Datadog, Inc. (DDOG) Earnings Call Transcript & Summary

Good day, and thank you for standing by. Welcome to the Q2 2026 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 first speaker today, Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead.

Thank you, Lauren. Good morning, and thank you for joining us to review Datadog's Second Quarter 2026 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 third quarter and the fiscal year 2026, and related notes and assumptions, 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 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 March 31, 2026. Additional information will be made available on our upcoming Form 10-Q for the fiscal quarter ending June 30, 2026, 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 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 to go over our Q2 results. Let me begin with this quarter's business drivers. Our revenue growth in Q2 has accelerated across our customer base. On one hand, our [ AI native ] customer cohort continue to grow and diversify, both in the number of customers we serve and the scale of those customers. But on the other hand, and as a great illustration of the breadth and strength across our business, revenue growth for our non-AI customers also accelerated again this quarter to the high 20% year-over-year, up from the mid-20s last quarter and 18% in the year-ago quarter. Overall, we continue to see healthy trends in customer demand. Our broad base of customers from the most nimble start-ups to the largest and most established enterprises are all adopting AI. We think this is accelerating the usage of cloud and modern technologies as well as their usage of the Datadog platform to observe, secure and act on their cloud and AI workloads. Regarding our Q2 financial performance and key metrics. Revenue was $1.12 billion, an increase of 36% year-over-year and above the high end of our guidance range. We ended Q2 with about 33,400 customers, up from about 31,400 a year ago. We also ended with about 4,720 customers with an ARR of $100,000 or more, up from about 3,850 a year ago. These customers generated about 91% of our ARR. And we generated free cash flow of $279 million with a free cash flow margin of 25%. Turning to product adoption. Our platform strategy continues to resonate in the market. For example, 58% of our customers now use 4 or more products, up from 52% a year ago; 37% of our customers use 6 or more products, up from 29% a year ago; and 13% of our customers use 10 or more products, up from 7% a year ago. So we're landing more customers and delivering value across more products, and our products are broadly delivering strong growth in usage and ARR. As an example, RUM or real user monitoring now exceeds $200 million in ARR and accelerated at its scale to over 50% growth year-over-year. Our customers are [ sending ] more user sessions and using RUM in conjunction with our newer product analytics to optimize our business outcomes. Moving on to R&D. We held our DASH User Conference in June, where we announced over 100 exciting new products and features for our users. So let's go through some of the announcements. First, we expanded Bits AI to accelerate and automate the DevOps loop. This is the look that goes from detection to investigation to remediation that engineers go through each time something breaks. At DASH, we announced a lot of new Bits capabilities for the DevOps loop. Bits can now create and maintain monitors, identify root causes within minutes of [indiscernible] signal, recommend and implement fixes, follow guardrails to add safety and controls, continuously learn and improve from prior incidents and detect symptomatic behaviors early to repair infrastructure issues before they escalate. Second, we announced Bits AI products to address the development loop. This is the loop that goes from coding to delivery to evaluation that develop and navigate to get code to production. For this loop, Bits Release now acts as an AI release validation agent, analyzing the impact of co-changes, running end-to-end checks and verifying production rollouts. Bits Code generates code fixes, running every fix in reproduction behavior, and Bits Testing also automates synthetic test generation and maintenance. Third, we expanded Datadog for AI, our products that observe, secure and optimize the AI stack from end-to-end. Data observability enables companies to trust the data being used by AI with line edge quality monitoring and jobs monitoring, Bits Data Analysis uses our rich data context to accurately answer business questions and Agent Console provides visibility into AI usage, cost and effectiveness. In Agent Observability, our patterns capability automatically clusters user interactions into behavior groups to identify quality or cost issues, and Bits Evals handles the repetitive parts of the agent development loop in order to improve the outcomes of agents. Fourth, we are broadening our platform to ingest, correlate, analyze and act on more data, whether on-prem or in the cloud. In Network Monitoring, we launched network path and network configuration management to track changes that cause complex network issues. Within database monitoring, Bits Database Optimizer now automatically simulate and evaluate the impact of AI generated changes in order to optimize logistics queries. In log management, federating logs enables users to acquire external data stores, including Databricks and [indiscernible]. And with Bring Your Own Cloud or BYOC, customers can now use a full Datadog experience on logs that are kept within their own infrastructure. And we've also announced that we are bringing BYOC to Metrics and Traces as well. In the digital experience space, [indiscernible] monitoring automatically gives a single shared view for every critical user flow. And for custom metrics data, we introduced infinite cardinality metrics, which allow our users to answer arbitrarily complex questions as they generate larger amounts of data with AI agents without incurring any extra costs. Finally, we launched a number of innovations to secure the AI stack and defend against a new class of AI-powered attacks. AI Guard agent discovery, finds and maps every known and unknown custom agent so security teams can see what is protected and what is not. AI Guard for custom agents provides run-time protections to block