Thank you everyone for joining us. My name's Keith Weiss. I run the U.S. software research group here at Morgan Stanley, and very pleased to have with us Mike Scarpelli, CFO, and Christian Kleinerman, SVP of Product from Snowflake. Thank you, gentlemen, for joining us. Thanks for having us. Thank you. Before we get started, a brief disclosure. For important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative. Excellent. With that out of the way. Actually I want to get started in the presentation with you, Christian, in talking about the market opportunity ahead of Snowflake. I think one of the most impressive parts of the story is how that opportunity's evolved over the past couple of years. I remember at the IPO, we were talking about roughly an $80 billion-$81 billion market opportunity, but you guys have developed into sort of the adjacencies around your core business, and now we're talking about a $2 billion-$48 billion in market opportunity. Could you walk us through the steps of how we got there, of how we expanded out that opportunity? Yeah. The early days of Snowflake were all about helping organizations break down silos and consolidate their data. If you look at pretty much every large organization, they have a little bit of Vertica and Netezza, all these different database technologies, and it's hard for them to think across. Our thesis was, let's help organizations combine the data and be able to think throughout the business. What we saw is even when customers have been able to consolidate data, they keep finding reasons to start to copy bits and pieces of their own data into different systems. "Oh, I have a, an application that does some AI, so I copy the data. I have an application that does some graph processing. I copy the data." Our whole thesis is instead of re-siloing the data, how do we help customers bring that application, those business logic, into Snowflake? That's when you hear us talking about Snowflake as an application platform, which dramatically changes the scope of what we do. Intersecting this type of business logic on Snowflake, we are also very focused on helping organizations collaborate with data. That's where our data sharing technology fits in, that's where our Data Clean Room technology fits in. The intersection of all of this says the opportunity for us keeps getting larger and larger. Got it. Got it. I think the data sharing element is probably one part of the Snowflake story I think people still under appreciate. The way I think about it, in sort of the old data warehousing technologies, pricing was based on capacity, like how big is your data warehouse? In the Snowflake model, 90% is compute. It's how many questions are you asking of the data? In every company that I talk to, one of the primary reasons for moving into a cloud-based data warehouse is to enable more sharing and enable more people to ask questions of that data. I think there's, like, an inherent expansion of the market opportunity that comes just from moving to the cloud and just from getting that data sharing. You guys kind of tracked this, these are the edges. Can you explain to us kind of what edges are and sort of how you see that developing within the base? Yeah. We think of sharing or enabling sharing of data both within an organization but also across organizations. Both are obviously very important and meaningful opportunity for us. The way we think of sharing relationships is what we call an edge, which is the connection of two organizations or two parts of an organization where they have activity, one querying data from the other, and that's what we call an active edge. If I share data with you, Keith, we have an edge. The number that you hear or the metric that you hear us talk about is what we call stable edges, which are edges that have a minimum threshold of activity over a minimum amount of time, which tends to suggest that this is not a one-off conversation Mike and I. Absolutely It's a persisted ongoing relationship. I don't know if you want to add anything. Yeah. No. That data sharing really creates a stickiness- Yeah ... in terms of, we're actually seeing RFPs out there from our, some of our customers are actually further asking their vendors questions, "Are you a Snowflake customer?" Because they want to do data sharing. Mm. Beyond just stickiness, it's driving new customer adoption on Snowflake because people are insisting on doing data sharing through Snowflake, and you really see that happening in the financial services industry, which by the way, shouldn't surprise you because the financial services industry has been sharing data for years and years and years. Unfortunately, the data you've been sharing has been through FTP downloads, which is such an old technology. Yeah ... or PDFs. We can avoid all that. There's no reason why the whole concept of a bank statement going to a company is irrelevant. You can do data sharing, so you don't have to actually transfer any data, and you can just run your reconciliations directly against that in Snowflake. Again, we're working on things like that for our own use case internally, and