Good day, and thank you for standing by. Welcome to the Q1 2025 Datadog Earnings Conference Call. At this time, all participants are in listen-only mode. After the speakers' presentation, there will be a question-and-answer session. [Operator Instructions] Please be advised that today's conference is being recorded. I would now like to hand the conference over to your first speaker today, Yuka Broderick from SVP of Investor Relations. Please go ahead.

Thank you, Antoine. Good morning, and thank you for joining us to review Datadog's first quarter 2025 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-Founder and CEO; and David Obstler, Datadog's CFO. During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the second quarter and the fiscal year 2025 and related notes and assumptions, our gross margins and operating margins, our product capabilities, our ability to capitalize on market opportunities and usage trends. The words anticipate, believe, continue, estimate, expect, intend, will and similar expressions are intended to identify forward-looking statements or similar indications of future expectations. These statements reflect our views only as of today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. For a discussion of the material risks and other important factors that could affect our actual results, please refer to the Form 10-K for the quarter ended December 31, 2024. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter ended March 31, 2025, and other filings with the SEC. This information is also available on the Investor Relations section of our website, along with a replay of this call. We will also discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com. With that, I'd like to turn the call over to Olivier.

Thanks, Yuka, and thank you all for joining us this morning to go through our Q1 results and what was a solid start to the year. Let me begin with a review of our Q1 financial performance. Revenue was $762 million, an increase of 25% year-over-year and above the high end of our guidance range. We ended with about 30,500 customers, up from about 28,000 a year ago. We ended Q1 with about 3,770 customers with an ARR of $100,000 or more, up from about 3,340 a year ago. This customer generated about 88% of our ARR. And we generated free cash flow of $244 million, with a free cash flow margin of 32%. Turning to platform adoption. Our platform strategy continues to resonate in the market. At the end of Q1, 83% of customers were using two or more products, up from 82% a year ago; 51% of customers were using four or more products, up from 47% a year ago; 28% of our customers were using six or more products, up from 23% a year ago; and 13% of our customers were using eight or more products, up from 10% a year ago. We are pleased to see that customers are adopting more products, and I'd like to highlight two of our newer products with you. First, Flex Logs is off to a fast start and now exceeds $50 million in ARR. Flex Logs has achieved this milestone in six quarters, which is the fastest ramp we've seen to that level, in which echoes its value to customers as well as the size of the log management market opportunity. I'll also note that by adopting Flex Logs, our customers are adding new use cases at the right economics. And these Flex Logs adopters ultimately spend more on data…

Thanks, Olivier. Q1 revenue was $762 million, up 25% year-over-year and up 3% quarter-over-quarter. To dive into some of the drivers of Q1 revenue growth, first, overall, we saw trends for usage growth from existing customers in line with our expectations and similar to the second half of 2024. We saw a continued rise in contribution from AI native customers who represented about 8.5% of Q1 ARR, up from about 6% of ARR last quarter and up from about 3.5% of ARR in the year ago quarter. AI native customers contributed about 6 points of year-over-year revenue growth in Q1 versus about 5 points last quarter and about 2 points in the year ago quarter. We continue to believe that adoption of AI will benefit Datadog in the long-term, but we remain mindful that we may see volatility in our revenue growth on the backdrop of long-term volume growth from this cohort, as customers renew with us on different terms as they may choose to optimize cloud and observability usage. Next, regarding usage growth by customer segment. Year-over-year usage growth with our enterprise customers remain healthy if a bit lower than last quarter, which we see as a product of the volatility that can occur among customers from quarter-to-quarter. Meanwhile, we saw strong booking activity from our enterprise customers in Q1. And as Olivier noted, this included some large TCV deals. From our SMB and mid-market customers, excluding the AI cohort, year-over-year usage growth was roughly similar compared to last quarter. As a reminder, we define enterprise as customers with 5,000 employees or more, mid-market as customers with 1,000 to 5,000 employees and as SMB as customers with less than 1,000 employees. Looking ahead to April, our usage growth was consistent with year-to-date trends. As usual, we have contemplated near-term…

Thank you. At this time, we will conduct a question-and-answer session. [Operator Instructions] Our first question comes from Mark Murphy from J.P. Morgan. Please go ahead.

