Good afternoon. Good evening. Thanks for joining day two. I think this is the last presentation of the day. We saved the best for last. We have Mr. Mike Scarpelli from Snowflake here in person, wrapping up day two for the software track. So Mike, thanks for making the journey out to New York City. Thanks for the support of our conference. Obviously, a packed house here. I think almost every seat is filled. So for investors that may be new to the story, I'm sure 90% know who Snowflake is, but maybe just give a 30-second overview on the company.
Sure. Thank you, everyone. Snowflake's twelve years old now. Snowflake really started out as going after the cloud data warehousing market, but it was really a cloud platform, and data warehousing just happened to be one of the workloads. We gradually grew into a lot of data engineering workloads, and one of the things what customers like about Snowflake, number one, is data sharing. No one does data sharing the way Snowflake does. People like the ease of use of Snowflake. Does not require a very sophisticated user. It just works. We do all the work in the background, and it's the exact same experience, whether you're running in AWS, Microsoft or Google, we can do cross-cloud, cross-region replication, very easy.
We have a number of data listings in our marketplace that are available around the world into any region that anyone wants to use it. Yeah, we just reported our Q2 results a couple of weeks ago, and we're off to a good start this year.
Great. So speaking of kind of the recent changes, the CEO transition was a big one earlier this year. Frank Slootman handing off the reins to Sridhar Ramaswamy. What have been the main highlights that you've seen, obviously, working with both executives? What has sort of Sridhar been focused on in the two quarters since he's taken over?
Sure. So Sridhar joined at the beginning of this year, but that was something we were working for a few months and discussions with him, and we got Neeva- or Sridhar through the acquisition of Neeva in May of 2023, when he came on board. And I remember I told Frank when we were doing that deal, I'm like: "Frank, you need to spend time with Sridhar. He could be your successor." Originally, Sridhar only wanted to stay six months. Agreed he'd stay for six months, and then he was going to go off. He wanted to be CEO somewhere, and he had other opportunities to be CEOs of public companies, as well as running all of the generative AI in, let's just say, one of the hyperscalers.
Mm-hmm.
And so when the opportunity presented itself with Sridhar, we had to move on that because he had some other opportunities. And Frank, we had been talking about succession planning for two years. When Frank joined the company, he had told the management he really only wanted to be there for about three to five years. He had come out of retirement to do this. So it may have been a shock to some investors, but it was definitely not a shock to me, on that. In terms of what are the big things and why we think Sridhar is the right person, you know, Snowflake has a very large engineering organization, a very technical engineering organization. The product is going down more into... And everything happening with AI, we really thought we needed a leader who had more technical chops. That is not Frank.
Frank is more of a go-to-market. He's really good at product positioning, understands product management well, but he's not an engineer. And Sridhar is. And also, Frank was getting. I don't want to say this, he's not listening. But Frank was getting old - and the energy level. I joke around with him about that. You know, he's sixty-five, sixty-six he'll be this year.
Yeah.
He was ready to retire. I hope I'm not working when I'm 65, 66. But Sridhar has really, really had an impact on the engineering organization and really driving the delivery of releasing new product features and capabilities faster, holding the engineering organization more accountable to what they're building with a go-to-market fit. He's really been driving a lot better alignment between engineering, product management, and the sales organization. And I'm seeing that in we have various, what we call war room weekly meetings on things, and some meetings are once a month, some are weekly, where they're he initiates in pulling together the stakeholders, and Sridhar is involved in so much of that detail.
Mm-hmm.
And then he's very focused on the go-to-market side, on really holding sales reps more accountable to better process, better reporting, not just on bookings, but meetings and who you're meeting with, and detail about pulling people in. Sales enablement, that's another big piece. We have so many new product feature capabilities that we need to ensure the salespeople are equipped to be able to sell that, and a lot of that is sales enablement and training, and he's really deriving a lot of that.
Yeah.
And it's very positive what I'm seeing coming out of that. The guy's a workaholic.