attacks that can only be detected with real-time observability data. AI Guard for coding agents applies the same deep observability to block [indiscernible] and packages in code. And we also announced Runtime Prioritization Engine to cut vulnerability noise by over 95%. And finally, we expanded Bits Security Analyst to run unknown Datadog teams, so customers can benefit from the smart central learnings of our broad data set regardless of which [ SIEM ] they deployed. As we continue to innovate, we are being rightfully recognized by independent research. We are pleased to see that for the 6 year in a row, Datadog has been named the leader in the 2026 Gartner Magic Quadrant for Observability Platforms. Let's move on to sales and marketing and look at a few of the deals our GTM teams have closed in what has been a very strong quarter. First, we landed a 6-figure annualized deal with a Fortune 10 company. This company is expanding its e-commerce business and they plan to use Datadog Log Management alongside 10 other Datadog products to improve customer experience and business outcomes. This win validates our expected go-to-market approach to focus on the world's largest companies and win opportunities in the most complex environment. Next, we landed 7-figure annualized deals with 2 new labs. These AI labs are rapidly scaling their AI model training workloads and preparing for major product [ launches. ] By deploying observability using Datadog, they gained visibility across our training infrastructure and GPU fleet and can iterate faster on their AI models. They are also using Bits AI to rapidly build monitors, dashboards and alerts for deep observability context. Next, we landed a 7-figure annualized deal with a South American bank. This bank's fragmented legacy monitoring stack and manual triaging caused significant application downtime that was often called in by customers. By consolidating into Datadog with 11 products, this customer enables visibility from their mainframe all the way to their micro services and has already reduced [ mean ] time to resolution on live production incidents. They are adopting Cloud SIEM and data security and evaluating other Datadog security products to improve their security posture. Next, we signed a 7-figure annualized expansion for an 8-figure annualized deal with a Fortune 100 health insurance company. This customer's biggest pain point is to deliver a great experience to their members throughout their care while protecting PII across dozens of business units. Datadog's HIPAA compliance and PII handling in RUM, log management and Cloud SIEM allowed us to differentiate and win over our competitive solutions. And Bits AI investigation is already speeding up the incident resolution and reducing expensive escalations. This customer will expand to 19 Datadog products. Next, we signed a multiyear over $30 million TCV deal with one of the world's largest online media companies. This customer [ shows a standard ] from Datadog across its business, displacing 4 commercial and internal tools. Datadog also proved value beyond core observability with product analytics, CI visibility, data observability and [indiscernible] cost management. This deal includes our largest win to date for Bring Your Own Cloud, displacing their legacy commercial logging tool at a [indiscernible] scale. And finally, we signed a 9-figure renewal with a leading AI company. This long time very large customer uses 17 Datadog products to enable unified visibility on production workloads at a very large scale, albeit with a usage reduction starting in Q3, which were considered in our guidance and which David will speak to. Before I turn it over to David for a financial review, let me offer a few words on our longer-term outlook. There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. But we now have an additional growth driver with AI as we help our customers deliver value with this transformative new technology. We are tremendously excited about the opportunities in AI. To summarize where we are and where we're going. First, AI is a tailwind for Datadog today as cloud consumption grows and drive more usage of our platform. As of Q2, over 750 AI customers use Datadog to monitor and improve their tech stacks. When we look at the largest companies driving AI, all 10 of the top 10 AI leaders are Datadog customers. Beyond AI native, we see AI activity growing across our broader customer base. We're also seeing signs of rapid growth in agentic activity with a number of MCP [indiscernible] quadrupling again quarter-over-quarter and growing more than 22x when compared to Q4 2025. Second, we are delivering AI for Datadog to deliver more value and greater platform capability to our customers. This includes our Bits AI products, chat, investigation, detection, code, testing, readings and many, many others. Third, next-gen AI introduces new complexity and observability challenges. We are addressing this with what we call Datadog for AI to observe and secure the AI stack from end-to-end. This includes the GPU monitoring, agent observability, agent console, data observability, AI Guard and many other products. Finally, our AI research team and our large volume of rich data using critical workflows enable us to conduct groundbreaking research. We have shown some of our work already with the second version of our time series model, Toto in May. Toto version 2 was exciting for 2 reasons. First, we've shown it to be state-of-the-art on key benchmarks. But more importantly, we've demonstrated for the first time true scalability for time service models, allowing us to target the same improvement path language models have followed since 2020. So now beyond Toto, we are working on larger and more ambitious dedicated models, [indiscernible] models to power Bits AI and bringing other modalities beyond time series data into world models that we think can lead to a step change in capabilities for our customers. And we plan to accelerate these research efforts with the acquisition of Adaptive ML, which we closed in June. Because of all of that, now more than ever, we feel ideally positioned to have customers of every size and every industry as well as all types of users, whether humans or AI agents, so they can transform, innovate and drive value to AI and cloud adoption. And with that, I will turn it over to our CFO, David.