Thank you so much, and congratulations on a great performance. Olivier, we noticed the CEO of Anthropic recently said that within 12 to 18 months that AI is going to be writing 100% of all code. I'm sure there's a bit of hyperbole there, but directionally, it's intriguing. Can you comment on the opportunity that might open up for Datadog if that sheer volume of applications being put into production starts to rise, because AI writes so much code so rapidly? And just does that AI-generated code require more or less monitoring than human written code? And then I have a quick follow-up for David.

Yeah. That's a great question. And there's definitely a big transition that is happening right now. Like, we see the rise of AI-written code. We see it across our customers. We also see it inside of Datadog where we've had very rapid adoption of this technology as well. While I don't think this is going to replace all our software engineering, and I'm pretty sure that Anthropic is still hiring software engineers too, I do expect big changes to come to the way software is being shipped and being run this way. The way we see it is that this means that there's a lot less value in writing the code itself, like everybody can do it pretty quickly, can do a lot of it. You can have the machine to do a lot of it and you complement it with a little bit of your own work. But the real difficulty is in validating that code, making sure that it's safe, making sure it runs well that it's performing and that it does what it's supposed to do for the business. Also making sure that when 15 different people are changing the code at the same time, all of these different changes come together and work the right way and you understand the way these different pieces (ph) interact in the way. So the way we see it is, this moves a lot of the value from writing the code to observing it and understanding it in production environments, which is what we do. So a lot of the investments we're making right now, including some of the acquisitions we've announced, built towards that and making sure that we're in the right spot, so we can tell you exactly what -- every piece of code you've written, make sure that it works well, you understand it well, and it does what it's supposed to do for the business.

Okay. Very interesting. And then, yeah, David, the -- it's just amazing, the booking stats so impressive, 11 deals over $10 million, only one a year ago. And then these big AI numbers, the combination there, it's just incredible. What do you attribute that level of booking strengths to, it's a quarter where you have the specter of a trade war and it seemed like it was weighing on business confidence. So I'm wondering, if you can comment on the bookings strength?

Yeah. We entered the quarter with strong pipeline and I think it's consistent with our investments we're making in go-to-market where we're increasing our investments in enterprise and across the board. And so -- and we've been good about proving value to clients across the platform, allowing us to land larger as you saw, consolidate and add more value. I think that much of this is related to product strength and expansion of quota capacity.

Yeah. And look, we -- like everybody, we read the news and we see -- we look hard for any signs of a trouble in our customer base. We haven't seen that in the deal making so far. We are -- our sales cycles haven't been affected, our pipelines are growing healthily. And I can tell you that at least one of the customers we mentioned in the call, one of the new lands, is also a company that is very affected by the tariffs and is having to moderate its own plans for the future. I think what this tells you is that cloud migration and observability in particular are deflationary for these communities and they help -- these are tools that help them save money and move faster and we are on the right side basically of the problem for them.

And to echo a little bit what Oli said in our prepared remarks, we continue to see that we said we have in Q2 the same quality of pipeline higher than last year. So it continues -- we continue to have that situation.

Excellent. Great to hear. Thank you.

Thank you. One moment for our next question. Our next question comes from Sanjit Singh from Morgan Stanley. Please go ahead.

Great. Thanks for taking the question, and congrats on the quarter. I wanted to pick up where Mark left off. And as we talk about kind of the core drivers, digital transformation, cloud migrations, what are the trend lines on the cloud migration side? It seems to -- I mean, based on your comment, Oli, it sounds like that's likely or could actually pick up potentially going into a lower economic environment. So in terms of just what we've seen through the early parts of the year, what are the trend lines on core cloud migration?

