Datadog, Inc. Q1 FY2023 Earnings Call

Good morning, and thank you for standing by. Welcome to the First Quarter 2023 Datadog Earnings Conference Call. 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 against the backdrop of continued macro uncertainty and the 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, 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. 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, generating about 85% of our ARR. 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. Overall, we experienced business conditions that were similar to the previous several quarters. In Q1, user growth from existing customers came in roughly as expected. We saw existing customer user growth in Q1 improve from the levels we saw in Q4, but remain a bit lower than the levels we experienced in Q2 and Q3. 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 logo 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 accumulate 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 proud to achieve several key milestones in Q1. 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 traditional monitoring product and our successful execution on the breadth of the platform. Remember that our APM suite includes 4 Datadog products: core APM, Synthetics, Real User Monitoring, and Continuous Profiling. Third, we continue to make steady progress with our cloud security products with continued growth in ARR and in customers. I am 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 several new security capabilities last month. We announced the general availability of Application Vulnerability Management, which provides visibility into the attack surface of production environments by automatically surfacing vulnerabilities. Instead of overwhelming users with thousands of vulnerabilities, this new functionality uses observability data to prioritize risks based on the estimated impact to the business, closing the loop between security, operations, and development teams. We also introduced several 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 their workloads. Moreover, we now offer vulnerability detection for containers, automatically scanning live container images for known vulnerabilities. Transitioning to observability, we announced the general availability of Data Streams Monitoring. This product specifically targets queuing, streaming, and event-driven pipelines, such as Kafka or RabbitMQ. These systems often span many different teams and technologies and are notoriously difficult to manage and troubleshoot. For this reason, 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 has seen significant growth over time and now represents high single digits as a percentage of revenue. I also want to take a moment to share our excitement for the latest wave of AI innovation. I’m referring to the recent advances in deep learning, large language models, and generative AI. From a market perspective, we believe AI will significantly expand our opportunity in observability and beyond over the long term. We anticipate massive improvements in developer productivity that will enable individuals to write more applications and do so faster than ever before. Like previous 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 will also increase the value of proprietary data, further driving digital transformation and cloud migration, as these are prerequisites for adoption. We do expect significant noise in the market as the technology stack progresses and changes very quickly. From a product perspective, we believe that Datadog is uniquely positioned to deliver value to our customers in this new world. First, we built Datadog from day one as a pure SaaS business, allowing us to leverage our data at full scale and train models to solve our customers' problems. Second, our large customer base provides us with the insertion points to make AI relevant. Third, we serve many of the largest builders and consumers of AI services and are quickly adapting to their needs in this rapidly evolving field. In summary, we are very excited about the potential of AI for us and for the observability and security markets, and I am sure we will discuss this topic further in the future. Let’s move on to sales and marketing. Our go-to-market teams had another productive quarter. To highlight some key wins, we signed an expansion into 8 figures ARR with a leading AI company. This customer saw a substantial increase in user demand and growth in new customers due to their innovation and interest in generative AI. As a result, this customer now utilizes 6 Datadog products and relies on our platform to track and correlate key business metrics, including uptime data and newer subscriptions and revenue. We also signed a high 7 figures expansion to another 8-figure ARR deal with one of the world's largest fintech companies. This customer expanded meaningfully over time, now using the Datadog platform across thousands of users and dozens of business units. With this expansion, they now employ 14 Datadog products while consolidating multiple open-source, homegrown, and commercial tools across observability and security into the Datadog platform. We also signed a 7-figure expansion with a Fortune 500 healthcare company. Before using Datadog, major incidents required mobilizing 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 reduce these numbers further. Additionally, we are replacing a commercial observability competitor whose new pricing model has been causing increased costs and lower value. This customer now expects to save over $0.5 million each year by switching to Datadog across several business units. Furthermore, we signed a 6-figure contract with a multinational clothing company that previously operated in silos with various monitoring tools. This resulted in issues affecting revenue and customer experience. They are starting with 5 Datadog products and expect to consolidate and replace a total of 13 commercial and open-source tools. Last but not least, we signed a 7-figure multiyear contract with a leading university in Australia. This customer historically relied on open-source solutions, evaluated a few commercial competitors, and chose Datadog for their needs, which spanned both cloud and on-premise architectures