marketplace. And I will emphasize for all of you. The philosophy that we have on allowing someone to publish in the marketplace is it has to be finished real products. We don't want demos. We don't want like a toy app. No. The analogy we've used internally and happy to share here is we want to be the YouTube of data -- we want to be the Netflix of data products and not the YouTube of data products. And I have lot sort of respect and appreciation for YouTube, but we want that every product in the marketplace, it's curated. It's known to work well. We run security reviews, we run assessment. We validate sample queries, because we want the maximum experience for our customers. Here are some of the logos that I mentioned we shared this morning. I don't know if there's anyone I want to call out DTCC. What they're doing with us is completely amazing. The Depository Trust Corporation, they're trying to bring settlement data faster into the financial system. And we have seen already customers come to Snowflake and say, I am interested in Snowflake because of such an app. And that's the beauty of what we're trying to do is not just bring value to our existing customers, but create the data cloud where other customers will feel compelled to come and join. Bloomberg is another recent addition that I'm very excited about. They are now, through a native app, enabling the ability to bring data towards Snowflake account. Third innovation team theme. Again, it's how do you get or how do our customers get value out of the data without trading off security compliance or capability, and very important for us. Every so often, we hear, well, but Snowflake doesn't do streaming. Like Snowflake has been doing streaming since -- a little bit over summit 5 years ago, a little bit over. That's when we introduced Snowpipe and Streams and [indiscernible]. So streaming has been in our product for a long time. What we've been working on is how do you lower the latency of that streaming. So where we used to do 1 minute to ingest data, now with Snowpipe streaming, our customer can bring data into Snowflake in a matter of single-digit seconds, 1, 2, all the way up to 5 seconds. The other thing that is very important to bring lower latency and make it simpler is how do you transform that in Snowflake. I mentioned, again, 5 years ago here at Summit, we announced streams and [indiscernible] is the way to do it. And what we've announced now, this is in public preview at the conference is the ability to do dynamic tables, which is a much simpler way to express data transformations. And Snowflake does the execution of all of that behind the scenes. Extremely -- the external framework, the open source framework, that thing is very healthy. The growth is amazing. The number of applications being created is great. It has always been the fastest way to productionize machine learning models. And of course, in the age of LLMs and Gen AI, it has become a tool of choice to productize and showcase AI and ML of generative nature. We shared at the keynote this morning that we counted over 6,000 stream need apps that are front-ending large language model and generative AI use cases. The big thesis by which we acquired stream, it was not just to have the open source framework which we love and continue to invest in, it's to be able to securely run extremely inside the security boundary of Snowflake. That's what we've internally called [ streaming ] in Snowflake. It's been in private preview with a larger number of customers. We've counted over 2,000 applications being used. And this will be going into public review in the next few weeks, maybe a month or two at most. It's in the final stages. We're finishing up some performance and fit and finish. But what we are hearing is quite exciting from our early adopters. Then a lot of what you have heard from us around bringing the competition to the data, the technology or the collection of technologies that enable that is what we call Snowpark. As a quick recap or reminder, Snowpark is the secure hosting of Python and Java run times with a number of libraries that make it easier to program to those run times. The most popular library we have right now is the data frame API. It went generally available in November 7 last year. And that is when you've heard Frank, myself and others speak about dear customers, do you want to save money from what you're doing with Spark is because we see the performance of our data frame Snowpark API be easily 2 to 3 to 4x faster than Spark. And then depending on which distribution and which pricing you're comparing, maybe you are only 10% cheaper all the way to 2% -- it's 2x cheaper. And there is an extreme case, like there's one customer that ran a POC, they ended up being 12x cheaper than what they were running currently, and we're now in the process of planning the migration. So this is probably one of the fastest -- actually, I'd say, the second fastest adopted technology we put out there. The first one was SQL-based [indiscernible] procedures. And we'll continue to invest in Snowpark, because we like a lot of what we see. Part of that investment, we announced this morning, two new libraries in addition to the data frame library that I mentioned, that are in public preview of the conference. One is around doing feature engineering. So how do you prepare the data to feed it into AI, ML, AI, LLM? The other one is how do you do training in Snowflake. Frank alluded to me here from Fidelity being on stage this morning. They were one of the early adopters of this technology, and they completely loved it. They ended up asking us for early permission to go into production, even though the technology is not generally available. We also announced this morning the introduction of a Snowpark model registry, because one of the challenges organizations have is, everyone is training models and what do you do with these models? How do you organize them? How are you versioning? How do you deploy them? How do you discover them? And that's the capability that we're doing. Right now, this is in early private preview. But again, early customer feedback tells us we're on a great trajectory. And probably the single most consequential announcement, not only for our customers, for our partners, but I would say for all of you, is the introduction of Snowpark Container Services, which is the continuation of we wanted to have more competition running closer to the data. But if we have done one programming language run time at a time, we'll keep doing Snowpark for C# and Snowpark for C++ for the next 5, 10 years. And we decided to just accelerate all of that. What we're doing is Snowpark for a docker container. So now either our customers who are partners can bring a docker container and run within the security contents of Snowflake. That's the foundation of what you heard Frank and Jensen talk about, how are we going to bring this amazing framework that NVIDIA has? They had it all containerized. And as Frank just mentioned, you can go to the Porter Pavilion and you see very interesting technology running inside of Snowflake. Here are some of the logos of partners and a couple of customers that have already done the work to integrate with Snowpark Container Services. I will emphasize, this is not people that said, oh, I would love to do something, and if you put my logo in the slide, I'll do some -- someday we'll do something. No, no. They've done work. We've seen solutions from every one of these partners running on Snowpark Container Services. And if I say there's another 30 or 40 in progress, maybe that's undercounting it. The excitement from both, partners and customers, for this technology is, I would call it, through the roof. Snowpark Container Service is very important. It's in private preview. So it will take some time to get us into the full cycle public preview [indiscernible], but the early signals we get are encouraging. This is how all of this comes together. And I think many of you walked into this room with questions on, okay, what are you guys doing in Gen AI, are you getting outcompeted or anything like that? And there, the answer is the whole thesis that we've been on for the longest time is entirely applicable to generative AI. All along what we said is we want to tear down silos from customers, and we want to avoid resiloing. And one of the biggest reasons why people resilo is because they're sending data to all sorts of third-party systems. It's only that now you have even more Gen AI third-party systems you can send. And what we want to do is turn around its head. You already did a lot of work as a customer on organizing your data, governing your data, setting policy, setting users, role-based asset control. We want to honor and leverage all of that but still be able to get value of Gen AI. We've shown the keynotes, not only our own first-party models but also third-party models. Nothing precludes any of our customers who are taking a hugging phase model or from whoever you want. And they can go and run it safely and securely close to the data. Most important is not just running scoring, but be able to fine-tune the models with enterprise data. We showed it a couple of times this morning on how you can take a base model and improve the results of those models by leveraging fine-tuning with the enterprise data. And of course, we have the ability to call into third-party APIs. If someone wants to call into OpenAI or Azure OpenAI, we've had extensibility for a long time. We've improved the choices and extensibility. So the message for all of you is customers want to be able to Gen AI on enterprise data securely, safely, with compliance. And that's exactly the platform that we showed this morning. This is not a, hey, someday, this slide will be -- no, we were showing many of the building blocks, and I'll be the first one also to say, we have more work to do. But the vision is clear the down payment from the technology is in the hands of our customers and partners, and we are very excited about what can be done, and extremely obviously plays a very important role. We announced partnerships with -- have we mentioned that we're partnering with NVIDIA? The partnership runs deep. To be very clear, we're hosting their Rapids library, and we showed this morning ML training running. I think it was 4 or 5x faster than running on CPUs. We're leveraging their enterprise AI technology to run inside of Snowpark Container Service. And probably the single biggest aspect of the partnership, they have this NeMo generative AI framework, which has both models, but a lot of software that helps you train and fine-tune large language models, and all of that is getting pushed through a container service to running through inside Snowflake. This is what Frank was alluding to. It's literally up and running here in the conference. If you -- any of you want to go and geek out. Two other partnerships, RECA, we're quite excited about the partnership with them. It's a small startup building a foundation model. But so far, they've been very aligned with the use cases that we're going to enable, and we always like companies that are aligned with our use cases. AI21 Labs, also a partner they're going to be bringing their models into Snowflake. And of course, we're having conversations with others to continue expanding this LLM partnerships. I'll end today where we started last year, for those of you that joined us last time. And the most important thing is we've started with three massive, I'd say, ginormous waves of innovation. We decided this year to do not only 24 -- 2014, plus -- because the plus means, it's not that we're done with analytics. That disruption is starting. Many of your organizations of you in the room here, you know that you still have 80%, 90% of data on-prem and still trying to break down silos. So that disruption continues. At the same time, the disruption and collaboration continues. This were data sharing and function sharing and application sharing. It's a part of it, and you're going to be able to have native application and container services. All of that is part of it. And of course, app development, how do we bring all of the interesting use cases, whether it's cybersecurity or supply chain or marketing analytics? We want to be able to deliver a different platform where data is not copied over and over and over and siloed and resiloed all the time, because we know that it just simplifies the time to value of all of these use cases by vertical and by horizontal. So we're extremely excited about all three waves of innovation. They're all making progress. And hopefully, at the conference, you'll get a validation of the progress we're making on all of these. That's what I had in terms of a quick recap of the announcement from this morning. Now what we want to do is -- I think Jimmy already introduced that we have Sridhar from Neeva here with us. If we can get some couple of chairs, coffee chairs for Sridhar and I to sit down. I'll give a chance to Sridhar to introduce himself, but I'll tell you in many of the conferences that I've ran, Sridhar is well known. I don't think anyone ever uses his last name. It's like Sridhar is Sridhar, you know who it is. And we're incredibly excited to have Sridhar here. And maybe what we do is we start with that background. I said some of us know very well who you are, but I don't think everyone knows. So we can start with your background.

Yes. So I joined Google early as an engineer, in fact, and grew with the team. I ran Ads and Commerce at Google for over 5 years. And it was quite the right for any company. I think Google revenue grew from $1.6 billion in '03 when I first joined to over $100 billion in 2018 when I left. An incredible way of technology changes were pioneered by the teams in search and ads that I ran. Many of the PlanetScale machine learning systems were built as early as '04, '05. About 4 years ago, I left Google and I started Neeva with a mission to humbly, rethink search. Little did Vivek, my cofounder and I know that the revolution in AI was up and coming. But a lot of being a start-up is about taking opportunity when you see it. And so early last year, we could see that generative AI was going to have a profound influence on how we consume information, how we talk to machines, how we talk to programs. And so we retooled our entire search stack, which we had built with a 50% team to release the first AI-powered search engine early this year. Of course, Satya, famously 'Thrown Down The Gauntlet' early this year for search. And it became clear that Google and Bing were going to be putting in billions of dollars into consumer search. And so Vivek and I came to the conclusion that we really would be better off taking a lot of the skills in data processing, in large language models done inexpensively. We were a well-funded startup, but we could still only spend $10 million a year, not $1 billion a year. And then when we looked around to who could be a great partner for us to take the journey forward, Snowflake was head and shoulders above everybody else that we talked to. We talked to a lot of folks. But we had amazing conversations with Benoit, Thierry, Christian and Greg and of course, with Frank. And here we are. We are 4 weeks in and very excited for what has been done, but much more importantly, what is possible. For me, AI, the non-high part of AI is really about fluid language interaction between humans and computers. I think it is really hard for us to understand how much of a profound influence that's going to have on all of the software that we're going to be using. But it very much feels like a right place, right time.