So it's very consistent. It's consistent with what we've seen before. It's also consistent with what you've heard from the hyperscalers over the past couple of weeks. So I would say it's a steady, unremarkable. It's not really trending up, not trending done right now, but we see the same desire from customers to move more into the cloud and to lead the groundwork so they can also adopt AI because digital transformation and cloud migrations are prerequisites for that.

Awesome. I want to talk a little bit about some of the expansion -- additional expansion opportunities into data observability. I mean, that's been a space that's been talked about sort of buzzy the last couple of years. I'm not sure it's gotten super big just yet. Maybe just some of the -- what the vision is about moving into data observability and how consequential of an opportunity could it be for Datadog?

So it's a space we've been watching for a while. Initially, we were worried that this was maybe a little bit too small in the impact, because if we're just talking about debugging the availability of reports for executives like it's definitely valuable, but we thought it might be like a smaller market opportunity in the end. What we see though is that the field is evolving into a big enabler or it can be the opposite of enabler, if you don't do it right for building enterprise workloads -- sorry, building AI workloads, sorry. So in other words, making sure the data is being extracted from the right place, transformed the right way and is being fed into the right AI models on the other hand. And so we're building towards that with the acquisition of Metaplane we just announced. And just to help you feed that into what we're doing, we already had some building blocks for data observability. We built a Data Streams Monitoring product for streaming data that comes out of queues such as Kafka, for example. We built a Data Jobs Monitoring product that monitors spark jobs and large data transformation jobs. We have a Database Monitoring product that looks at the way you optimize queries and optimize database performance and cost. And by adding data quality and data pipelines with Metaplane, we have a full suite basically that allows our customers to manage everything from getting the data from their core data stores into all of the products and AI workloads and reports they need to populate that data. And so we think it's a big opportunity for us.

Understood. Congrats on Q1.

Thank you.

Thank you. Our next question comes from Raimo Lenschow from Barclays. Please go ahead.

Perfect. Thank you. Congrats from me as well. Two quick questions, actually more for David this time. David, if you think about the guidance, I know you've been -- you've always been a conservative team. You raised it more than the Q1 beat. So obviously, you're kind of seeing good trends in Q2 there as well. What was the thinking process in terms of kind of showing us that upside that you kind of might expect in Q2 versus kind of buffering that and just kind of given the uncertain environment? It's first question. Second question is on the gross margin change for the full year guidance. And can you just remind us like obviously, that's three months after you guided the first time. Can you remind us a little bit like what you saw there and what's the action that you're taking there to kind of change that? Thank you.

Yeah. So on the first question, we haven't changed any of our strategy towards guidance. We look at the recent trends. I think it's important to note that we obviously beat in Q1 and we sort of looked at the run rate and still discounted the current use growth rates has still put the conservativism into Q2 and this was the result of the guide. So it indicates the pace of business and where we are, but still discounts the trends as we always do. And another thing that's important to note is, because of the uncertainty in the market, we left the second half of the year unchanged. We essentially have done that, there's no change in our strategies that we do all the time, but as we go out more in time, we have less visibility. So I think there's no change in philosophy, it's just sort of a follow-through on our run rate. In terms of gross margin, as a reminder, we've always said that our gross margin would fluctuate within the range. And we've been in the range of the upper 70s to low 80s for a long time. And so what we sort of designed the business that way. At sometimes, we're going to lean into investment and in some cases, we'll optimize. In Q1, as we talked about, we did lean into investment and we also had a little more of a spiky pattern of -- from our customers, which we're learning about. We're learning as we grow in size how to deal with that. And so from those learnings, we feel like on that, we can do a bit -- a better job in provisioning. And as we always said, we run in the spectrum. So I think there were higher cloud costs than we expected. It is largely due to those two factors. And we said some of that will persist and some of it we will work on optimizing. This is no different than on that sort of pattern than we have over time of growth and then optimization. Oli?