Yeah, I've heard that, you know, people think of Frank as kind of this hard charger. Sridhar might be an even harder charger than and worker than-
I've worked with Frank for seventeen years.
Yeah.
Sridhar is much more hard charger.
Yeah.
I used to joke when he... For some reason, Jim Cramer used to like Frank, and I remember he was going to say, "Oh, he has a cot in the office." I'm like: "I don't think I've seen Frank in the office after five o'clock." Sridhar just doesn't sleep. There's no, there's not even a cot. It's Sridhar actually shares his full calendar with me. Not just sharing that he's busy, I know exactly what he's doing. I can unequivocally tell you, that guy, from the moment he gets up in the morning till he goes to... He literally has eight hours for himself. He likes to sleep seven hours, one hour to work out, and everything else.
Yeah
... he books with anything for work.
Wow. Wow, that is, that is hard.
Weekends, too.
Yeah. So as you think about the demand environment and kind of on the most recent set of results, I'd be curious, just what do you think some of the, you know, biggest questions or misconceptions are coming out of last quarter? Obviously, you had a quarter where, you know, you delivered upside to your guidance range, and you also raised the full year product revenue by, I think, the biggest magnitude you'd ever done. But what are kind of the biggest issues that you think investors might be missing?
Well, I would say what I think investors are concerned about is they see our revenue growth slowing down, and people saw we had a beat of 5.5% in Q1, and expected that we would be beating by a similar magnitude every quarter. And I've been saying this consistently since the time we went public, that the way we guide the business is a 3%-5% beat is a big beat. Just like I don't get excited when we're 5.5%. I actually got upset once when we were 9.5% because we did a bad job of forecasting. That tells me this is a consumption model. You're gonna have variability on literally a daily basis by customers, and just like we beat Q2 by 2.5%.
But what I would say is, we gave a good guide for the full year with the raise, and that's really because we look at most recent consumption trends, and the consumption trends are very stable with our customers. And so I feel good about that. Now, obviously, that could accelerate or it could decelerate-
Mm-hmm
... the consumption model.
Yeah. Yeah. One of the themes you also talked about, on the GPU side, we'll go into kind of the more, GenAI specific questions in a moment, but you did talk about some capacity constraints in certain regions in terms of GPU availability. Can you talk a little bit about those dynamics and, you know, anything else beyond just the consumption volatility that investors should be aware of?
Sure. Well, first of all, we can get the small GPUs, the A10s, A100s, it's the H100s that are a little bit more difficult to get in certain regions. Where the bulk of our customers are, we have coverage.
Mm-hmm.
There is some demand from some of the smaller deployments where salespeople are asking for them, and I'm telling them: "Let's see the customer demand before, not just you asking, before we commit more dollars to that." Because the problem with the GPUs is, you're making a three-year fixed commit.
Mm-hmm.
It's under a reservation model, and whether you're using them or not, you're paying for them, and that's hitting our margin. I personally... And by the way, those customers that are in those regions, you can still we have the ability, through your existing region, to call into one of the regions where we have them if you want. Now, some customers don't wanna do that because they wanna make sure their data stays in their region, but I haven't seen a huge demand. When I see the demand from customers, we will deploy more, but I'm not expecting a lot of dollars in that. I think we have more than enough, and until we see revenue-
Mm-hmm
... we're not gonna spend more on the COGS side.
Right.
We have a fixed amount in R&D, and that will not change.
Right. And some of the other moving pieces that I think are more applicable in the second half of the year versus the first half are things like some of the performance optimizations that you put forth into your product every year-
Mm-hmm
... as well as, some larger customers looking at Iceberg tables and-
Mm
... and open table formats. Can you just walk through those dynamics for-
Sure
... for the audience?
Sure, so first of all, ever since we've gone public too, we've said we estimate long-term. It's 5% of yearly revenue headwinds associated with performance improvements.
Mm-hmm.
