Datadog, Inc. Q3 FY2024 Earnings Call

Good day and thank you for standing by. Welcome to the Q3 2024 Datadog Earnings Conference Call. At this time, all participants are in a listen-only mode. After the speakers’ presentation, there will be a question-and-answer session. 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, Senior Vice President of Investor Relations. Please go ahead.

Thank you, Liz. Good morning and thank you for joining us to review Datadog’s third quarter 2024 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 fourth quarter and fiscal year 2024 and related notes, our gross margins and operating margins, 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-Q for the quarter ended June 30, 2024. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter ended September 30, 2024, 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. We are pleased to report our Q3 results as we continued to execute against our goals to help our customers grow faster, safer, and more efficiently as they modernize their applications. We kept broadening our platform in observability and beyond, including in next-gen AI, where interest continues to rise. And we added new customers while expanding with existing ones as they grow into the cloud. Let me start with a review of our Q3 financial performance. Revenue was $690 million, an increase of 26% year-over-year and above the high end of our guidance range. We ended the quarter with about 29,200 customers, up from about 26,800 a year ago. We had about 3,490 customers with ARR of $100,000 or more, up from about 3,130 a year ago. And these customers generated about 88% of our ARR. We generated free cash flow of $204 million with a free cash flow margin of 30%. Turning to platform adoption, our platform strategy continues to resonate in the market. As of the end of Q3, 83% of customers were using two or more products, up from 82% a year ago. 49% of customers were using four or more products, up from 46% a year ago. 26% of our customers were using six or more products, up from 21% a year ago. And 12% of our customers were using eight or more products, up from 8% a year ago. We continue to execute on growth across the three pillars of observability and we are pleased to report that infrastructure monitoring, our APM suite and log management together represent more than $2.5 billion in ARR. As a reminder, within the APM suite, we include core APM, synthetics, real user monitoring, and continuous profiler. We also want to call out our newer products, which are increasingly contributing to our business. Of our 23 products, 15 now exceed $10 million in ARR. These include even more classical products as well as CI visibility and cloud cost management. So we have many products beginning to contribute to our revenue growth, and we're continuing to build greater capabilities within those products for our customers. Now, let's discuss this quarter's business drivers. Overall, the business environment for Datadog has remained stable, and similar to what we have seen throughout 2024. Our customers overall are growing their cloud usage, while some are continuing to be cost-conscious. In Q3, we continued to see existing customer usage growth broadly in line with our expectations. Our usage growth with existing customers continued to be higher than in the year-ago quarter. We saw healthy growth across our product lines, with newer products growing faster than more mature products of a smaller base. Finally, churn continues to be low, and growth revenue retention was stable in the mid-to-high 90s, highlighting the mission-critical nature of our platform for our customers. Moving on to R&D. In the next-gen AI space, customers continued to experiment with new AI technologies, and as they do, they want to get visibility into their AI use. At the end of Q3, about 3,000 customers used one or more Datadog AI integrations to send us data about their AI, machine learning, and LLM usage. As some of these experiments start turning into production AI applications, we are seeing initial signs of traction for our LLM observability product. Today, hundreds of customers are using LLM observability, with more exploring it every day. Some of our first paying customers have told us that they have cut the time spent investigating LLM latency errors and quality from days or hours to just minutes. Our customers don't want to just understand the performance and cost of their LLM applications, they also want to understand LLM model performance within the context of their entire applications. So they are using APM alongside LLM observability to get fully integrated end-to-end visibility across all the applications and tech stacks. Meanwhile, we continue to work to make the Datadog platform the best place for customers to monitor, secure, and take action on their systems, no matter where they deploy. In September, we launched Datadog monitoring for Oracle Cloud infrastructure for general availability. With this launch, our customers get visibility into their OCI stack and they can manage in real-time the performance of OCI cloud services, servers, VMs, databases, containers, and apps in Datadog. Customers can now unify their monitoring across OCI, other clouds, and open environments. We also continue to expand our platform in new ways to bring value to our customers. At our Dash User Conference this summer, we announced Datadog On Call, our newest product in the cloud service management space. Our customers use Datadog extensively during their workdays for alerting and