Thanks, Olivier. Our Q2 revenue was $1.12 billion, up 36% year-over-year. Within that, our 11% quarter-over-quarter revenue growth is the highest since Q2 2022. And our quarter-over-quarter revenue added of $115 million is a record by a significant margin. We continue to see robust usage growth from existing customers as well as a strong ramp in our new customers. Revenue growth accelerated with our broad base of customers, excluding AI customers to the high 20s year-over-year, up from the mid-20s percent last quarter and 18% in the year-ago quarter. We saw robust growth across our customer base with broad-based strength across customer size, spending bands and industries. Meanwhile, our AI customers continue to grow rapidly and diversify in the quarter. This 750 strong customer group includes a broad range of AI start-ups as it has in the past, but now also includes hyperscalers using Datadog for in-house AI labs. In Q2, this includes 31 customers spending more than $1 million annually, of which 8 customers spent more than $10 million annually. We also achieved strong new logo dollar bookings with particular strength in enterprise, where new logo annualized bookings more than doubled from a year ago. And we are seeing new logos ramping faster, contributing more to revenue growth. The portion of our year-over-year revenue growth that relates to new customers was about 30% in Q2, up from 25% in Q1. Geographically, we are performing well in all regions with growth acceleration across the regions. We see particular strength in the Americas as much of the AI activity is occurring in the U.S. as well as -- in addition, we are executing strongly in LATAM. Regarding retention metrics, our trailing 12-month net revenue retention percentage was in the low 120s, similar to last quarter, and churn remains low with gross revenue retention in the mid- to high 90s. We believe this metric highlights the mission-critical nature of our platform for our customers. Now moving on to our financial results. Billings were $1.18 billion, up 38% year-over-year. Remaining performance obligations or RPO was $3.47 billion, up 43% year-over-year. Current RPO grew about 40% year-over-year and RPO duration increased year-over-year. As we previously mentioned, we continue to believe revenue is a better indication of our business trends than billing and RPO. Now let's review some of the 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. Our Q2 gross profit was $892 million for a gross margin of 79.6%. This compares to a gross margin of 80.2% last quarter and 80.9% in the year ago quarter. As we've discussed in the past, our gross margin varies from quarter-to-quarter with investments into innovations for our customers, offset by efficiency efforts. There's no change in our expectations for gross margin, which has been in the 80% plus or minus range historically. Q2 OpEx grew 26% year-over-year versus 31% last quarter and 36% in the year ago quarter. We held our DASH Conference, User Conference in June, and as expected, the event cost about $15 million. Q2 operating income was $257 million for a 23% operating margin compared to 22% last quarter and 20% in the year ago quarter. Turning to our balance sheet and cash flow statements. We ended the quarter with $5 billion in cash, cash equivalents and marketable securities. Cash flow from operations was $316 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $279 million for a free cash flow margin of 25%. And now for our outlook for the third quarter and the fiscal year 2026. Our guidance philosophy overall remains unchanged. As a reminder, we based our guidance on trends observed in recent months and imply conservatism on these growth trends. Regarding our largest customer, we have seen a usage reduction, which is incorporated in our Q3 and full year 2026 guidance. As Olivier noted, this customer has recently renewed with us. For the third quarter, we expect our revenue to be in the range of $1.135 billion to $1.145 billion, which represents a 28% to 29% year-over-year growth. Non-GAAP operating income is expected to be in the range of $260 million to $270 million, which implies an operating margin of 23% to 24%, and non-GAAP net income per share is expected to be in the $0.63 to $0.65 per share range based on approximately 378 million weighted average diluted shares outstanding. For the full fiscal year 2026, we expect revenue to be in the range of $4.45 billion to $4.47 billion, which represents a 30% year-over-year growth. Non-GAAP operating income is expected to be in the range of $1.01 billion to $1.03 billion, which implies an operating margin of 23%. And non-GAAP net income per share is expected to be in the range of $2.50 to $2.54 per share based on approximately 376 million average diluted shares outstanding. And for some additional notes on guidance, we expect net interest and other income for the fiscal year 2026 to be approximately $180 million. We expect cash taxes in 2026 to be about $30 million to $40 million. We continue to imply a 21% non-GAAP tax rate for 2026 going forward. And finally, we expect CapEx and capitalized software together to be in the 4% to 5% of revenue range in the fiscal 2026. Now finally, to summarize, we are pleased with our execution in Q2. Our investments in R&D and go-to-market are yielding positive results, and they position us well for continued execution. I want to thank all the Datadogs worldwide for their efforts. And with that, we'll open the call for questions. Operator, let's begin the Q&A.