So you were at the forefront. I think you built a search engine, leveraging a limb before anyone else, is that factual?

That is absolutely correct. We set a high bar. We didn't know ChatGPT was coming. But when we played around with language models, hallucination was a real problem. At one level, like middle of last year, it was clear that you could give a 1,500-word blog to 1 of these models and have it do what's called an abstractive summary is somebody not done by picking out words but by actually abstracting the concept in the blog and turn it around in like a second. It's like it's real magic because like humans, we just can't treat that fast, for example. But on the other hand, if you ask Marvel's questions about things that they were not really sure of, they would just hallucinate, they would make up stuff. So the bar that Vivek and I set for ourselves was that the AI answers that we provided, they had to be referenceable, meaning we needed to tell our readers where this information was coming from. It had to be real time. The world keeps changing. And so you can have 2021 is the limit of my knowledge. And also, it needed to understand authority. And the way we did that was by using a phrase on a technique that's now, I'm sure familiar to all of you, It was called retrieval augmented generation. A way to think about that is LLMs combined with tool use. Just like humanity and culture exploded when we develop not only language, but also tool use and the ability to, therefore, then convey this tool use through generations. Similarly, there's an evolution happening with large language models where they have incredible linguistic power, but they're also savant. But when you combine them with tools like search, the ability to call APIs, there is going to be a big revolution in how we get information, how we consume information and hopefully also many real-world things like how we go about purchasing things, but that's a whole other story. Multiple sectors are going disrupted in big ways, most definitely enterprise data, which was part of what excited Vivek and me.

Yes. So I think you and I were chatting recently that in the hype of Gen AI, which we know, as [ Sridhar ] just said, it's a technology that is going to disrupt and change. But it's often lost is a notion that these things make up stuff sometimes. I think you and I were commenting on the, yes, people ask ChatGPT and then go and verify Google Search.

Yes. I mean even now, it's a common thing with me. I pay for ChatGPT, I have it on my home screen. By the way, I forget who it was that said it, it might have been our friend Brad Gerstner, who said like, "Hey, listen, if you don't have ChatGPT on your home screen, you're like behind the times." But I have ChatGPT on my home screen, but have to go through a process of trying to figure out, is this question mainstream enough that I can actually ask it off ChatGPT and get an answer that I can trust? Or if it's obscure, I have to worry that it will just manufacture something, and I have to go and verify. Just the other day, I was asking it for -- this is just a random thing that happened. I was to -- I listened to song. I was like, oh, tell me the characteristics of this [ prog ] that was playing, it completely manufactured an essay for what this [ prog ] was, I mean it had nothing to do with the structure of the music. But these are solvable techniques, and I feel we are just on the threshold of being able to solve it. But it's really language models, tool use, how we orchestrate them together. And it's a little bit like the early Internet. You know you're on the threshold of something magical. You can't wait for it to show up.

So then maybe tell us a little bit more about the team, Neeva team and the technologies that you bring because it's a very unique world-class team and technology you put together.

Yes. So this was a team of 50 that built a search engine from scratch, we crawl the web at a scale that we imposed on ourselves 200 million, 300 million documents. We have called crawled many petabytes of data as single team paying for S3 storage. We ran an index that was 6 billion, 8 billion pages and an incredibly low-cost system that cost on the order of a few hundred thousand dollars to be able to serve web search traffic. But we also had to pick up really, really important skills last year through this year and this was driven by the fact that yes, we did not have access to 5,000 A100s, that was out of even our budget. As I said, we were a well-funded startup. We were spending $10 million on OpEx. But them fleets of 5,000 A100s cost a bit more than.

A100 GPUs. Which are in short supply?

Roughly think of them as $8,000 a pop. And so if you are like I want 5,000, $8,000 a pop per year, if you're like you want 5,000 do the math, that's $40 million, you're like that's not going to happen. So what we did a lot of was we began to understand things like you can do with fine-tuning. You can do with human feedback problems that can be solved by much larger models. And so we pioneered a set of techniques in what's called transfer learning, where you really can use the output of large models in order to train small models. That is, again, a profound change for how models are trained because I've done evals like for the last 20 years, a lot of Google search quality, ads quality, have all been based on humans very painstakingly labeling stuff. So -- but a lot of companies, certainly Neeva, but also Google and OpenAI. What they're doing is they're taking the very largest models, taking their output as then training data for the smaller models which get to be just as good as the big models for specific classes of problems. We also had to pioneer a whole bunch of techniques in order to make them fast. I'm sure some of you have used Sydney, and I've openly expressed frustration with Kevin Scott, the Microsoft CTO that Sydney is so slow. That's because it runs on a very large model. So we also did a whole bunch of techniques to speed up inference. But these are, again, the building blocks for how you go about using large language models in practice. Part of what is really exciting now about things like Snowpark containers is that we took our original fine-tuning scripts for some of the problems that we were solving and worked with the Snowflake team over the last 2 weeks, mind you, to be able to push our fine-tuning script into a Snowpark container, so where we can download a model from Hugging Face, we can fine-tune it and have the output be stored somewhere. Again, then we'll be figuring out how to run inference on them. We have shown some demos to Christian already. But we want to turn these into recipes so that all of our customers can natively use language models. All of us are very enamored by things like chatbots and tool use and yes, those are the sexy applications, but there is incredible value to be created in things like understanding documents in things like sentiment analysis, translation of all kinds of feedback that comes into your product, a lot of boring business problems that carry incredible value for all of the enterprises. Those are the things that we want to make sure that we enable. Of course, you also have to be where the cutting edge of technology is. And so making sure that we can use the same technologies to power copilots both in a 1P sense, where you get assistive experience for everything from writing sequel to writing streamlits to being able to produce dashboard, plus also making sure we have this technology be usable by our customers as they think about what are transformative experiences they want to build. So these are all in our road map. We are like kids in a candy store, pretty excited.

And by the way, that's super important for all of you, which is the -- even though on the surface, oh, yes, Neeva is consumer search, something get into consumer search, No. But everything Sridhar is saying is the techniques, the technology, the approach, how do you put into production at 1 of these language models. Easy to say, really hard to do it and do inference in 100 milliseconds, 200 milliseconds.

100%. For something to be an interactive app, you have to finish up search to retrieve context on then a language model run to produce a fluid answer. But you don't really have more than 800, 900 milliseconds before people are like, this is sort of slow and to create high-quality experiences needs a lot of deep technical expertise. And -- but again, something like Snowpark containers is the ideal vehicle for us to be able to take some of these, as I said, turn them into recipes that our customers can use, and there's a lot here.

One last comment on the applicability of what Neeva built is think of use cases like search for our marketplace. And it's really until already took pointed their crawler into the marketplace, tell us some of the things that you showed me, which is incredibly amazing.

Yes. I mean, as I said, language models have hallucination problems. Language models have authority problems, an increasingly important layer of software that is going to exist between us humans and the language models is going to be this piece that sits on the side that is going to provide context for the language models. In the context of Neeva, the consumer search engine, this meant that we would run search on whatever it is that the user type, but we would also make sure that we took care of intent disambiguation. A word like Swift, for example, has all kinds of meaning. Obviously, it means fast, but it's also a banking code. There's a laptop with the same name. It's a programming line.

Taylor Swift.

There's also Taylor Swift. But disambiguating so that you can feed the right context into the language model becomes important. So expertise in doing really good retrieval and clustering is a fairly important part of how you make a language model work. In fact, all of the cool kids that are doing start-ups these days, they operate on what's called a Langchain, Pinecone, openAI kind of stack where Pinecone is the 1 that's used to do vector retrieval, Langchain is the orchestration layer and OpenAI is the model. It's a cheap way to get a nice demo going, but you need industrial strength versions of this. So on the marketplace, for example, we just looked up a retrieval engine to crawl all the entries of the marketplace, combined it with the language model and all of a sudden, you have an interactive app where to quote Jensen from yesterday, you can basically talk to the marketplace. But we want to enable this on other subset of docs to be able to do the same thing for analytical questions that you might have on your data, but it's the same technique, you set context on the language model understands the context to answer your questions.

So we are incredibly excited to have Sridhar, the [ browser ] Neeva team, the technology, the expertise they have. We wanted to have Sridhar here just to give you a glimpse of what's in front of us that has not been shared at the conference. And it's a super exciting and thank you for joining us.

Thank you, Christian. Thank you all for your time.

Thank you, everyone. Now I'll introduce Chris Degnan, Chief Revenue Officer. And -- Yes. I'll let Chris introduce our customer guests. Thank you.

Perfect. Hey, everyone. My name is Chris Degnan, I'm the Chief Revenue Officer of Snowflake. I've been with Snowflake for about 10 years. Rob, why don't you introduce yourself?

Yes. Hi. My name is Rob Smedley. I'm the Vice President of Technology at Disney Parks, Experiences and Products responsible for all things data. So data platforms obviously -- data platforms, data products, data management, data governance. Parks Experiences and Products, if you don't know, is our global Parks and Resorts business. It also includes Disney Signature Experiences, which is Disney Cruise Line, Disney Vacation Club, Adventures by Disney. And it also includes our Consumer Products business, which is our Disney stores or shopDisney e-commerce business as well as licensing, publishing and games.

Awesome. Well, Rob, thanks for doing this with us today. I appreciate it. Maybe just give the audience a sense of what motivate you to move to Snowflake in the first place? And what other solutions did you look at when you're doing that?

Yes, we're at this kind of crossroads. We had a large Teradata warehouse kind of on-prem in our data center in Orlando, and we had just reached capacity. As data got bigger and more complex and our use cases got more advanced, being constrained by compute and storage, just we were stuck. We're in the midst of this attempt at a data migration into a Hadoop platform. It wasn't going particularly well. And we got accelerated pressure to get out of our data center. We had to migrate to the cloud. So we started looking for some solutions that allowed us to really just lift and shift what we had in Teradata into the cloud and tremendous cost savings associated with getting out of the data center. And it's still also opened up scalable compute and got rid of some of the problems that we had. And that's where we found Snowflake. That was, I don't know, maybe 3 years ago or so. And we completed that migration in the last year, I'd say.

So that was kind of my next question is kind of you've gone through some of the migration. You've got some of your legacy systems. Where -- what's the phase or how far along are you in your enterprise migration?

Yes. I mean in terms of moving to Snowflake, we've retired all of our kind of legacy platforms that we had, our Teradata platform, our Hadoop platform. We had some data in RedShift. All of that's been migrated to Snowflake. Due to the speed at which we were moving, we really didn't have any other option but to just lift and ship that into Snowflake. So great new modern platform, really old patterns and data architecture. So in terms of really being able to advance our use of data, we needed that refactor. So that was kind of the next step. So we started that in the past year, I'd say we've started that refactor, taking our most valuable data and saying, "All right, we're going to modernize our data architecture and get that ready really for that next phase." Our initial move to Hadoop was motivated by AI and ML readiness. We didn't really get there. I think this -- where we are in terms of modernizing our data architecture, that's, I think, really what it's about now for us.

