DigitalOcean Holdings, Inc. Q2 FY2025 Earnings Call

Ladies and gentlemen, thank you for your patience. My name is Krista, and I will be your conference operator today. I would like to welcome everyone to DigitalOcean's Second Quarter 2025 Earnings Conference Call. I will now turn the conference over to Melanie Strate, Head of Investor Relations. Melanie, you can begin.

Thank you, and good morning. Thank you all for joining us today to review DigitalOcean's Second Quarter 2025 Financial Results. Joining me on the call today are Paddy Srinivasan, our Chief Executive Officer; and Matt Steinfort, our Chief Financial Officer. Before we begin, let me remind you that certain statements made on the call today may be considered forward-looking statements, which reflect management's best judgment based on currently available information. Our actual results may differ materially from those projected in these forward-looking statements, including our financial outlook. I direct your attention to the risk factors contained in our filings with the SEC as well as those referenced in today's press release that is posted on our website. DigitalOcean expressly disclaims any obligation or undertaking to release publicly any updates or revisions to any forward-looking statements made today. Additionally, non-GAAP financial measures will be discussed on this conference call and reconciliations to the most directly comparable GAAP financial measures can be found in today's earnings press release as well as in our investor presentation that outlines the financial discussion on today's call. A webcast of today's call is also available in the IR section of our website. And with that, I will turn the call over to Paddy.

