 Hi everybody, welcome back to Caesars Forum in Lovie Las Vegas. I'm here with Frank Slutman as the chairman and CEO of Snowflake, friend of theCUBE. Thanks for coming on, making some time for us. Absolutely, good to see you. So investor day was yesterday. They obviously liked what you said, the stock's been up two days in a row. Of course, you had Jensen on Monday night. How did that go investor day? What was the conversation like? I think what was important in investor day is we had to fully lay out our strategies for enabling AI on our vast amounts of data that we manage and host on behalf of our customers. And they felt that it was a very, very convincing strategy, very compelling strategy. And obviously that was the number one topic coming into the day. But we reiterated and up long-term guidance in a couple of places. And it's just good to sync up. I think the conference content helps in enormous amount to help them understand the vastness of the strategy and how far we've come. So I mean, this is where it all comes together. It all becomes real, right? There's no longer words. You get to see it, touch it, and really assess the reality of everything. Well, when you turn the engineering investments into product, that's kind of what it's all about in tech. But they obviously bought the story. I'm sure there was some skeptics in the room asking you some hard questions. They talk about the competition. How did you address sort of the, they're always asking you about the 10, but how did you address the expansion into new markets? Like I say, they obviously bought into it. But what was the narrative there? Well, the narrative for Snowflake as a data cloud is it's a multi-layer kick, right? We obviously have infrastructure, elastic consumed by the drink. We have live data in extraordinary amounts. We have the complete workload enablement layer. The programmability platform, which is Snowpark, the marketplace, and then the transaction model. Transaction model, people can monetize data and applications. So the strategy really is, we enable data engineers big time. That's sort of our, I call them our homies. Those are the people that we're super close to, historically, because we're a database company from way back. But we now have completely embraced the functional layer that lives above the data layer. We have data engineers and software engineers. And we now said, look, software engineers and data engineers, we address both these audiences. It is a big vision, but we think in the cloud, you have to have this. In a non-premise environment, it's very, very different. You can really stratify these things. But in the cloud, it's like, wait a second. All of a sudden it's like, who manages security and governance here? Well, it's not you, it's them. So in other words, unless we step into that void and say, no, no, no, we own it. If you're on a snowflake, you're safe, you're compliant, all these things, that's a really important thing because if we're not doing it, who's going to do it? So there's a lot of discomfort around, where do software engineers live? Where do data engineers live? How do they interact? And that's really, we want to really assert that space, if you will. Well, governance and security is obviously a big part of your promise. Now yesterday at your keynote, you had a little joke. You said, every time I say AI, the audience has to take a shot. So I thought, Frank, here's the thing. Turnabout is fair play. So I got a little Jameson here. I don't know. Yeah, that's my son's favorite drink there. I had to go up and down the strip yesterday to find these beauties, little flamingo shock glasses. Okay, I'm going forward. So we'll put this aside, maybe later on tonight. Noah, keep count of how many times we say AI. I have to consider an entire bottle probably. You can't have the CEO of a public company doing shots on camera, but anyway. Okay, I want to go back to Monday night. Sarah Goh, I think it's how you say her name. She asked you a question. Why snowflake? And then you said, because we're the best. Yeah. Okay, what makes you the best? I want to dig into that. You ask a general question, you get a general answer, right? But what makes us the best? I mean, our core is an extraordinarily optimized database engine, right? I mean, a lot of the superior performance comparisons in the economics are because of our database engine. Okay, so when we move into snow park, right? The database engine backends the snow park later. So people are seeing these two to four X, literally two to four X comparisons in performance and economics relative to their spark jobs and the Duke jobs. And they're like, how does this happen? Well, it's because of the database, right? There's a bunch of other things as well. But fundamentally, the database is superlative, right? So we bring that to every single workload, right? I mean, you're running Python, you know, you're running our database engine at the back end. And that's what creates all these opportunities for extraordinary performance and economics. Never mind the governance and the simplification operationally. So I want to dig into that a little bit because the high level messaging, I think is really clean. All data, all workloads, very powerful. And when you go down deep and you talk to folks like Benoit and Terry and the technical people, it's really solid, a deep understanding. I find, Frank, in the middle, when I kind of get lost in all the products, it's hard to sort of connect the dots. And I want to run something by you and see if we're understanding this correctly. I take a lot of notes when I'm on a show like this. So it seems to me you've got a lot of ways