Awesome. So what business cases do we support or does Snowflake help you support within Disney Parks?

Yes. I'd probably categorize it in 2 ways. I'd say, first of all, I mean, data drives everything that happened at Disney Parks, right? You can't walk into a park with a ticket and order food and ride a ride and stay a resort, every decision in the business is powered by data. So just day-to-day running of the business, for sure, is coming out of Snowflake. I think the next piece of that, though, which is -- we're a little bit in the present and a little bit in the future. And this is something I saw a few years ago, I started talking about it Disney is that we used to see kind of the big data analytics world as an analytics function. And then there was this operational function. And it felt like, I don't know, maybe back in 2018, those lines were starting to blur and now start to disappear. So that's kind of the next wave of Snowflake capabilities for us is, yes, all of the analytics, I can wait 5 minutes, 15 minutes to get my data, but now really powering customer-facing use cases. That's really the next frontier for us.

Awesome. And so you're obviously a large customer. And so how do you evaluate getting more budget and spending more budget -- or money with Snowflake?

Yes. I mean every time there's a new system or a new thing that generates data, we're very project-driven at Disney and everything come -- we kind of fund tech almost the way we fund construction projects sometimes. But every time there's a new system, something generates data or every time we have some new use for data, that's a project for us. So we evaluate what's the benefit of that project revenue or cost efficiency or whatever, and you balance that with the costs of implementing. So we take that into account. I'd say the other side of that, though, is there's a clear recognition that data is a differentiator for us. The data strategy is extremely important to us. We tend to forgive a little bit the difficulty that you have sometimes tying data directly to revenue. It's hard to make those jumps sometimes. So we are investing in our foundation. So it's not always a matter of, okay, I have a top line, bottom line benefits.

So probably a lot of these folks in the room have a question because you've been on this journey for a while, you've been using the consumption model. So how do you manage the consumption model at Disney?

Yes. I mean I think, first and foremost, you have to put the effort into optimization. When we did our lift and shift from Teradata to Snowflake, and we did this eyes wide open, we had code that was probably written in 2005, we're lifting into -- if you expect that to run efficiently. I mean it's not going to, you're going to have to do something to make it run efficiently. So you do have to put that effort in. When we first did that migration, we were like 2, 3x what we thought we were in. We've had no idea how to estimate and this may be a slight panic, but a reassurance, we knew this was going to happen. It didn't take long for us to make incredible gains in optimizing and controlling those costs. And then the process we got very mature in our modeling of how expensive is this new thing going to be, it's very easy for us to not predict. I know what this is going to cost. So we're able to make those decisions pretty well informed.

I mean it's important, I think as you guys scale your business, you have to be able to budget accordingly. And you guys have done a good job with that. Okay. So where does Snowflake actually sit in your data management stack?

I mean right in the center. Snowflake is kind of the core of what we've designed. We run Snowflake and AWS. So we tend to use some native AWS kind of components to get data in, we use Kinesis and Amazon's data migration service, things like that. We have invested in Alation as our data catalog for data governance. I'm very, very bullish on DBT. I think -- as someone who comes from a very traditional software engineering background, kind of the shock that I had when I got into the data space years ago was like, wow, the engineering practices that I'm so used to, I don't see those in the data engineering space. And with tools like DBT, it's really enabled us to use Snowflake and also have CI/CD and test automation and all these things that were kind of second nature to us. We have other tools that we look at. We're looking at DBT, BigID to help us with compliance and governance, things like that. So.

That's great. And all these are Snowflake partners which is great. So you just heard Sridhar talk about Gen AI. What do you -- what's your perspective at Disney Parks around generative AI?

Yes. I mean it's real. I mean I don't -- there is a ton of hype around it. And there's a lot of work to, I think, to be done to see the big revolution come. But I do believe it is 1 of the most disruptive innovations that we've seen top 3 disruptive innovations in my lifetime for sure. So it's a very real thing. And we're absolutely -- I mean, Disney is a company, especially Parks that was built on the idea of innovation. We're absolutely looking at Gen AI and hope to be a leader in that space. But the first, focus on the data. And I talked about how we're in the middle of that kind of refactoring of the data, the cleansing of that data so It's ready. It's not ready today. And we can go and do some Gen AI use cases and will be flashy and everybody will be pretty happy with it. But we're going to hit a wall really, really quick if we don't first focus on the data. I just -- we kind of ran over here from base camp, I just finished a talk myself where I talked about, if you don't invest now or you haven't already started to invest in cleaning up the data, you have no chance, and every day that you wait, you're going to get further and further behind in that race. So that's, I think, really where our focus is today.

So similar story that we heard today from [ Maher ] at Fidelity. He said the same exact thing. So it's great to hear. The final question I have is, do you have any plans to adopt newer technologies from stuff like Snowpark, Iceberg tables, et cetera? Anything that's coming up for you?

Yes, a lot. And the line was probably a little bit longer than it was maybe on Sunday, Sunday evening. There's some pretty interesting things coming. I think Iceberg is important to me. That external storage of data, that helps me mitigate risk. Snowflake has a choice for our warehouse, help me mitigate risk because I didn't have to lock myself into GCP, right? I can go where I want in terms of cloud -- having cloud flexibility. External storage of data gives me flexibility and risk mitigation in the storage of my data. And that allows me then to take bigger bets on some of these new products, some of these new features that are coming out on Snowflake. I can go harder into some of those, knowing that I'm mitigating my risk kind of elsewhere. So that's really big for me. Streaming, for sure. As those operational use cases get more and more important. It used to be 15-minute lately, it was okay, and it was 5, and it was 1. Now we've got to move faster. So we'll be early adopters of Snowpipe streaming. Snowpark for sure. We are very big on data applications and data products, and I think now Snowpark gives us a ton of capabilities there. I'll be very interested when Unistore is -- when we're ready for Unistore because I think that's going to be a big innovation, especially as we're going towards those operational use cases.

So Christian, we have to type faster. Yes. So yes, Rob, thank you. I think we love -- at Snowflake, we love customers like you who are actually willing to try our new technologies, give us feedback early. So thank you for the partnership, and we look forward to continuing to partner here with you in the future.

Thank you, Rob. Thank you, Chris. Thank you, Christian and Thank you, Sridhar. All right. For our final segment before Q&A, I'd like to welcome Mike Scarpelli, our Chief Financial Officer.