Thank you, Melanie. Good morning, everyone, and thank you for joining us today as we review our second quarter 2025 results. We continue to make meaningful progress on the strategy we laid out at our Investor Day back in April. This is evident in our strong second quarter results and supported by the fact that we are raising our full-year guidance on both revenue and profitability metrics. My comments today will include a recap of our Q2 financial results and an update on our progress in product innovation and our enhanced go-to-market strategy across both core cloud and AI, which are enabling over 174,000 digital native enterprise customers to scale on our platform. Let me start with the second quarter financial results highlighted in our earnings deck. The growth momentum from Q1 continued into the second quarter with revenue of $219 million, growing 14% year-over-year. We saw excellent strength in our AI/ML business with revenue growing over 100% year-over-year. Revenue from our Scalers+ customers, or those with an annual run rate of $100,000 or more, also saw strong growth during the quarter at 35% year-over-year, making up 24% of our total revenue. Finally, we achieved incremental ARR in the second quarter of $32 million, marking our highest incremental ARR since Q4 of 2022 and the highest organic incremental ARR in over three years. Given our strong top line performance in the first half of the year and our confidence in the second half outlook, we are raising our full-year revenue guidance range to $888 million to $892 million. We are also excited about the traction we are getting with larger customers and the increase in committed contracts. I spoke last quarter about a multiyear $20 million plus committed deal, which contributed to the material growth in our remaining performance obligation balance as we continue to seek and secure large multiyear deals with our higher spend customers and key strategic partners. Our momentum not only carried over to the second quarter, but the growth also came with healthy profitability, including adjusted free cash flow of $57 million, which is 26% of revenue. As a result of this performance, we are raising our full-year free cash flow guidance to 17% to 19% of revenue, demonstrating our ability to accelerate revenue while maintaining attractive free cash flow margins. Turning to the balance sheet, we continue to make progress on our capital allocation priorities and remain on track to address the outstanding 2026 convertible debt before the end of this calendar year. Matt will provide further details on this front in his prepared remarks. Now let me give you some updates on the product innovation we continue to deliver for our digital native enterprise customers, which are highlighted in our earnings presentation. During the quarter, we released over 60 new products and features that address the needs of our higher spend customers, including builders, scalers, and Scalers+ customers who now drive 89% of our revenue. Notably, 64 of our top 100 customers have adopted a product or feature released in the past year, and 26 of the top 100 customers have adopted a new capability released in the last quarter, which clearly demonstrates the impact our product innovation is having on our digital native enterprise customers. Let me provide a few product highlights from the quarter, starting with core cloud. This past quarter, we officially announced our Atlanta data center, which is now available to all customers. This is our newest and largest data center, purpose-built to deliver high-density GPU infrastructure optimized for AI inferencing. This data center includes our core cloud stack, featuring compute, storage, and other cloud elements that are essential for enabling AI-native customers to manage full-stack applications supported by AI. This comprehensive cloud data center infrastructure is a key distinguishing factor for us compared to other neo clouds, as it provides a complete stack for running sophisticated AI applications that have extensive requirements beyond just GPUs. More on that later. During the quarter, we continued to build capabilities for larger digital native enterprises. These customers typically require high-quality storage, especially for AI workloads. To support that, we enabled NFS or network file systems for GPUs, allowing customers to run demanding GPU applications with access to higher performance object storage to meet enterprise workload demands such as video streaming and data lakes. We also introduced two advanced networking features in public preview: Bring Your Own IP address (BYOIP) and Network Address Translation gateways (NAT gateways). These are crucial capabilities that will assist more and larger digital native enterprise workloads in migrating to this solution. BYOIP allows customers to use their existing publicly routable IP addresses on DigitalOcean, eliminating the need to acquire new specific IP addresses, facilitating smooth transitions to our platform without requiring extensive application modifications. Meanwhile, NAT gateways allow a customer’s resources to securely access the Internet from within their virtual private cloud on the DigitalOcean platform. These innovations on the core cloud platform are enabling us to scale and capture more workloads from our digital native enterprise customers. To leverage this traction, we are complementing our industry-leading product-led growth strategy with a dedicated migrations team to support customers transitioning existing workloads from hyperscalers and other clouds to DigitalOcean's platform, facilitating 76 migrations during the quarter. One notable example is Xcitium, a next-generation cybersecurity provider offering innovative, no-cost incident response as part of its fully managed security operations center. They signed an 18-month contract with DigitalOcean to migrate from other cloud providers, drawn by our compelling total cost of ownership, performance, and ease of use, which enables them to deliver cybersecurity solutions more efficiently and at scale. Servd.host, a Scalers+ customer providing managed hosting for the craft content management system, has already adopted our newly released network address translation gateway, allowing their customers secure Internet access within their DigitalOcean Virtual Private Cloud. We’re also thrilled about the progress we are making with our AI/ML platform, now known as the DigitalOcean Gradient AI Agentic Cloud, which complements our full stack general-purpose cloud. The power of having these two platforms side-by-side allows our customers to fully utilize the integrated stack required to build and operate AI-powered applications in the future. The Gradient AI Agentic Cloud comprises three components: Gradient AI Infrastructure, Gradient AI Platform, and Gradient AI Agents. I’ll start with the Gradient AI Infrastructure, where we've significantly expanded our GPU Droplets lineup to now include eight major types, such as the H, L, and