troubleshooting, whether that's for observability or security use cases. Now, with Datadog On Call, we are bringing a modern paging experience directly into our unified platform. We know for a completely integrated solution that covers incidents from end-to-end, from detection, alerting, and paging, to incident management, troubleshooting, and resolution. Even though On Call is still in limited availability, we are already seeing very strong reception for the product, and we are beginning to see customers including On Call as part of their deals. In particular, new customers are interested in including paging as part of their land with Datadog. We're working hard to deepen and broaden our platform. Our innovations are being recognized by independent research firms. We are pleased to see that for the fourth year in a row, Datadog has been named a leader in the 2024 Gartner Magic Quadrant for observability platforms. We believe this validates our approach to delivering a unified platform that breaks down silos across teams. Datadog has also been named a leader in Gartner's very first Magic Quadrant for digital experience monitoring, which includes Datadog’s products across synthetic testing, real user monitoring, product analytics, session replay, and error tracking. Now, let's move on to sales and marketing. Our sales team continued to execute this quarter, and we added some exciting new customers while expanding with many more. So let's go through a few examples. First, we landed a seven-figure annualized deal with the leading e-commerce company in India. With its previous observability vendor, the customer faced quickly increasing costs while lacking the enterprise-grade observability it needed. By switching to Datadog, they expect to support their goals and rely on us for tracing, granular profiling, and cloud integration support. I will note that we are pleased to have landed a large new logo customer in India, and we are continuing to invest to grow our presence and opportunities there. Next, we signed a six-figure annualized deal with a major U.S. federal agency. This agency is beginning to move some of its workloads to the cloud and is expanding the service it offers to every single U.S. citizen through cloud applications. They have chosen Datadog to observe and secure their cloud environment and deliver a faster, better experience to end users. This deal includes eight products on the Datadog cloud, including cloud security and cloud cost management. Next, we landed a seven-figure annualized deal with a large American financial services company. This customer has a very seasonal business and experiences thousands of major incidents during the annual peak season, with an average downtime per incident of about five hours. They estimate millions of dollars of lost revenue for each hour of downtime. By replacing its cloud provider's monitoring with Datadog and particularly using our real user monitoring product, this customer targets substantial reductions in downtime. They are starting with five Datadog products and are trialing on network monitoring, database monitoring, cloud security, and cloud cost management products, as they look to consolidate dozens of homegrown commercial tools. Next, we secured a seven-figure annualized expansion with a major airline in Europe. This customer has adopted Datadog for its customer-facing website. They are now moving hundreds of applications from on-prem to AWS and want to de-risk their cloud migration. They estimate that each incident can cost tens of millions of dollars in lost revenue and customer impact. By using Datadog across five products, this customer expects to significantly improve the mean time to resolution and has already seen progress in that respect during our evaluation period with Datadog. Next, we achieved a seven-figure annualized expansion with a division of a hyperscaler that will bring next-gen AI models. This customer is very technically capable and already has a homegrown observability solution requiring time-consuming customization and manual configuration. They will be launching new features for their large language models soon and need a platform that can scale flexibly to support proactive incident detection. By expanding the use of Datadog, they expect to efficiently onboard new teams in the environment and support the rapidly increasing adoption of data. Finally, we find a seven-figure annualized expansion with a leading online food delivery company in Latin America. Before Datadog, this customer suffered from excessive alerting noise, siloed teams, and lack of visibility, resulting in thousands of lost orders for each minute of downtime. By using Datadog, this customer has experienced meaningful reductions in mean time to resolution and false alerts, while cutting costs in their community environment. This customer is expanding to ten products in the Datadog platform. And that is it for another productive quarter from our go-to-market team. Now let me say a few words on a longer-term outlook. Overall, we continue to see no change to the multi-year trend towards digital transformation and cloud migration, which we believe is still in its early days. We are seeing continued experimentation with new advances, such as next-gen AI. We believe this is one of the many factors that will drive greater use of the cloud and other modern technologies. So we are helping our customers every day to observe, secure, and act on their business-critical applications and workloads. With that, I will turn it over to our CFO, David.