Most of them, we feel, come from our software. Some do come from the hardware, but not all the hardware improvements impact our software the same. And so this year, entering the year, we said 6%. We still see that happening, and a big piece of that is associated with what we're doing around what we're calling our warehouse optimization, where we can give customers visibility into their warehouses and utilization of that, so they can choose to pick smaller warehouses. A lot of customers still over-provision-
Mm-hmm
... their warehouse. Now, some do it on purpose to get better performance-
Mm
... but most don't need to do what they're doing, and that will have an impact on revenue. Now they have better visibility into that. There are other things like Iceberg we do expect, and we've always said we expect it to be more back-end loaded, more Q4, where some of our customers, now that we have the support for open file formats in Iceberg, where they're going to choose to move some of their storage out of Snowflake directly into open format, Iceberg tables. We've yet to see that happen. The only thing we've seen to date is we have about 400 customers that are playing with net new workloads in Iceberg format. They're smaller workloads. They're still just trying to make sure it has the same performance of Snowflake.
But I do expect some of our larger, more sophisticated customers will move their storage to Snowflake.
Mm.
But the vast majority, I don't see that happening, and I think there's gonna be a group of people, too, that will have be in a hybrid format. You may say: "Why would you do that?" Even some of the ones that have been playing with Iceberg... They're like, "There's a lot more work for us-
Mm.
when it's done in Iceberg," and they didn't realize that.
Right. Right.
Um.
Right.
And then, they have to take care of... They own- they have the data, they have to control the security and governance around that data.
Yeah. One question we've recently got around Iceberg tables is, if a customer decides to sort of move the storage layer away from Snowflake and go to Iceberg, is there... What's the magnitude of that sacrifice on the query performance? Is it-
So in Managed Iceberg, virtually the same-
Yeah
... the performance. And the reason that is, is because through Managed Iceberg, we're controlling the writes to-
Mm
... Iceberg, and we put them in the right format so that they have the best performance. If it's the case of unmanaged, the customer is controlling the rights to Iceberg, and a lot of times there's more work to get it into the right format, and they just don't have the expertise that we have in doing that in our system. A lot of customers who had originally said they wanted unmanaged are realizing now, "I wanna do managed instead.
Mm.
And whether it's managed or unmanaged, that customer can still choose to use other query engines on that data too.
Right. Right. Okay.
And that's why we're obsessed with price performance, because the cheaper we are from a price performance, they'll use our engine versus-
Right
... someone else's.
I guess, do you have a view on the customers that do go to Iceberg, or is it gonna be sort of a mix of managed and unmanaged, or how do you think that plays out?
It, it's too early. I do think it's gonna be a mix, but I think the majority will go with managed-
Yeah
... when they realize how much more overhead there is for them to do it in an unmanaged-
Right
... way.
Right, yeah. Open source-
Just like we've heard from some customers, too, and big customers, a top 10 said, "I am not interested in Iceberg. Our data lake is Snowflake and it works well. We are very efficient how we use it, and we're gonna continue to do it that way. Why? So I don't have to worry about it.
Mm-hmm. Right. Right. Moving to the topic of Gen AI, maybe we can start high level and then get into the specifics. But what is Snowflake's Gen AI strategy? We get a lot of questions of just how does a traditional data warehouse look in the world of LLMs and JSON documents? How do you sort of see yourself playing in this evolving Gen AI landscape?
Well, there's an example. We have a logistics company that is using Cortex right now, and what they've done is they've developed a chatbot, replaced a legacy BI tool for their people to interact with the data warehouse. And that's a prime use case, and we see there'll be more of that. We have an insurance company who's using Cortex to take all of their support cases, notes, and everything, to get insights into what's going on in their customer base from a support standpoint. Those are the types of things that we're seeing being used in Snowflake right now with-
Right
... GenAI.
Right. Okay.
There's a lot of things we're also doing on our own. This is not a customer thing, but it will help customers, is using AI to help speed up migrations and make them...