RTX Series GPUs from NVIDIA, along with the latest Instinct series GPUs from AMD. A major update enhancing the Gradient AI Infrastructure for inferencing is the introduction of a new inference-optimized GPU Droplet, simplifying the setup and deployment of large language models (LLMs) using Docker. This new GPU Droplet comes preconfigured with vLLM and features optimizations like multi-GPU parallelism, smart batching, faster token generation, built-in support for Hugging Face model downloads, speculative decoding, prompt caching, and multi-model concurrency, allowing customers to go from deployment to serving tokens in mere minutes without the need for extensive manual setup. We recently announced a collaboration with AMD that provides our customers with access to the AMD Instinct MI325X GPU Droplet, alongside the MI300X Droplet. These GPUs deliver high-level performance at a lower total cost of ownership and are ideal for large-scale AI inferencing workloads. Another example of this collaboration is the Gradient AI Infrastructure powering the recently launched AMD Developer Cloud, enabling developers and open-source contributors to test AMD Instinct GPUs instantly in a fully managed environment. This allows developers to start AI development with no hardware investment and speeds up time to value in tasks like benchmarking and inference scaling, furthering our mission to democratize AI access while delivering the quality, performance, and flexibility our customers expect from DigitalOcean. Let's look at how customers are taking advantage of our Gradient AI Infrastructure. Featherless.ai is an AI inference platform offering API access to an extensive catalog of open-weight models, primarily Hugging Face models. They chose DigitalOcean for its simplicity and price performance and were early adopters of our AMD MI300X GPU Droplets, which provide exceptional price performance and ease of use for inference workloads. Another customer utilizing GPU Droplets is ScribeAI, specializing in AI-generated documentation and serving 94% of the Fortune 500 companies. They migrated their AI/ML training workloads to DigitalOcean from competitors and are now using our GPU Droplets to build and train their knowledge-sharing platform. Moving to the next layer of our Gradient AI Agentic Cloud, we recently announced the general availability of DigitalOcean's Gradient AI Platform, the industry's most user-friendly and cost-effective solution for developing production-grade AI agents with automatic safety and security measures. The Gradient AI Platform, as shown on the right side of our earnings deck, caters to the complete life cycle of agent development, allowing AI-native, SaaS, and software application customers to build, test, deploy, monitor, and operate agentic AI software. Customers can access a rich set of proprietary and open-source foundation models, including OpenAI, Anthropic, Mistral, DeepSeek, and Llama as high-performance serverless endpoints that automatically scale to meet real-time application demands, freeing customers from managing their own compute resources. The Gradient AI Platform includes built-in guardrails to verify AI behavior and offers an advanced agent evaluation framework to ensure high accuracy of AI results along with the capability for robust experimentation to optimize AI performance. Since the platform's announcement, over 14,000 agents have been created, almost double the previous quarter's number, with over 6,000 customers leveraging this platform since January, 30% of whom are new to DigitalOcean. One customer using our Gradient AI Platform is Quickest, a leading AI-powered collaborative workspace tool that helps product marketing and sales teams create strategy documents and campaigns using shared AI personas. Quickest utilizes the Gradient AI Platform to generate persona agents, facilitating model comparisons and managing tasks on the platform to fetch and summarize content. They selected DigitalOcean for its flexible and scalable infrastructure to handle complex AI workflows and appreciate how easily agents can be deployed and integrated into their product line with minimal coding. Moving on to the Gradient AI Agents layer, our first commercial AI agent is the Cloudways Copilot, which continuously monitors crucial server components to identify issues in real-time, diagnose root causes, and provide actionable recommendations faster than traditional alerting systems. Mint Media, a full-service media and marketing company that specializes in video production and digital marketing, uses our Cloudways Copilot Gen AI Agents to automatically detect and resolve web hosting issues. They manage over 180 websites and have experienced considerable time savings by using Cloudways Copilot, which turns what once took hours of manual debugging into minutes with detailed actionable recommendations. In addition to the new product innovations we delivered, we also made significant strides in our go-to-market strategy this quarter. In terms of new customer acquisition, we observed noteworthy progress at the top of the funnel from our product-led growth enhancements. Revenue from core cloud customers within their first 12 months exceeded growth from previous years, which is a promising leading indicator for future growth potential. Our direct sales approach and strong ecosystem partnerships are attracting more AI-native customers with large-scale inferencing needs than we've seen previously. The increasing success with these marquee customers is reflected in the uptick in remaining performance obligations previously mentioned, and we expect this trend to continue as we expand our AI capabilities. In conclusion, I’m pleased with both our second-quarter results and the progress we are making on the strategy articulated during our Investor Day in April. We maintained our top line growth momentum from Q1 to Q2 while achieving healthy profitability metrics, allowing us to raise our guidance for both revenue and profitability for the fiscal year 2025. We continue to innovate our products, enhance our industry-leading product-led growth model, and gain traction with our direct sales efforts, particularly in AI. The full general availability of the Gradient AI Platform represents a significant advancement in our offerings, merging our general-purpose cloud capabilities with a modern agentic AI cloud. These integrated systems allow AI-native customers to conduct inferencing at scale while enabling digital native customers to seamlessly incorporate AI into their applications without dealing with the complexities of AI infrastructure. This unique combination of cloud and AI stacks is generating increasing momentum with AI-native companies requiring larger scale inferencing, and we are strengthening our partnerships with key players in the AI ecosystem. Furthermore, we are making solid progress on our balance sheet and refinancing priorities, putting us in a strong position for 2026. Thank you, and I'll now turn it over to Matt.