[Operator Instructions] Our first question comes from the line of Sanjit Singh with Morgan Stanley.

Congrats on the acceleration in revenue growth again this quarter. David, thank you for giving us the color on some of the guidance assumptions, particularly headed into Q3. With respect to the largest customer, I was wondering if you could share any additional details in terms of the new contract? Was it a similar duration? And in terms of the lower usage. Is that a function of the customer getting lower unit price because of making a new commitment? Or is there some churn or downsell that we are seeing through, not only for Q3 but for the balance of the year?

Yes. So maybe I'll take this one. I think we -- So overall, we, as usual, we don't want to comment too much on any specific customer because we're also not really in control what's happening with any specific customer. We wanted to be transparent about this on the call because we did see a reduction in usage, and we took the liberty to fully derisk the guidance for the rest of the year with respect to that customer. And again, the reason for that is we don't control what's happening to a specific customer, but we do have a great amount of control on what's happening to everything else in the business, and the business is booming, and we don't want that to overshadow basically the acceleration we see pretty much everywhere else in the business. So the -- as we mentioned on the call, we renewed the customer. It's a long-time customer, uses many of our products. But there's not a lot more we can share.

Yes, I think just to get specific on the guidance, we, last quarter and previous quarters, said that we essentially have a level of commit, and we can derisk our guidance by using that. And then as you know, in most of our large customers, we have variability relating to the commit. So take that into consideration.