Mm
... more efficient. Using AI to help maintain pipelines for customers.
Right. So, so maybe going back to the, the products, and we're, we're obviously a few months post the Snowflake Summit, where there was a lot of product announcements. Sridhar's been, been hard at work. As we think about the AI-enabled products at Snowflake, what's sort of the, the vision and roadmap? You talked about Cortex. I think that's, that's one of kinda multiple ways that you're sort of addressing this market. But just sort of give us the, give us the vision and, and how, how investors should think about that product.
Well, Cortex is really just... You have Cortex Search, you have Cortex Analyst. We think ultimately Cortex Analyst is gonna be the big thing, where an average person in the company can just ask questions in plain English, simple text, of the data and generate a SQL query on the back end and get the results back.
Mm-hmm.
That is the biggest thing that we see happening with-
Right
... AI and Snowflake.
Right. Right. And then on the LLM side, using-
We're using LLMs on the back end to do this.
Right.
We're not looking to train LLMs and stuff, and things like... That's just not interesting to us.
Right. Right. But for customers that may have a preference on, you know, using something from Hugging Face or some of the... I think you support a number-
We support a number, whether you want to use Mistral, Reka, Llama 3-
Right
... we have our own Arctic model. There's other models as well, too-
Right
... you can bring into Snowflake. You use the open source, it's free. You use the, Mistral or Reka, there's an upcharge for that because we-
Right
... have to pay-
Right
... them.
Right.
By the way, the AI stuff has much, much lower margins on that.
Mm-hmm. Right. Right. As you think about these new, new products, Cortex, we talked a lot about that, how is adoption going? I know it's early, but how are you thinking about the adoption curve and how those... You know, the timing of when those become a meaningful part of the business?
Yeah, we have over 2,500 customers today actually using Cortex. A lot of it's not in full-blown production, a lot of them are just playing with it. We have a number of other non-customers that are trialing Cortex as well too. I'm not including those. So we're pleased with the uptick we're seeing in-
Mm-hmm
... customers engaging with Cortex on our platform. I'm super excited about Notebooks and what that's doing. That's gonna be a big catalyst for Snowpark. That will be GA later on this quarter. We already have 1,600 customers using that, and the feedback has been very positive. Sure, it has more. There's still some feature gaps that we need to deliver on that, but our version one of Notebooks, we're very pleased with that.
Got it. I think Sridhar has talked about seeing, you know, significant contribution from some of these emerging products, maybe as early as next year. I mean, how should we sort of think about the pace in which these ramp up? 'Cause on one hand, I mean, 2,500 customers is, you know, it's 40% of your install base.
Mm-hmm
... or somewhat, which it's a pretty good start, considering these products are only out for a couple quarters.
Yep. So, you know, obviously, we wouldn't have built these products if we didn't think they could have a meaningful impact, and I'm not gonna guide to it now. It's still too early to tell, but we'll definitely be talking about that in Q4 when we finish the year.
Yeah. Yeah.
I think that's when it will have more of an impact. Remember, a lot of these things with Cortex, there's two components to it. There's the GPU component to generate things, but then actually running the query is also driving a lot of data warehousing, revenue as well, too.
Mm-hmm.
That has the higher margin.
Right. And I think you're... No contribution expected from these new products embedded in the-
We don't have anything embedded in our guide for this year.
Right. Even though you're seeing something.
The only thing we have in there is Snowpark-
Right
... where we said at the beginning of the year it would be roughly 3% of revenue, and it is on track for the first six months to be that $100 million trailing twelve-month revenue at the end of the year.
Right. As you think about Snowpark, we talked a little bit about Notebooks, which sounds pretty exciting.
Mm-hmm.
Are there additional products or feature improvements that you're looking to that can potentially grow that business? Obviously, I think $100 million is great, but they could be orders of magnitude-
Yeah
... larger over time.
I do think, Matt, we, and we heard this from many of our customers, and you know, Snowpark is really going after the data science persona.
Mm-hmm.