Yeah. And just to echo that, we have the same engineers basically shipping functionality and optimizing performance. And we -- there's a certain range of margin we're comfortable with. I would say last year, we were very comfortable with what we were. Now we're getting a little bit less comfortable with what we have seen over the last quarter. And so, we're shifting some resources from -- towards optimization, so we can write that. As David said, in addition to shipping a lot of new functionality that might not be as optimized out of the gate as the rest in terms of the system utilization, we also saw some spiky growth from some of our largest customers. And that might continue and especially, as they deploy new workload. But our job is to optimize and we feel very confident that we can remain in the range we've been in before for gross margin or even better in the future. There's no question about that.

Okay. Perfect. Thank you.

Thank you.

Thank you. Our next question comes from Kash Rangan from Goldman Sachs. Please go ahead.

Hi. Thank you very much for taking my question. Oli, one for you and one for you, David. Oli, when you look at the AI market, certainly, it looks like we're about to move from training into inference. Where does it leave Datadog from a product perspective where you can add more consistent value without being optimized? And one for you, David. As you look at the stepped up research development sales marketing investments, how from a financial perspective are we to see the incremental benefit of those incremental investments? Thank you so much.

Yeah. On the workloads, turning more towards different. So there's definitely more product to build there. So we have a -- so we built an LLM observability product that is being -- that is getting increasing usage from customers as they move into production. And we think there's more that we need to build both down the stack across the GPUs and up the stack across the agents that are being built on top of these models. So you should expect to hear more from us at our conference on those topics because there's quite a bit to -- that is emerging as a set of needs from customers as they move towards inference.

And on the investments, I think you're seeing it, we're trying to report on it regularly when we're making disclosures of being over $50 million or being around $50 million in database and then in Flex. That is the evidence of being able to monetize the investments in R&D and continue to do that in the various product offerings. So that's how we look at that and we've gotten a lot of evidence over the years and continue to report on that. In turn -- we always said that the investment for R&D takes two -- sometimes three years to realize itself because of building in products, but we're reporting on investments that we made previously and the result of that in products. In sales and marketing, it's the same thing where we identify end markets or customers, which we haven’t have covered, feel like we can add additional capacity and there we have to go through the ramp period. So the increase of capacity that we've been executing on, we said will pay back in sort of the year plus as we ramp our reps and get returns, and we'll continue to report on that.

Yeah. And just as a quick anecdote, like, we -- so we talked about DB, Database Monitoring as a product that has taken off. And that's really a product that shows the power of the platform. So we have a product that is over $50 million or closing on $50 million in ARR with large number of customers and that's a product that was built basically by 10 to 15 people. And that's a -- just the ability for a small team to build on top of our platform and on top of all of the existing services we have that already collect customer data and deliver the data to them and to make a large difference in terms of revenue in a short amount of time.

Super. Thank you so much.

Thank you. Our next question comes from Jake Roberge from William Blair. Please go ahead.

Yeah. Thanks for taking the questions. Could you talk a little bit more about what you're seeing with the AI native cohort? It sounds like growth remains solid in that segment, but still some potential for optimization there. So could you talk about what you're seeing from some of those AI native contracts that have already come up for renewal and just how those conversations have been trending?

Yeah. So I mean, all the contracts that come up for renewal, they're healthy. The trick with that core is that it's growing fast. There's also a revenue concentration there. We now have a largest customer in that core and they're growing very fast. And on the flip side of that, we also have a larger number of large customers that are also growing. So we -- I think we mentioned more than 10 customers now that are spending $1 million or more with us in that AI native cohort and that are also growing fast. So when you add up all of that like a number of newer customers that are growing fast and some concentration in one customer is larger than the others, like there is definitely some potential volatility there and we want to be careful. We've seen that movie before. We've seen that with the cloud natives a few years ago, as they were growing very, very fast out of COVID and they had to optimize after that quite sharply. Now, of course, situation is different, like it's a small part of our business today, whereas a very large part of our business was cloud natives a few years ago. But we want to be mindful of what might happen next and that's also part of how we incorporate that revenue and its growth in terms of our guidance for the rest of the year.