across logs, user experience, and network device monitoring. They plan to migrate from over 10 tools to the Datadog platform over time. That covers Q1's highlights. I want to thank our go-to-market teams again for their continued execution in Q1. Now, let’s switch gears and discuss our longer-term outlook. Overall, we continue to see no change to the multiyear trend toward digital transformation and cloud migration. Customers are optimizing their cloud usage, and while visibility remains limited as to when this optimization cycle will end, we firmly believe it eventually will. We are confident that we will continue to deliver value to more customers in their digital transformation and cloud migration journeys. It is increasingly clear with each wave of technical innovation that every company in every industry and geographic region must leverage the cloud, microservices, containers, generative AI, and more. By relentlessly broadening the Datadog platform, we will continue to help our customers save costs, operate with greater engineering efficiency, drive competitive differentiation, and deliver value to their own customers. Our long-term plans remain unchanged. We will continue 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-over-quarter. To delve into some drivers of the Q1 performance, we had an unusual outage in March, and we estimate that it impacted our revenues by about $5 million. As we mentioned last quarter, we saw subdued usage growth in December, creating a lower growth trajectory to start Q1, which led to a seasonally weaker sequential growth in the first quarter. During Q1, we experienced a linearity pattern typical for us, including usage growth in March that was higher than in January and February. Overall, the existing customer usage growth in Q1 improved from the levels we saw in Q4, though it was slightly lower than in Q2 and Q3 last year. We continue to see larger spending customers growing slower than smaller-spending customers. From an industry perspective, we see the slowest growth in the consumer discretionary vertical, particularly in e-commerce and food delivery. Geographically, we saw faster year-over-year growth internationally than in North America. Our trailing 12-month dollar-based net retention rate, or NRR, remained above 130% as customers increased their usage and adopted more products. However, based on our current growth trajectory, we expect our trailing 12-month NRR to fall below 130% in Q2. While our net retention rate may 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 and examples of our strong Q1 renewals that Olivier discussed. Our dollar-based gross retention rate remained stable in the mid- to high 90s, indicating the mission-critical nature of the Datadog platform for our customers. Moving on to our financial results, billings were $511 million, up 15% year-over-year. We had a large upfront bill from 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, stood at $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 more stable indicator of our business trends than billings and RPO due to fluctuations in invoicing timing and customer contracts duration. As for 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 for 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 expect gross margin to hover in the high 70s percent range. Our Q1 non-GAAP OpEx grew 45% year-over-year, a decline from the 54% year-over-year growth in the previous quarter. We continued 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, showcasing an 18% operating margin, flat sequentially from Q4 2022's margin of 18%. In the year-ago quarter, operating margins were 23%, benefiting 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 accounting for capital expenditures and capitalized software, free cash flow was $116 million, reflecting a free cash flow margin of 24%. Looking ahead to our guidance for Q2 and the remainder of fiscal year 2023, we have maintained conservative assumptions for organic customer growth compared to historical periods. We base our near-term guidance on recent activity with our customers. While existing customers are still expanding with us, we assume in our guidance that cloud optimization will continue to affect their expansion rate for the remainder of 2023. For Q2, we expect revenue to range from $498 million to $502 million, representing 23% to 24% year-over-year growth. Non-GAAP operating income is projected to be between $82 million and $86 million. Non-GAAP net income per share is expected to fall in the range of $0.27 to $0.29 per share based on approximately 349 million weighted average diluted shares outstanding. For fiscal year 2023, we expect revenue to range from $2.08 billion to $2.10 billion, indicating 24% to 25% year-over-year growth. Non-GAAP operating income is anticipated to fall between $340 million and $360 million, with non-GAAP net income per share in the range of $1.13 to $1.20 based on approximately 351 million weighted average diluted shares outstanding. On additional notes regarding our guidance, we continue to balance near-term financial strength with investment in our large, long-term opportunities, executing efficiently. We expect continued moderation of headcount growth, coupled with the lapping of COVID-affected historical expenses will result in slowing OpEx growth throughout the remainder of 2023. We plan to grow our non-GAAP operating expenses, excluding COGS, by approximately 30% year-over-year in fiscal year 2023, targeting a Q4 exit rate in the low 20s percent year-over-year. We estimate net interest and other income for fiscal year 2023 to be approximately $75 million, with tax expense expected to be in the range of $14 million to $16 million. Finally, we anticipate capital expenditures and capitalized software collectively to account for 4% to 5% of revenues in fiscal year 2023. To reiterate Olivier's comments, we remain excited about our long-term opportunities as our customers embark on and expand their cloud migration and digital transformation plans. We continue to invest to enhance how we engage customers and support these journeys. Thank you to our Datadog team worldwide for their efforts this quarter. With that, we will open the call for questions. Operator, let’s begin the Q&A. Thanks.