And data scientists want a notebook out of the gate. They just want to have a notebook available-
Yeah
... with the product. We missed the boat on doing a notebook. We said we would rely on partners, but people don't want to go and procure a separate notebook. That's how Databricks got into a lot of the data scientists. By having a notebooks, people started playing with it, and then they started doing that. And, and Databricks has a very good notebook, by the way. Hex has a very good notebook, who's our partner, that we partnered with. But it was clear from customers they want to have a notebook just available for free-
Mm-hmm
... to them, and that will drive more Snowpark consumption, we feel, so I do think the faster we can get that into GA, it's going to really benefit Snowpark for us.
Yeah. Well, you brought up your friends at Databricks. So I'd be curious how you're thinking about the competitive landscape. Obviously, they're private, so we probably get selective disclosure from them, but they seem to be, you know, on par to adding similar amounts of new business this year, as you are. We'll see, obviously. But how often are you running into them? Do you view them as your number one competitor? Just give us a sense on the competitive landscape.
So our biggest competitor today is Google. I've been saying that all along. From a technology standpoint, BigQuery is probably the closest to Snowflake. And I would say number two is Microsoft, probably three would be Databricks-
Mm-hmm
... and AWS. And what I would say is we coexist in many of our large accounts with Databricks, and they have done very well with the data science persona. I know they're talking a lot about their SQL Warehouse, but I'm not disputing their numbers they give because I don't know them. Just don't see them in the SQL data warehouse space-
Right
... within it. Because they even quoted a bunch of customers that I think, "Well, every one of those people you quoted-
Mm-hmm
... is a Snowflake customer, and they're growing.
Right, but I guess I'm a little surprised to hear you say Microsoft is a bigger competitor to Databricks.
Microsoft, if they can get Fabric right. Now, similar to they said Synapse, and they did not deliver on Synapse, but they still have a lot of stuff they need to do on Fabric. But if they can get that right-
Mm-hmm
... from what they're saying from a marketing standpoint, that will ultimately be a bigger competitive threat.
Yeah. Yeah, we
Yet we still partner with them.
Right.
I know it's not ready today because they want to partner with us on certain-
Right
... accounts too.
Right.
More in particular, when Google is the competition.
Yeah. I guess on the partnership front with the hyperscalers, you know, how are those relationships going? I think, you know, Snowflake is still sort of a tier one partner in terms of-
Mm-hmm
... the Azure marketplace, and AWS, you have a good relationship. But any changes just as, you know, they're rolling out Fabric and arguably more competitive products or-
Yeah, well, they've been rolling out Fabric for a while now.
Yeah.
Most of their Fabric revenue is really their BI, Power BI revenue-
Mm-hmm
... they call Fabric now. So I would say our relationships with Microsoft are stronger now than they ever have been. We have a very good relationship with the entire sales organization there, and Sridhar has a good relationship with Satya. He's known him for-
Mm-hmm
... years. And but it's gonna be coopetition-
Yeah
... with them. Data, obviously, they still bring Databricks into a number of accounts. I think that maybe is souring a little bit.
Mm
... from everything I hear. But they have a big investment in Databricks, so you know, it's a very competitive market.
Yeah.
It's not like when I was at ServiceNow, there was no competition.
Right.
There is real competition here with the-
Right
... hyperscalers and Databricks, and-
Right
... who knows who else is building a new mousetrap to come compete with us?
Yeah, yeah. Definitely.
But it's a-
Big markets attract-
It's a massive market opportunity-
Right
... and there's gonna be many, many winners in this market. It's not-
Right
... a winner-take-all.
Right, right.
That's why we're focused on the better price performance we deliver to customers, the more workloads they'll move to us.
Right. Right. And I guess just going back to the last couple quarters, we talked a lot about the consumption volatility. I think one thing that's been, you know, much stronger and less volatile, frankly, has been the bookings and commitments. You've seen very strong growth there. Can you talk about the strength that... You know, what are... And a lot of these are multi-year, right? What are those conversations like with those customers? What are they sort of buying into from the platform vision that's causing them, in some cases, to commit nine figures of business your way?