Thanks, Olivier, and good morning. Q3 revenue was $690 million, up 26% year-over-year and up 7% quarter-over-quarter. To dive into some of the drivers of our Q3 revenue growth, overall, we saw trends for usage growth from existing customers that were consistent with our expectations. Conditions remained roughly stable throughout 2024, with continued movement towards cloud and modern DevOps technologies, and with customers remaining cost-conscious and seeking efficiency and value from their spend. In Q3, we saw usage growth from existing customers that was higher than usage growth in the year-ago quarter, as well as higher than usage growth in the prior quarter. Some of our growth is coming from AI-native customers, who this quarter represented more than 6% of our Q3 ARR, up from more than 4% in Q2 and about 2.5% of our ARR in the year-ago quarter. AI-native customers contributed about 4 percentage points of year-over-year growth in Q3 versus about 2 percentage points in the year-ago quarter. While we believe that adoption of AI will continue to benefit Datadog in the long term, we are mindful that some of the large customers in this cohort have ramped extremely rapidly, and they may optimize cloud and observability usage and increase their commitments to us over time with better terms. This may create volatility in our revenue growth in future quarters against the backdrop of long-term volume growth. Regarding usage growth by customer size in Q3, similar to the last quarter, we saw the strongest performance among our largest customers, who spent multiple millions of dollars with us annually. As we looked at usage growth by segment, similar to recent quarters, we saw the strongest growth from our enterprise customers, where year-over-year usage growth has accelerated over the past several quarters. Meanwhile, our SMB customers remain solid, with year-over-year growth similar to the past several quarters. As a reminder, we define enterprise customers as our clients with 5,000 employees or more, mid-market customers with 1,000 to 5,000 employees, and SMB customers as those companies with fewer than 1,000 employees. Regarding our retention metrics, our net revenue retention percentage was in the mid-110s in Q3, with continued improvement from last quarter. This is a trailing 12-month measure. We have continued to see an increase in recent quarters as we look at the NRR quarterly trend. Our trailing 12-month gross revenue retention percentage remains stable in the mid-to-high 90s. Now moving on to our financial results. First, billings were $689 million, up 14% year-over-year. Billings duration decreased slightly year-over-year. Pro forma for changes in billing timing, and the slight change in duration, billings growth would have been in the mid-20% range. Our billings and billings growth can be volatile on a quarterly basis depending on the timing of our deals. Our 12-month trailing billing growth is similar to our trailing 12-month revenue growth, with both in the mid-20%. Remaining performance obligations (RPO) were $1.82 billion, up 26% year-over-year, and current RPO growth was in the high 20% year-over-year. RPO duration was down slightly year-over-year. Normalizing for duration, RPO growth would have been in the high 30% year-over-year. We believe that revenue is a better indicator of our business trends than billing and RPO as those fluctuate on a quarterly basis 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. First, gross profit in the quarter was $560 million, representing a gross margin of 81.1%. This compares to a gross margin of 82.1% last quarter and 82.3% in the year-ago quarter. Our Q3 operating expenses grew 21% year-over-year, the same growth as last quarter, although it would have represented an acceleration from last quarter, excluding the impact of our Dash user conference in Q2. As we have said before, we are investing in headcount in 2024, and the acceleration in operating expenses reflects our execution on our hiring and sales, marketing, and R&D so far this year. Q3 operating income was $173 million, or a 25% margin compared to 24% last quarter and 24% in the year-ago quarter. Turning to the balance sheet and cash flow statements, we ended the quarter with $3.2 billion in cash, cash equivalents, and marketable securities. Cash flow from operations was $229 million in the quarter, and considering capital expenditures and capitalized software, free cash flow was $204 million for a free cash flow margin of 30%. Now for our outlook for the fourth quarter and for fiscal 2024. First, our guidance philosophy remains unchanged. We base our guidance on trends observed in recent quarters and apply conservatism to these growth trends. For the fourth quarter, we expect revenue to be in the range of $709 million to $713 million, which represents a 20% to 21% year-over-year growth rate. Non-GAAP operating income is expected to be in the range of $163 million to $167 million, which implies an operating margin of 23%. Non-GAAP net income per share is expected to be $0.42 to $0.44 per share based on approximately 361 million weighted average diluted shares outstanding. For the fully fiscal year 2024, we expect revenue to be in the range of $2.656 to $2.660 billion, which represents 25% year-over-year growth. Non-GAAP operating income is expected to be in the range of $658 million to $662 million, which implies an operating margin of 25%. Non-GAAP net income per share is expected to be in the range of $1.75 to $1.77 per share based on approximately 359 million weighted average diluted shares. Now, finally, some additional notes on guidance. We expect net interest and other income for the fiscal year 2024 to be approximately $140 million. Next, we expect cash taxes in 2024 to be in the $20 million to $25 million range and we continue to apply a 21% non-GAAP tax rate for 2024 and going forward. Finally, we expect capital expenditures and capitalized software together to be in the 3% to 4% of revenue range for fiscal 2024. Now to conclude, we are continuing to execute on our strategy, investing in our innovation, and expanding our platform to deliver more value to our customers. Lastly, I want to thank all Datadog’s Worldwide for their efforts as we close out 2024. With that, we'll open the call for questions. Operator, let's begin the Q&A.