Okay. That's helpful. And then great to see the strong ramp in Flex Logs over the past year. Can you talk a little bit more about what you're seeing in the log management market specifically? We've definitely been hearing about some more disruption at the start of the year from the recent acquisitions in this space. So just curious, if you're seeing an even more meaningful opportunity on that front this year?

We see tremendous opportunity there. So we validated that Flex Logs were -- was really solving a big problem for customers. We've also validated the fact that this was net new use cases for us and this was not really a -- just a cheaper product that can be managed with the rest of what we have, like, we do see customers adopting Flex Logs and then consuming a lot more of everything after that. So this is great for us. We also see a ton of opportunity to displace some existing players in log management. We've built a lot of functionality towards that to close whatever gap in functionality or specific implementation of functionality that might have been perceived by customers on that end. And so we see a ton of opportunity there. One of the reasons, we're growing the sales capacity in the way we are currently is that we see those opportunities in the market. We see great return on investment when we deploy more sales capacity to go after them because these tend to be large enterprise opportunities that require some sales capacity and we intend to fully capitalize on that. So we're super bullish about this.

Very helpful. Thanks for taking the questions.

Thank you. Our next question comes from Gregg Moskowitz from Mizuho. Please go ahead.

Okay. Thank you very much for taking the questions. I have two. I'll just ask them concurrently. First, the AI native cohorts, getting back to that. So the percentage of total ARR coming from this cohort rose really impressively. Just so that we have a little better understanding of this dynamic because Oli, you just mentioned or referenced customer concentration. Can you say if the increase from a percentage basis was driven by perhaps one or two of these larger customers or was it broader base than that? And then secondly for David, just given the second half step up in sales and marketing investment that we saw last year at Datadog, that seem to have continued into 2025. How is your productivity tracking at this stage compared to your expectations? Thank you.

Yeah. So I mean, look on the AI side, we do have, as I mentioned, one customer larger than the others there that are contributing more than your revenue than the others, but we see growth in the rest of the cohort as well. So again, fairly typical. We've always had -- in every single one of our SaaS growth cohort, we do have some form of revenue concentration. And I remind you that a small fraction of our customers are the ones that pay us more than $100,000 of ARR and they represent almost 90% of our revenue. So that's sort of how the business is structured. But we do -- we are careful about the size and the rate of growth of those customers there, which is also why we call attention to potential volatility there in the future. David, do you want to take the next one?

Yeah. So that's something we watch a lot. So once the reps are ramped, we look at productivity and we have productivity from those added that are similar, and the answer is yes. It is due to where we're putting the reps. We're not doing it sort of in a non-targeted way. We're looking at territories. So when we see ramps rep, ramps -- reps ramp, that's a hard one, we see similar productivity and that gives us the signal that we are doing the right thing and we can continue to add additional reps. Yeah.

Just as a quick reminder, the -- when you think of growing capacity, I think it's a little bit different for sales at least from engineering. For engineering, you can do it in a top down fashion, you can decide how much you're going to grow the team and then you can distribute your engineers on different types of products after that, more or less. On the sales side, you need to do it in the bottom up fashion because you need to understand which territories can carry how many new reps, which ones have the right productivities, which one have the right market opportunity and where you're going to put people. So it's not as easy as saying, we're going to grow the team by 30%. You actually need to figure out who's going to go where and who you're going to grow the teams and who you're going to make everyone successful. So we've done the hard work for doing that. We've done it for many years now. And the growth in capacity we are seeing today is a result of that.

Terrific. Thank you.

Thank you. Our next question comes from Brent Thill from Jefferies. Please go ahead.

Thanks. David, on the 25% to 30% sales capacity that you're seeing now, is that what you're continuing to plan for the year or do you expect that growth rate to fade as you get into the back half of the year? Just give us a sense of the shape of that quota carrying ramp in 2025. Thanks.