Our first question comes from Raimo Lenschow with Barclays.

Could you speak to the optimization, Olivier, a little more in detail? Obviously, it's a journey for customers and there are initial steps, followed by subsequent ones. In terms of customer behavior regarding optimization, are we still seeing the same steps being taken? Or are we moving into round 2, round 3, etc.? I am trying to understand where we stand in this optimization journey with more color. And then I have one follow-up.

Yes. The short answer is we don’t know exactly yet. I’ll give an answer similar to the one I provided last quarter, as we see customers reassessing their own workforce, similar to what we've seen in several companies this morning. Customers themselves are not entirely sure when they will be finished with this process, making us prudent in our assumption regarding an end to it in the near future. Our data and feedback from hyperscalers indicate no concrete signs that we can predict an end to optimization next quarter or the quarter after that. As far as our guidance and plans for the year go, we are assuming this will continue at a similar level throughout the coming months. While we do see some customers that seem to be finished with their optimization efforts, there are others yet to do so. We continuously monitor this and model it, but we’re not including any of that in our guidance.

Okay, perfect. And then, David, regarding costs, thank you for providing the OpEx rate for the year and the exit rate. When considering the balance between investing for the future versus surviving or addressing today's challenges, how do you view your investment philosophy in terms of balancing immediate concerns with future readiness?

We have made substantial and consistent investments in both our go-to-market and R&D over the last few years. This year, we are balancing the continuation of those investments with prioritization and optimizing or injecting some efficiency into those investments. We are recognizing the opportunity to obtain returns from previous investments while also concentrating on prioritizing them.

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

I have a question, Olivier, on AI. It appears we're on the cusp of another compute cycle driven by AI. During the last cycle, you were ahead of the trend regarding the shift from monolith to microservices. As we enter this new cycle, how do you see applications and the application stack changing? What implications do you foresee for Datadog as a monitoring vendor? What potential factors could affect this regarding the future of application development?

It’s a fascinating time with rapid innovations in AI. However, it remains early to determine the future market landscape. Building conventional models and chatbots has become nearly a commodity, accessible with just an API call, thanks to many options available today. This rapid advancement is enabling more customized, deeper AI applications, and it remains uncertain how many will be built by a few vendors versus many companies. At Datadog, we anticipate that this will drive more compute and increased business value from data being gathered. It will accelerate digital transformation and cloud migration since adopting AI necessitates having data and a modern architecture. In the mid-term, we view this as a clear accelerant for our business, albeit with potential noise regarding which technologies will ultimately prevail. In the short term, as developers become more productive, it’s likely they’ll produce more applications but potentially lack a comprehensive understanding of those applications—this creates further need for our services as we help customers manage the complexity they've built. We are confident about our long-term position.

Thanks for those insights, Olivier. As a follow-up, can you comment on what you observed in April compared to March? Typically, how does April line up for the company? Also, are any new customer cohorts beginning to engage in the cloud optimization process that you hadn't seen in 2022?