Thank you, David. The first question comes from Mark Murphy with J.P. Morgan. Your line is now open.

Thank you very much, and congrats on another very healthy performance. Olivier, we noticed your AI contributions surged to about 6% this quarter. We're watching all the advances in the foundation models, including OpenAI’s Strawberry version. We're watching the multi-step reasoning, how they're becoming multi-modal, the longer duration inference, the video models. Does it seem reasonable to you that we are on the cusp of a pretty interesting period in the next couple of years for the inferencing loads and that might drive some incremental traction for Datadog that is tied to AI? Then I have a quick follow-up for David.

Look, it's definitely a very interesting period to say the least. We see tons of innovation across the customer base, still largely more around experimenting and testing new applications. Though, as we reported, we are seeing some customers moving to production and using our production-minded availability products, for example, being used by real paying customers, which is generating real volumes and applications in real production workloads. So that's exciting and healthy. I think it's a great trend for the future. Generally, in terms of the workloads, you are right that we're starting to see more inference workloads, but they still tend to be more concentrated across a number of API-driven providers. So there are a few others both on AI lines and other kinds of models. This is where I think most of the usage, in production at least, is today. We expect that to diversify more over time as companies get further into production with applications and start customizing more on their models.

Okay, understood. David, you've mentioned that revenue is a more reliable measure than billings and RPO. However, given that billings growth may have been impacted by timing, as we've seen in the past, is it possible that some invoices scheduled for September were actually sent out in October? In other words, can we anticipate a recovery of that timing impact in Q4 or possibly early next year, or is there a different explanation for this?

Yeah, it's really some other dynamic. It's that the timing of billing for last year was slightly different than for this year. So it was more a factor of billing timing last year that didn't repeat this year. We think that the weighted average what we talked about, the average over the 12 months is a better indicator of the relationship between billing and revenues. When you look at that, they're much more closely aligned.

Excellent. Thank you again.

Please stand by for the next question. The next question comes from Sanjit Singh with Morgan Stanley. Your line is now open.

Yeah, thank you for taking the questions. Olivier, in the framework that you have laid out for the business, observe, secure, and act. I wanted to focus on the last two pillars, secure and act. When we think about how the security cloud security sales motion has been going this quarter compared to prior quarters, any trend lines there, and any sort of early indications on the uptake of some of the service management products and more of the automation features within the Datadog platform?