it's a much more efficient way of doing things. Right. More importantly, because the data isn't getting transferred, it's secure, and governed, and you know exactly who's accessing it. Right. Just to continue down this thread a little bit, you talk about it in terms of creating stickiness. From an investor perspective, I think one of the sort of holy grail that we're always looking for in our investments is where are there really defensible moats around companies? Because technology and software evolves so quickly, it's hard to get a technology moat that remains durable, but ecosystems that people create around certain technologies, and data sharing being one of them, could potentially be that defensible moat. You talked about financial services. Can we segue a little bit into sort of the industry focus? Because I'm sure this is probably one of the kernels of why you have this industry focus is to try to create these ecosystems. Financial services is one. Can you walk us through some of the other verticals that you think you could develop these types of ecosystems in? Well, it's happening in the media streaming area with advertisers and media companies, and Data Clean Rooms is another form of data sharing, and especially with all the privacy concerns today. That's definitely a key one. Healthcare, there's all kinds of opportunities on both the payer side and as well as in pharma with the development of new drugs and stuff. There's a lot of data sharing that happens between companies in that. Many times the pharma companies use third parties to do part of the work on those things, and that's an important piece as well too. You can pretty much apply it to any industry, data sharing. Well. It's funny, when you talk to people. I was actually talking to someone the other day, a CIO of a bank. I was talking about data sharing, and he's like, "Well, we don't really do any data sharing." I'm like, "Okay." That's what most people say. Then when you dig into it, "Oh yeah, we send these reports to Fidelity. We get these things." You are doing data sharing, you're just doing it in an inefficient way. Right. Yeah, on industries, we even heard state governments- Yes ... interested in this. Yes. Yeah. That's true. Imagine how many agencies are there. They all would like or would benefit from collaborating. Right ... I think it permeates every industry. Right. It all comes back to asking more questions of the data and utilizing the data more fully. If we go one step further and talk about the concept of Data Cloud that you guys talked to, now becoming a platform for application development, That's probably even a bigger expansion of kind of the market opportunity in terms of app dev. Why is Snowflake the platform for doing this application development? Can you talk to us about some of the tools, the capabilities you brought on board, like the Native Application Framework and the Streamlit acquisition that enable that application side of the Data Cloud to really come to fruition? The core thesis for us in this topic of collaboration is that any organization that leverages second-party data, third-party data, second-party and third-party services will do better. Now that at this point there are many studies where they show you will outperform your peers if you figure out how to not only leverage your own data, but how do you enrich and put your data in context. That's the concept of the Data Cloud for us, and that is what is unique about Snowflake. Technology, yes, we can deliver technology, and we're very proud of the technology we have, but when a customer buys into Snowflake, he buys into this Data Cloud. Data Cloud is where all this ecosystem of players. Data providers is one form of partnership, but more interestingly, there's a lot of interesting IP, interesting business logic that organizations are creating. What we're doing with this concept of application platform and Native Apps in Snowflake is, can you package that logic? Can you make it available to other customers? Now when a customer buys into Snowflake, they're buying into this ecosystem, and we've seen customers that had passed on Snowflake- Mm-hmm ... like, "I'm good. I'm not interested." When they see some of the applications that are coming onto Snowflake, that I can do this data sharing, that I can repurpose a team of 30 people that were doing pipelines and ingestion and encryption and decryption, all of that goes away, that is the appeal, and that's how we think of the Data Cloud unique for Snowflake. Yep. No, I agree. Yeah. It all, from a monetization standpoint, it all comes back to more questions being asked of the data. That's one of the really interesting things about Snowflake. It's such a straightforward pricing model. It's such a straightforward monetization model. It's actually a beautiful model that, you know, we really have one product, three different flavors of that product, depending on which edition you want. Every new feature we have, our salespeople don't need to go in and get a PO out of a customer. They just need to go in and educate the customer so a customer can consume more. The