Good afternoon, everyone. I'm glad I'm here in person this year, having not made it last year, and I'm glad -- I apologize, I didn't get to talk to many of you after our last earnings call as I think I was dying with pneumonia. But Anyways, hopefully, you guys are getting a really good view of what we're doing. I know a lot of people have had questions about Generative AI and what we're doing. And this is not something that we just thought about and I hope you've seen that. We've been thinking about this for many years, what we're doing. And that's 1 of the things that I've said in the past, too, when Snowflake does something, we don't just roll out a new feature tomorrow. Many of the things we work on take years to develop. And we have other things we haven't talked about, and we're not going to talk about them until we know technically we've proven it. We're going to have a product. So what I want to do today is I really want to give you some of our highlights from 2023. We're really proud of 2023. We grew our product revenue by 70%. Not many companies that scale to go to $1.9 billion, have been able to do it. We expanded our non-GAAP operating margin by 700 basis points in 2023. We generated more than $500 million in free cash flow on a non-GAAP basis in 2023. Those are pretty spectacular numbers with the growth that we've done, and we're going to continue to show you guys leverage in the future, and I'll show you. So let's talk about where our growth opportunity is from here. Gartner has come out with this new market thing. Our market is growing [indiscernible]. I really want to get across here, whether it's $290 billion or $270 billion. It's a massive market opportunity. This is not 1 person take all. There's going to be many successful people in this market, but we think we're going to get more than our fair share of this market. And I want to remind you, too, it's also a very competitive space with the 3 largest hyperscalers in the world, Google, Microsoft and Amazon. You can see from our announcements that we've had with our recent move with Microsoft and AWS, we have very good partnerships there, a very good go-to-market alignment, which is getting better. Google is the 1 we still need to work on, and we're open to that. They're just not as open to it. And we do think this market is just going to continue to grow with the proliferation of data, and I'm sure generative AI is going to create even more data. So I bet these numbers were even understated. But this is what gives us the confidence for our long-term model, and I'll talk about that a little bit more later on. We're really focused on landing the largest organizations in the world. 3 years ago, we had 16% of the Global 2000. In Q1, we had 30% of the Global 2000. We're really looking to land these large quality customers. It's all about quality of customer. And we have a long runway in front of us with these customers. Many of them are in the very early innings. Some are still at in the warm-up stage before even going into the game in terms of migrating data to Snowflake. So I talked about quality and I've said this before, many people ask about, "Oh, your customer ads." I really don't focus on our customer ads. I focus on our large customer ads, those quality customers. And this is what I really focus on who are those customers that are going to be able to pay us $1 million a year, $5 million or $10 million a year, $20 million a year. We will have customers and many customers paying us north of $50 million a year. And we're at that stage right now with a few of our customers. So as of the end of 2023, we had 330 customers paying us north of $1 million a year. You can see that was up from 80 in 2021. We have 60 customers paying us more than $5 million a year. That was up from 13 in 2021 and 20 customers are over $10 million, and we have 10 million or 10 customers that are north of $20 million a year. These numbers will continue to grow with Snowflake. And it's surprising to me even our largest customers, I thought 2 years ago, they were probably as big as they were going to get, and we're still identifying opportunity and they are continuing to grow with us. I really want to focus on -- a lot of people don't understand, when we land customers, we land customers small. It's rare that customers start off at $1 million year. Our average -- if you look at all of our $1 million dollar customers, the average size these customers land at is $150,000 a year, and then they grow. That's what really leads to our net -- where you see our net revenue retention continue to grow as well, too, because most customers start small. And that's the beauty of a capacity business. When you talk to our customers, they're really just buying capacity whether it's -- and they may sign a 3-year contract, but they're buying capacity and whether they spend that in 6 months, a year or 2 years or 3 years, it doesn't really matter to us. I actually don't get that hung up on what the bookings is, I get more hung up on what the revenue is from those customers. And migrations. This is 1 of the things that gives us confidence that our customers are going to continue to grow. We started tracking. This is really self-reported by our GSIs. But our top GSIs and this is not all of our GSIs, they tend to be our bigger GSIs. In 2022, the self-reported $847 million in services revenue associated with Snowflake work. Last year, $1.34 billion in services work. Customers don't spend this type of money on services around Snowflake if their intention is not to grow with Snowflake and consume Snowflake. And when customers are making a decision to go with Snowflake, these are multiyear decisions. These are not a 1-year decision. We're replacing systems. Some of the Teradata systems we're replacing are 20 years old, 15 years old. And so when customers are looking to do these migrations, they're making technology decisions for the next 5 to 10 to 15 years on Snowflake. That's what gives us the confidence that we're continue to grow and hit our longer-term numbers. In terms of time to migrate, this is the 1 thing and I get asked a lot of questions by people. What are you doing to accelerate the time to migrate? The biggest challenge to migrating customers is customers' timeline. How quick do they want to -- how quick do they want to go? Many of our new customers that we're landing now want to go at their timeline because they want to make sure they do it right. You heard Rob from Disney Park, he was talking about, well, they just migrated all their data and didn't really focus on the quality of data and rearchitecting it, most of our customers now when they're doing a migration. There's a lot of work that goes into ensuring that the data when it goes to Snowflake is architected properly, so they can take the full advantage of that data in their business. That's 1 of the things that takes the time. So it's still taking us on average, 240 days for a customer to actually ramp to their initial contract value and what they're consuming. But once they do that, then they start to grow. And I also want to stress to. It's labor intensive, too. You can have the best migration tools to convert code, but there's still -- it's billions and billions of lines of code that are getting translated, but you got to make sure everything is right, and there's time that still goes into this manual time to make these migrations successful. Our expansion patterns. If you look at our recent NRR in the 150 range, that's actually back to where it was in 2021. I would say that 2022, there was probably a little bit of euphoria with customer spending. And now we're seeing it more back to what we would say was normal. Not saying it's not going to go down. I don't think it's going to stay at these rates forever, as our customer base continues to grow, but it will be above 130, and I've said this many times for a very, very long time. But what's interesting is when you look at our Global 2000, our large customers, this is what I like, their net revenue expansion rate has been very stable. These are the ones that are a lot more disciplined on doing these big migrations, they take their time, but they are growing and they will continue to grow. So I really like the net revenue retention within these Global 2000. There's still a massive growth opportunity in front of us. If you look at the G2Ks alone, our average spend today for Global 2000 is $1.4 million. Our customers that -- those 330 customers I talked about that are paying over $1 million a year, their average spend is $3.7 million. There is no reason why a Global 2000 can't spend $10 million-plus on Snowflake. I'm not going to say it's going to happen overnight. But these guys have massive IT budgets in what they're doing. That is not a big dollar for Global 2000. Our top 25 customers on average, spent $18 million a year. And these numbers will continue to grow over time. We believe the G2K customers will get to similar or larger size than our average million dollar customer between now and 2029 in our 10 year and our longer-term model. And when you talk to these customers, you talked to Rob was just up here from Disney, they have aspirations to do more on Snowflake. And that's what we're really working on. And that's 1 of the reasons why we talk about all these new product announcements is to get our existing customers to grow more in Snowflake. And it's really our existing customers today with some new customers coming on that are going to get us to where we need to be for that $10 billion target that we had put out. And I also want to say too, as of the end of last year, only 17 of those top 25 customers are G2K. There's still some non-G2K relatively smaller customers that are big spenders on Snowflake. But we do see a shift with our largest customers from a revenue standpoint, shifting to those large enterprises. And more so because they're just growing faster than some of those other smaller customers that were significant consumers of Snowflake. In terms of our go-to-market strategy, I think this is really important. We're really aligned, and this is something we've done over the last few years. We're aligned by theater. In theaters, we have U.S. verticals. We have our enterprise. Our enterprise is really focused. Our verticals is focused on the largest customers within the verticals we play in, think of financial services, health care and other, the enterprise is really focused on accounts with 500 or more employees. You have our corporate that is focused on those sub-500 employees. And our corporate sales is really an inside sales motion. And then we now are aligned by industry. And the industry is these 8 industries that we have lined up here. And then we're also aligned by workload. That's something relatively new that we're really focused on within the sales going in, because it's really important we understand to actually go in and sell customers on workloads. This really is a workload-by-workload sales when we're going into our customers to get them to grow. But it's so important, we really sell 1 product at Snowflake with 3 different, 4 versions you could say. We have our standard, our enterprise business is critical and then VPS. One of the things I do notice is very few customers actually start out today on VPS, Virtual Private Snowflake, used to be most financial institutions wanted that. Now we're finding they're comfortable with the security within our own regular Snowflake multi-tenant environment that they're comfortable going into that because VPS, you do sacrifice some of the things of data sharing and other things. And I really want to get across too. And our product really supports this full spectrum of workloads from data engineering to data science, AI, MI applications. We're super excited about the native app development within Snowflake and the things you're going to be able to do with Streamlit. And 1 of the things we've really been focused on in the last few years is really ensuring that our salespeople are equipped to sell the specific workloads. A lot of our sales enablement in the last year has all been around Snowpark, how do you sell Snowpark going into a customer. Because many times, you're really -- that's a very different conversation than a data warehouse migration. And so it took time to train people. And we're learning and sales enablement for these specific workloads is really key where we're investing a lot of money. In terms of when organizations land on Snowflake, they arrive with complex data states. It was interesting, the gentleman from Fidelity, if you saw when he was up there, he talked about they had 170 different data silos that they want to migrate. And what's interesting, they've been 3 years into that journey, and he still said they have another 18 months to go. These are long, they'll be 5 years into this journey before they fully migrate all the stuff. And I want to get that across to people. These take time. They take a lot of time. And some customers, I think, are going to take 10 years. But the first thing most customers want to do is they want to really consolidate their data silos. What Frank talked about to really get the benefit of AI. You need to have your data in a very structured architect properly in 1 location. That's what our customers are focused on doing. And I also want to stress too, we migrate a variety of legacy vendors into Snowflake whether it's Teradata or your traditional data warehouse is Hadoop, a lot of SQL, a lot of things we're migrating. But when we win, we're really winning workload by workload and these expansions take years. And each opportunity, this is important to, we may have won 1 workload, but we're trying to do another workload, we're competing with someone else for that workload. It's not just given to us. Our salespeople have to be involved in those customers. And that's really important. A lot of people think, well, it's kind of like an annuity once you sell a customer, why do you need to have salespeople. You always need to have salespeople in there because you're always going to be attacked workload by workload, whether it's by the hyperscalers or some other technologies out there. Yes, I do listen to you, Chris. That's why we need to pay salespeople. It's actually really important. And we do a lot of migrations. In 2023, we did 2,410 migrations. We replaced over 3,000 vendors within our customers and these will continue. And it takes a lot of -- and this is why it's super important to us that we have relations with GSIs. We get them to build practices because we can't do all this work. Yes, we have a PS organization, but we need our partners to help this that are in at our customers, and also you need those partners to actually advocate for you so that you're getting the work and not someone else. Snowpark. November 2022, we talked about Snowpark for a while. But in November 2022, it became available for Python. It was generally available. This new capability brings a new competitive landscape. It's really unlocking new workloads for us. And it is taking off within us. We're now replacing Spark, EMR and Databricks and I have the data, you can see that. These are not legacy solutions. We compete against each incumbent vendor in this space. Snowpark is taking its share. What this graph is showing you is 2 Spark technologies that are running within our customer base. I can see that the blue at the bottom is Snowpark. And you can see how now Snowpark consumption. This is looking at daily credits. What they're consuming has now outpacing Spark #1, and it's going to surpass Spark #2. And so what you're seeing also is those ones we're growing within our customer base, we're growing much faster than them. So we feel that Snowpark is being very successful, and it will continue to grow. But once again, we're still in the very, very early innings. Christian talked about the price benefit of Snowpark, and it's anywhere from 2x to 12x. We have 1 financial services customer that actually replace Databricks and they save $4 million a year by doing this and a huge cost benefit to our customers. And you may say, well, why is it that it saves you -- saves those customers that much money? You have to remember, these Spark workloads that are running in our Snowflake customers, they take the data out of Snowflake. There's a cost to move that data. They do the Spark outside of Snowflake, then they push that data back into Snowflake and there's a cost to do that too, plus they're incurring the compute and storage costs while it's out of Snowflake. And if we can show customers, you can do it at the same performance or better running it in Snowflake at a fraction of the cost. Why would you move that data outside of Snowflake. Not to mention, you have much better governance and security on that data. You know exactly where that data is. And so we are seeing a lot of really good traction with Snowpark and we're pretty excited about that as well. And what I will say is we've done a lot of successful POCs. We've done a few customers that have migrated into production. Those are in the very early innings and there's a lot that are planned over the next 6 to 12 months. Because once again, it's not a -- it is a migration again. It takes professional services do these migration of the Spark workloads to Snowpark, and customers need to prioritize it and they need to have their people involved in doing these things, so they take time. In terms of early signs of adoption and really looking at here, so if you look at our customers over $1 million a year, 100% of those customers are data warehousing customers. 