SaaStr 807: Snowflake's CEO on the AI Data Cloud, Partner Strategy, and What's Next

0:01Welcome to the official Sastr podcast where you can hear some of the best Sastr speakers. This is where the cloud meets. Up today on the Sastra podcast. The teams that close, the customers are off the deal when it closes. They keep it for a year, year and a half. We will continue to evolve that. But the set of people that get new accounts are completely distinct from the set of people that are growing existing accounts. Honestly, it's a different skill. The person that is managing an account that's spending$50 million a year, that person's a manager. They have 20 people. This is a big, complicated beast where you have to learn.

0:35Often these folks, our folks know the architecture of their customer often better than the customer because they talk to everybody within the company. I see. And the folks in the first group, they're still sales, right? They're both sales. Are they, they need to have a more technical background to be real and expert in everything across the enterprise? I tell, I have a lot of these conversations with sales folks. I tell them you have to be able to have a credible conversation with either a business executive or somebody that is technical and be able to distill out what's the problem that really matters to them and how Snowflake applies.

1:11You need to know that much pattern matching. You don't need to know what's like the technology underneath. So, for example, with Snowflake, you can upload a bunch of PDFs or images and do things like image recognition. You know, you want to do receipt totals? Not a problem. You can just upload stuff. You do that. As a salesperson, you don't need to know that's a tilt model, that's 7 billion parameters, that Snowflake developed, that's all blah, blah, blah. But you need to know, hey, if you want to extract numbers from a bunch of PDF contracts, Snowflake can do that. So it's like you need to know the application of technology to business.

1:45Those are our best account exams. Hey, everybody, get excited. We just hosted 10 ,000 of you at the Saster Annual and AI Summit in the SAP area. It was insane. It was off the charts compared to last year. It was a deep dive on everything new, everything AI, everything go-to-market. And we're getting ready because Sastra AI is coming to London in December. It's Christmas with Sastra. On December 2nd and 3rd, we're bringing Sastra AI to the heart of Europe. This is your chance to connect with thousands of SaaS and AI executives, founders, and investors, all sharing the secrets to scaling in the age of AI.

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3:41So we've got a treat. We asked folks last year what they most wanted to see this year and they said a board meeting. They said they most wanted to see a board meeting. That was what they had. They'd seen everything else. A lot of scaling revenue. so um so we've got this so obviously everyone knows snowflake although many folks may not know as much about snowflake as they think i did we'll tell some stories but it's pretty interesting not only jeremy you've been on the board right since before sridhar was acquired right since the beginning that's right i'm since pre-revenue actually since pre-revenue okay so you know longer than anyone should be on a board on board and we'll get into it jeremy is one of the most esteemed gdm executives and i mean you were evp at oracle president at semantic ran half of the emcee did i mean pretty much everywhere you've run some part of it that's a big enterprise leader is that right no that's right yeah okay and then we're going to talk about it but so here's pretty interesting we'll talk about snowflake but his little old startup was acquired by snowflake for a billion dollars right which seemed like a lot of money back then do i have that right if i remember yeah sadly wasn't a billion oh wasn't i thought it was everyone's a unicorn today wasn't a billion dollars and then you became the ceo so we it's really interesting founder ceo but not founder ceo is a very interesting perspective and we've got the board so it's a unique dynamic um so i want to use this to talk about we've talked about nothing but ai but the few snowflakes thinking a lot about this right i mean few have been more successful in the explosion of the cloud than snowflake we'll talk about it but few are thinking more about the disruption right from from ai today right and for folks to know a little bit less about observe interesting too and also the other dynamic is if this is a partner relationship that's right this i forgot the third dimension so jeremy was on the board of snowflake before it started right Right.

5:40Observe now is a key Snowflake partner. Snowflake is a significant investor in Observe. And Observe in part is an observability platform on top of Snowflake, right? That's right. I get the inside scoop on how good we are as a partner. You do, right? So I want to talk, at least in our time that we have, I want to touch a little bit on observability. Over the years, since we started Saster, we've had, and I'm not a total expert, but we had Lucerne in the beginning. Really, New Relic in its day was incredible, right? We had Olivier came from Datadog when it hit 100 million. That seems like forever ago, right?