We're continued doing that, but what we see is that the ramp capacity because we essentially have this six month, 12 month ramp. If our plans are realized, the investment in headcount will translate into a higher ramp capacity increase. That's dependent upon getting the right people, training and enabling the right people and retaining them. But that's the idea, and we're hopeful for that.

Okay. Oli, that was it. Thank you so much.

Thanks.

Thank you. Our next question comes from Kirk Materne from Evercore ISS (ph). Please go ahead.

Yeah. Thanks very much. Yeah. Thanks very much. Good morning, guys. Oli, can you just talk about whether you've seen any discernible change in terms of the AI natives that you're working with in terms of what you're helping with -- them with, meaning I assume it's majority is training at this point in time, but I was curious if you're seeing inferencing come into the equation in a bigger way, or even if some of your bigger enterprise customers are starting to think a little bit more about inferencing? Just trying to get a sense on where we stand on that front. Thanks.

For the AI natives actually what we have them with mostly is not training. It's running the applications and their inference workloads, awareness, customer facing. Because what's training for the AI natives tends to be largely homegrown one-off and different from -- between each and every one of them. We expect that as and if most other companies and enterprises do significant training that this will not be the case, this will not be one-off and homegrown. But right now, it is still the AI natives that do most of the training and they still do it in a way that's largely a homegrown. So when we see growth on the AI native cohorts, that growth of AI adoption, because that's growth of customer-facing workloads by and large.

Okay. And then just one follow-up, maybe this is for David. Just I realize you guys are in investment mode right now. I'm just kind of curious internally, how do you think about AI from an efficiency perspective, meaning I think Oli addressed it a little bit on the R&D side, but we've seen from other software companies that they are starting to see real yield in terms of using AI internally that might be masked with you all given the -- just the growth rate on your investments right now. But I'm just kind of curious how you think about that perhaps longer term relative to where your margins are today versus where they could be over a longer period of time? Thanks.

Yeah. And for right now, I think we're seeing the returns in productivity, whether that be salespeople getting more information or R&D. We're essentially trying to create an environment where we're encouraging the various departments to use it and learning from it. Long term, there might well be efficiency gains, but there may be efficiency gains that can be manifested in headcount. But right now, we're essentially trying to make the headcount investment that we are making more efficient and more productive.

Remember, we're constrained by sales capacity and we're concerned by generating capacity. So any gain of productivity we gain right now are basically reinvested in the -- in producing more, whether that's on the revenue side or on the products and engineering side. Long-term, obviously, we're strong believers in the fact that AI is a big driver of efficiency and we can see how it can be if you squint to see that it can be 10 times cheaper to produce the same amount of software and things like that. So I think we definitely hit it there.

Thank you. Our next question comes from Howard Ma from Guggenheim. Please go ahead.

Great. Thanks for taking the questions. I was hoping you guys could reconcile the consumption downtick in the Enterprise segment versus the strong new logo ARR growth that I believe you said it was over 70% year-over-year. Thank you.

Yeah. I mean, there's no contradiction there. I think the -- there's always viability in the usage. We see that month-to-month, we see that quarter-to-quarter. And what we see there is within the balance of that.

Yeah. We obviously -- we take a strong look. So we -- so I think the first one is what we talked about already, which is product sales capacity winning market share in the market. The second one is, we've always said we have some volatility now. Back when we had the post-COVID, we saw some correlation around broad parts of our customer base, indicating there were broader trends. In this case, we've looked at it and it's sort of volatility of usage, but not a broad trend.

Yeah. And remember also when we say we have strong bookings, it takes time for bookings to turn into revenue for us. For example, when we have a great new logo deal, we have mentioned a few on the call, that usage will take many months to materialize and revenue will take months to materialize it. But then the back end of that we expect to see go far beyond what was the initial lens with those customers. So there's definitely a timing effect there.

Got it. Thank you.

Thank you. Our next question comes from Matt Hedberg from RBC. Please go ahead.