85% are using Snowpark, I would say many of them are still in the kind of the POC trying it that aren't fully deployed with Snowpark, 65% are using some AI, ML capabilities, 70% are using data sharing and data sharing will continue to grow. That data sharing I want to stress with people, too, is probably 1 of the key differentiators of Snowflake, and data sharing drives new customer adoption for us because when we become the standard in certain industries and you want to get data, we have customers telling their vendors, you must be a Snowflake customer because that's how we want to get our data through data sharing. If a company like DTC is successful in what they want to do, that is driving financial services firms to Snowflake because they want to be part of that. This whole network effect created by data sharing is unique to Snowflake. But if you look at all of our customers, 95% of our data warehousing, only 35% are using Snowpark today, 20% are using AI and 25% are doing some type of data sharing. But we do expect, as those customers grow, you're going to see more of these other workload adoptions within those customers. M&A. M&A is not something we just do haphazardly. All of our -- when we're doing an M&A deal, and I spend a lot of time on M&A with Christian and Benoit and Greg and others, it's all about how is this going to accelerate our product road map. When we're looking at M&A, we're looking for things that will accelerate our product road map. You can see in 2022, we did 2 acquisitions. Those were more talent acquisitions. We made the decision back in 2022, actually 2021. We're going to build up an engineering focus in Poland. Most of those people are focused on connectors, think of ServiceNow connector and other things, but they're doing other things too. To accelerate that opening of that office, we did 2 acqui-hires. 2 deals in Poland. But then in 2023, we acquired Streamlit. That was an unbelievable acquisition, and we're super excited about that Christian talked about, the Applica. That was a year ago when we did Applica. It was in Q2, end of Q2 when we did that. And this is all about the document AI. So it's not something we were just thinking about today when AI has been talked about. We've been thinking about this for a long time. And then recently, Neeva, Touk, Snowconvert. Snowconvert is mobilized, one of the reasons we bought Mobilize was to help with our customer migrations, whether it's off of Teradata, Netezza, Exadata, whatever, they help a lot with that. And by the way, and we're going to continue to do M&A, but it's always focused on how is it going to help accelerate our product road map. But the most important piece of all M&A is does the team have the right DNA to fit into our engineering organization. That is super important. And I got to tell you, Christian, Benoit and the team, they do unbelievable diligence on the people. And there was -- would I hire these people if they were stand-alone people, really important. Forecast visibility. This chart is showing an early journey of a top 10 customer. And over the course of a few years, as customers sign multiple contracts to support their consumption. And you can see the dark blue line is -- that's the contract they signed, what the contract rate is. The blue line is their product revenue. And you can see here that some quarters the ACV is below the revenue. Other quarters, it's above the revenue. And the important piece about this is -- what I'm trying to show people is we don't predict our future revenue based upon bookings. We predict the future revenue based upon what our customers are consuming today, looking back historically and how we expect them to grow. If you look at our models, they're not driven by bookings at all. Yes, it is driven by a number of new customers, but it's not the dollar amount or the bookings amount of those new customers. And that's a really important thing and it's different in a consumption model like Snowflake versus a SaaS company. That's why we never talk about bookings or billings, yes, we disclose RPO because you're required to do that. But that's not how we forecast our business. A big topic that a lot of you have been talking about recently. It's not something new to us. We've talked about optimizations all the time. And I will tell you, optimizations will continue to happen in a year from now, 3 years from now, 5 years from now, just like they've happened since day 1 of the history of Snowflake. But there's really 3 buckets of optimizations that happen at Snowflake. We control 2 of them. The third, we don't control. The first 1 is the CSPs, Amazon, Azure, Google. These are hardware improvements. And I want to stress too, and I've said this in the past before, not all hardware improvements benefit our software. Yes, Graviton too had a big impact on our software performance. And our customers get the benefit of all of these things. Snowflake software improvements, probably last year or 2 years ago, 1 of the big ones was storage compression that Christian talked about. I want to remind people, every 2 years, we generally have new storage compression that comes out and our customers get the benefit of that. But there's also things like our warehouse scheduling service. There's going to be another big 1 that's going to come out next year, which is going to be auto warehouse sizing for customers. That's 1 of the biggest challenges for customers today is how do you size the right warehouse so they're not -- you don't have a bunch of -- you're not paying for a bunch of capacity you're not using for when you're sizing those warehouses. That will be a software improvement. We control those. We control how they get rolled out. If you were at the session this morning, 1 of the engineers, Allison Lee, she talked about last year, they built this tracker. And this is gross, not net. They estimate that it was a 15% performance improvement for our customers because of the improvements had happened in software, but it's also the hardware coming into there as well, too. Net benefit, I want to remind people, we expect a net 5% revenue headwind every year due to the hardware and the software improvements. The third bucket is customers. And customer optimizations are very different. We do expect they're going to continue to happen. You heard Rob from Disney talked about, well, they just loaded all their data into Snowflake and then they had to kind of really clean up that data. That is inefficient spend they had on Snowflake. That was an optimization they went through. Those are typical things that customers do. I've talked about in the past that another thing that customers do when you load your data into Snowflake, you index your data. When you have that data index, it's easier to search that runs more efficiently. If you're loading data and you don't index it or your indexing gets out, your queries don't run as efficiently and it's not as accurate. So we have customers that are going in and reindexing their data. These are normal things and that will always happen within customers. They're not new to today. We talked about on the last earnings call, we had 1 large customer. It was another division of maybe the person who was up here, they decided to reduce their amount of storage from being 5 years retention down to 3. That was 7 petabytes of data in that customer's mind that was an optimization. They're still doing the same amount of work on Snowflake, they're just not running those queries against the same amount of data, so it saves them a lot of money both on the storage and the compute side. That is a choice that a customer makes, but we don't control that stuff. So I really want to stress that customer optimizations are always going to happen. We don't control them. But they will always happen, and we expect them to happen. FY '29 targets. We still feel very confident that we will reach $10 billion in revenue and product revenue in 2029. We expect our product -- our non-GAAP product margin to be 78%. And what I will say there, right now, we guided this quarter to -- or this year we're in right now, is 76%. You may say, why aren't you going to see more? Yes, we got better pricing out of Microsoft. Frank talked about that new contract. We're getting good pricing out of AWS. I will say Google is almost 50% more expensive on egress storage and compute right now with us. One of the reasons why I don't -- I'm not forecasting it to go above, there's things like Unistore that are going to come out. Unistore requires double the storage to happen within a customer. We expect there may be new features that come out that could be a headwind to expanding that product margin more than that. So I don't feel comfortable right now going above 78% longer term. And if you non-GAAP operating margin, we do expect that's going to expand. We're taking that from 20% to 25%, and we're taking free cash flow from 25% to 30% for 2029. One of the things here that I think is important is net revenue retention. We've been spending a lot of time looking at net revenue, looking at various maturity cohorts customers in their second year at Snowflake, what we call the year 2 cohort grow at a faster rate than those at the 6-year cohort. And in 2024, we've seen both young and old cohorts expand beyond their historical rates. And we do feel this is something of the environment in 2024, where customers have been looking to save money because of uncertainty in their business. These are what's driving some of the optimizations. I don't see migration slow down within customers. It's really some of the expansions of workloads and customers trying to figure out how to use Snowflake more efficiently. We do view that there's going to be a slower ramp than we've seen historically, but it's going to be a stable expansion. And so we still feel comfortable that we can get to that $10 billion with a lower net revenue retention rate. And we think that's reasonable based upon the customers that we have and what we're seeing. And shifting to margins. I talked about margins already. So I'm not going to spend more time here. But we have seen dramatic expansion on the product margin. I think I told people at the time we were going public, you're never going to see product margins the same way you see in a traditional SaaS business, think of a salesforce or a ServiceNow, why? Storage is a big component of what is in our COGS 10% to 12% depending on the customer, on average, is storage. And that will be a headwind to -- in storage. We don't make much margin on storage. It's pretty much a pass-through. We make a little margin on it. But that will be a headwind to our product margins. This is an important piece here, too, and other people ask, how do you make the determination when to invest in your salespeople in particular. And I got to tell you, sales is what drives a lot of our budgeting when we do things in terms of looking at productivity. And when we see productivity be above that, one, the way we define it. We add more people. When we see a drop below the 1 we slow our hiring down. You can see in 2023, we came out at 0.9x below the 1. So we have slowed our hiring down. It doesn't mean we're not hiring people. We're not hiring people at the same pace because we really want to focus on getting our salespeople productive. In terms of free cash flow, this is an important thing too and we've been getting the benefit of this in our free cash flow is, I've expected from the time I joined the company that customers are going to pushback on payment terms upfront. But it's surprising most customers still want to pay upfront. If you look here, 80% of our customers in 2023 paid annually in advance, 20% pay on other payment terms. That's either quarterly or monthly, or monthly in arrears. I do expect more customers are going to move over time to quarterly or monthly in arrears. Why? Because that's how Microsoft, Google and Amazon charge their customers. And so far, customers haven't pushed back. We're willing to do that with customers, but customers generally like to do annually in advance to get the benefit of a bigger discount. And so it's a trade-off. If we're -- if the payment terms are going to change, it's going to have a positive impact on the product margins. But I'm not seeing that pushback yet from customers, but I do expect that to happen. And that will impact free cash flow. That's a really important piece. And I want to remind everyone as we're talking about free cash flow, the quarters where you have the highest cash flow are always going to be Q4 and Q1, it is where we have our highest cash flow every quarter. And that's really the timing of most of our customer contracts. In terms of modeling considerations, important thing, we assume in our 2029 targets, less than 5% contribution from Snowpark based upon current consumption patterns. That could go up. It could go down, but that's based upon what we see today. We assume insignificant contribution from Iceberg, Streamlit and Unistore. As I said, when we do our forecasting, it's based upon our historical consumption patterns of our customers, based upon the products they're using. So I would say that's upside. We're also not assuming any additional tailwinds for public sector or China. We are looking to launch into China. We will be in China next year, but it's not China for all customers. It is China for our Global 2000 customers that are outside of China that have operations in China that want to be able to leverage Snowflake in China. And so it's not a massive subset of customers, but they happen to be some of the largest in the world that are asking us to be there. The other thing too is, as most of you know, we've been working on FedRAMP High and IL5. These take time. We will have FedRAMP High this year. We haven't forecast anything for that because we don't have the historical data to forecast that. We do expect that we will have more than $150 million in interest income this year that is factored into our cash flow. We expect to earn interest above 3% through 2029. I'm not an economist. I can just forecast based upon the data today where interest rates are for us, and we feel good about that. I also want to stress too, that a lot of our interest income is not cash. Like you, you'll buy commercial paper at a discount. And when you get the amortization that flows through on interest income, but it's not all cash to us during the quarter. So don't just take that interest income in the quarter and take it back it out of cash flow to figure out what the true cash flow from the business is. We will give you that number. What is the cash and noncash piece in our Qs. We forecast an effective tax rate on a non-GAAP basis of 26%, but our cash tax rate will be below 5% because we have so many NOLs in the U.S., but we do pay cash taxes in countries around the world where we have significant presence under cost-plus reimbursement. Dilution, this is one of the biggest topics that people always want to talk about dilution. When we look at dilution, we really don't focus on SBC. I look at dilution, and it's -- reason being is dilution is the best indication of current grant behavior, where SBC really reflects the trailing 4 years of grants coming through. In 2023, the vast majority of our grant amounts went to new hires and R&D employees. I'll just show you this, so you got our R&D, most of our grants and then our other new employees, you can see 14%. And then our refresh grants to our existing employees were 25% of that pool. R&D employees did account for 61%. I do think in the future, we will continue to grant more skewed towards R&D. Salespeople tend to get more cash compensated. On the sales side, you perform, you get paid. But it's a really important thing though that engineers all want R&D or all want equity, and we're competing with the largest technology companies in the world for those who are all granting that R&D, too. So dilution is not going to go to zero unless we do a massive buyback to offset that. But I do think dilution, I'm just going to skip through these things, will come down over time, and it is coming down this year, partly because we're hiring less people. As we slow our hiring down, the grant expense will decrease. I will say we do use RSUs, and we heavily compensate on M&A transactions. We will continue to do that. But we are benefiting from fewer grants and lower grant amounts. We -- and as our stock price, if it goes up, we give less equity. As it goes down, we'll end up having to do more equity. And the reason being is when you grant equity to an employee, it's not about the number of shares, it's the value of those shares on that date. In terms of where we're investing, you can see here R&D. I talked about they're going to continue to grow, but I do expect in 2024, 50% of our grants are going to go to net new employees in R&D. Dilution target. We're reiterating our long-term target of 2%. You can see in 2023, we are at 3%. We're forecasting it to be 2%. And we have introduced a buyback to help manage dilution, be transparent, have a bonnie thing this quarter. We did buy $193 million worth at an average cost of about 120 -- $136 million -- or $136 in Q2. And with that, I'm going to invite Frank and Christian. But before we do that, we're going to bring chairs up here and we'll go into Q&A.