to query data, right? You got SQL, you got data frames, you got Neva now, you got search, you got supervised machine learning libraries, maybe with AI, do a shot later. You got unsupervised. You also support a lot of different data types. You start with relational, rows, columns, streaming, vector. We saw all of that this week, especially when you squinted through there. What doesn't come across, I think, to people, and I want to get your take on this, is the magic is that's all integrated. I can query those different data types, those storage platforms, and then you take care of it and give me a consistent, coherent return. That's magic, that's not easy to do, because Amazon has a lot of different data types. They have a lot of different query options, but it's that last piece that creates the difference. Is that the correct understanding of the magic? Yeah, one of the things that's been set a few times in the last couple of days, Snowflake is a single product, which is quite an extraordinary thing because in software engineering, in the spirit of expediency, people lose their religion very, very quickly and they start hacking separate engines. Sometimes they have three or four different flavors of the same thing, because it's just quicker, right? I spin up a separate team, I build a separate engine, and I get to market quicker that way. We have resisted that. We still have one product, which is an extraordinary engineering feat, not just one product, but also we can do two men and a dog as well as the Fortune 10. It scales from small to the largest around. But in terms of your comment, we have to support all the different user types on the data cloud, right? You're an end user. We support you, and a lot of the generative AI is going to be very much aimed at end users because if you're literate, you'll be able to get considerable value from data now. Well, that was a heck of a lot harder for most of our lives, right? But you move up the spectrum, dealing with hardcore programmers, Python, Java, and they have a very different world view and their life experience is completely different from a SQL engineer. We have to support all these people, but behind these interaction methods is the exact same product. You're interacting with the exact same engine, the exact same governance layer. It's just you engage with us differently based on how you want to do things. We've historically, SQL engineers, SQL analysts, basically data people. Data engineering people were really our folks, right? But now the whole software engineering culture is coming into Snowflake as well because we got data, we got function. You heard me hear from Fidelity yesterday talk about data and function. Data engineering, software engineering. Well, they both live on Snowflake now. Yeah, in 170 databases, I think. Somebody said yesterday that we're all data engineers in a way, that's true. Something more sophisticated than this. The other point you mentioned is small to large. That's Snowflake's spectrum. You don't really have that at service now. Great company as it is. It was really kind of mid to large. I want to come back to that example because one of the things you talk about a lot is supply chain. And it's company Blue Yonder, which is interesting because it's a legacy company. It's an old man logistics firm, right? And they're kind of replatforming on Snowflake and relational AI. So my understanding is with the container services in Snowpark, they can bring in all those legacy apps. Exactly. Containerize them and then actually have a consistent data platform. And a fully governed data platform. And using our database engine again at the back end. Yeah, there's two things that really matter in supply chain management. The first one is complete visibility across the supply chain. This has historically been an unsolvable problem because the supply chain is made up of, I don't know how many independent entities and they're like, well, this is my data and you're not going to see it or touch it and building network connections was hard. We have EDI, remember that term, right? I mean, we're just hacking data connections from A to B and people are like, I'm giving up. This is just too hard. So that problem gets solved with Snowflake because you got two parties on Snowflake, it's a matter of minutes for them to have visibility to each other's data. It is a data integration problem first and foremost. But after that, yes, you mentioned all these legacy engines that Blue Yonder has. They can go and rebuild those and rewrite those. I mean, some of these things are 20 years old, but they're still used by many, many manufacturers, retailers and so on. Now they can just containerize those and use them as a service. And they get all the benefits of a modern cloud platform. This is pretty great. So the other thing is they have to bring enormous compute to bear on these engines because they are very, very, very short burst and there's an event in the supply chain and now they have to run all these scenarios and what do we do, what do we do? And it requires tons of compute. And Snowflake is ideal for that because you can stand up the cluster and the workload in seconds, massively provision it, run it for its duration and then you back off. So it's just the elasticity of the compute and the data is ideal for supply chain. When you say back off, you mean dial down the compute? Yep, unwind it. Which others have tried to set, oh yeah, we're going to separate compute from storage. They quite get there but you guys were the first. I want to ask you about the ecosystem because the ecosystem continues to grow. It's critical. I did a little mini video yesterday