Datadog, Inc. (DDOG) Q1 2023 Earnings Report, Transcript and Summary

Good morning, and thank you for standing by. Welcome to the First Quarter 2023 Datadog Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded. I would now like to hand the conference over to your speaker today, Yuka Broderick, Vice President of Investor Relations. Please go ahead.

Thank you, Michelle. Good morning, and thank you for joining us to review Datadog's first quarter 2023 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 2023 and related notes and assumptions, our gross margins and operating margins, our strategy, our product capabilities and our ability to capitalize on market opportunities. 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 our Form 10-K for the year-ended December 31, 2022. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter-ended March 31, 2023, 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. Thank you all for joining us this morning. We are pleased with our execution in Q1 as we continued broadening our platform, delivering new use cases for our existing users as well as signing up more customers, all on the backdrop of continued macro uncertainty and optimization of cloud workloads. Let me start with a review of our Q1 financial performance. In Q1, revenue was $482 million, an increase of 33% year-over-year and above the high end of our guidance range. Note that this number factors in the impact of a service outage we experienced in March and which reduced our revenue for the quarter by about $5 million. We ended with about 25,500 customers, up from about 19,800 last year. Also note that we are now including customers who joined following our acquisition of Cloudcraft, representing about 1,400 net new customers to Datadog this quarter. We ended the quarter with about 2,910 customers with ARR of $100,000 or more, up from about 2,250 last year. The customers generated about 85% of our ARR. And we generated free cash flow of $116 million, with a free cash flow margin of 24%. Our platform strategy continues to resonate in the market. As of the end of Q1, 81% of customers were using 2 or more products, in line with last year. 43% of customers were using 4 or more products, up from 35% a year ago. And 19% of our customers were using 6 or more products, up from 12% last year. Now let's discuss this quarter's business drivers. Overall, we experienced business conditions that were similar to the previous several quarters. In Q1, user's growth from existing customers came in roughly as expected. We saw existing customer users growth in Q1 improved from the levels we saw in Q4, but remain a bit lower than the levels we experienced in Q2 and Q3. And as in recent quarters, we continue to see customers optimize their cloud spend, particularly those further along in their cloud migration and hosting a larger portion of their infrastructure in the cloud. Additionally, our new logo acquisition and bookings in Q1 were solid for what is a seasonally slower quarter. New local bookings reached a new record for Q1 and were up slightly from last year as we continued to add many promising new logos, which I'll discuss in a bit. With our land and expand model, we expect many of these new logos will turn into much larger customers as they add up more of our products over time. Despite a more cost-conscious demand environment, we have continued to land new customers and expand existing ones, and we are very proud to achieve several key milestones in Q1. So first, our total ARR exceeded $2 billion for the first time, a true achievement for all of us at Datadog even though we all know we're only getting started. Second, our APM suite and log management products together exceeded $1 billion in ARR. This demonstrates the expansion of our business well beyond our [indiscernible] monitoring product and our successful execution on the broad of the mobility platform. Remember that our APM suite includes 4 Datadog products: core APM, Synthetics, Real User monitoring and Continuous profile. Third, we continue to make steady progress with our cloud security products with continued growth in ARR and in customers. And I'm very pleased to announce that we now have more than 5,000 customers using our cloud security products. Now let's move on to R&D. We introduced a number of new security capabilities last month. We announced the general availability of Application Vulnerability Management, which provides visibility into the attack surface of production environment by automatically surfacing vulnerabilities. And instead of submerging users with thousands upon thousands of vulnerabilities, this new functionality is observability data to prioritize risks based on the estimated impact to the business and closes the loop between security, operations and development teams. We also introduced a number of new capabilities to our Cloud Security Management product. Workload security profiles allow customers to flag anomalous activity and improve overall accuracy of threat detection directly within the workload. And we now offer vulnerability detection for containers, automatically scanning live container images for known vulnerabilities. Now moving on from security to observability, we also announced the general availability of Data Streams Monitoring. These products specifically targets queuing, streaming and even driven pipelines, such as Kafka or RabbitMQ. These systems often span many different teams and technologies and are notoriously difficult to manage and troubleshoot. And for this, even standard APM and log management solutions are not specialized enough. Data Streams Monitoring automatically identifies the topology, interdependencies and key metrics of complex streaming data pipelines, allowing customers to maintain availability, correctness and latency for what is now a critical part of their business. Lastly, we were thrilled to unveil our newest data center in Japan last