6:15Jody Vonsal's been here twice with AppDynamics, and then it seemed like things were quiet, right? And then it's re-exploded, right? It's re-exploded. I don't know why, so maybe we'll get into that too. Yeah, I can help with that. But maybe just to set the stage, there's a bunch of stuff I want to ask, but I'll tell a story. And by the way, I hope it's a lot of hands. How many folks here have used Snowflake or their team uses Snowflake in some capacity? Okay, that's good. Thank you. That's good. I think it's badass. I will just tell you a story. We can use this to learn about the future. Did anyone see Marcelle from Manglement?

6:51She was the VP of sales of the salon software company. I said she's really good, right? So her CEO's here. They're at 20 Million ARR. They do software for salons and spas. But he is a data engineer by background. And he had this funny thing. He was wondering if his best sales reps were really his best sales reps. And he goes into Salesforce like we do the dashboard and he sorts and Jason and Jeremy at the top and three hours down sort of at the bottom. And he thinks he knows the answer. But he's a data engineer. He exports data, puts it in Snowflake, figures out how to run a report, takes a few minutes, which we'll get into, but finds out maybe an obvious but profound learning from putting the data into Snowflake, which is at his top reps for some of his worst reps.

7:32Why? who bear in mind is SMB, they had the highest churn. They had the highest churn because they were pushing deals through. They were so good at sales. They were so his top reps were not his top reps. So one meta topic for Snowflake is talking to your AI, right? Which you used to need, maybe you still need a data scientist and a data engineer and the whole team. But tell us like versions of that story and where's Snowflake going in 2026 in this age of AI? Where's it going? Yeah, I mean, first of all, Snowflake is the AI data cloud. Meaning we basically want to be a cloud computing platform centered on data, whatever you want to do with it.

8:13Obviously, there's simple stuff like analysis. You bring some data in. You want to make it easier for people to bring that data in. We're going to release a pile of new connectors. But then analytics is what we were always known for. Infinitely scalable analytics. Difficult. Everything from AML to predicting next best action, or as you pointed out, churn detection. But for us, AI is a big unlock as far as getting value from data goes. If we can make any business user be able to ask questions off of data quickly, that's a big plus. You just showed me your chatbot on your website. It's pretty amazing.

8:52If you folks haven't tried it, you should. But now imagine being able to do that on every data set that your company has. That's sort of, that's our thing. And the pieces to do that are already there. And the next steps then are going to be, okay, how do you stitch these data flows together? If you're a salesperson and you want to make a new pitch for a use case to your customer, you want to know perhaps like what are open use cases that other customers like this have deployed that this person should. You also want to make sure you know the latest about what's going on. How do you bring that into a single experience?

9:25That's kind of where Snowflake is headed, which is all of your enterprise data, but now the intelligence of something like ChatGPT Deep Research with access to every single data set that you have. We have a bunch of internal examples of agents. It's pretty magical in terms of what you can do just by bringing it all together. just like I was totally blown away when I first started using ChatGPTD for search because the ability to get a high quality research report on a pretty arcane topic was amazing. But I think that is going to happen over and over again and that's where we are headed as a company.

10:00And let me, and whatever the number is, I know it's stunning, I should know it, I've written that, but how many million plus customers does Snowflake have? I know you have to do it every quarter. It's several hundred. Hundreds, right? Yeah, several hundred. Seven, seven fifty, maybe more. What are they, and I'm sure it's a spectrum, that's why I asked the question. When you talk to them today, what are they asking for you in the age of AI? What do they want you to do more of in the future? What are they pushing you for on? I mean, it falls into several buckets. The most important one tends to be, how do I get more value from the data that I already have in Snowflake?