Yes. The last thing I will say because I know it's on people's minds is if you back out our largest customer from our growth, you get pretty much the same growth rate as the rest of the business has been accelerating very steadily. Actually, we've seen, I think, now 5 quarters of continuous acceleration from the rest of the business. And we feel very good about the -- what we see in the market.

Yes. No, I appreciate the thought. Let's talk about maybe the rest of the business. What we've seen in the past couple of years, sort of AI Native is sort of leading the charge. It sounds like the enterprises are getting on board with their AI initiatives. And so just in terms of like the enterprise AI app dev cycle, what does that look like for Datadog over the last couple of quarters?

Well, we do see broad adoption, and we see it in 2 ways. One is we see it manifest itself in just more transformation, in more cloud adoption, more workloads, more [ modernization ] from customers. And that's what drives the majority of the known AI customer acceleration. So we mentioned also we've seen continuous acceleration from customers that existed before AI and are not majority AI businesses. And it's been pretty remarkable, like the acceleration that we gave the numbers on the call, but their acceleration since last year has been constant and very significant. And we -- it keeps happening as far as we can tell. So it's a very positive trend there. That's the first thing we see. The same thing we see is a very rapid increase in the usage of all of our AI first surfaces. So that would be the products that measure agents and LLMs, where we see an explosion of traffic in terms of the LLM and [ tool calls ] that we're getting. That would be the amount of closed book gain to our MCP endpoints. So we see that explode completely over the past 2 quarters.

Our next question comes from the line of Raimo Lenschow with Barclays.

Perfect. Could I stay on that AI theme, please? At the moment, like if you think about the large customers, there's a lot of model training, et cetera. But if we broaden it out, the inference is really becoming the bigger part. Can you talk a little bit about like how much more observability is needed? And I'm thinking there, if I do inference, I need to think about vector databases. I need to think guardrails. All of these agents are going to be in containers that need to be monitored, et cetera. So what do you see in real life at the moment in terms of if some people do more inference, how much more observability gets triggered by inference? Is that kind of an opportunity that we should probably pay more attention than that one renewal? And then I had one follow-up.

There's opportunity at every layer of the stack in inference. So we do think at the end of the day, inference will be the dominant workload. That's -- any time you train, you probably would want to infer more than you train as a rule of thumb. We see opportunity at the low level when it comes to the infrastructure, the GPUs and the consumption you have there. There's opportunities at the very top end, when you measure what the agents are doing and whether you're getting the right outcomes and whether you're getting the right alignment, and there's opportunities at every layer in between, just looking at the LLM itself, just looking at the tool calls and the applications that are being called by the agents, like everything is an opportunity in there. We see growing adoption from the products we already have there. We mentioned our GPU monitoring product is actually getting quite a bit of usage in a number of new labs and AI first types of customers. We're also seeing an explosion of volume in our agent monitoring products. And so we're well positioned there. But we think this market is going to change quite a bit. And the [ occupations ] of customers, they also change over time. So for example, last year, our customers were mostly trying to validate correctness and validate that they were getting some form of [indiscernible] scale-up. I would say, 3 to 6 months ago, the focus has moved quite a bit towards cost. Now customers were spending a lot on AI and they were wondering how to optimize costs. And I think we'll see some variations in the concerns over time as customers get further into the adoption and new products emerge for that.

Just want to add that when you look at what we described as some of our deals in the quarter and you look down our script, you'll see that a number of them have the AI products included. And so that is indication that those large enterprises are using the platform and buying the AI products as well.

Okay. Perfect. And then David, one for you. It's like, obviously, you're always in a tough position if you have to guide these large contracts. How did you do it historically? So did you always kind of put in the base level and then what happened, happens? Or has that approach changed? Or I don't [indiscernible] you on having to do this.

No. We essentially use, as we've talked about over the many years, we kind of use the inputs of what we see. And what we said, I think, in the last quarter or 2 is that we have certain base levels. As you know, we have a commitment and a usage model. And we've factored that in, in providing our guidance. So our -- as we said in the prepared remarks, our methodology for guidance hasn't changed. We've always used those inputs and looked at the commitment in usage in doing that.