Thanks, Paddy. Good morning, everyone, and thanks for joining us today. As Paddy discussed, we are very pleased with our Q2 2025 performance, and we are confident in our ability to sustain and build on this momentum in the latter half of the year. In my comments, I'll walk through our Q2 results in detail, provide an update on our balance sheet and capital allocation strategy and share our third quarter and full year 2025 financial outlook. Starting with the top line. Revenue in the first quarter was $219 million, up 14% year-over-year. Our annual run rate revenue, or ARR, was $875 million, which was $32 million above Q1. This incremental ARR of $32 million was the highest incremental ARR since Q4 of 2022 and the highest organic incremental ARR achieved in over 3 years. We continue to build and strengthen our relationships with our higher spend customers and key strategic partners. This is evidenced by the material increase in our remaining performance obligation balance as we continue to secure large multiyear deals with our digital native enterprise customers, which is an early but promising new go-to-market motion for the company. Our product innovation and go-to-market enhancements are resonating with this target customer base. In Q2, revenue from our Scalers+ customers or customers whose annualized run rate revenue in the quarter was greater than $100,000 and who represent 24% of overall revenue, grew 35% year-over-year with a 23% increase in customer count. This is clear evidence of the increasing traction that we are getting with our largest customers as they expand their use of our core cloud products and adopt our new AI offering. Q2 revenue growth was primarily driven by improvements in customer acquisition across both core cloud and AI as well as strong customer adoption of our AI/ML products. As Paddy mentioned, revenue from core cloud customers in their first 12 months significantly outpaced growth from prior years, which is a great leading indicator of future growth as these stronger recent cohorts not only drive up revenue from customer acquisition but also should positively contribute to net dollar retention when they reach their 13th month and become part of our NDR cohort. Our Q2 net dollar retention was 99%, up from 97% in the same quarter last year and within the expected range that we communicated on the prior quarter's call. We also delivered strong AI/ML revenue growth in Q2 as we continue to see a robust demand environment, particularly for inference workloads, with AI revenue growing north of 100% year-over-year. Turning to the P&L. We delivered strong performance on all of our key profitability metrics. Gross margin for the second quarter was 60%, which was 100 basis points higher than the prior year. Adjusted EBITDA was $89 million, an increase of 10% year-over-year. Adjusted EBITDA margin was 41% in the second quarter, approximately 100 basis points lower than the prior year. Non-GAAP diluted net income per share was $0.59, a 23% increase year-over-year. This increase is a direct result of expanding per share profitability by driving durable revenue growth while exercising ongoing cost discipline. GAAP diluted net income per share was $0.39, a 95% increase year-over-year as we continue to grow revenue, drive operating leverage, and prudently manage stock-based compensation. Q2 adjusted free cash flow was $57 million or 26% of revenue, up significantly from our front-loaded Q1, which included a large portion of the upfront investment required to bring the Atlanta data center online. As I'll detail later in my comments, we remain confident in our ability to deliver attractive adjusted free cash flow margins for the full year although the timing of capital investment payments will continue to create quarter-to-quarter variations in adjusted free cash flow margins, hence, our highlighting of the trailing 12-month adjusted free cash flow margins on Slide 15. Our balance sheet continues to be strong as we continue to maintain material cash and cash equivalents and ended the quarter with $388 million in cash. We also continued to execute our share repurchase program in the quarter with $20 million of repurchases in Q2, buying back approximately 691,000 shares. This brings our cumulative share repurchases since the IPO to $1.6 billion and 34.8 million shares through June 30, 2025. At the end of Q2, we had $3.4 million remaining on our current share repurchase authorization. On the debt front, we continue to actively evaluate the market and our financing alternatives and remain committed to fully addressing the 2026 convertible debt over the balance of this calendar year. We have multiple attractive financing options available to us, including convertible debt, bank debt, and bonds. And we plan to tap into these markets as needed to optimize our long-term cost of capital. Before we move on to guidance, I'll highlight one noncash item related to both the balance sheet and the P&L. We continue to evaluate the necessity of our valuation allowance on certain existing tax deferred tax assets each quarter in accordance with U.S. GAAP. While the valuation allowance is still necessary for Q2, in the latter half of fiscal 2025, we may release all or a portion of our valuation allowance of $109 million, which was discussed in our most recent 10-K as well as in our most recent 10-Q. When released, we estimate this would have the financial impact of decreasing our noncash tax expense by the amount of the release, resulting in a corresponding increase in net income. When this occurs, it will be a positive noncash event and will have no impact on non-GAAP financial metrics. Moving on to guidance. For the third quarter of 2025, we expect revenue to be in the range of $226 million to $227 million, representing approximately 14.1% year-over-year growth at the midpoint. For the full year 2025, we are raising our annual revenue guidance to the range of $888 million to $892 million, representing approximately 14% year-over-year growth at the midpoint. Given our strong Q2 performance, visibility into our customers' usage trends, and the strength of the AI/ML demand environment, we are able to raise our full year guidance with confidence. For the third quarter of 2025, we expect our adjusted EBITDA margins to be in the range of 39% to 40%. For the full year, we raised our adjusted EBITDA margin guidance to the range of 39% to 40%. For the third quarter of 2025, we expect non-GAAP diluted earnings per share to be $0.45 to $0.50 based on approximately 102 million to 103 million in weighted average fully diluted shares outstanding. For the full year 2025, we expect non-GAAP diluted earnings per share to be $2.05 to $2.10 based on approximately 103 million to 104 million in weighted average fully diluted shares outstanding. Turning to adjusted free cash flow. We raised our guided adjusted free cash flow margins for the full year to 17% to 19%, increasing our projected cash flow margins; at the same time, we are accelerating our revenue growth outlook, which speaks to the confidence we have in our ability to maintain attractive free cash flow margins while we accelerate our top line growth. Consistent with our historical guidance practice, we are not providing adjusted free cash flow guidance on a quarter-by-quarter basis, given it is heavily influenced by working capital timing as you saw in our year-to-date results. That concludes our prepared remarks, and we'll now open the call to Q&A.