In general, April has been consistent with what we've observed in Q4 and Q1. There are no significant deviations to report at this point. It’s too early to define the quarter based on April alone. Regarding customer optimization, we have a large group of customers who are early in their cloud migration and Datadog adoption, growing quickly without any optimization seen from them as of yet. It’s possible we might see optimization among some of these customers, which is why we remain cautious in our guidance—not assuming that optimization will stop with customers who have already optimized.

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

David, considering the large upfront bill that didn't recur, which seems to amount to about $65 million, could you provide more insight regarding whether you anticipate recapturing any of that in Q2? What type of customer dynamics are we dealing with at that level? Then I have a quick follow-up.

Yes. That customer of ours is the one we referenced. What we noted was the timing and size of the billing frequency changed. For this client, the bill will be proportionately spread out over time. This particular company was involved in the crypto sector and continues to be a client. They were among the early optimizers. We communicated that some industries faced significant pressure with optimization, and we restructured that contract to better suit their business profile. This approach allows us to maintain a positive relationship with them moving forward.

The situation with this customer is one where their industry experienced significant reductions over the past year, leading to considerable revenue cuts for them. We worked closely with this customer to restructure their contract with us, ensuring we remained part of their solution, not their problem. We believe this will benefit both parties for many more years.

Since going public, we have pointed out instances like this involving unusual bills. When those have arisen, we have attempted to pro forma the impact to provide a sense of the underlying health of our business trends during those times.

I appreciate the clarity on that. As a follow-up, Olivier, congratulations on surpassing $2 billion in ARR—one of the fastest software companies to achieve this milestone. I wanted to ask about optimization. Currently, Microsoft seems more optimistic that optimization activity will normalize within the next few quarters compared to Amazon, who has cited deceleration in April. Does this align with your telemetry and reflect that perhaps your Azure monitoring business might begin to turn the corner sooner, while AWS may lag?

It's premature to make any definitive calls. The challenge lies in projecting our numbers from what the cloud providers report, as it's not a direct correlation. Different providers showcase varying dynamics in their reports. Generally, vendors that manage larger bills may experience more push from their clients. In this case, AWS would be more heavily impacted. From our perspective and data, we don’t see any particular cloud recovering from optimization yet. Despite reviewing hyperscaler comments, the sentiment has fluctuated. Ultimately, it doesn't materially impact us; we are positioned to leverage workloads effectively across Azure, AWS, and GCP. The only part we don't cover well from Microsoft's stack is their proprietary technology like Office, which better fits their own tools. Regarding any market share gain moving forward from Microsoft or others, these will likely stem from cloud workloads that gather data for AI models, areas where we are well-positioned to capture business.

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

Olivier, regarding generative AI, is it merely a matter of waiting for these workloads to surface? Given your strong presence in infrastructure monitoring, should we expect these workloads to run primarily on the larger clouds and optimize from there? Or is there something specific you need to do on the product side to ensure Datadog is prepared for generative AI workloads?

There's no doubt productivity levels are going to rise. Historically, when productivity increases, businesses produce significantly more output, leading to additional complexities that need managing. One person might yield ten times more output, yet that same individual may not comprehend everything they’ve created. Our outlook suggests that companies will deliver more functionality to their users at greater speed. They will solve many problems in software development, but teams might find it challenging to fully grasp what they’ve built and its possible flaws or breakpoints. This reflects the patterns we're currently seeing with tools like Copilot, helping solve smaller problems but not capable of building coherent software platforms. Looking ahead, the increased productivity will generate more complexity that our services will assist customers with effectively managing.

Microsoft mentioned the anniversary effect of optimization, suggesting headwinds may subside soon. Meanwhile, AWS indicated a slowdown in April. Your business seems steady in April, indicating a potential decoupling from AWS's deceleration. Can we infer that your consumption trends remain steady despite AWS's trajectory?

Looking forward, we are not projecting any changes in our trajectory for the remainder of the year. Comparing ourselves to other cloud providers, if you check the sequential growth numbers quarter-to-quarter, you will find that the three main players have slowed to about 1% quarter-to-quarter growth during the last quarter. We are still significantly higher than that as our ARR growth is steadily above theirs, indicating that we have already begun to decouple ourselves from the growth of hyperscalers to some extent.