follow-on capacity purchase orders follow. It's a very simple model, I love it. The customer also, because of our model, the way that we price, we sell a customer credit. A credit is a unit of measurement with the amount of compute you use, and we charge you by the terabyte of storage you have. The beautiful thing of every software improvement, every hardware improvement that improves the price performance, you can do more with that same credit every year. We become cheaper to our customers every year. That's good because the better the price performance, the more workloads they move to us. The more performant the speed at which we have, more workloads can come onto us that otherwise we weren't fast enough for. Right. Our whole product roadmap is focused on more features that are gonna drive consumption, but then improving that price performance. Yeah. The speed at which we operate. Right. We have the data. Like, one thing is to say it, the other thing is we can show that the amount of compute credit that we generate per query, per question asked, it keeps going steadily down. We've publicly shared the last three years, roughly 20% better economics for Snowflake as a platform. Our customers see that it's- Right ... not only giving faster answers, but better economics. You can see that too. I think we're right about 3 billion queries a day running through Snowflake. We crossed it. We crossed 3 billion. I know as of last week we were 8 million queries short of 3 billion a day. You can see how the number of queries have grown in Snowflake. The revenue doesn't grow as much. Why? Because the price performance improvements-. Right ... to customers. We give them. Right. That improving price performance, like we talked about a $248 billion TAM, but there's the addressability to that TAM, and you need to have the right price performance to address the entire TAM. Correct. As you improve that, more and more of that potential market opportunity has become serviceable over time. Yes. Can we talk about the ML and AI opportunity within Snowflake? I think it's been a somewhat of an investor debate of whether a data warehouse, whether the Snowflake architecture is correct for building ML AI type of models on top of and workloads on top of. You guys announced Snowpark for Python, which I think makes it more applicable, but can you explain to us why, like, the data warehouse and why Snowflake is the right platform for building out these applications? Yeah, yeah. core to what we want to do and enable for our customers is deliver programmability of data. How do I get value? How do I extract value out of my data? Without trading off governance and security. That's what's different from what you will hear from everyone. Everyone has Python. Like, we get asked a lot, "Why did it take you two, three years to incorporate Python into Snowflake?" 'Cause incorporating Python in an unsecure way, it's easy. We can do it in a couple of weekends. You can ask CIOs, "How do you know that your data science team did not download some library from the internet, and it may have had a vulnerability, and potentially exfiltrated data?" That's where the answers get a little bit less clear. Oh, yeah, the networking team was in charge, or someone was in charge," like. What we offer, and I'll get to your AI part of the question, what we offer is a secure way to program data. When we say program data, it can be just transformed data, or it could be doing AI and ML. For us, AI, ML is one additional workload that we wanna support running close to the data in a secure fashion. Then you can say you wanna do training. We have customers coming on to Snowflake to do training. You wanna do machine learning scoring. We have customers coming onto the platform, do the scoring. At our user conference, we introduced this low latency storage mode we call Unistore, which is very low, very fast reads and writes. That's very common for online feature stores, online recommendations, applications of ML. You come and say, "Well, there's a new thing called language models." Language models is nothing but here's another form of pre-trained machine learning. I wanna be able to score that based on data that I have in Snowflake. I may wanna be able to fine-tune that based on data I have in Snowflake. For us, it's a continuum. I'm not trying to dismiss the importance of AI, but what is really important is do everything you wanna do, program data, do AI, do proprietary computations, but do so without trading off security, governance, policies, privacy. That's the value prop of Snowflake, and it resonates to no end with customers. Yeah. It's something I hear a lot, when I'm talking to CIOs, particularly in, like, regulated industries, when they're thinking about these large language models, and stuff like ChatGPT, is the security implications haven't really been explored. Mm. It is a real threat of sort of data leakage on a go-forward basis. If I'm hearing you correctly, you today have companies that are utilizing Snowflake for training these models? Yes. For sure. We have not only customers leveraging Snowflake for machine learning. Part