Your first question comes from Patrick Walravens with Citizens.

Congratulations, Paddy. Could you talk a little bit more about the AI/ML revenue and the over 100% increase there? And maybe walk us through a little bit the history of this offering and why the current version is really starting to kick in?

Yes. Thank you, Patrick. Good way to get started. So the AI/ML revenue, as I mentioned in the call, grew more than 100% year-over-year. So if you remember, last Q2 is when we brought a lot of H100 NVIDIA gear online. So more than doubling that this quarter was a significant step for us. And what is different is, as I explained, we have a 3-layer AI stack. On the foundational level is our Gradient AI infrastructure stack, which is a network of GPUs, both from AMD as well as NVIDIA. And then in the middle layer is our Gradient AI platform that we just took from private to public preview all the way to general availability. And then on the topmost layer are agents. So the type of customers that use these 3 layers are slightly different at this point. So AI infrastructure is consumed typically by AI-native companies that have their own model or have taken an open-source model and are doing some tweaks to it and hosting those models and scaling them, especially in the inferencing mode, are typically consuming the AI infrastructure. And a majority of our revenue comes from the Gradient AI infrastructure stack. And that's not very dissimilar from the rest of the industry. The Gradient AI platform that we recently pushed out to GA is where any software application like a SaaS provider, for example, can start consuming AI into their own applications without having to do the heavy lifting of building and managing their own GPU infrastructure. So we have serverless endpoints for these LLMs, for example. And we have a bunch of other tools and modules that are critical building blocks for consuming AI into your own application. So it becomes very, very easy to build AI into your existing applications. And that's what is powering the growth of our AI revenue is predominantly on the infrastructure side, but we are driving a lot of adoption and mind share with developers with the AI platform. And on the Agent layer, the first commercial application of that is the Cloudways Copilot that's typically adopted by end customers as a way to automate some of the manual tasks that we are seeing in managing and operating cloud-based applications.