month. We see a large opportunity to serve our customers in the Asia Pacific region, which have seen significant growth over time and now represents high single digits as a percent of revenue. I also want to take a moment and share our excitement for the latest wave of AI innovation. And I'm going to use AI in this community here to refer to the recent advances in deep learning, large language models and generative AI. First, from a market perspective, over the long term, we believe AI will significantly expand our opportunity in observability and beyond. We seek massive improvements in developer productivity will allow individuals to write more applications and to do so faster than ever before. And as with past productivity increases, we think this will further shift value from writing code to observing, managing, fixing and securing live applications. In the short to medium term, we believe the rise of AI will increase the demand for compute and storage to train and run models, but it will also increase the value of proprietary data and further drive digital transformation and cloud migration as these are all prerequisites for adoption. We also do expect quite a bit of noise in the market as the technology stack is progressing and changing very quickly. Now from a product perspective, we believe that we at Datadog are uniquely positioned to deliver value to our customers in this new world. First, we built Datadog from day 1 as a pure SaaS business precisely to be able to put our data to work at full scale and to train models to solve our customers' problems. Second, our large assets of contact with our customers gives us the insertion points to make AI relevant. This is where we see the value of having a variable customer base and being designed to be used every day by every single engineer. And third, we serve today some of the largest builders and consumers of AI services and are quickly adapting to their needs in a rapidly changing field. So in other words, we are really excited about the potential of AI for us and for the observability and security market, and I'm sure we'll discuss this topic further in the future. Okay. Let's move on to sales and marketing. As I said earlier, our go-to-market teams had another productive quarter. So let's discuss some of our wins. First, we signed an expansion into 8 figures ARR with a leading AI company. This customer saw an order of magnitude increase in user demand and a third in new customers following enormous innovation and interest in generative AI. As a result, this customer now uses 6 Datadog products and relies on our platform to track and correlate key business metrics, ranging from uptime data to newer subscriptions and revenue. Next, we signed a high 7 figures expansion to another 8-figure ARR deal with one of the world's largest fintech companies. This customer has expanded meaningfully over time, and today see Datadog platform used by thousands of users across dozens of business units. With this expansion, this customer now uses 14 Datadog products and is consolidating multiple open source, homegrown and commercial tools across observability and security into the Datadog platform. Next, we signed a 7-figure expansion with a Fortune 500 health care company. Before using Datadog, major incidents would mobilize up to 150 employees for an average of 3 to 4 hours. With Datadog, they only need 20 employees for about 30 minutes with an opportunity to further reduce these numbers. I will note that we're also replacing a commercial observability competitor whose new pricing model was causing an increase in cost with lower value. This customer now expects to save more than $0.5 million every year by moving that to Datadog across several business units. Next, we signed a 6-figure land with a multinational clothing company. This company was previously heavily siloed, with each team using different monitoring tools. And as is often the case, this caused issues impacting revenue and customer experience. This customer is starting with 5 Datadog products and expect to consolidate and replace a total of 13 commercial and open-source tools with Datadog. And last but not least, we signed a 7-figure multiyear land with a leading university in Australia. This customer has historically relied on open source solutions. They evaluated a few commercial competitors, and Datadog won as their requirements involve both cloud and on-premise across logs, user experience and network device monitoring. This customer plans over time to migrate from more than 10 tools to the Datadog platform. And that's it for this quarter's highlights. I'd like to thank our go-to-market teams again for their continued execution in Q1. Now switching gears. Let me speak to our longer-term outlook. Overall, we continue to see no change to the multiyear trend towards digital transformation and cloud migration. We do continue to see customers optimizing their cloud usage, and visibility remains limited as to when this optimization cycle will end, but we firmly believe it will. As before, we remain confident that we will continue to deliver value to more customers in their digital transformation and cloud migration journeys. And it is increasingly clear with each wave of technical innovation that every company in every industry and every geographic region has to take advantage of the cloud, microservices, container and generative AI and more. By relentlessly broadening the Datadog platform, we will continue to help our customers save on costs, execute with better engineering efficiency, drive competitive differentiation and deliver value to their own customers. So our long-term plans have not changed. We are continuing to invest to capture our long-term opportunities. And as David will discuss in a moment, the strength of our business model allows us to balance that with delivering financial performance. With that, I will turn it over to our CFO. David?