Oh, I need the pillow.

Am I supposed to take other pillow? You want to? I mean it was a big disaster.

Brad, since he was keen to sit in the front row.

Be brave.

Awesome couple of days, a ton of innovation on display. I've got one for you, Christian and one for Mike. Christian, you put up a simple yet powerful slide showing in 2014, disrupting analytics, then disrupting collaboration and now you're disrupting the development of AI/ML apps. How does your prior disruptive innovations in separating storage from compute and data sharing uniquely position Snowflake for this next wave? And what are the milestones and metrics we should look at to appraise your progress in AI apps? And for Mike, your long-range guide, in the past when you've talked about it, you've explicitly said that you expect to be growing 30% in fiscal '29. Just want to clarify if that expectation has changed and if that at all impacts the margin update that we see as well?

Want to go first?

Sure.

Oh, you want me to go first.

Oh, I'll go first. I removed that because I don't need 30% growth to get to the $10 billion in 2029. So I don't expect it right now to be 30% in 2029. It'd close to it.

Some of the architectural innovations from the early days of Snowflake. You mentioned separation of storage and compute, are foundational to everything else we're doing, to the 3 waves of innovation. Data sharing, which is the collaboration disruption would not work as well and as magic as it is without that separation. Same thing happens for applications. Our vision is we want to have a common data substrate and all sorts of apps working in it, that would not be possible without that same abstraction. So for us, it's very important that we have actually a very clean architectural blueprint that enables all of these waves of disruption. In terms of metrics, you asked, how do we track it? Mike shared some of the numbers that we use on -- we have perfect telemetry on what customers are doing, and we will be able to report and share with you progress on workloads, data warehouse, data science, but also specifically is it LLM or not. We have visibility into all of that.

[ Catherine ], you can just decide.

Kirk Materne with Evercore ISI. Maybe for you, Frank, there's a lot of discussion, obviously, about AI. But it's interesting listening to you all that without data preparation, the ability to take advantage of a lot of the things that are associated with LLMs and AI just won't be the same. When do you think your customers sort of make that connection more fully, meaning if -- it seems like there's going to be a FOMO element of this where if one company falls behind another from an AI perspective, they're going to have to catch up. But if they haven't been doing the right things around data, that could be really difficult for them to catch up, say, a year from now or 2 years from now. So can you just talk about that a little bit in terms of are the customers making that connection right now in terms of data preparation, data cleanliness and AI? And then just really quick for you, Christian, a long presentation today. I'm sure you're tired of talking, but we're getting a lot of questions about vector databases. And I was just kind of curious if you could just touch on that really quickly about how that fits into the architecture.

I'll go first. So first of all, I'll reference Mihir from Fidelity again, 5-year journey to basically clean up the mess from whatever the -- over how many previous decades. It's not a quick turn, okay? So they're not going to wait for that. What they will do is they will take certain business segments with certain data sets, and they will enable those with large language models, and I'm already seeing that, okay? Now hopefully, along the way, they get some religion around having a data cloud because that's the reason why you hear us talk so much about it because everything gets harder when you have a siloed environment, but it's not going to stop people from lighting up specific segments, specific businesses, specific data sets. As we said earlier, people want to go to their board meetings and show what they're doing. And that's why I said augmented query and things like that are sort of might term for that low-hanging fruit. You're going to see a lot of that stuff and people are going to be high-fiving and like, yes, we're doing that. We're part of the party. Great, right? But we're looking much harder at the ability to ask very hard questions of the business. And we're already -- we want to sort of look towards what that challenge entails, and that's what our partnership with NVIDIA is about. I know Jensen is super interested in that because it's the frontier, right? Where you get a copilot who is like who's way smarter than you'll ever be in terms of understanding your business. I mean, that's the -- and there's no endgame in this. It goes on and on and on, but that's sort of a state that we're aiming for here.

Yes, vector databases, we subscribe to the notion that it is not a different database. It's a specific representation of a vector and embedding and then you run some operations along those -- or on those vectors. If you look at how we've done everything around data science for Snowflake is, we first got extensibility to enable all the use cases, and then we go on a first-party enable or simplify the use cases. So we showed this morning the extensibility version. And I'm not ready to announce anything else, but we're looking at how do we simplify the use of vector derivatives.

Mark Murphy with JPMorgan. So Frank, Microsoft recently commented that it expects that its AI services will drive about 1 point of growth in Azure in this current June quarter. And I'm just wondering, given all the work that you're doing with NVIDIA, with Microsoft, Satya Nadella commented on this with the open AI services and the linkages there. And the fact that most of the Global 2000 is going to have to prepare its data estate, right, for training these models. Can you come up with any kind of rough estimate, for instance, what percentage of all the compute that's happening today at Snowflake do you think is related or tied in with these generative AI models or large language models? And if you -- if there is no way to approximate that, is there a way to step back and say given your favorable positioning in this arena and all the developments that you're launching here, can generative AI be sort of a tangible tailwind on the growth of Snowflake going forward several years into the future?

Well, I'll actually I think Christian might be able to comment on whether we can see what workloads are of that sort versus everything else. But I absolutely expect it to be a tailwind just by virtue of democratizing access. They will -- it will be much easier for many more people to ask many more interesting questions of the data. So I could go along, it's what Sridhar talked about earlier. This natural language interaction to really redefine our relationship with data, I think we're going to benefit from just positively. In terms of how we separate that out, I know I mean the things are running inside containers. We want to be able to identify that, but why don't you...

Yes, so we have really good visibility into what type of activity compute is being spent on. The number Mike just showed on percentage of customers doing AI/ML, we see which Python libraries are being used, and we classify them as AI/ML. So there's going to be a direct number and at some point, we can give you update on what Mike shared today. The hard thing to piece apart is people are going to run to try to cleanse the data and to -- what Frank was saying this data strategy that is input into AI, I think that's going to be harder to correlate. And we're already hearing from customers, someone told me last week, I want to start tearing down silos because there's no having to operate with your data, if your data is in 5 different database systems. That second part is partly of the core idea. But the direct impact, yes, we have visibility, and we'll be able to share.

Kash Rangan with Goldman Sachs here. Congratulations on an amazing summit. One, I guess, for Frank and one for Mike, maybe Christian, you can chime in as well. We've listened to a lot of software companies saying how they are uniquely positioned to take advantage of generative AI and all those explanations are very compelling. Frank, what in your view, makes Snowflake very unique in this generative AI? And a follow-up for Mike. You laid out your long-term projections, and it didn't seem like you were incorporating aggressive assumptions for Snowpark. There's a string of initiatives, the Iceberg, Streamlit, Unistore, et cetera. Let's say you do hit a home run in 1 or 2 of those non-core adjacencies, what is your best possible outcome -- upside outcome to the $10 billion? And there's a third question, sorry. if optimization ends, can you reaccelerate your top line?

So Kash, the answer to your question is, we're sitting on exabytes of proprietary enterprise data -- structured enterprise data. So that's number one. It has gravitational pull. People are going to want to, as Jensen said last night, you just turn on the AI factory, and then we have natural language interfaces, and then we're going to be asking all kinds of interesting questions. But that's sort of level one of the answer to your question is the amount of proprietary data that we're hosting and managing on behalf of our customers. But the second thing is this proprietary enterprise data holds the key to levels of intelligence about institutions and businesses that are far more interesting than, what I call, planning your next trip to Yellowstone, not that I have anything against that. But it's like, holy cow, that's the sort of -- and we're summarizing the Great Gatsby, and all that stuff. This is about redefining the economics of industries, right? Think about what's going to happen in health care and in call centers and in telco. So we think that the potential economic impact of these models by virtue of the fact that we're on structured enterprise -- proprietary structured enterprise data can be extraordinary. So I do -- and I don't -- are we unique? No, because we're not the only people that live in this world. Are we extraordinarily positioned for the opportunity? Yes, absolutely.

Yes. I'll say in a consumption model, just as quickly as a customer can slow down consumption, they can increase their consumption. So it is very possible for revenue to reaccelerate. We've seen that historically with us. There's nothing to say it could not happen in the future. And in terms of what is the upside, your guess is as good as mine. I'm not going to speculate how big Streamlit, Unistore, Iceberg tables can be. As I said, we forecast our business based upon historical consumption. I don't have any data to support that yet, so I'm not going to forecast that, if we see some uptick in that next year, expect that the model will be updated, and I would expect it will be on the upside. If that is the case, if we see that.

One thing just a very quick follow-up, right? I mean customers are already taking off without us, right? Because as I said earlier, like an Azure account, Microsoft is hosting its own open AI instance and people are just querying those services. So they don't even need us to do that level of implementation. So this is already happening while we're sitting here talking. So I do expect there's going to be -- there's a lot of push for people to adopt levels of these kind of services, yes.

Michael Turrin with Wells Fargo. Appreciate all the content and the time this -- the past couple of days. I wanted to spend time on the industry cloud strategy. The clouds up in the go-to-market segmentation. That's been a clear point of focus on the product side over the past year. So I think the question is, do those help in terms of current positioning around some of the AI conversations that are coming up? I'd imagine customers are looking for industry-specific ways to solve their data problems. And so I'm wondering if already having those cloud industry-focused products, is it all helping jump start some of those conversations?

So I'll comment. First, look, the industry clouds, they really shaped the contours of the data estates, okay? Because it has like in financial services, the S&P and FactSet and all these different people that are part of it, and through Cybereason and people like that, we're really augmenting, enriching, even weaponizing data. So that all becomes part of the contours that people then can enable that with these large language model. So I think it will have an effect on that. I think it's -- I think the industry data clouds are really important because the network effects, which are incredibly powerful and obviously, we've seen that in financial services very pronounced. But I'm expecting this in supply chain management. It really means all the manufacturer, all the retailers, really that is a huge part of the backbone of the economy, we're going to see tons of network effect there just to get visibility and understanding of supply chain, which we historically have not had. So I think the way we're shaping the data estates in these industries and then the incremental opportunity of driving incremental intelligence from that, that's going to be great. And that is the strategy. I mean it's not a sort of a horizontal abstract thing. I mean, industries are really the coalescing around their unique issues. Every industry is a totally different conversation.

It's Brent Thill with Jefferies. Mike, I think you said in the last earnings call, that things were flat and you weren't seeing a big inflection. When do you expect those customer behaviors to change? Are you seeing any signs of light out of the tunnel here in terms of optimization? Just to give us a sense of what you're seeing.

Well, as I said, optimizations are always going to happen and they're going to continue. I'm not seeing any big optimizations now, but a lot of them, we only find out about if we're not involved on the PS side. We don't find out about them until they're happening. In terms of what we've commented on in the call was literally the month of April kind of starting about day 10 or whatever is pretty much flat week-over-week growth in revenue. But coming into May and into June, consumption is back where we'd expect it to be. So we look at it on a daily basis, week-over-week growth. I will add to that in talking to the sales team, the sentiment from the sales team is a lot -- and once again, this is more from a bookings perspective. This isn't how we forecast revenue. The sentiment with customers over the last 30 days seems to have improved a lot.

Over here. Ittai Kidron from Oppenheimer. Question to you, Christian. From talking to customers over the last couple of days, the benefits that you bring to the table in data warehousing are absolutely clear from a cost and performance standpoint. Some of the concerns, however, that were raised with respect to what you're trying to do with analytics and machine learning is that the cost benefits and the performance benefits don't quite translate in the same way in those type of use cases. So maybe you could talk about the ROI for the customer in those type of implementations, nobody doubts the technical capability of the platform of doing it all, but does something in the math change in that perspective and performance?

All right. It's a very interesting question. When we did Snowpark, the bigger benefit that we believed in was the ability to eliminate these trade-offs between doing data science and compromising potentially privacy or security. I mentioned in the session this morning, organizations oftentimes are at odds. They -- the data science team trying to do cool downloading libraries from the Internet and then the team that is in charge of guarding it. And the whole thesis of Python was really Snowpark was removed that trade-off. And that continues to be the top line, or the headline benefit. It just so happened that our processing engine is so much better than Spark, which is what most people use but then we saw this massive not only performance benefits, but by implication of the business model, cost benefits. I don't think we would have ever started saying this is a -- we're going to do a cheaper version. No, it's all about governance. And frankly, Spark was -- it is not very good, and that's how those benefits. But those 2 still stand. Even for machine learning and training, we showed this morning something that was 5x faster than Snowpark that was 2x faster than Spark. And so economics, but probably most important is governance.