Yeah, on the security side. I think there's quite a bit of focus right now on the cloud scene as we see a number of very exciting opportunities there. When you look at the competitive landscape with what other companies are using, most companies have seen it already, at least for some part of their business. I think there's a very interesting opportunity for us there and the product is mature enough to win in best of both situations across the existing products. So we are making quite a bit of a push, and we are still investing heavily in the rest of the platform, bringing all the products together to be a unified platform for security. I would say the tip of the spear for this quarter is a very specific opportunity there. On the service management side, we are seeing very exciting trends from customers. We mentioned the On Call product, which puts us directly in the paging loop and the start of many incidents. The product is receiving stronger reception than we initially thought to the point where customers are clamoring to buy it, even though it's still in preview. We feel good about providing a full loop for them, starting from when we detect something in observability all the way to full incident resolution, and automation is a big part of it. We believe On Call can be a watershed for us to do quite a bit more on that. We’re also excited and we're doing what we can to accelerate the roadmap there because we think there's a very good opportunity ahead.

That's great to hear. One follow-up: going to sort of the spending intentions from your customers, it feels like most of the past 12 to 18 months, the sales playbook, particularly in the enterprise, has been around consolidation. Is that still the theme in terms of driving new bookings, new expansion deals, or are you starting to see customers focused less on the consolidation opportunity, more on innovation and bringing their innovation budgets to bear to invest in AI and cloud, bringing Datadog along for the ride?

There's always been innovation and new things. You're right that for the deals we talk about in the earnings, there tends to be more consolidation, but that's the nature of it. Innovation usually happens gradually rather than through a big bang customer switching from five vendors to another. We discuss these less frequently now, but that's been the trend throughout. We certainly see further room for consolidation both among existing customers and new logos. At the same time, we're excited to see what's happening with AI innovation as it transitions from testing and experimenting into real production applications. We observe signs of that with our LLM observability product. We also see that with some of the workloads we monitor from our customers on the infrastructure side. It's still early days in terms of customers being in production with their AI applications.

Great. Thank you for the thoughts, Olivier.

Thank you. Our next question comes from Raimo Lenschow with Barclays. Your line is now open.

Thank you, and congratulations to you as well. Can I stay on that topic a little longer, Olivier? One significant driver for you in the past has been workload growth, which we are all monitoring in relation to the hyperscalers. It appears there are stable trends among the hyperscalers and for you as well. How much of this is purely macroeconomic and how much involves reallocating project resources—not just funds but also time—toward AI, considering we are still in the early stages of this lifecycle? Can you share your perspective on how this might develop, as it's something we are all focusing on? Thank you.

The key thing to remind everyone is, we think the general move of workload growth in the cloud or cloud environments might be public cloud-based, but they might also be private cloud. So this growth and move to the cloud is going to last and remain at a fairly high level for a very long time. You are right that when we look at numbers from the hyperscalers and try to factor out what's GPU driven, the growth looks stable. We think that growth is still high and will last for a very long time, and that's one of the big trends we expect to continue in the years to come. Where the workload might have grown more, instead of 20%, they could grow 25%, perhaps some of those 5% are going into AI, which is largely right now in experimentation and model training. We see that as a precursor to further workload growth in traditional applications. So for us, it doesn't change the equation. It does create a bit of a decoupling between all numbers and those reported by hyperscalers, where much of their short-term growth rides more on their capabilities of GPUs they bring online for those experimentation workloads.

Okay, perfect. Thank you. And then one follow-up for David: if you think about capacity, where are you in terms of sales capacity? Do you think things are changing or how do you maintain capacity as we approach the New Year? Thank you.

Long-term, we believe our revenue is highly correlated with our sales capacity. We have been attempting to increase our sales capacity similar to our top-line growth rates. This considers bottoms-up planning, putting sales capacity where we see under coverage, and ramping and training our salespeople. Our philosophy is to scale our sales capacity roughly in line with the top-line.

Okay. Alright. Thank you.

Thank you for your question. Our next question comes from Kash Rangan with Goldman Sachs. Your line is now open.

Thank you very much. I appreciate it. I'm curious about two things. I'll keep this brief. In the subsequent weeks after the quarter closed, I'm curious, especially with rate cuts, how you feel customer feedback has been coming along, particularly on the SMB side, since the rate of change has not been discernible at least at the end of the quarter. And one for you Olivier: as you look at GPU workloads, what about the company's existing portfolio geared towards CPU applications, and how do you monetize an instance of a GPU versus a CPU, if that makes sense?