Thanks, Olivier. In Q1, we continued to execute well and deliver value to our customers. Revenue was $482 million, up 33% year-over-year and up 3% quarter-to-quarter. To dive into some of the drivers of the Q1 performance. First, we had an unusual outage in March, and we estimate that the impact to our revenues from that outage to be about $5 million. As we mentioned last quarter, we saw subdued usage growth in the month of December which created a lower growth trajectory to start the first quarter and drove seasonally weaker sequential growth in the first quarter. During Q1, we experienced a linearity pattern that is typical for us, which included usage growth in March that was higher than that in January and February. Overall, we saw existing customer usage growth in Q1 improved from the levels we saw in Q4, though it was slightly lower than the levels we experienced in Q2 and Q3 last year. Next, we continue to see larger spending customers grow smaller -- grow slower than smaller-spending customers. From an industry perspective, we continue to see the slowest growth in the consumer discretionary vertical, particularly in e-commerce and food delivery. Geographically, we saw faster year-over-year growth in international than in North America. Our trailing 12-month dollar-based net retention rate, or NRR, continued to be over 130% as customers increase their usage and adopted more products. Based on our current growth trajectory, however, we expect our trailing 12-month NRR to be below 130% in Q2. While our net retention rate is expected to go below 130%, we continue to execute strongly on our platform innovation and our land and expand business model, as evidenced by our latest product announcements, our expanding cross-sell products and the examples of the strong Q1 renewals that Olivier discussed. Our dollar-based gross retention rate remained stable in the mid- to high 90s, an indication of the mission-critical nature of the Datadog platform for our customers. Now moving on to our financial results. Billings were $511 million, up 15% year-over-year. We had a large upfront bill for a client in Q1 '22 that did not recur at the same level or timing in Q1 '23. Pro forma for this client, billings growth was in the low 30s percent year-over-year. Remaining Performance Obligations, or RPO, was $1.14 billion, up 33% year-over-year. Current RPO growth was in the high 20s percent year-over-year. We continue to believe revenue is a better indication of our business trends than billings and RPO as those can fluctuate relative to revenue based on the timing of invoicing and the duration of customer contracts. Now let's review some key income statement results. Unless otherwise noted, all metrics are non-GAAP. We have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. Gross profit in the quarter was $388 million, representing a gross margin of 80.5%. This compares to a gross margin of 80.6% last quarter and 80.4% in the year ago quarter. We continued to experience efficiencies in cloud costs, reflected in our cost of goods sold this quarter. In the mid- to long term, we continue to expect gross margin to be in the high 70s percent range. Our Q1 non-GAAP OpEx grew 45% year-over-year. This is a decline from 54% year-over-year growth in the previous quarter. We continue to grow our headcount in R&D and go-to-market, but at a more moderate pace than last year. Q1 operating income was $86 million or an 18% operating margin, flat sequentially to Q4 2022 margin also of 18%. In the year ago quarter, operating margins was 23%, and it benefited from lack of in-person office, travel and event costs due to our COVID policies during the pandemic. Turning to the balance sheet and cash flow statements. We ended the quarter with $2 billion in cash, cash equivalents, restricted cash and marketable securities. Cash flow from operations was $134 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $116 million for a free cash flow margin of 24%. Now for our outlook for the second quarter and the rest of fiscal year 2023. In forming our guidance, we continued to use conservative assumptions as to the organic growth of our customers compared to historical periods. And as usual, we are basing our near-term guidance on recent activity we see with our customers. While existing customers are still expanding with us, we continue to assume in our guidance that cloud optimization is affecting their expansion rate for the rest of 2023. For the second quarter, as a result, we expect revenue to be in the range of $498 million to $502 million, which represents 23% to 24% year-over-year growth. Non-GAAP operating income is expected to be in the range of $82 million to $86 million. And non-GAAP net income per share is expected to be in the $0.27 to $0.29 per share range based on approximately 349 million weighted average diluted shares outstanding. For the fiscal year 2023, we expect revenue to be in the range of $2.08 billion to $2.10 billion, which represents 24% to 25% year-over-year growth. Non-GAAP operating income is expected to be in the range of $340 million to $360 million. And non-GAAP net income per share is expected to be in the range of $1.13 to $1.20 per share based on approximately 351 million weighted average diluted shares outstanding. Some additional notes in our guidance. First, we have continued to balance near-term financial strength with investment in our large, long-term opportunities, and we are executing well on our plans to invest efficiently. We expect the continued moderation of headcount growth, and the lapping of the COVID-affected historical expenses to result in continued slowing of OpEx growth during the remainder of 2023. We now plan to grow our non-GAAP operating expenses, excluding COGS, in fiscal year 2023 by approximately 30% year-over-year with an exit rate in Q4 in the low 20s percent year-over-year. We continue to expect net interest and other income for fiscal year 2023 to be approximately $75 million. And we expect our tax expense in fiscal year 2023 to be in the range of $14 million to $16 million. Finally, we continue to expect capital expenditures and capitalized software together to be in the range of 4% to 5% of revenues in fiscal year 2023. Now finally, to reiterate Olivier's comments, we remain excited about our long-term opportunities as our customers embark and expand on their cloud migration and digital transformation plans. We are continuing to invest to further expand the ways we reach our customers and help them along these journeys. I want to thank our Datadogs worldwide for their efforts in this quarter. With that, we will open the call for questions. Operator, let's begin the Q&A. Thanks.