Great. Thank you for taking my questions. Two quick ones. I guess, Oli, your customers can obviously change usage trends due to macro…

We can't hear you. I'm sorry, we can't hear you.

You're breaking up.

Philosophically speaking, customers optimized in 2022 and 2023, are they still operating at fairly optimized levels?

Well, customers, I think are running tighter optimization cycles now. So, we don't think they -- the vast majority of customers don't build as much overhang of unoptimized usage that they need to come back to later. We do still see the same regular cycles, right. Typically when they renew their card their cloud commitments or when they renew their observability commitments is when they're going to look at what they need to optimize and come back to it. But we think, in general, that cycle has become much tighter for customers.

Got it. Thanks. And maybe just a quick one for David. It sounds like $10 million of expenses for Eppo and Metaplane. Curious, if your guidance includes any impact from a revenue perspective for those two? Thank you.

Well, for the second half of the year, we did not change the revenue guidance. So I would say, net-net, when you put everything together, we did not include it.

Thank you.

The impact is small.

And the impact -- yes, and I think that's important to say. The impact is small., the combination of the revenues per -- in the quarterly in the second half. Based on what we acquired, now we're obviously going to try to accelerate that. It's small anyway.

Yeah.

With high expectations. Yeah.

Yeah.

Thank you. Our next question comes from Brad Reback from Stifel. Please go ahead.

Great. Thanks very much. Oli, there's been growing kind of customer focus on bring your own cloud. How should we think about your -- well, I should -- what are you thinking philosophically as it relates to that maybe on-prem workloads in general? Thanks.

Well, I think it's two different things. There is on-prem workloads because some customers seem to have a lot of large on-prem workloads that are not going anywhere, and we're seeing that. And we have new product offerings that goes query after that. So we can cover from the cloud their on-prem workload in a way that is cost effective for them and that they can use in conjunction with the monitoring of their own cloud environment. So that's one thing, and we are addressing that. We see a big opportunity there. The second thing is, the desire for customers to run on their own infrastructure, whether it's clouds or some on-prem version of a cloud. We think there's definitely a future where that becomes more important, especially if there's more geopolitical like separation between the various parts of the world. Philosophically, we go wherever customers want us to go. So if that's the way they want to consume software at the end, that's definitely the way we're going to deliver it. We are testing right now some ways for customers to manage part of their data directly on their own cloud. We have a new product that is being in customers' hands for that. And if we see strong market pool for that and if we see the world going in that direction, we'll definitely lean harder into that. So for us, this can be a big opportunity in the future.

That's great. Thank you very much.

Thank you. In the interest of time, we have one more question. Our next question comes from Andrew Sherman from TD Cowen. Please go ahead.

Great. Thanks for squeezing me in. The net-new adds 100K plus of 160 were the strongest since 4Q '22. Maybe just talk about the drivers of expansion there across new use cases, new products, share gains? And it sounds like some boomerang customers coming back to you as well. Thanks.

Yes. All of the above, that's what we have. And look, I'm happy this number is high, but it's -- the reality is that it's consistent, and sometimes a bit lower, sometimes a bit higher, but all of our customers are going up the curve with us as they consolidate more and more of what they're doing into our platform and bring us more and more of their workloads and they grow with us. Of course, we're also very happy to see some customers coming back. So the -- one customer we mentioned on the call here has decided to sort of build their own using some commercial open-source and they decided to come back. And it's a motion we do see. We are very happy to see that in the end, some customers who've tried everything, including us on the competition, we've seen as being the best long-term choice. And maybe we can close it on that.

Thank you.

Thank you. The question-and-answer session is now closed. I will now turn it over to Olivier Pomel for closing remarks.

All right. Thank you very much. So again, I want to thank all of our employees and customers for working with us and for us during the quarter. And I remind everyone that we are hard at work preparing our conference DASH on June 10 and 11 in New York. We have a lot of exciting stuff to show there. So we expect to see as many of you there as possible.

Thank you for your participation in today's conference. This does conclude the program. You may now disconnect.