of the Snowpark for Python integration enable that. We introduce a type of cluster that has more resources. We call it Snowpark warehouses, which are just about this. The one piece that you can say, well, you don't have is GPU support, that is for cases, that's where it is deep learning. You can stay tuned. We'll be sharing more about this at our user conference in June. Fundamentally, we just think about it, the broad vision and broad goal for Snowflake is bring computation, whatever the nature it may be, to run closer to the data, and AI and ML is just one such example. Got it. Got it. Perfect. I wanna dig a little bit into Unistore. That's something that you guys talked to us a lot about at the last Analyst Day. It enables Snowflake to now address more transactional workloads, right? For the broader audience, there's analytical workloads and transactional workloads, and historically, never the two shall meet. Today, given sort of the computational resources you guys have at hand, and it's not constrained, it's now more amenable. Like you can bring those two together. One, can you talk to us about sort of the underlying technology that enables you to bring those two together? Two, what's the market opportunity that opens up when you can look at the data from both perspectives, both in terms of using it for transactions, but also for the deep analytics? Yeah. I'll rewind a little bit on database history. In the very early days, a database was a database, was a database, and it did both transactional and analytics. It's only that... That was before my time. Way, way before my time, or I got the tail end of that. Then specialization happened, and for example, Teradata, credit where credit's due. Say we're gonna build a database focused on analytics. Many others follow, the Verticas, Netezzas, et cetera. Oracle and Db2 and others went on the transactional side. For 20, 30 years, they progressed separate tracks, and as you said, they would never combine because the specialization was for each type of use case. What's changed and what's different, which is the question we get asked, "Okay, if this thing has never happened, why do you think you have a shot at succeeding here?" is the cloud helps us present a unified product, a unified experience for our customer, even if behind the scenes, there are different ways to store the data. That's what Unistore does. The implementation of Unistore, we call it Hybrid Tables. Hybrid because they have a storage system optimized for analytics and a storage system optimized for fast reads and writes. We can, behind the cloud, tie all the details on is data replicated, data moved back. What this does for us is now we enable customers to store data in Snowflake, build applications, application state, or machine learning pipelines, or machine learning inference at low latency with very fast reads, very fast writes, but also the data is seamlessly available for analytics. It's the technology and the cloud, the fact that we're delivering a hosted service that enables us to do this, and I believe it's a big part of our application stack. I don't know if you want to say something about the market opportunity. Yeah. No, it's. Well, we don't know how big the market is. It really could be a good opportunity, but I think it's important, too. I think it'll be a revenue. First of all, the product is still in private preview today. It's not in public preview. Right. It will be in public preview at the end of this year. We've learned a lot from customers, and we're revising some of the stuff on the engineering side. That will have an impact on margins because of the fact that there's duplication of data. Definitely it will drive revenue, but it's not gonna cause our margins to decrease, but it puts a gate as to how big the margins can get, the product margins in the company. Definitely opens up a massive market opportunity for us as well, too. And- To be determined how big that is. Yeah. I would add that the bigger goal for us is enable full applications to run inside Snowflake. If you look at the elements of an application, there's the core storage, so we have Snowflake analytics, as well as Unistore. You want a middle tier to be able to do processing. That's where Snowpark fits in. You want a presentation tier, which is where Streamlit fits in. Right. The combination of all of those change the art of what's possible, and how we think modern applications will be built and deployed in a secure way. Got it. That's a good summary. I want to shift gears a little bit and talk about the business model, and get into near-term results, maybe just stick with the theme of history lessons. I think one of the really interesting things about Snowflake is, like, how pure of a consumption model it is, and if we think about it holistically from where we came from with perpetual license models, where all the risk was put onto the end customer. Like, you gotta figure out how to set it up, you have to figure out how to get productivity out of it, but upfront you give us a couple million dollars. With Snowflake, nobody's paying you until they're starting to