saying the hallmark of any cloud company is its ecosystem and that's proving true. It's critical as you become the platform for application, for data apps. How have you thought about the ecosystem, its growth and what are you specifically doing to advance that ecosystem and grow it? Well, you look at things like Streamlade which has a huge community around, right? I mean that's just, Python program is reflexively reached for Streamlade when they want to publish something, visualize, animate because machine learning models are for programmers, right? Or at minimum, fairly sophisticated technical people. We have our native apps framework, right? In other words, what we're trying to do with the data cloud and I said this yesterday in the keynote is like we're trying to set up a renaissance and software development by really lowering the bar, right? If you wanted to build and publish and monetize an application, right? What does that take to do historically? I mean, you got to raise venture capital and you got to staff up, you got to buy tons of hardware, right and then you need to have a scalable enterprise grade high trust platform. You pretty much give up before you've gotten started. So we created a full stack where not only can I build it, not only can I sell it to the enterprise because it's on the Snowflake platform and but I can now market it through our marketplace. By the way, you can find it through Neva Search, you know, very easily and I can monetize it, right? I get paid on the transaction and we announced yesterday also that people can use their commit dollars to Snowflake to also buy data and apps, right? So if you're too many a dog and I want to build the service, I want to publish it and monetize it. All you have to do is guess to check at the end of the day. So we've massively lowered, you know, the, you know, what it takes to start a software business and it's very, very fine grant and we're hoping to set up a renaissance and software development because that, you know, we've been in software for most of our lives here. It's been hard and risky and expensive, all these things. You know, we bring in that way, way down. You know, that's our agenda. That's what our native apps framework, you know, I compared it to the iPhone. Obviously, you know, Snowflake is our version of the iPhone, but that's what it is, except that it's multi-cloud. You can build one app and it runs on all of them. And it's the app store for data. It's like the next generation of cloud, as I see it. In other words, when Amazon started, if I were a startup, I didn't have to go buy a bunch of servers and Oracle database licenses and that was great. Then this is next generation, which is the integration. Well, you know, cloud really hasn't had the software development paradigm, the way we've had it in mobile, for example. You know, and so we're really asserting that paradigm as a cloud company. Like, this is how you build apps in the cloud, right? Now, there will be other short, but we've got one, you know, and it has a lot of advantages. You know, it has life data, right? It has full governance framework. It has full workload-enabled marketplaces, transactional models. Just think about it, you need all that stuff. Well, the other thing that the nuance that a lot of people might not have caught is that how you leveled the playing field with iceberg tables. There was, you know, a penalty for keeping them external. You could have an advantage coming inside a snowflake. That's gone now. If you want to leave them in an external iceberg table, you're going to get the same performance. So that's, again, a little nuance of how you're, it seems to me, you want to be the best place possible to build apps, not just take a box on open source. People can choose. Look, you know, you want your object to be, you know, an iceberg open table format object. Do you want us to manage it? Do you want us to be the custodian? Do you want some other tool to be the custodian? You get to make those choices. Do you want to manage the storage? Do you not want to manage the storage, right? So people will learn over time, you know, what is, and by the way, you can change your mind. Okay, it's not the decision you have to make for all time, right? But these decisions all have trade-offs. People are going to learn, you know, what really fits for their particular circumstance. Sometimes people are just reflexively reacting to, well, you know, I don't want to duplicate my data, but there's, when you duplicate your data, there's actually value to that because it's highly optimized. It's highly organized. It's sanctioned and trusted, you know what I mean? A lot of data lakes are struggling and the reason is people are like, I don't know what I'm dealing with. It's untrusted, unsanctioned, and then people don't trust the results, you know, that the workloads generate, right? So they back off and they go back to their data warehouse saying, well, that's sanctioned data. Everybody uses it, everybody believes that, right? So, you know, data lakes really need to come up, you know, in terms of the level of function that we have already brought to the data. And by the way, we know, we of course, you know, are the data lake for most of our customers, they view us as a data lake. Yeah, totally. The other day, the other night with Jensen, it was kind of funny, you guys going back and forth, you did say that consumption models require discipline and he said, you know, AI or LLM is going to take that to a new level. So, explain what you meant by that and how you plan to deal with it. Well, look, it's all very fun, you know, to sort of feed the entire great despy, you