The other thing I would add is operational simplification and I think that's -- people are not looking for more complexity. They're looking for less. And if you can run it in the same platform, I mean, you're eliminating a lot of steps, not just cost. So...

Yes.

Tyler Radke from Citi. And glad that you're still here, Frank and I hope you're feeling better. So a couple of questions. The first question, maybe for Mike. So you had an interesting slide just kind of talking about how some of the customer cohort expansions this year have been a bit slower than typical seasonality. But I think as you look towards your FY '29 targets, you're expecting that there is still some bit of a headwind on that initial consumption, but then it sort of normalizes in year 2 to year 4. I was just wondering if you could talk about what's driving that confidence? And should we expect a return to that normal type of consumption expansion starting next year? And then second question, just related to the marketplace capacity announcement. Just any more color you could provide on the potential impact for the financial model, be it bookings or margins?

Yes. So actually, I'll deal with that first. So when you allow customers a capacity drawdown on a contract, you can't include that amount in your RPO because remember -- and so that will have an impact on RPO, even though we have a contract and it's a firm commitment. So we will limit the maximum amount that a customer can apply towards that marketplace drawdown. And as an example, if it was a $1 million customer and we said, you can have -- $100,000 of that could be applied against buying through the marketplace app data or applications, we'd only be able to record, assuming no revenue was recognized, $900,000 in RPO. The $100,000 doesn't show up in RPO. And when the customer, if they consume, say, $50,000 of that $100,000, the only thing that hits our revenue is the revenue piece that we take because it's a pass-through to the customer. So say we made 4% on it, we'd only record $2,500 in revenue. We wouldn't get to $50,000 in revenue is the way the accounting works for those marketplace deals because we're actually not reselling. We're just being the intermediary and collecting the cash on behalf of the partner. And I'm going blank on what your first question was. Repeat it, please?

Yes. Just around the normalization and consumption pattern.

Yes, yes, so one of the things that we would see historically that your 2 cohorts, their consumption just spiked. And what a lot of that was with customers just trying to move their data as quickly into Snowflake and there was a lot of inefficient usage of that data. And what we're seeing now with customers a lot more disciplined, they have been hiring people that have lived through Snowflake migrations to be with them. So I don't see quite that same ramp in the early years. Yes, it's still ramping a lot, but you don't expect that. But what gives me the confidence in the net revenue retention expansion, you saw the Global 2000, that stayed pretty consistent. I expect the Global 2000 to be a much, much bigger percentage of our overall revenue by 2029 than it is today. And those guys will continue to grow for years.

Yes, let me add just one thing to this because I sort of see this happening in, for example, large financial institutions. Some of you are representative of these institutions so you probably know how this works, right? But they plan over 3, 4, 5 years, they have an extremely disciplined regimented rollout. And it frustrates the hell out of our sales people, by the way. They're like holy s***. I'm going to make money here, right? But they're like, no, we're on a plan and they are methodically marching down the field and nothing and nothing is going to distract them. But I will tell you one thing, it is going to materialize. It is just not a wang bang quick. I'm pushing it into the end zone. Now you see less and less of that, in a larger institution that is not how they operate.

Brent Bracelin, Piper Sandler. I actually wanted to drill down into this push into apps and AI. Mike, for you, specifically on monetization, should we think about Snowpark as the primary vehicle to monetize apps in AI? Or is it broader? And then for Christian, can you talk a little bit about and clarify the app ambitions. I think, of Snowflake being an app development platform unclear if Snowflake might want to build their own apps as well. So just a clarification there.

I think they are both questions for Christian.

Okay. I'll take it in reverse order then. Will we build our own apps? We've had a conversation in many contexts. Right now, the horizontal opportunity is so large that we would much rather have partners go and develop that for us. Maybe at some point, there's a category where we want to go and do more ourselves, but right now, it's partners primarily and literally the 3 of us have this conversation ever so often. What was the first part, I'm sorry?

Monetization, Snowpark or [indiscernible]?

Yes. So Snowpark is the run time of our app stack, whether it's Python, Java or containers, that truly is what drives the bulk of the compute. There are other parts that play a role in our app stack. Streamlit, which is how you build a UI, but Streamlit itself runs on Snowpark. So I would say it is fair to say that Snowpark is the core engine for the app platform. It is at the end of the day, what we will monetize. And I think you said in different forms that if all of this plays out the way we think, you could see at some point, revenue from the app run time, Snowpark be larger or comparable or meaningful to what we see on the data side.

One thing I would add to that, and it was kind of interesting to listen to Mihir, the CTO from Fidelity this morning because he said, look, there's data engineering and there is software engineering, right? And data engineering is Snowflake. Software engineering is Snowpark. There's function and there's data. And they are 2 hemispheres, right? Now we have -- and in my opinion beautifully integrated these spheres.

It is beautiful.

It is beautiful, and we like beautiful. So it's important that it's beautiful. We're not hackers, okay? But I think it's important for you to have an appreciation that we're addressing the function in addition to the data, which is a massive scope expansion for us as a company. But we think this is really important in the cloud because when we were living on-premise, we access databases, ODBC, JDBC, because you had a security parameter around it. People weren't worried, right, but in the cloud who's managing that? It has to be you, right? So you can no longer say like, oh, it's not my problem. Well, sooner or later, it will be your problem. So we took a highly secure, high-trust posture towards that. So it's not just a function, it's the way that we deliver a lot of function to be delivered is what this is about. So I -- my belief is that software engineering is a much bigger deal than data engineering into -- I mean, if we set up this renaissance in software development that I talked about this morning because it's sort of how I think about it, because we have so lower to bar in terms of investment, in terms of all the things that have to happen for you to build and sell a software application, you're going to get these orders of magnitude software explosion because you just now can. You don't have to put any money upfront. Two men and a dog can build something in 4 days, put it in the marketplace, sell, monetize. All they got to do is cash the check, right? That's really what we're trying to do. We're trying to redefine the software engineering sphere from what it historically has been because we think the cloud enables and allows that. So I just want to give you a little bit more sort of background on how we're coming at this. This is -- I don't want to say it's revolutionary, but it's definitely a really different take on software engineering from what it historically has been.

Patrick Colville from Scotiabank. I thought the most interesting slide you put up was the product adoption by customers. 95% of customers using data warehouse, 35% using Snowpark, 20% AI/ML and 25% data sharing. I mean do you think Snowpark AI/ML and data sharing are going to reach that like 90% penetration? Or do you think the mid-market customers, and I guess smaller enterprises won't adopt those products? And I guess my second question, if possible, is we didn't hear too much about Unistore. Is there any update there in terms of when that might go GA?

Yes, so on your question there, those customers, I broke it into customers consuming $1 million and customers over $1 million. Just because they're consuming less than $1 million doesn't mean they're not a big customer.

No.

And there are many of the Global 2000 that we've landed are not $1 million customers yet because they're in the very early innings. I don't see any reason why that won't be closer to the $1 million plus. And I still think even in those $1 million plus, there's a lot of upside with getting more adoption, especially on data sharing. I do think data sharing is going to be a norm that all of our customers.

Yes. Yes, I think data sharing is going to go to 100%. Snowpark's going to go to 2,000%, okay? I've said this publicly over and over, right? If you read or write -- read and/or write to Snowflake, we're going to own that work. I guarantee it. We will not stop at anything because it's cheaper, it's faster, it's safer and it's simple. There's no d*** reason in the world why you wouldn't do that, right? Because a lot of things that people are doing are unnatural acts it's incomprehensible what's going on. But in fairness, we didn't have all the primitives to support it either, but we do now.

Yes, and the prior question answers part of your question, which is, if Snowpark is a run time for applications, customers are going to end up using Snowpark maybe directly or indirectly. Like if you're a bank and you want to use DTCC's new native app, you will be doing Snowpark even though you may have not set out to do that. So your second question was on Unistore. I shared the -- we will be in public review, give or take, at the end of this year. And feedback ultimately informs general availability but we've front-loaded all the big architectural changes, and we expect to be no more than 6 months from public review into GA. So planned for roughly a year from now.

Hello. I'm [ Andy with Hypergrowth Blog ]. What is -- what price structure are you guys considering for Snowpark container services? And can you discuss the option for the GPUs build through? Is it going to be built through NVIDIA? Or is Snowflake going to manage that and pass it through?

Christian.

Yes. I'll take it. We did actually a lot of research on what should be the margins for Snowpark container services. And we found 2 extreme perspectives for enterprise customers that want to consolidate infrastructure, simplify data governance, those use cases our traditional margin structure is like that's easy. You're simplifying my life so much. There's a lot of value. For application developers that will compare Snowpark container services to similar orchestration -- container orchestration products from the cloud providers, they needed a much, much smaller margin structure. So we ended up going into preview with an in-between margin and we will have to adjust and figure out things as they go, which is one of the reasons why Mike was saying, new products may have different margin structures.

In terms of the GPUs, those will be managed through us that we will procure them that with the hyperscaler and then we'll just go through the normal billing through us. The interesting thing now is with our buying pattern power at AWS, we get easier access than many people to GPUs and they're really hard to get today.

Yes, because we -- that's actually important because if you're -- let's say, Databricks, right? They rely on their customer to be able to procure the GPUs. What if they don't have them? We have some real -- I mean we're the largest ISV that Amazon has. We're obviously one of the largest that Microsoft has as well and that changes the relationship.

[ Brad Ursener with Ultimeter ]. Mike, I really -- I want to drill down on the $10 billion. I think you said we believe we can still get there. But when I listen to the drill down, I think less than $500 million of that, so less than 5% is going to come from Snowpark and de minimis from the other places in the forecast. So less than $500 million from that. We think that AI is going to be a general tailwind because every corporation has to cleanse their data, get their data into the cloud. So generally, we think that's a tailwind. And Christian says Snowpark could be as big as the underlying kind of data warehouse itself. So we all have to leave here and try to build a forecast. And the hardest thing I struggle with is if we thought core data warehouse was $10 billion before. And if we think AI is an accelerant, and you've got all these concentric rings now coming together around the core data warehouse, why still $10 billion? And why do you think perhaps the exit run rate is not as high as you did before? And then if you'll allow me a second question, Frank, I thought the segment with Satya was amazing. And the piece you both focused on appropriately was frontline alignment, getting that sales force alignment. He talked about it publicly, which was great. Can you give us any more detail on what's your objective? What's success for you in terms of sales force alignment?

Yes, I'll start with I think it was 3 years ago, we said that we'd do $10 billion in revenue in 2029. And felt very good about that, and there was a lot of cushion in that. And the next year, I said we'd do $10 billion, and there was a lot more cushion in that. This year, we're saying $10 billion. There's still a cushion in that. But once again, we forecast based upon the historical consumption patterns we have. I don't have any support for how much is Unistore, and how much is going to be AI and that's upside. I just don't have that, and I'm not going to guide to that. Once we start seeing that, we'll update that model every year, but I feel good about the $10 billion based upon what we have today.