Yeah. You know, when people are choosing Snowflake, it's not a one-year decision. This is why these are long sales cycles. We're replacing systems that have been in place for 15-20 years, and so these are long-term decisions, and most customers and most of the Global 2000, the average deal size, they started as $150,000, and then they get that up and running. They usually buy a lot more in our next contract, and they typically enter into a multi-year contract with us. Because large organizations don't want to go to procurement-
Mm-hmm
... every year. And I'll give you an example. There was a big bank I remember. We signed the contract with them right when I joined the company, got nothing out of them. A year later, we got a contract out of them. They barely consumed, but then we started doing a big migration. We did a three-year contract with them, which was a pretty good-sized contract, and now they're running out of capacity, and they have the potential. They're a top 10 customer now. They grew 401% year over year, last quarter, and they will continue to grow like that for some time because they have so much data. We're talking to them now about a potential five-year contract-
Wow.
and they're open to that because they know they're going to be on Snowflake for a long time.
Wow. Wow. And on the migration point, I mean, there's still a lot of legacy business out there, whether it's you know, IBM or you know, Teradata. You got even some of the Gen1 cloud data warehouses like Redshift. You talked earlier about AI potentially accelerating some of those legacy migrations.
Mm-hmm.
How are you approaching that? Is there some, you know, relational migrator tool that you guys are-
Yeah
... introducing, or how do you think about that?
No, no, very good question. Before I answer that, first, when Snowflake really started, what we were going after at the beginning was not the big Teradata migrations and stuff. It was really the first gen cloud, data Redshift. We were replacing Redshift left and right. Then we got into Teradata and Netezza, and now we do a lot of Oracle, but then we also did a lot of Hadoop.
Mm-hmm.
A lot of Hadoop, a lot of SQL Server. This one bank I was talking about, where we started with the Teradata, and now we're doing a big Oracle migration with them. We actually acquired one of our partners called Mobilize, who was an early partner, who had built a lot of tooling around migrations, and we call it SnowConvert, where now we're letting our big customers... We actually just give it to them to use for them to do their own migrations of workloads. We're letting partners use it as well, too. It's been very good at doing Netezza and Teradata migrations, and we have another partner that we use a lot, too, or subcontract to that is doing a lot of the Oracle migrations and still doing Teradata migrations and stuff, too, for us.
They have all kinds of tooling, and they're building this tooling with AI to make it more efficient. I see that.
Okay, so a lot of partner-led stuff on the AI front-
Correct.
Okay.
Yep.
And if you-
And by the way, we're giving the partners our tooling too, SnowConvert.
Right. Okay. And if you were just to sort of think about the biggest opportunities on the legacy migration side, and maybe throw in AWS in there, too, how would you sort of stack rank it? It sounds like you're seeing maybe more Oracle now than you had in years past, but-
Mm
... what does that mix look like?
There's still a lot of SQL Server out there.
Uh.
There's still, which is a lot running on Hadoop. There's a lot of Hadoop out there. There's still a lot of Teradata out there. And it's a mix of everything. It varies by customer. I can tell, there's one customer, we literally signed them five years ago when I joined the company. I think it was in the first quarter I joined the company, and was supposed to do a Teradata migration. That flipped completely, just do a big Hadoop migration, and now we're talking to them about going back to, "Let's do the Teradata migration now." So there's still a lot of on-prem out there.
Mm-hmm
... to do, and it's gonna be many, many years.
Right. Right. Okay. As you think about your go-to-market, I think you made some changes over the last few quarters just to incentivize consumption more than pre-committed bookings. Obviously, the bookings have continued to be strong. Several companies in, like, call it cloud consumption software, have talked about some sales executions this year.
Mm.
How have those changes been executed at the sales force? Or how are you seeing kind of productivity levels and just overall considering some of the, you know, CEO-level changes and kind of the dynamics in the market?