Yes. I mean one thing I'd say is in this case, we did chose to fully derisk our largest customer. And the reason for that is we don't want that to be an overhang on what is otherwise a business that is accelerating and performing extremely well. So we extended that. We have the same overall conservatism as we always do when we look at our numbers. But in this case, we also weighed this one a little bit differently.

Our next question comes from the line of Gabriela Borges with GS.

I wanted to ask you both about one of our observations of DASH, which is the [indiscernible] love the pace of innovation. They talk very positively about the product. The CFOs love to complain a little bit about their data on [ bills. ] So my question for you is talk to us a little bit about how the CFO level conversations are evolving. Clearly, the ROI is there, but maybe give us a little bit more on where the budget is coming from? And something like infinite cardinality, is that now part of the conversation with CFOs in solving some of those very particular cardinality cost portion?

I mean, look, at a high level, there's only 2 reasons people buy software. It makes them more money or it sells them money. And any time we sell, any time we got a renewal, we've got an [indiscernible] or we land a new customer, that's because we do one of those two things for them. And we always have to make that case. So I wouldn't say that's any different from what we've seen before. What we do for our customers today, especially as they keep adopting AI is we help them sell a lot of the money they would spend on building, running operations or running AI agents. When you [indiscernible] customers, that's the one thing they kept mentioning, hoping you help me running my AI cost. This is growing very fast. I don't have any control on it. And I don't know whether I'm reaching the right outcomes with that. And so that's one of the reasons we've invested in all these products we've mentioned earlier. And also, we're seeing some of the great returns on that product already. In terms of Infinite cardinality, that's -- I would say it's been one of the longer-standing source of frustration for customers when sometimes they send more data or they send more fine-grain tags with their data and to get some unpredictability on the [ bills ] because of that because it increases the [indiscernible] of the data we're getting. And we saw that from a technical perspective and from a commercial perspective by packaging our metrics a little bit differently. And we think it's particularly important and relevant as customers are building more applications with AI and as they want to send basically more [ tech, ] more information and ask more complex question and get more fine grain answers to those questions. And so that fits well within their plans basically. So we've got great feedback on that so far, but it's still early. Sometimes we get it right, sometimes we get it slightly wrong, and when we get it slightly wrong, we fix it. That's not different from what we've done in the past.

Our next question comes from the line of Mike Cikos with Needham.

I wanted to come back to the significant size of the lands that you had this quarter, and it's great to see the sustained traction especially with those AI labs. But if I'm thinking about the 2, 7-figure AI labs that you landed this quarter and then going to David's commentary around winning some of these in-house AI labs with the hyperscalers. Are those one and the same here? Or are those two separate customers that we're talking to?

These are different customers. The ones we mentioned on the new lands are [ neolabs. ] So these are companies that didn't exist a few years ago. And what's interesting about them on the use case there is that very often, we land customers when they go into production and they release products and they start selling their customers. In this case, these are customers we're getting as they are training models, and they're using us to observe and improve and optimize the training of the model. And so that's an exciting new area that was not really a business area for us a couple of years ago, and we've seen a number of new proof points around that. In addition to that, and we've mentioned in previous calls, we've also landed the AI labs or super engineers labs or the number of hyperscalers. And I would say the workloads are similar in that it's largely training of the models, but the customers are a little bit different. These are very large companies that in that case, previously had a lot of in grown -- a lot of homegrown technology to observe and run workflows.

Excellent. And for a follow-up, I know you had cited the new logos ramping more strongly than what we've seen historically. And correct me if I'm wrong, but I feel like that's a newer phenomenon that you guys are calling out this quarter. When I think about those new logos ramping, is that a function of pull-through where maybe some of these AI capabilities are pulling through the broader platform? Or is it vice versa? Anything you can do to help us think through what is creating that catalyst, if you will, when the new logos are contributing to the model?