know, into your prompt and then let it summarize and everybody goes, ooh, enough. What is the economic value of that? I mean, the reason that search became such a gigantic thing is because there was a business model to pay for it, right? There needs to be a business model that's going to pay for AI as well. I mean, once the fun and games were off, people are going to have a very hard-nosed look, what am I getting for this? And you can ask the question, what should I have for dinner tonight? But how does that translate, you know, to the business model? Where's the alignment, you know? And if you can't do that, people are going to go like, well, this is an expensive hobby, you know? And you are sure in academia and all these places, but in business, we need to see returns for spend. And that's a very important thing that's going on in cloud in general because people are consuming, consuming, consuming and you see if O comes in, wait a second, you know? What's the relationship with the business side here with all the money that we're spending? This is really important. They go hand in hand. It is the technology and the business model. There needs to be a business model, you know? Meaning consumption is aligned with value. Yeah. And then the more you consume it. Re-turn. Yeah, yeah. It'll be a little bit more pointed. Yeah, yeah, ROI, right? We're going to spend a dollar, we're going to make 10 or whatever the IRR is going to be. You need to be able to pay for it, you know? So in observing you and Mike, my understanding is you don't incentivize your Salesforce to go hard after new logos, rather you're focused on consumption. Is that the right understanding? No, we do both. You do? We actually have selling motions that are strictly and only focus on new logos, specific new logos. Not all logos are created equal, so we're very targeted in that way. And then there are the people that are dealing with existing Snowflake accounts. Those are consumption-driven and based because they need to be. So two very different models. Yeah, okay, so I didn't realize you had both. And how's that former going in terms of the new logos? Well, it's actually working much better because we have created full separation because landing is a totally different muscle motion than expanding. You know, expanding is all about use cases and workloads, developing the account where there's landing. Those are technical battles and CTOs and CIOs. You know, all the stuff that you guys, you know, revel in. Yeah, so you're a software guy, but you've had a couple of scents at some hardware companies, Data Domain and EMC a little while. Hardware companies, they don't want to announce a new product while there's an existing product in the field because it'll cannibalize sales. You guys take a different approach. You're giving the network, the ecosystem, visibility on what's coming. Explain your philosophy in terms of a lot of stuff in private preview or public preview. What's the philosophy there? Well, I mean, a couple of things. First of all, the preview is a signal very precisely what's coming. So, you know, you know, secondly, you know, we need to put the thing through its paces. I mean, the long-pwned attempt for us to deliver content is always the security and governance aspects. And that, in the case of Python, it took us literally years to fully plug, you know, Python as a high-trust, enterprise-grade programming framework because you can't have developers sort of download their libraries really nearly and stick them in and who's watching that? You know, who's certifying that, right? And so we took an approach where we're like, wow, we have to look through the entire supply chain and understand that this is bulletproof because if we're going to give it to an enterprise, they're going to say like, hey, Snowflake, I can take it to the bank, right? And we're going to have to say, yes, you can take it to the bank, you know, we're liable and all these kinds of things, you know, for them using it. So that takes time. So these previews, and I heard a little bit of, you know, of the analysts, you know, complaining about that. Yeah, sometimes it takes more time than we'd like, but that is a commitment we have to the large enterprise. We are, look, we sell the two men and the dog as well and they may have less concerns around governance, right? But it's the same product that we provide to the largest institutions in the world. Christian said we'll ship it when it's ready. So we wasn't apologizing for it, neither are you. There's a lot of fun, a lot of smiles, a lot of, the culture here is very playful. You having fun? I'm having, look, you know, this is such a great time to be alive in this industry. I've been waiting for this for decades. You know, we've been grinding it out, you know, since the late 80s with technology that was just agonizingly difficult. And all of a sudden we're in this place where, you know, wow, you know, the acceleration, it's warp speeds. You know, I think I'm against it the other night, I mean, it's very funny, you know, you turn the AI factory on and while you sleep, it's generating all these amazing intelligence and all that, maybe a slight overstatement, but probably not by much, you know? I mean, this is coming like a freight train, you know? Well, we've seen a lot of waves, Frank, and it feels like, and I think most of us agree, this is potentially the biggest one we've ever seen. Yes. Frank Slubin, thanks so much for taking some time with us. Really appreciate it. You're back. All right, keep it right there. We'll be right back with our next guest. You're watching theCUBE live from Snowflake Summit 2023. Right back.