[Operator Instructions] Our first question comes from Raimo Lenschow with Barclays.

Could I speak -- could you speak to the optimization, Olivier, a little bit more in detail? Obviously, it's a journey for customers and there is like the initial steps. And then there's a follow-on. So if you think about the customer behavior in terms of what they do on optimization, is that still -- are you still seeing the same steps that are getting taken? Or are we going to kind of round 2, round 3, et cetera? So I'm just trying to understand a little bit where we are on that optimization journey if you have more color. And then I have one follow-up, please.

Yes. I mean, the short of it is we don't know exactly yet. So we -- and I'm going to give an answer that's going to be very similar to the answer I gave last quarter, but we see customers taking another bite at their own workforce, like we saw it in a number of companies this morning actually even. So I don't think customers themselves know where they are done. So we're very prudent in terms of assuming an end to it in the near future. So when we look at our data, when we look at the -- what we hear from the hyperscalers also, we also listen carefully to their commentary on what they foresee in the near future. We don't see anything that gives us confidence that we can call an end to optimization next quarter or the quarter after that. So as far as our guidance goes and our plans for the year, we assume that this is going to continue at a similar level for the rest of the year. Now obviously, when we look at our customer base, we see some customers that obviously seem to be done with it and others that haven't done anything. So we try to keep an eye on that and model it, but we're not including any of that in our guidance.

Yes. Okay. Perfect. And then, David, on the cost side, so thanks for giving us the OpEx rate for the year and the exit rate. If you think about the -- there's always that situation of investing for the future and at some point, you did come out versus like surviving or like living in the current environment. How do you think about that investment philosophy of balancing like today versus getting ready for tomorrow?

Yes, I would say that we've made substantial and consistent investments both in our go-to-market and our R&D over the last few years. And this year, we're balancing -- continuing to do that with 2 things, with prioritization as well as injecting some optimization or efficiency into the investments we had made. We're seeing the opportunity to get returns from the previous investments and to think through a little more the prioritization of those.

Please stand by for our next question. Our next question comes from Sanjit Singh with Morgan Stanley.

I had a question, Olivier, on AI. It seems like we're on the cusp of another sort of compute cycle driven by AI. The last compute cycle, you guys were all over in terms of being ahead of the curve, in terms of the shift from monolith to micro services. And so with this new sort of compute cycle that we're about to embark on, what do you see -- how are sort of applications and the sort of the application stack, how is that going to change? And what are the implications for Datadog as a monitoring vendor? What are potentially the puts and takes with this in terms of how applications can be built going forward?

Yes. So first, I'd say it's a -- [indiscernible], it's fascinating time to be alive, to see all these, rapid innovation in the world of AI. The first thing I'd say is that it's still fairly early in terms of what the market going to look like in the AI world. Right now, there's one particular thing that's been -- that used to be very hard, which was building conventional models and chat bots and things like that, which almost overnight became almost a commodity basically. Anybody can incorporate it in any application. It's an API call away, and there is even a number of different options, commercial open source you can use today. So that just happened. That one has massive traction. You see it everywhere. But it also is opening the gate to many, many more, I would say, more customized, deeper applications on AI that may be built by a few vendors or may be built by a large number of companies instead, it's not quite clear yet. What we see on our end is that it's going to drive more compute, it's going to drive more value in the data that is being gathered by companies. It's going to drive digital transformation. It's going to drive cloud migration because, again, you can't actually adopt AI unless you have the data. You can't actually adopt it without having a modern architecture and an application you can scale up and down and infrastructure you can quickly provision and de-provision. You need to capture all of your -- to capture your data, you need to be digitally transformed. So you have data of all your customer interactions and everything that is proper to your business. So in the mid-term, we see that as a very clear accelerant to our business. Maybe with a little bit of noise, as I mentioned on the script earlier, in terms of what technologies end up being the winning ones and what technologies end up being sizzling after a few years, because it's still -- it's so early and there's so much innovation that out of 200 new things, there's probably only 10 or 20 that will matter to you somehow, but it's hard to know which ones are today. So short answer is, mid-term, a lot more of the current workloads, the types of workloads we see maybe with different sets of technologies. Longer term, I think we can all glimpse at the future where productivity for everyone, including software engineers, increases dramatically. And the way we see that as a business is, our job is to help our customers absorb the complexity of the applications they've built so they can understand and modify them, run them, secure them. And we think that the more productivity there is, the more people can write in the amount of time. The less they understand the software they produce and the more they need us, the more value it sends our way. So that's what makes us very confident in the long term here.