It's been happening and building up some with the number that we have in our Qs, which is the percent from customers of growth that we didn't have a year ago. That number, we said, has gone from 25% to 30%. So this has been building, and we wanted to point that out because of that disclosure indicating that the customers that we're landing that it's not only the new logos, but it's also the growth of the new logos that we've added over the last couple of years -- last year, sorry. So it's a compounding of that.

Our next question comes from the line of Alex Zukin with Wolfe Research, LLC.

Oli, maybe first for you, just on the -- a lot of headlines around security over the course of the last few weeks, particularly AI breaking containment. And it occurs to me that with your positioning and observability and security increasingly, the notion of a guardian model and development around that could meaningfully increase kind of your ambit on what you can do and achieve for clients, both AI natives and legacy. Can you maybe talk to what -- the increasing opportunity around this crossover in this AI agent and what that means for Datadog? And then I've got a quick follow-up for David.

I mean look, there's a complete switch in the way the AI security products need to work. So you can't wait basically for putting humans in the loop. You can't have the typical path when you have 12 or 15 different products that are going to aggregate signal and then you get it linked into a system [indiscernible] prioritize them for humans, then humans will leave them when they can. Like you need to integrate everything a lot more. You need to operate a lot closer to the application into the infrastructure. And you need to have AI agents solve the issues first. So it's a complete rebuild for most of the industry. And I think it plays into our approach, which is to have an integrated platform and have all of the different data streams come directly from observability straight into the security agent and have all that integrated from end to end. So obviously, the data field is moving very fast. We see new classes of issues pretty much every week at this point. We are quite busy building that up, but we think it displays into our strengthening to where we are basically already on and what we're building for our security products.

Perfect. And then, David, maybe just for you. On the largest customer renewal. Is there anything you can tell us around maybe just any changes around the duration or anything that makes this new contract maybe a little stickier in terms of the discounted rate card, the amount of products that they're able to kind of use for better value? Anything that increases the conviction level around stickiness?

I'll comment on this other than to say that most of our enterprise customers, as we talked about for a long time, have annual plus and then the pricing is generally volume-based pricing. So I would say, overall, our customers transact in that way. And then we have that level of commitment. And then as we talked about over a lot of years, then there's usage and then we transact. So similar to what we have with most of our larger enterprise customers. Oli, anything you want to add there?

No, I think clearly there's a lot of continuity in that renewal. I think that's what you gain. That's one way to put it.

Our next question comes from the line of Eric Heath with KeyBanc Capital Markets.

This is [indiscernible] on for Eric Heath. I would love to get more color on your 3Q, guys, specifically. It seems like it's a little below your sequential levels of how you've guided your previous 2Q. So I would love to just hear more about what trends you're seeing going into 3Q and maybe what some of the assumptions in the guide are?

Yes. I think it's similar to the methodology we take, what we see and provide some conservatism. And I think we had mentioned in the script that we've been -- renewed our largest customer, but we've seen user declines relative to the previous quarter. We said that. So that's all taken into consideration in trying to develop a guidance that is consistent with the methodology of conservatism that we've used as a public company.

Got you. And if I could just ask one more for Oli. I'd love to just get your thoughts on the impact of diversification of AI model usage in your customers and what you're seeing there?

Well, we think it's great. Like there's a lot more options for customers to choose from. And that creates -- that opens up a lot of doors and opportunities for them. It also creates a lot of complexity, and we're here to help them with that complexity. So for us, these are great opportunities. And by the way, we see -- like we've had that thesis since the early days of AI that we would not just end up with 1 or 2 big AI companies and everybody using them, the same way we didn't just end up with 1 or 2 big cloud companies and everybody just using software from them. Like the ecosystems are very, very, very rich. There are a lot of providers. There are very large providers, there are smaller providers and everything in between, and there are many compositions of those different systems that are used by any given customer. And so we think the same is going to happen in AI. We think also that the [indiscernible] application of model as an open source model in particular opens the door to customers doing a lot more training on their own. And so that's a new market for us. We see some signs that we have a very good role to play there. And so we're building towards that as well. So overall, it's very positive for everyone.