10:33I was talking to the CMO of one of the biggest mobile carriers today. And he's like, I have all of this information on Snowflake. I want to build a better detection engine for who's going to fall off in some area. How do I automate this process and make it much, much easier to do? While others basically go, I was talking to this other company that came together by agglomeration. 60 different companies is now one company. And that poor person's like, has 60 instances of SAP. If you were to ask me, how much money am I making with this one particular customer? honestly, I can't tell you. What can you do to help me bring it all into a single panel class?

11:15Those are the two extremes of what do people want from Snowflake? It's been a while. When I was briefly a VP at Adobe, I had to install SAP locally in my Windows. That was one of my worst software. But I couldn't imagine you could even rationalize 40 instances, right? So that's a winner for Snowflake right there, right? It's probably impossible to move to one, right? It's really hard to do things like that. And part of the value we bring to the table is we can help these folks do it incrementally. I tell people, never sign up for five-year projects. I don't. If any of mine is doing RE, like any team comes to me and says, I have a great idea.

11:48It's going to take five years to implement. I go like, this is the age of AI. I just don't want to hear you. Talk to me about what you can deliver next month and next quarter. But are there areas where you meet with them now that 12 months ago, they weren't pushing you in hard in the age of error? Are there areas where the customers are pushing hard? They're more aware of their, or even just at their expectation. I think it's raising all of our expectations, right? in terms of what they want. I mean, agent AI is a thing because people can interact with a chatbot. People can run that report on ChatGPT and go like, but wait, I want that done on my data.

12:19Yes. There's definitely a push. And we honestly try to temper expectations in terms of making sure we distinguish between what is doable today versus what will be doable sometime from now. You know, if you take a complex process, like a loan underwriting process, that takes like a set of analysts. If you're trying to ensure a hundred million dollar building or trying to decide whether to make a loan like that. That's like half a dozen people working for a week. That's a big deal to decide a decision like that. In cases like that, we can probably simplify many pieces of it, but not automate the whole thing.

12:53And so managing expectations is a big part of what we have to do. But it all starts with the data. Yeah. If you have the data, if it's easily accessible, if you have clean semantic information about the data, you're already well on your way to how do I get value from it? And there is this now hunger to do, okay, what else can I do with this? Yeah. And so, and I want to come back to a bunch of that, but Jeremy, so was Observe founded before Snowflake or before you joined the board? No. It was, yeah. I joined late 2018. Late 2018. Okay. And then, because partnerships, they're always interesting and they're always sort of what they look like on the outside, but the real story is usually more interesting, right?

13:38And how much of the business today is snowflake-oriented? I should know. How much of Observe is? 100%. Oh, it's 100%. There's no other platforms. You're tied. Committed. Good, bad, and ugly. You're only in, right? And so walk through how you manage this round-trip relationship. Yeah, so when we were founded back in end of 2017, actually, We had a couple of early Snowflake engineers on the founding team. I, Philip Unterbrunner, who worked on the query engine, Vidim Antonov, who did the unstructured data support. And so we were quite predisposed to building something on Snowflake. It was actually controversial in the founding team.

14:18And in fact, we lost a number of members of the founding team because of that decision. That came from Snowflake or other? No, that didn't come from it. There was a bunch of folks who had a theory that every query had to return in 100 milliseconds. which I would like that as well, but that's just not required in an enterprise SaaS application. And so they wanted to build their own database. Oh, I see. The folks who came from Snowflake were like, no, why don't we build it on Snowflake? And for me, I was on board fairly quickly because at the end of the day, customers are not paying us really for the database.

14:53They're paying us for the analytics. They want to answer questions. And so my thesis here was quite simple in that if we've got our engineering team adding value above the database, and we let the Snowflake team build the database, then we should be able to add more value and therefore charge more money. If we've got half the engineering team building a database, then we could be in trouble because that's a long road. And many in our space, I mean, a couple of the folks that you mentioned earlier have built their own database. And our strategic decision, if you like, was when we are not going to build a database, we're going to use Snowflake.