Yeah, so what I would say is the biggest change to our sales compensation plan this year was actually not the consumption. We started migrating more and more of their comp to consumption five years ago. That was the first thing I did.
Mm.
It was really more just saying there's 35% of our reps, you are only paid on new logos, period. Because what was happening in the past, up until this year, is reps had a consumption quota and then they had a growth quota.
Mm-hmm.
That growth quota could come from an existing customer, or it could come from a new customer, and you got paid each dollar you did. It was the exact same amount. The reality is, it's a hell of a lot harder to land a new customer than it is to sell more, get a new booking out of an existing customer.
Mm.
And so reps naturally gravitated to what was the easiest, and just focused on our existing customers and ignored the landing new customers. Not to say they didn't do it, but there was a lot of opportunity they never focused on closing.
Right.
And so that is a big change that we think will have a big impact on our customer count going forward. On the consumption quotas for reps, and then roughly 55% of our reps moved to predominantly just paid on revenue by customer.
Mm-hmm.
We pivoted away from them, calling out bookings on growth bookings, and what we want them to focus on are identifying new workload opportunities and the size of those workload opportunities. I will say, one of the things that we did make a tweak in the second half to also pay those reps more on the bookings as well, too.
Right.
We were always paying them on the bookings, but it was a very, very small amount.
Mm-hmm.
I upped the amount we're paying to incentivize reps to also get bookings as well, too.
Right. Right. Okay, so.
But I will tell you, bookings generally follow consumption.
Right. Right. Okay, so sort of prioritizing the new logos which got lost, and... 'Cause there's still a lot of Fortune 500 and Global 2000-
Yeah
... that aren't Snowflake customers.
Correct.
Yeah. Internationally, how's execution over there? I think you made some international sales leader changes in the last year or two.
Yeah, so in EMEA we hired a new sales leader, but then I think it's about a year and a half now.
Yeah.
I would say he's doing very well in the high end of the market. We're building out more of a commercial sales function in EMEA right now, we've been doing this year. APJ is doing very well.
Yeah.
I would say Japan is doing well, Korea, Australia. We're actually doing well in Indonesia, and Singapore.
Mm
... is starting to take off. We have some really nice accounts. We actually did. I was shocked, they actually did a $9 million cap one. It was a five-year deal, but with a telco in Indonesia, which I was shocked-
Yeah
... that they did. Usually, why I'm shocked, I wasn't shocked we got them as a customer, I was shocked typically from a new customer they don't start that big.
Right, or that long of a-
Yeah
... that long of a deal. Got it. Got it. I guess as we think about data sharing, you mentioned that earlier as kinda one of the key drivers and, or you know, key use cases. Any way of sizing, you know, that opportunity or I guess that contribution across your base? And do you feel like we're still sorta in early innings of that adoption? I remember that was a huge kinda killer app or killer use case-
Yeah
... around the time of the IPO, but is that still the predominant driver that you're having in a lot of these customer conversations?
I would say in financial services, that is a key thing, and it really leads to stickiness, and it also leads to new customers. It leads to new customers through a few ways. We have some of our financial services customers that are on Snowflake are actually demanding their vendors deliver their data through a Snowflake data feed, which data sharing, which makes them, forces them to be on the Snowflake platform. We have customers that are building, and financial service customers building, applications on Snowflake for servicing their customers, which forces them to do stuff on Snowflake. Like Fiserv has built something that they're rolling out, and what's Fiserv have? 3,000 financial institutions.
Right.
Eventually, if they want to get the full benefit of it, they will become Snowflake customers, so... And there's people like DTCC, and BlackRock, and State Street, and BNY Mellon, that are all doing things on Snowflake that want to do data sharing-
Mm
... with their customer base.
Right. And the disclosure, I think you give every quarter around stable edges, is that sort of the best way for investors to-
That's the best way, and the majority-
Okay
... of the biggest users of having the biggest number of stable edges on average is within the financial services. We think the healthcare space, there's a huge opportunity for data sharing, and healthcare is starting to take off.