I appreciate the thoughts, Olivier. As a follow-up and getting back to sort of the topic of cloud optimization. What have you -- what did you sort of see in April versus March and typically, maybe sort of baseline what April typically looks like for the company? And are there any customers that began optimization that maybe didn't start in 2022? Are you seeing incremental new customer cohorts get onboard the cloud optimization train?

So I would say April is broadly consistent with what we've seen in Q4 and Q1. I think it's -- there's no major difference to call out there. And it's too early also for us to call the quarter, obviously. So there's nothing very sharp to point out about April. On the customer optimization, Look, we have a large number of customers that are early in their cloud migration, early in the Datadog adoption and that are growing very fast. We haven't seen any optimization from those customers. It's possible we see optimization from some of those, which is why we remain very careful in our in our guidance. We don't assume basically that the optimization will stop at the customers that already have done it, and that the rest of the customer base is going to be fine after that.

Please stand by for our next question. The next question comes from Mark Murphy with JPMorgan.

David, looking at the math on this large upfront bill that did not recur, it seems to be about $65 million, if I'm running that correctly. Can you possibly shed a little more light? For instance, will you recapture that or some of that in Q2? And what type of customer and customer dynamic is operating at that level? And then I have a quick follow-up.

Yes. We -- that is a customer of ours. What we said was the billing frequency changed and the size. So that customer's bill will, one, be spread out more over time. That company -- that was a crypto company and continues to be a customer of ours, but that was an early optimizer. We had always talked that some of the industries that were most affected optimized. And that is -- so we will get that -- we will get that bill at a smaller size than was billed last year in a more of a chunked up billing way.

And look, this is one of the situations where this customer was in an industry that -- vertical that got pretty much decimated over the past year. And their own business was cut in 3 or 4 in terms of their revenue. And when that's the case, that we really work with customers to restructure their contract with us. We want to be part of the solution for them, not part of the problem. And that's what we did here, we restructured that contract. So we kept them as a happy customer for many more years and do deal that works for everyone with their business profile.

In our company - since we've been public, we've pointed out when we have an unusual bill. So we don't have multiple of these types of situations. And what we've done, if you look back to our commentary, is when we've had one of those, a change of timing or a change of the duration of a bill or a size of a bill. We've tried to pro forma it in order to give everyone a sense of what the rest of the business is doing.

That's much appreciated on our part. And as a follow-up, Olivier, congrats on passing $2 billion in ARR, one of the fastest software companies ever to do that, pretty amazing. I did want to ask you as well on the optimizations. At the moment, Microsoft seems more optimistic that the optimization activity would start to normalize in the next couple or few quarters than Amazon does. And when we tear it apart, Azure has less exposure to tech companies which are doing layoffs. They have more exposure to generative AI, which is booming relative to AWS. So I'm wondering if that part lines up with your telemetry and your forecasting that perhaps your Azure monitoring business is going to start to turn the corner sooner, and then perhaps AWS would follow after that.

It's too early to tell. And by the way, it's a bit hard to just project our numbers from the -- this is just a reminder, it's hard to project our numbers from the numbers of the cloud providers because it's not a one-to-one in terms of what they report and what concerns the infrastructure and the applications directly. Different cloud providers have different things in there. And also, these vendors have -- in addition to the volumes that drive consumption on our end, they also have their own pricing dynamics. And typically, in those situations, the vendors with the largest bills are the ones that get pushed the most. So in this case, that would be AWS. But in terms of what we see in our data, there's no -- nothing to suggest that any particular cloud is recovering from optimization just yet. We also tried to read the hyperscaler comments. And depending on what you've read before and after, they look more or less positive, so I'm not quite sure I would project as much enthusiasm in the Microsoft comment. But what we know is that, at the end of the day, it doesn't really matter for us. We're equally well positioned to capture workloads on Azure as we are on AWS and GCP. And the only part I would say of the Microsoft stack that we don't cover as well is everything that is a lift and shift of purely Microsoft Technology Office or proprietary because that can typically be done very well with the built-in Microsoft tooling. But when you think of any market share gain that might happen now from now on from Microsoft and the others, if you can imagine that, that would be a more cloud workloads, next-gen workloads, workloads that have to gather data and go into AI models, and these are things that we are very well positioned to capture.