15:30And I think the most important thing when you make that call is you have to commit to it. Yeah. Because in the short term, your gross margins are going to be worse than comparative companies because obviously you've got to pay the snowflake bill. But if you're committed, you'll figure out a way how to exploit the unique features of the platform. You'll figure out a way how to be the most efficient application on that platform. And look, as we sit today, I mean, our gross margins were tens of millions of revenue. And the gross margins are already up around 60%. And so can we reach normal SaaS margins, 70s and 80s at scale?

16:08Why not? But that's only come through being committed and making sure we are the best got on application on Snowflake, bar none. Yeah. So there's a big commitment there. And then on the partnership side, it's sort of like a sibling relationship. It's like brothers. There's like a love-hate relationships. Many days we love Snowflake. Sometimes we love to hate Snowflake. But again, if you make that decision early on that you're going to build on a platform, you've got to realize you are picking up a dependency. You have to build relationships. Certainly, technically, we were quite fortunate in that regard.

16:40We could sort of get bootleg bug fixes because we had relationships engineer to engineer. It is right. It is right. It's super important. But then as the company grows, what you realize, look, Shridhar has got this amazing distribution channel. And so you need relationships, not just in engineering, but across the board. Because we want to be able to leverage the sales team and the access that they have. They're a very credible enterprise player. We're not. Can we leverage that? Well, you're not going to do that unless you've got the relationships with the sales team and they understand your value proposition.

17:12And do you guys actively cross-sell? Is that the motion? Starting to. We're sort of big enough now to be relevant. And really, although our sales motion is a little bit different, we go to sort of DevOps and SRE people. And I think the Snowflake guys predominantly go to DataOps folks. Snowflake have excellent relationships with the top people in the organization. And so if you've got like a warm, credible introduction into an enterprise account for a startup that's, you know, 30, 40 million ARR, that's precious. Does Snowflake benefit from observed deals when they're trying to close up sales or big deals?

17:49or is it downstream or what does it happen in the timing? When do folks realize they need the platform or want the platform, the Observe platform? Yeah, I mean... You can buy Snowflake without, I mean, you can buy Observe without really buying Snowflake and vice versa, but they also go to a set of customers together. Yeah, I think there's, I mean, our motion, we go sell Observe and think of Snowflake as sort of the man behind the curtain. It's embedded. So our users, they don't buy Snowflake, they don't log into Snowflake, They don't even know Snowflake's there. I should know this. They don't buy Snowflake.

18:21No, they pay for Observe and everything is included, completely embedded. But where it does play out in the larger accounts, obviously, through, let's say, the Snowflake marketplace, an enterprise customer may have a big commit with Snowflake. Well, they can actually burn down that commit by buying Observe. So it's a huge benefit. And this relationship works great with Amazon and GCP on their marketplaces as well. Yeah. So, you know, there is a big benefit, you know, to your average Snowflake rep because we can help turn down credits that have been sold into that account. I get the credits. They've made the big pre-commit, right?

18:56It helps. How does the rep make money out of it? I don't mean to be nerdy, but I'm curious. They now get paid on that. I do? Our reps are based on consumption. For sure. Snowflake. Oh, even if there's pre-commits, they're still incented on consumption. So Snowflake recognizes revenue only when customers actually consume. Even if the pre-comit merely ensures supply of credits at a certain rate. But they actually have to consume it for us to recognize the revenues. I'm embarrassed to say I didn't know that. I study Snowflake and I get the model, but I didn't realize from a gap perspective, even if you sell a five-year deal, you can't recognize it until it's used.

19:34And so you don't want to pay the sales reps until the consumption is. Even if it's a 500K deal a year for five years, if they backload it and use it all year three, four, and five, you can't recognize that ratably over the contract? No, we don't get to ratably recognize the contract. It is recognized when it's consumed. There are limits on how much you can roll over and stuff like that. But the consumption model is fabulous because we get paid on usage. Our refs are now focused on making sure that you get value from whatever it is that you signed for. That's a super aligned incentive. I'm thinking a lot of startups might get this accounting wrong until they go public, but let's keep...