Yeah.
One of our nine-figure deals last quarter was with a healthcare provider.
Got it. Got it. I guess turning to profitability, you know, since the IPO, the margins have come up significantly-
Mm
... particularly on free cash flow. This year, you are reinvesting. We talked a little bit about the pressures on the gross margin side with GPUs. Also, Sridhar, you know, acquiring, not acquiring necessarily, but hiring great talent, right, to build out these emerging products. How are you thinking just about the pace of profitability investors should expect here, and what are sort of the guardrails that you have in terms of ensuring that these AI contributions are ultimately gonna be high margin long term?
What I would say, first of all, I don't think the AI is ever going to be high margin.
Okay.
I really don't, from everything I'm seeing. It will be margin positive, but I don't think they're gonna have the same margins as what you're seeing-
Mm
... in the core Snowflake. In terms of what I... It's a fixed amount, and unless there's revenue, there's no more that is going in. I have Sridhar's commitment on that and others, and I recognize the fact that our operating margins have come down from where they were last year, and I am working right now on fixing that... Now, it doesn't fix overnight, but we are gonna take steps that you will see expansion in our margin next year, period.
Okay.
Now, unfortunately, I got people like Databricks that are just spending, like. Well, the rumor has it they actually have more salespeople than we have, and we're still a much bigger company than them, and I know they're burning cash like you wouldn't believe.
Yeah.
I'm still very focused on free cash flow, focused on operating margin leverage. And I realize, too, that a lot of people are unhappy with where our revenue growth has gone, and first I wanna stabilize that, and then we are working on plans. And how do we re-accelerate revenue? And we have some thoughts, and we're-
Yeah
... doing some things. I'm not guiding to that, but that is what Sridhar is very focused on.
Right. What do you think on the revenue reaccel- is that more just the contribution from new products, or are there, you know, other things investors should be thinking about?
New customers.
Yeah.
And that's where we're very focused on getting new customers in the door. Ultimately, it's those new customers that are gonna drive future growth. New products are going to expand the opportunity within existing customers.
Yeah.
The one thing we've been spending a lot of time on, the federal space, that is a real upside for us because it's pretty small today.
Yeah, yeah. Got it. And on the efficiency side, I mean, obviously, you know, the faster you grow revenue, the easier it is to expand margins, but what are some of the ways that you would look to optimize efficiency, spending-
So I'll give you one example. You know, we made a conscious decision and started last year to improve developer productivity. Well, to do that, there's a lot of different tooling that's required, and the full switchover of that tooling will happen at the end of this year. Right now we're running parallel systems that consume a lot of internal cloud spend. My internal cloud spend, I'll just tell you, it's over $200 million this year that's going through OpEx. It was, like, $117 million last year. That is going to come down because there's a lot of duplicate costs we're running there.
Wow.
And I've been in meetings on a regular basis on how we take that down.
Yeah, some optimizations.
A lot of optimization.
Yeah. Um-
But we knew going into that, to switch over the tooling, you still have to run things in parallel.
Right. Right. Yeah, no, it makes sense. Probably a lot of, a lot of transition costs this year with-
Yes
... the new engineering team. We got about a minute left. I thought I'd turn it over to you, just if there was anything else that you wanted to hit on or stuff that maybe we didn't ask you about, that you wanted to leave with investors before we wrap it up.
Yeah, no, I just wanna say that it's gonna take a few quarters to see the impact. And, you know, Sridhar's a new CEO. He's having an impact internally in the company, and I think it's gonna take a few quarters before you're gonna see in external results the impact of those things that he's doing. You know, a lot of people think it just happens. It doesn't happen overnight.
Right.
It takes time.
Right. Right. Well, we'll look forward to that. Thanks for coming out to the conference, and appreciate everyone staying late with us. Thank you very much.
Thank you.