run queries against the data. Well, I'll correct that. They're paying us many times upfront, but they're not incurring the expense... They're not incurring the expense. ... until they actually use the product. Right. Like, there's a commitment. Yes ... it's... You're taking on a lot more of the risk. Like. Yeah, we take on the risk, and that's why it's super important that we are there for our customers' success, and why we spend a lot of time, and why we insist our sales people stay engaged with customers in the seat model. I know this. When I was the CFO of ServiceNow, and I know we buy a lot of licenses from other people. It's painful when you buy a license, and you start having the expense even though you may not start using it for six months. Right. Yes, we do bear that at Snowflake. The benefit of that, though, is just as you can see a slowdown if people are tightening their belts, you can see an acceleration in our business as well, too. Right ... people have more visibility into their business. Right. people have taken advantage of that flexibility. Yeah ... a tightening spending environment. Just to bring it back to sort of the current sort of results, and the sort of what we've seen throughout 2022, obviously customers are taking advantage of that, and we've seen optimization in all sorts of cloud models, and including Snowflake. How do you get an assessment of kind of where we are in that cycle? I think optimization is an overused term by many companies today. You know, we've been talking about optimizations as far as two years ago. Right. At our financial analyst day, we talked about this is, this is nothing unique, and this will be ongoing with any customer, but there are no big optimizations out there. Optimizations, just to be clear what they are, is we find instances where Snowflake people have not written the most efficient queries that are taking up too much compute. We spend time out of professional services time to help them rewrite, re-engineer the queries so they use less compute. One of the biggest and low-hanging fruits on optimization, we've seen customers store data that they've never accessed. Why are you doing that? Yeah. We've seen customers store the data twice in Snowflake when you don't need to. We've seen customers where they would choose bigger warehouses than what they really need. They would disable the auto-suspend function. Well, today we help you pick the right size warehouse that you need, and take away a lot of that. Customers can still choose, but we help you size the warehouse correctly. When you disable the auto-suspend function, there's a lot more alerting that happens on that, and we're monitoring constantly to make sure that warehouses aren't left hanging. That's what optimizations are for us. We will continue adding product capabilities to do all of this proactively or automatically for customers. Nobody wants the cycle of I grew, I optimize, I grew, optimize. We believe in you're always optimized, and that's good for everyone. Right. I have a team of people that literally look at spikes in revenue on a daily basis, and when they see something, we reach out to the customer, driven by finance with the rep, to do that, to understand what's going on? You may say, "Why am I doing that?" I know if we have an unhappy customer, that they left a warehouse running or they're using Snowflake inefficiently, they're gonna ask for a credit back. I'd rather get in front of that. More importantly, we reforecast our revenue on a daily basis based upon the prior day's consumption. If I'm incorrectly reforecasting on spikes that aren't real spikes, like ongoing consumption, then I have a problem. We do that. I don't know a single vendor that's ever reached out to me to tell me when I'm consuming too much of something. Right. So you feel comfortable that you've flattened out that curve, if you will? Yes. That you've taken out the, any excess- There are no big optimizations. Right ... that I'm aware of. It's now systematic. Doesn't mean there's not some small ones, but there's no-. Right ... big ones out there when I look at the top customers. When we think about the adjustment of the forward year kind of revenue guidance that you did on the last conference call, that was less about optimizations, or not about optimization, it was about consumption patterns. It's about customers ramping more slowly. What I would say is early adopters, by their nature, tend to move faster, and a lot of our early customers were, "Let's get up and running on Snowflake. Who cares about costs? We'll worry about optimizing later." The more, I don't want to say, laggards, but the later adopters tend to be more methodical. They tend to be more cost conscious. A lot of our early customers were the digitally native companies that were very fast-moving, and were growing so quickly they didn't really care about cost. When you're dealing with well-established Global 2000 companies, these guys have always cared about cost, and they just move at their own pace. What we're seeing is we've landed so many of these large customers over the last three, four years, they just grow slower than these digitally native early adopters. Got it. They're still gonna get to the same end... Right ... end state. Okay. the destination remains the same, it just takes longer and longer to-. Destination's the same. ... to get there. Maybe one of the dynamics that's been really interesting in the model throughout the year is the large customer growth has still been very robust. I think the last three quarters have each been the largest net new additions into the million-dollar-plus spenders. Can you dig in with us a little bit about the sort of the life cycle of that million-dollar-plus customer? How long did it take them to get there? I think the average million-dollar-plus customer now is like 3.7, 3.8? 