Please stand by for our next question. The next question comes from Kash Rangan with Goldman Sachs.

I was curious, Olivier, that the generative AI, is it merely a fact of just waiting for these workloads to come on? And since you have such a strong presence in infrastructure monitoring, these workloads will just run on these big clouds and you will optimize them? Or do you have to do something specific on the product side to tool Datadog to better handle these generative AI workload? And I have a follow-up question.

Well, so I think the -- clearly, there's going to be more productivity. And we -- the way this has played out in the past typically is you just end up generating more stuff and more mess. So basically, if one person can produce 10x more, you end up with 10x more stuff and that person will still not understand everything they've produced. So the way we imagine the future is companies are going to deliver a lot more functionality to their users a lot faster. They're going to solve a lot more problems in software. But the they won't be as tight and understanding from their engineering team as to what it is they've built and how they built it and what might break and what might be the corner cases that don't work and things like that. And that's consistent with what we can see people building with a copilot today and things like that. So they're very, very good for solving a small problem, but they don't help you build consistent code babies or didn't help you build software platforms like that, that is still out of reach. So the -- again, the way we see it in the future is we'll see our customers do a lot more, and they will still need help to catch up with everything they are doing. And we'll be the ones to do that for them.

Got it. And also Microsoft talked about the anniversary effect of optimization that the headwinds would be less going forward, whereas AWS called out deceleration in the month of April. But it looks like your business is still steady in the month of April. So it's fair to say that you're sort of decoupling away from the AWS deceleration and maintaining its steady pace as far as consumption trends are concerned.

Well, I mean, look, we're -- for the rest of the year, we're not assuming any change in trajectory. So now in terms of where we are compared to the other cloud providers, I mean one thing you can do is you can look at the sequential growth numbers quarter-to-quarter. And you'll see that if you look at the 3 cloud manager providers, they've decelerated to about 1% quarter-to-quarter growth for the last quarter. We're still significantly higher than that. And when you look at our ARR, considering the fact that we ended -- we exited slower in '22 and we exited higher in end of Q1 because the quarter is actually differently around the holiday, we actually maintained an ARR that was already higher also than the -- on ARR growth that was higher than the -- sequentially than the cloud providers. So we already decoupled from the growth of the hyperscalers to a certain extent.

Please stand by for the next question. The next question comes from Fatima Boolani with Citi.

This is [ Joel ] on for Fatima. So just to check in another vertical here. Given the continued uncertainty in the financial services vertical, just wondering if you could speak to your exposure here and perhaps any related behavior you're seeing with your customers, if at all? And then I just have a quick follow-up.

So we don't-- I don't think we'll have numbers to share on the exact exposure to financial services. Obviously, it's been a growing vertical for us as the financial services are early adopters of software. And I would say it's not necessarily earliest adopters of the cloud, but definitely adopter at scale today of cloud technology. We haven't seen any changes in customer behavior on that side. And that includes, when there were all this trouble -- we saw this trouble with SVB and other banks failing, we were still seeing great upticks from our products from their financial services, new logos as well as expansion deals. So nothing to report.

Okay. And then just -- you mentioned the expansion deal with a large fintech, which displaced open source software in addition to other tools. So I just wondered if you could speak to the competitive dynamics versus open source, especially in this cost-sensitive environment and why Datadog still wins and perhaps works as a consolidation destination.

Yes. I mean the situation is very similar to what it's always been. It's -- we win because we deliver more value. It's -- at the end of the day, it works better and cheaper with what we do and what we provide than trying to build it yourself and mobilizing your own team and trying to stitch together different parts of open source. For some customers, they will still want part of that. They will still want to build somebody, that's more a cultural thing. But for the vast majority of customers, it's not a rational thing to do. And that's why we win in the end. The dynamic there is remarkably unchanged from -- I could have said the exact same thing 10 years ago. Obviously, the open source projects, we're different. Our footprint as the community was different, but the dynamics when we sold the customer was very, very similar.

Please stand by for our next question. The next question comes from Brad Reback with Stifel.

Olivier, Earlier, you mentioned sort of the opportunity or your sort of focus on the Azure ecosystem. What types of things on a go-to-market perspective can you do to increase your penetration there as you've sort of historically been much larger inside of AWS?