Your next question comes from the line of Mike Cikos with Needham & Company.

I wanted to continue discussing AI and machine learning. It's great to see over 100% revenue growth, which reflects some recent trends in annual recurring revenue. However, I'm looking for more details on the underlying components of the new annual recurring revenue. Last quarter, you mentioned over 160% year-on-year growth. I may have missed this information, but I'm interested in how the new revenue is developing in the AI and machine learning area for the June quarter.

Mike, it's Matt. Our ARR has been experiencing growth, with AI ARR increasing over 160% in previous quarters. This figure refers to the actual ARR, not just incremental ARR. The north of 100% still indicates strong growth. This quarter, our incremental ARR was at 32%, showing a good balance between AI and core cloud, marking our highest incremental ARR ever. The decline from 160% to north of 100% is due to the fact that we are comparing against a high growth quarter last year when we launched our AI capabilities, which created a lot of pent-up demand. As a result, the Q2 growth in the AI business was substantial, making for a tough comparison. However, the incremental ARR we are adding in that business is on an upward trajectory.

Got it. And for the NDR, I know that the 99% here is in keeping with that commentary you guys have provided last quarter. Can you just explain what actually acted against that? Because I would have thought there would have been at least some benefit from you guys lapping that Cloudways price increase in April.

Yes. When we examine the NDR, we've noted that it will likely fluctuate within the current range this quarter and possibly for the next couple of quarters due to market conditions. We haven't observed any deterioration in the market since April. However, when we look at some of our larger customers, there is a mixed impact. Some customers are cautious and either optimizing or hesitant to expand, whereas others are thriving and increasing their business and workloads with us. This is reflected in the growth of our Scalers+ at 35%. While there's strong growth among certain customers, others are being more reserved, leading us to believe that we'll maintain these levels. The positive aspect is that even though the NDR dipped slightly to 99%, we've managed to raise our guidance. We're achieving the best incremental ARR we've seen in a long time, which is promising. Although NDR is a lagging metric and may take some time to improve, it won't hinder our revenue growth. The new product acquisition in our core cloud is performing exceptionally well, along with the migration motion and growth in our AI business. We're very optimistic about our growth prospects, which is why we raised our guidance for the year.

Q - Gabriela Borges:

I wanted to touch on the unit economics of the AI business. Matt, I know in the past, you've talked about the 3-year payback period, but you've both been very consistent in saying, as you move from bare metal GPUs to more differentiated services, exactly as you've illustrated in the graphic in the slides, you should be able to command more gross margin essentially. So maybe give us an update on how those efforts are tracking? How do you feel about the gross margin and the LTV CAC of the AI business relative to the core business?

Yes, we are very encouraged and comfortable with the margins we are achieving in the AI business. The higher layers of the stack have better margins compared to pure infrastructure. However, we also feel confident about the returns at the infrastructure level, especially considering the long-term value we expect to generate from those customers. As mentioned previously, inferencing customers, which we are seeing more frequently even at the infrastructure layer, will also require additional cloud services such as databases, storage, bandwidth, and standard compute CPU. While we are still making investments in infrastructure, which has lower margins than the higher layers, this baseline capability is essential for offering the higher-layer services. We view this as a solid investment and a wise use of our capital. We are optimistic about the returns we’re seeing and the potential for greater returns as this business develops, leading to increased pull-through revenue and a shift of more revenue to the higher layers of the AI stack.