I can address GPU. There are two parts. What can we do for GPUs that’s different from CPUs? In general, there are quite a few distinct aspects regarding how that machine operates with the rest of the application or system performance. There's a lot that's new and differentiated around profiling and understanding how you optimize the usage of GPU bandwidth. We're currently working with several customers who are dealing with large inference workloads and how we can assist them on the GPU profiling side for inference. We are doing less on the training side mainly because training jobs are usually bespoke and temporary, lacking a consistent application. They're generally large clusters of GPUs, closer to HPC than traditional applications. But we're also experimenting with what we can do there. Today, we do not monetize GPU instances as well compared to CPU instances. A GPU instance costs many times more than a CPU instance, which doesn’t directly benefit us.

As for your question about the period since the closing of Q3, we've seen trends very similar to year-to-date in Q3 in terms of customer growth. We have strong pipeline strength in enterprise stability and SMB, and we are working hard on harvesting that as we approach the year-end.

Thank you. Please stand by for the next question. The next question comes from Brent Thill with Jefferies. Brent, your line is now open.

Hi, good morning. David, on RPO, it has been decelerating, which has become a focus for investors. I know you said to focus on revenue. However, many are concerned about this trajectory, and I know you've said that the backlog is strong and you're adding sales capacity, but your backloger may be bigger than the numbers we are reporting. Can you add any more context on the pipeline and your thoughts on the metrics regarding that deceleration?

As we talked about duration, we had a surge last year in longer-term contracts that was really customer-led. We may have this in the future, but the timing comparing last year to this year did not repeat. I would say when you look at adjusting for duration over the long term, we find all metrics are circling around revenue at mid-twenties. There's a lot of noise there, and it has to do with the timing of multi-year deals. We'll report at the end of Q4 how that progressed. We have much business to do in Q4, particularly in the larger customer and enterprise side. We'll see where that lands, but again, revenue is the North Star.

Those figures don't correlate with the sales pipelines because a lot of it has to do with when customers recommit. There’s considerable variability there; they might choose to extend for one year to three years and that has a significant impact. This adds a high degree of variability to billing numbers, which is why we focus on usage and sales pipeline for new business.

What Ollie is saying is the output to natural flow of business, which has more volatility than revenues do.

Great. Thanks.

Thank you. Please stand by for the next question. The next question comes from Karl Keirstead with UBS. Your line is now open.

I have a couple of questions regarding your comment about some of your larger AI native customers. First, why do you think this is happening? Is it simply a natural occurrence that these AI native customers are growing larger and seeking better pricing and optimization, or is there something more at play? Secondly, when you set your Q4 guidance, did you account for the trends in pricing and observability related to these AI startups, or were you cautious, thinking that it might be more relevant in 2025?

Look, what we see is that we have a small number of AI-native companies, many of which are model providers or infrastructure providers for AI that serve the rest of the industry. This group has been growing very fast and we mentioned it's 6% of our ARR, which is about 4% year-over-year growth. We expect optimization at some point, and customer recommitment because many of them are way over their last commitment with us. It’s similar to what we've seen with cloud-native consumer companies in the late 10s and early 20s, with the key differences being that the AI cohort is growing faster and represents a smaller fraction of our overall ARR. We made that comment because we want to be transparent; there's potential for volatility there in the short term while the mid- to long-term growth remains strong.

As for the fourth quarter, we maintain conservative assumptions regarding usage growth that are lower than we've provided. Naturally, the effects of Q4 are more limited than they would be on next year, and as we report, we'll update everyone on the trends we observe in this cohort at that time.

We didn't change guidance principles for this; we made this extra comment for transparency about what we see, and we didn't bake anything specific into the guidance.

All very helpful, thank you.

Thank you, please stand by for the next question. Our next question comes from Kirk Materne with Evercore ISI. Your line is now open.

Thank you very much. Ollie, you mentioned a larger federal customer this quarter. Can you discuss the broader trends you are observing within the federal government? What insights do you have, and how did the fiscal year-end in September potentially impact this?