So there's a lot of things we're doing, whether that's working directly with other cloud providers in addition to our very strong relationship with AWS, whether that's building more integration specifically to the ecosystem, technology integration. And if you look at our announcements, product announcements, you will see that we've done quite a bit in partnership with Azure and Microsoft, for example. And we -- part of it is also expanding our sales force in territories that tend to be -- to lean more towards Microsoft in terms of their tech stack. So when you look at the early customers we had, which tended to be a lot of software companies that used to be based on the West Coast in the U.S., these typically didn't lean hard Microsoft. But when we look at the more recent enterprise teams we started in the past few years, for example, in U.S. Central areas, like these tend to be way heavier on the Microsoft stack. So it's a combination of all of the above, basically. Like it's not just one single thing we do, it's a lot of different things at many levels to make sure that we have the right product to show in front of the -- it can be from the right customer and have the right sales force to renew that.

Please stand by for our next question. The next question comes from Matt Hedberg with RBC.

Thanks for the APM and log management data point, over $1 billion in ARR. I'm wondering, can you provide a frame of reference for maybe the growth of these 2 businesses versus core infrastructure? And maybe between the 2 APM logging, are they about equal in size? Or is one sort of relatively larger than the other?

Although it might look the same, I mean we're not going to give specific numbers there. But -- and the growth rates, of course, like the -- generally speaking, the smaller products grow faster than the products that are bigger than them. That's pretty much true over the whole set of products. The growth of all of the products have come down a little bit, especially the ones that have a lot of volume component, like, for example, logs, where if you -- when you think of optimization, like there is optimization that happens at the cloud provider level and there some that can happen at the observability level, too. Overwhelmingly, at the observability level, that's logged and everything that's for which customers have different knob to turn. For everything else, it's the utilization to where the cloud provide [indiscernible].

Got it. And then on the outage that you referenced, the $5 million. I'm curious, what did you guys learn from that to prevent maybe this in the future? And outside of the $5 million hit that David talked about, are there any other repercussions from a customer perspective?

So well, we've made a lifetime of learning in a day. So that's the positive part of having an incident like that. I want to say I was personally very impressed by the response from our team. We shared some information on postmortem on what happened. I encourage everyone to read it because it's a fascinating document. But because of the very wide nature of this issue, we ended up having 3 shifts of 500 to 600 engineers working on this outage. And that worked -- that part worked beautifully. Obviously, we've learned about a number of things beyond the root cause of the outage that is almost anecdotal, like it's one of those small things that can have a big impact, but I don't worry so much about it happening again. What we've learned is more about the various things we can do better to recover faster for our systems and to provide a better way for our customers to mitigate an issue when that happens. So these are the most of the learnings and most of the new sources of work that we have internally to follow up on that to make sure that we reduce the chance of it happening again. And when it does, we will recover faster and better for our customers. Overall, a very humbling experience. And I want to make sure that we drive our customers in the future there. We don't see any long-term impact. The way we've handled it is by being very transparent with our customers about what was happening and addressing any possible consequences with them after that. And we think in those situations, when you do things right and when you don't repeat these mistakes too often, the effect is actually to strengthen the relationship with the customer. And I hope we succeed in doing that.

Please stand by for our next question. The next question comes from Brent Thill with Jefferies.

David, just on the large customer adds, you were at 130 versus an average of 170 to 240 over the last 4 quarters. Any color there? Can you just talk to, is this just more customer selling because of the economy? Or is this execution-related? How would you the characterize the large enterprise traction?

Yes. A couple of factors. One, first quarter is our seasonally lowest amount of new logo ARR. The first quarter this year was very similar. I think we said slightly higher than the year before and similar to our other quarters. That's one factor. Secondly, as we've talked about, most of our customers who are in that larger customer group aren't born there but expand into that. And when the organic growth rate goes down, you will naturally have a slower evolution or graduation of customers into that larger customer classification. So both of those factors caused that slowdown in accumulation of customers over $100,000.

And Olivier, just a quick follow-up on the cloud authorization solution. Are you -- can you give us a sense of the traction of what you're seeing in terms of adoption there?

Adjustment for what? Sorry, I missed the beginning of your question.

Even build a cloud optimization solution to help your customers identify where there's potential opportunities, are you seeing an uptake in that product?

Yes, we're seeing extremely strong demand for that. And this is -- so this is definitely a product that customers want. And we -- and our immediate road map for it is very clear. So that's -- there's no doubt about it.

Please stand by for our next question. The next question comes from Koji Ikeda with Bank of America Securities.