3.7. 3.7. Yeah. Like, how long does it take them from when they cross $1 million to get to sort of like that average? You know, when we sign up a new customer, a Global 2000 is a little over $100,000 is what they start at. Okay. A normal non-Global 2000 is in the $50,000-$60,000 a year. They quickly grow. It usually will take a Global 2000 to get to that $1 million 2-3 years to get there. Why? Because. Some get there much faster, they generally move pretty slow, these companies. Got it. Got it. I want to shift gears to the margin side of the equation, 'cause that was the other really a pretty spectacular part of the equation if you look back at calendar 2022. Consumption models aren't supposed to see expanding margins as growth slows down. It should be harder for you guys to grow margins. Subscription models are mechanically geared that when growth slows down, you should see more margin. You guys saw a really robust expansion in your overall free cash flow margins during 2022. How are you able to do that? If the demand environment gets better and then the consumption picks up, shouldn't that be incrementally even sort of more positive for margins on a go-forward basis? I've been with the company now for a little over three and a half years. Since day one, I remember that first year when I joined, we were expected to burn $220 million. We quickly turned that around. We've always been focused on free cashflow, I would say it's revenue growth, product margins, and free cashflow. You know, it's pretty simple math as to how that's working. We continue to show product margin improvements. We continue to show operating margin improvements. I will say what's kind of surprised me is I was expecting early on more of a shift in payment terms with our customers. Most of our customers still, they sign a three-year contract or a one-year contract and pay us annually in advance. 80%-plus of our customers still do that. I have expected customers to want to move to quarterly or monthly payment plans. Why? Because the cloud vendors give everyone monthly payment terms. That is an option to customers. We give them that. Right that option, but it's all about discounting. They would rather get a higher discount and pay up front. I do think with people earning real returns on cash balances overnight now, that there will be a shift in that, and that's one of the reasons why we kept our free cashflow flat at 25% next year. That's 1 piece. Also, we got some surprise early payments in January that I wasn't expecting. That influence made that free cashflow higher in Q4 than it otherwise would've been. Got it. One other thing I wanna make sure we touch on, we have about a minute left, is the expansion of the AWS relationship. AWS, obviously a major infrastructure partner for you guys as well, but you've well expanded that relationship. It's not just being in the marketplace. There's go-to-market commitments being made on both sides of the equation. Can you dig in a little bit about that, of what, you and Amazon are now doing together on a go-to-market? Sure. We have committed headcount out of AWS aligned to our verticals globally. We're committing headcount to We've had those headcount anyways, but we're matching our headcount as well too on the alliances side. There's more dollars that are committed for migration funds to Snowflake on AWS, and there's a lot more free credits. There's a lot of POCs that we run, and those POCs could be a new customer. Also when we're looking at doing Snowpark, we offer free credits to customers to do evals. That stuff is funded by the cloud guys. Got it. We fund some of it, and they're willing to throw money in. It's a pretty big financial commitment. We're making a big financial commitment to them. They're making a big financial commitment to us as well too. It also promotes... You know, AWS is really good about everyone thinks, you know, you compete with AWS Redshift. They talk about all these product improvements. You know, the reality is in large accounts, AWS partners with us out of the gate because they wanna see those customers land in AWS. History has shown that Snowflake helps those customers land in AWS, and that's good for AWS because they can sell a lot of other software services around Snowflake. Outstanding. Unfortunately that takes us to the end of our allotted time slot. Mike Christian, thank you so much for joining us.