20:12I think they might just think if I at least can somehow recognize most of it radically, but it creates incentives. I guess it's a good incentive, right? You want as much data, as much workflows, as much going. So what is that? I mean, it relates to Observe. It is interesting. So what does that mean for your average AE? What are they doing with the customer 3, 6, 9, 12 months out? Because they're super incentive to grow that account, right? Yeah. And so they absolutely, they sell deals to the customer. Our typical first deals are like 50k, 100k. They tend to start small. And then we go in, implement a project, either Snowflake or the customer or an SI system integrator partner.

20:52And that drives a certain amount of consumption, a certain amount of value. And then they turn around and sign a bigger contract. Some of our biggest customers have gone from like zero to one to two to five for a space of two, two and a half years. But all of it is driven by what are use cases you can create. And every week, Snowflake lives and dies by how many new use cases did you create, how many did you win, how many went live. That's the motion that we're living. If I've got a great account, how do you get the reps to do new accounts when all the money isn't getting their existing ones to grow?

21:25Great question. We have completely separate reps for acquisition. The team that closes it is off the deal? Sorry? The teams that close the customers are off the deal when it closes? They keep it for a year, year and a half. People continue to evolve that. But the set of people that get new accounts are completely distinct from the set of people that are growing existing accounts. Honestly, it's a different skill. The person that is managing an account that's spending$50 million a year, that person's a manager. They have 20 people. This is a big, complicated beast where you have to learn. And often these folks, our folks know the architecture of their customer often better than the customer because they talk to everybody within the company.

22:07They know what the pain points are. They make business proposals for, hey, we can do this project over here. We can make your regulatory reporting a whole lot simpler or we can do this other thing. And so they are on that cadence of how do you create value with the customer? Then we have a different team that's focused on, you know, us winning 500, 600 new logos every time. I see. And the folks in the first group, they're still sales, right? They're both sales. Are they, they need to have a more technical background to be real and expert in everything across the enterprise? So we do have solution engineers.

22:39So we have both account executives as well as solution engineers. But honestly, you have to be pretty technical today to succeed because your customer is going to ask you about agent EKI. Yeah. You can't white code your way through answering what the agent TKI can do for the customer. It's hard. So we're on the spectrum of being technical for sales. How technical do you think you have to be one to 10 to succeed at Snowflake? I have a lot of these conversations with sales folks. I tell them you have to be able to have a credible conversation with either a business executive or somebody that is technical and be able to distill out what's the problem that really matters to them and how Snowflake applies.

23:20You need to know that much pattern matching. You don't need to know what's like the technology underneath. So for example, with Snowflake, you can upload a bunch of PDFs or images and do things like image recognition. You know, you want to do receipt totals? Not a problem. You can just upload stuff. You do that. As a salesperson, you don't need to know that's a tilt model, that's 7 billion parameters that Snowflake developed. That's all blah, blah, blah. but you need to know, hey, if you want to extract numbers from a bunch of PDF contracts, they can do that. So you need to know the application of technology to business.

23:55Those are our best account execs. Yeah, I have a founder I work with and they closed a seven-figure deal with OpenAI and they had what they thought was a relatively technical CRO, but when they went to close the seven-figure deal, they just left them in the lobby. I mean, it says it all, doesn't it? It's like because the questions were just so good that they really felt I have no value. Now, they're not at Snowflake scale, but they do have a brand. It's an extreme case. But I think about this. There's just no value having that guy in the room because the questions from the OpenAI team are just too good.

24:30There's just no point. You can't have any, you know, talking about golf or who won the NBA finals is just not helpful to convince the VP of engineering to buy. I mean, I really encourage all our folks to use the products. or tools that are built on our products. If you use Observe, you'll know a lot about like, hey, what's going on underneath? Similarly, if you use Sigma as a dashboard and it runs on top of Snowflake, you have a good feel for data, what it's going to do. I think applications is something that they should be able to do. And then pattern matching, like business problems to get our things we can solve and separating out.