Just one for me here in the interest of time. I wanted to ask you a question on the cloud security management platform and specifically the sales motion for it. So how is that shaping up this year? And how should we be thinking about the investments and strategy for that segment this year? And as you attack that opportunity, how has the competitive environment been shaping up as you head into deal bake-offs? Is it coming in as expected?

Yes. There's no dramatic change from what we've seen before. Our focus is really on getting the product in as many hands as possible. And as we mentioned on the call, it's working. We have more than 5,000 customers on those products. And this is really what gives us the -- basically, closes the loop so that we can keep building those products and bring them all to maturity. The one thing I'll say is our strategy or approach for security is ambitious. We don't have -- we specifically are not trying to build a point solution, we're building -- which means we're moving in many different directions at the same time for this. And everything we do in terms of go-to-market and driving customer adoption for that is in service of this product development so we can build out that platform. So with that in mind, the go-to-market hasn't changed from what we've done before. And we tend to land heavily on crossing over from DevOps team into security, and that's working well to get a lot more customer adoption. On the community side, there's no big changes there. We see the same user suspects as before. The dynamics are a little bit different because they typically have narrower solution and more defined cell motions around those. And -- but we're very pleased with the customer adoption so far.

Please stand by for our next question. The next question comes from Gregg Moskowitz with Mizuho.

Okay. Olivier, you laid out a pretty compelling case for why generative AI will have a positive impact on Datadog and presumably the observability market at large, both over the near to medium term as well as long term. But do you see any offsets, any partial offsets from organizations having greater intelligence and automation at their fingertips? Are there certain workloads that may no longer need to be monitored by an observability platform?

Look, in the long-term future, everything is possible, but I don't think -- today, I don't think that's in the -- that's not what we hear or see. I think the -- again, the workloads are getting more complex as the integration management is getting better, and we see basically a continuation of that. So in terms of what customers do today, like it's hard to project the current adoption of AI into what it might look like into the future because right now, AI is mostly used as an API call for most companies. But we don't think it's necessarily going to be the case 1, 2 or 5 years now.

Okay. Got it. And then just for David, on the outage, are the credits, et cetera, is that now fully behind you? Or is there any lingering financial impact in the Q2 that you're embedding in the guide?

Yes. We provide for what we know in our -- at time in our accruals. So we believe that we've provided for that through the revenue impact in Q1.

Please standby for our last question. The last question comes from Mike Cikos with Needham.

I guess a question for Oli. The first question is really around the ARR discussion. I appreciate the color you guys provided around that APM and log management. Can you highlight -- is Datadog winning new customers on pure APM and logging at this point? Or is infrastructure still the heavy lean as far as where those new logos are being predominantly won? And then secondarily, can you talk to maybe some of the newer products in the portfolio which you're seeing the strongest uptake today, just given we have, I think, it's 17-plus modules in the portfolio more broadly? Just where would -- where should we be focusing when thinking about growth going forward from here?

Yes. So look, motion is still to land with infrastructure, as you know, PMO and our logs when we start. I would say it's getting increasingly difficult to separate all of those because those market in general is converging and to -- I think, all 3 under 1 roof. So it's -- whether customers start the conversation about APM or logs, whether they start over from infrastructure, it doesn't matter a ton when we land new customers. I would say though that we definitely have best-of-breed products also in APM and logs, knowing those products win on their own. And sometimes when we enter a new customer that we already have solutions for other things, we can start by displacing a vendor on the APM side or the log side and expect from there. That's definitely something that we do on a regular basis. On the question of the products that are growing the fastest, like the -- so all the newer products are actually getting pretty good uptake. So it's hard to pick one. I mentioned earlier, so cloud cost optimization is actually a very popular product right now. Still very early [indiscernible], but it's a very popular product. And we also see a lot of interest actually for some of the new products we announced at the ASH last year, and some of which are still not generally available. So there's a number of things we're doing in terms of estimating response to issues and workflows and sorting through issues and managing issues for our life cycle that are getting a lot of attention from customers to that, too. So we're excited about what's coming next. Obviously, the expectation for some of those products are changing over time, too. Not everyone can see what can be done with AI. Everybody expects to see a lot more of that. So I guess we'll share more on that in the near future.

I would now like to turn the call over to Olivier for closing remarks.

Thank you. I want to thank again all -- everyone at Datadog for crossing these really important milestones. Obviously, we're already focused on the next ones. But I want to thank everyone for their hard work, shipping product, getting in front of customers. I also want to thank our team for responding to that system outage we had and our customers for very kindly working with us through that. And on this, I will speak to you again after Q2.

This concludes today's conference call. Thank you for participating. You may now disconnect.