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

This is Joel, on behalf of Fatima. On the financial services vertical, considering the ongoing uncertainty, could you discuss your exposure here and any customer-related behavior you've noticed? I also have a quick follow-up.

Unfortunately, we don’t have specific numbers to share regarding our exposure to financial services. This vertical has been growing for us, particularly as financial services firms are early software adopters. However, we haven't observed any changes in customer behavior on that side. Even during the troubles at SVB and the failures of other banks, we still registered strong product upticks from financial services, both in terms of new logos and expansion deals. So there's nothing unfavorable to report.

You mentioned an expansion deal with a large fintech that displaced open-source software. Could you discuss the competitive dynamics against open source, particularly in this cost-sensitive environment, and why Datadog continues to win as a consolidation solution?

Ultimately, we're winning because we deliver more value. Clients find our solutions work better and offer more value than trying to stitch together open-source tools with their teams. Though some clients may prefer to build their solutions, the majority find that approach less rational. This dynamic has remained consistent, as it would have been ten years ago. Factors such as differing features of open-source projects and our evolving dynamics are present, but the fundamental reasons why we close deals remain unchanged.

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

Olivier, earlier, you noted a focus on the Azure ecosystem. What are some go-to-market strategies you are employing to increase penetration there, considering your historical strength in AWS?

We are undertaking numerous initiatives, including direct collaborations with cloud providers, along with our strong relationship with AWS, and building more integrations specifically for the ecosystem. We have made several product announcements this year in partnership with Azure and Microsoft. Additionally, we are expanding our salesforce in areas where the Microsoft tech stack has a strong foothold. Our customer base historically consisted mainly of software firms on the West Coast, who typically favor AWS. However, more recently, we've established teams focused on enterprise clients in central U.S. regions, which tend to lean more toward Microsoft technology. In essence, we are executing multiple strategies at various levels to ensure that we showcase the right products effectively across Azure.

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. Can you give reference to how these two segments are growing compared to core infrastructure? Additionally, between the two, is one growing faster than the other?

While I can’t divulge specific numbers, generally, the growth trajectory of smaller products outweighs that of larger ones. The overall growth of our products has somewhat declined, particularly products with significant volume components like logs, where optimization has been more prevalent and poses challenges for us.

Regarding the mentioned outage that resulted in a $5 million impact, could you share what lessons were learned from this incident to prevent recurrence in the future? Moreover, apart from the revenue hit, have there been other repercussions from a customer perspective?

We have gained invaluable lessons from such incidents, making it a deeply educational experience. I was genuinely impressed with our team's responsiveness. We documented a postmortem outlining the event's details, and I encourage everyone to review it as it’s quite insightful. Given the scope of this incident, we engaged around 500 to 600 engineers across three shifts to address the outage, and that operation went seamlessly. We’ve acquired insights not only surrounding the root cause but also ways to enhance recovery and assist customers when issues arise in the future. Overall, it was a humbling experience, and I believe we've strengthened our relationship with customers through effective communication during the crisis.

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

David, regarding the large customer additions, you indicated a 130 net addition versus an average of 170 to 240 over the past four quarters. Can you provide more context? Is this a result of customer behavior in response to the economic climate, or is it more related to execution?

Several factors played a role, including the fact that Q1 is seasonally our lowest for new logo ARR. This year's first quarter matched the previous year and was slightly better than year-ago performance. Additionally, most customers who ultimately achieve larger classifications aren’t necessarily born into that segment; they evolve there over time. As the organic growth rate slows, this naturally leads to a slower rate at which customers progress into larger classifications, thus corresponding to the lower accumulation of $100,000 ARR customers.

Olivier, can you provide feedback on the traction of your cloud optimization solution? Are you seeing demand for it?

We are observing extremely strong demand for this solution. This product hits a critical need for customers, and we have a clear roadmap for its future development, so there’s no doubt about it.

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