Just to your second question, [ Brad ], we really wanted to get Microsoft to a similar place where AWS is in terms of field alignment, incentives, how people are getting paid and so on because we know that model works. And when I say that model works, look, we compete, okay? We win the technical wins a lot, okay, most of the time. What happens to people that do not get paid at all in such losses, they're going to try and double and triple down in any way, but Sunday and it ends up in a really ugly mix and that creates headwinds in the relationships and the distrust in the field builds and partnering becomes almost impossible, right? So I just want to get to a state where -- and I told Satya literally, I said, look, it is when you lose, you guys are throwing millions and millions of dollars of free services at it, right, to try and reopen the conversation. And if that doesn't work, you're going to trot out Databricks as a first-party product. He says, you just don't stop when you lose. He says, that can't happen, okay, when we have to sign up for a much bigger relationship. And he agreed with that. He's like, yes, we need to normalize that because we compete either we win or you win and then after that, we partner. And by the way, that happens with Amazon. Amazon loses plenty of times. They don't lose their mind over that. They don't. And that's where we need Microsoft to be. Don't lose your mind. We're still consuming Azure here. We're going to consume all the other Azure products. It's like this is not a bad thing. Azure -- Snowflake on Azure is a win for Microsoft. Now will we just get through day 1? I mean I just had a Microsoft guy take a selfie with me today. S***. That has never happened before. So I mean, look, little baby steps, I guess, but we codified these things in the agreement, right? And that's really -- we can win technically against Microsoft. We do it all the bloody time. As long as we have a normal posture in these relationships, it's going to work. And that's really what we wanted to convey to you today. And I want you to hear Satya tell you. You don't need to hear it from me. It means it's very little, but you can hear it from him. He's aligned with us on that sentiment, and I was willing to step up to that.

Derrick Wood at TD Cowen. Frank, you've mentioned Databricks a couple of times, so I thought I'd ask about them. I mean, you guys started in the cloud data warehousing market with the relational database. You're now going into unstructured data and AI/ML, and they've kind of started at the Apache Spark side of things and are trying to get into SQL. And so obviously, you talk about this market as very big. It's going to support a lot of players, but you do have road maps that are starting to kind of converge a little bit. So just curious how you think about your philosophy of the market versus theirs and what your advantages are going to be going forward?

Yes. Look, you're correct. There are different world views, first of all, right? I mean I've said before that Databricks is great for people who want to adjust their carburetor with a screwdriver. The rest of us are driving EVs or at least they have fuel injection, okay? And it's just a different type of thing. We're -- we view Databricks really as the descendants of Hadoop and that whole generation of platforms and technology. We're the descendants of Apple and Tesla. We're trying to abstract people from complexity, right? It's a very different choice. I sometimes talk to public sector organizations and they're like, I'm so glad -- running Snowflake, we can just get SQL engineers. I said, we can get those all day long. He says, we couldn't lay a finger on a Python guy to save our lives. He says, they want to stay. We can't afford to pay them. It's a very different approach. Now will there always be Python guys like Databricks in accounts? Yes, I think there will be. That is just part of the makeup of our industry. But Databricks is also lives much more on the function, the software engineering domain rather than the data engineering domain, and I think that what -- yes, you're right, they're trying to come to the database domain. And we sure as hell are coming into this software engineering domain with a vengeance. I mean Snowpark is aimed at that. And we have the advantage that starting with data is a really, really good starting point, okay? So I'm feeling good on that dynamic.

If I may add, I would say, structurally, many of the choices from Snowflake early on have been validated really strong. The model running on RVPC, on our hardware, that we can normalize the service across cloud providers, how do we extract metadata from data. All of those things, go look at where Databricks started and how they're slowly coming and following our model. So competition is good, but we think that we have a better foundation, and we continue leaning on that.

Yes. Single product, right? I mean how many engines do they have over there? I've lost count. It's hard to build a single product, but it's incredibly powerful and it benefits the customer greatly. So those are convictions that we have. I mean the product does have to be good rather than I'm just going to throw something out there with a small group of engineers and just check a box. That's not Snowflake's style. And I love that about the company from the -- I didn't bring that, that was here when I joined. And I really admire that.

It's Gregg Moskowitz from Mizuho. Christian, when we were sitting here a year ago, you had talked about Iceberg tables and the belief that many customers would standardize on that over time. Curious to hear how the uptake has been versus your expectations over the past year? And more importantly, does the addition of unified Iceberg tables with 2 modes, right, managed and unmanaged, do you think that accelerates the adoption curve? And then for Mike, circling back to your comment, I think it was in response to Brent's question about customer sentiment seeming to have improved a lot over the past 30 days. Anything you're hearing from Chris and his teams, anecdotally speaking, that might shed some more light on that?

I'll start with Iceberg. Yes, the point is very accurate. We think that the unmanaged, managed mode will accelerate adoption. What we learned in the last 12 months was if I already had all my data in a data lake and in Parquet files, and I want to go to Snowflake with Iceberg, we created, frankly, too steep of a step to go to a point where all operations need to be coordinated by Snowflake. And the announcement from today is we're introducing a step in between this unmanaged mode which meets customers where they are, and lets them leverage their existing files in Parquet. And then if and when they choose to, they can go graduate to a managed mode where Snowflake takes more. So it was entirely driven out of accelerating adoption, we're quite excited about it.

And my answer for Brent, and I'll reiterate, daily consumption patterns that we've been seeing in June have been very good. I would say, back to where more we'd expect it to be unlike in April and into May where we didn't see very much growth week-over-week. And as I said, customer sentiment and talking to salespeople seem to be good from a bookings perspective and deals are shaping up. We've been closing deals. We closed a big deal with a financial institution. We closed another one with a big health care tech company today. So I feel good about bookings, but that's not revenue. So -- but sentiment is shaping up in terms of the sales call when I sit in on that on the weekly call.

I don't think he likes you very much.

It's John DiFucci from Guggenheim. So Snowflake was the pioneer in data warehousing in the cloud, and you took advantage or -- not took advantage, you leveraged the architecture of the cloud and you did it first and you did it better. But now as you've acknowledged, there's competition out there. Frank has spoken, I think Christian too, about data gravity, and we know that's real. And so you have to expand it beyond data warehousing to data adjacencies and now even to app development, which is, frankly, pretty cool, and it's actually what good companies do. They expand for their customers and make it better. But if you stay at the current rate of monetization for all these opportunities and the overwhelming part of it is data warehousing, do you think Snowflake can continue its success as measured by growth, which is also reflective of customer satisfaction, you know that, in the medium to long term? And I guess as it relates to that $10 billion target out there. So I know this is a little bit like [ Brad's ] question over there. But it's really like if you just -- if you stay where you are doing what you're doing, but it's still data warehousing, is that opportunity big enough to allow you to hit that target?

Well, I'll start first, right? Data warehousing is really the -- in most companies, is the foundation of data engineering. This is the only place where they have trusted, sanctioned, optimized data. So I wouldn't sneer at data warehouse and like, oh, it's not big enough. It is foundational to the world of institutions and enterprises. The problem historically has been, and I think I've said this a few times, it's been a business of begging for a 2:30 a.m. time slot 3 months from now because on-premise, it was extremely capacity constrained because you would consume a cluster in no time, right? So the growth you've seen from Snowflake, which has been extraordinary, thank you very much, it's been created because of that enormous pent-up demand that has built up literally over decades. I've been in a world of analytics not nonstop because as you know, I've been in other places. But I've seen this in the '80s, this problem. I've seen this in the '90s. It's been excruciating. We're now finally in a place where data is starting to become a real thing rather than reporting yesterday's news. So you need to get some context on what this is really about before like, oh, it's data warehouses. That's yesterday's news. We threw -- we look at data warehousing really as a starting point for customers, right, because they need to be able to report what happened yesterday and update their data. And of course, we're going to streaming and observe a build and all these kinds of things. These are all natural things that are going to happen because they can. Before, we didn't have any player of really getting beyond reporting what happened the day before, the week before, monthly closing the books was extremely hard. We couldn't even focus on the more elaborated, more sophisticated pieces. So I think cloud computing as a foundational platform has opened up everything, has opened up the opportunity for Snowflake. Snowflake wouldn't be here without cloud computing fabrics. But now, like I think it's -- I -- as a question, I take exception to what's really going on here.

Yes, I was going to say exactly the same thing, which is -- and you touched on it a bit but I don't even know if traditional data warehousing exist anymore.

Right. Exactly.

Frank and Jensen talk about, you're going to have a natural language conversation with your data. You tell me if that's data warehousing or BI or a new thing. But it's the new normal.

Yes.

Okay. And we have time for one more question, I'm being told because we unfortunately have a customer event. And then Jimmy and the IR team will stick around for questions.

He knows all the questions for sure.

Yes.

Yes. I hope it's worthy of the last question. Brad Reback from Stifel. Mike, I think you had a slide up there that showed 58% growth in your GSI business year-over-year on a self-reported basis. A couple of weeks ago, you announced a new Head of Alliances, Tyler Prince. So what's the opportunity there? Obviously, he comes with a tremendous background on the app side from Salesforce. So how should we think about that playing through?

Sure. So Tyler has or Tyler has very good relationships with the large GSIs, whether it's Accenture, Deloitte, EY and others. And we think the GSIs are going to be very important, those GSI practices. We're already seeing kind of an inflection within Accenture. I think in Q1, we booked over $300 million and Accenture was our #1 or #2 in Q1. Once again, these are self-reported numbers. But there's no reason why our top GSIs can't have $0.5 billion to $1 billion annual practices, and that's what Tyler is really...

Tyler.

Sorry, Tyler is -- we're bringing him on to do, but it's not just the GSIs. It's also resale partners as well, too, as we move into Asia more and other -- and alliances you will own as well too in that group.

I do think the GSI relationships, and Accenture is a good example. I mean I personally wrestle with these guys, okay, especially Accenture because they're growing like a weed. I mean they're doing incredibly well, but they do it because they just bump into it. They back into it because we are selling, we are spawning all these projects and because of their high level of presence, they go like put me in coach and the business just takes off. But after a while, even the people at Accenture are like, Christ, are we organized here for Snowflake? Well, they weren't, right? I mean, they did -- they think there was nobody in Accenture who owns Snowflake as a business. And it's like pushing a rope, right? Now they have gotten to the point where they have taken a very, very senior person off another line of business that this is Snowflake. This is Snowflake only it's becoming a business group. I cannot tell you how important that is in your relationship and with SIs to get to the point where they become a business group, where they have targets, they have provisioning, they have to report on it on a weekly, monthly basis. Everything changes at that point. It's very, very nonlinear. So you've got to do hundreds and hundreds of millions with them, for them before they start paying any serious attention to you. And then we are now reaching these thresholds with these SIs. And I think it's really important.

Okay. And with that, thank you, everyone. I really appreciate you guys making the trip here today. And for those of you on the call, thank you for joining us and we will probably see you around later on tonight.

Thank you.

Thank you.