It's a huge opportunity for us and I would say it’s quite early with some interesting successes. We have some exciting logos there. There’s this one agency we mentioned, which has tremendous opportunities for growth and upselling with us, and another agency that already has a multimillion-dollar ACV footprint that we didn’t talk about this quarter but has been a long-term customer. There is significant upside in this area, and we are working hard on the product side to capture that fully. Over the past few years, we’ve worked on FedRAMP compliance, and we are working to get into more regulated and tougher workloads with FedRAMP and other certifications. So, we have a long roadmap and big plans for growth in this sector.

Thank you. Please stand by for the next question. Our next question comes from Mike Cikos with Needham. Mike, your line is now open.

Hey, thanks for getting me on the call here, guys. I just had two quick questions for you. The first on gross margin: can you speak to anything relating to those AI-native customers, the intensity of those workloads? How do your products fit into it or maybe even the broader portfolio? I'm wondering if those newer products may detract from gross margin in the near term versus some expansion that we can expect as these products scale? For my second question, it was great to hear that the usage growth continues to trend higher, especially for existing customers. Does it feel where we sit today in this new environment like this is the baseline growth you expect, or could there be acceleration? If things could move higher, what would drive that?

Regarding gross margins, we’re happy with their position, and there's been some small movements that we wouldn't overanalyze. Underneath that, we keep raising functionality while optimizing our code and using our products to achieve that. We continue to improve agreements with our cloud providers as we scale. We've got to balance investing in new functionalities versus optimizing. When gross margins dip slightly, we put effort into optimization; otherwise, we allocate more resources to new products and functionalities. So, product mix doesn’t significantly impact gross margins. We expect plenty of opportunities to improve margins long-term but for now, our focus is on shipping products to customers.

For growth of workloads, we see growth across the customer base. We observe classical workloads in the cloud, with very large growth on the AI-native side. The key catalyst for future acceleration will be AI applications going into production, leading to expanded usage among non-AI-native customers. As they do, they will resemble traditional applications, combining various components like databases and security layers to support their functionality.

Thank you very much, Ollie.

Thank you. Please standby for the next question. The next question comes from Gray Powell at BTIG. Your line is now open.

Alright, great. Thanks for taking my question. I wanted to follow up on Datadog On Call. It was good to hear the commentary earlier in the call, and it has been coming up more in our field work. So I'm curious how we should think about the opportunity there. Is that something that could completely displace a product like PagerDuty, or is it more of an add-on feature since my understanding is it mainly works with the Datadog ecosystem?

We'll take it where the demand takes us. Initially, we built it as a fully integrated experience for our ecosystem. The strategic part that's most interesting is the automation of resolution, rather than simply paging customers. The demand for integrating with many different sources has been strong, and we're very happy to accommodate that.

Understood. Thank you very much.

Thank you. Please standby for the next question. The next question comes from Ittai Kidron with Oppenheimer. Your line is now open.

Thanks and nice numbers, guys. Ollie, a question for you. You mentioned in your prepared remarks that 15 of your 23 products are now running over $10 million. If you look at the ones that are still under $100 million or under $50 million, where do you see the highest odds of crossing the $100 million mark?

I don’t want to single out any products for which we didn't disclose metrics, but we've mentioned in previous calls that several products are growing fast. Database monitoring, for example, has been growing rapidly and delivers clear value. There are numerous products across security and user experience I believe will reach that threshold. Every product has the potential to surpass $50 million, with some reaching up toward or beyond $100 million. We feel confident about the product set as a whole.

Very good; appreciate it. Thank you.

David, anything to add?

I think we've noted there has been stability to upward trend in usage across many clients, particularly in enterprise where digital applications are becoming more prevalent, and that's generating our strong pipeline.

Thank you. Appreciate it.

Thank you. This concludes the question-and-answer session. I would now like to turn it back over to CEO, Olivier Pomel, for closing remarks.

Thank you all for attending the call. I want to give a few shout-outs. Firstly, to the product management team for building great products last quarter, to the customers that spent their time with us to ensure those products work right, especially for the new products like LLM observability and On Call. I want to thank the go-to-market team as well, as we have a very full slate for everyone over the next month and a half. I know everybody is working hard, so thank you all. On this, we'll wrap the call.

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