And just to add to what Matt said, we are making significant investments to optimize our Gradient AI Agent Cloud for inferencing. I mentioned our inference optimized Droplet, and on the right side of Slide 8, you can see our investments in model optimization and infrastructure optimization. Our goal is to enhance the scalability of inferencing workloads on our platform, which often have very long tails. As Matt pointed out, these workloads also influence other cloud services, so they drive the overall performance as they scale globally. We are optimistic about our position and the early successes we've experienced with notable customers who are expanding their inferencing capabilities with us.

Yes. That makes sense. And Paddy and Matt, the follow-up I have here, just on these comments on highest incremental ARR, highest organic ARR in over 3 years in terms of the net new that you're adding. Can we think of this as the new high watermark? And looking at what's being implied in guidance, talk to us about your ability to consistently deliver growth of that metric and whether there's any unevenness whether because of seasonality or company-specific factors like the timing of new AI capacity coming online that we should be aware of as we think about the forward model.

Yes, I can start, and then Matt can add. We did not experience anything unusual in the last quarter. We did not bring a lot of new capacity online, and there was no seasonality to note. As mentioned in our prepared remarks, we are fine-tuning our product-led growth strategy for our core cloud customers, which is beginning to yield positive results. Our migration strategy is attracting a new segment of customers, typically digital native enterprises, and we are seeing growth in this area. On the AI front, we are starting to onboard some larger inferencing customers. It's a combination of these factors rather than a single large contract or a significant increase in GPU capacity. The momentum we are experiencing in acquiring new customers is steady and sustainable. Matt?

I agree with that, Paddy. I want to reiterate that ARR is not determined by bookings or sales; it's tied to actual customer revenue and usage. We believe this will serve as a reliable indicator of our future trajectory and is an essential metric for us. As Paddy mentioned, we are optimistic about our ability to enhance it. While it will fluctuate from quarter to quarter, we are confident in our capacity to improve this metric as we move forward.

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

Perfect. Staying on the topic of AI and inferencing, can you elaborate on how you differentiate in that space? Where does the industry currently stand regarding capacity constraints? Is that still a relevant factor for you, or is it now more about differentiation? I have a follow-up on that as well.

Thank you, Raimo. Capacity constraints are a way of life in AI as we are scaling like everyone else. So we are trying to stay ahead of it a little bit, but there's just so many factors there in terms of the real estate footprint and the power and the cooling and the actual gear. So there's just a lot of variable factors here. But I think for us, it all boils down to why some of these marquee AI-native customers are starting to choose us over the other alternatives that they have. And it is really the twin stack cloud that we have laid out in Slide 8. So I don't think there are too many cloud providers that can claim to have both sides of that equation. And we certainly feel like we are driving home that point in terms of not only offering a world-class AI infrastructure, but increasingly, those same customers are also starting to leverage some of the guardrails and the agent evaluation framework and the agent observability and things like that, going up the stack on the right side of the agentic cloud. But also, as Matt mentioned, they also have very sophisticated storage, data processing, and CPU compute requirements as well because at the end of the day, these are very sophisticated applications that require the might of a full-stack general-purpose cloud. So I think that is the differentiator that we are leaning on, and we feel really confident. I've been talking about this for about 4 quarters. And finally, we have the twin stacks that we have described on Slide 8 of the earnings deck. And we feel really good. We're just getting started, and some of the RPO and the large contracts that we have been talking about, they have not even started hitting their full stride as we are scaling those customers. So we feel really good about the forward momentum that we are building.

That brings me to my next question for Matt. Considering the second half of the year, I've received several inquiries from people noting that we're likely raising the full year guidance by more than the amount we exceeded in Q1 and Q2. This indicates a strong confidence in the second half. Should we consider that the increase in RPO provides greater visibility, which influences some of that guidance, especially given your typically conservative nature?

I wish I could attribute our full confidence solely to the RPO. While we’re pleased with the increase, it still represents a very small part of our business. Nevertheless, in looking at our performance in the first half, our visibility into customer usage patterns, the migration trends we’re observing, and the momentum we’re gaining with AI and through direct sales and partnerships, as well as discussions with large AI-native companies, we have enough opportunities that bolster our confidence in raising our revenue guidance. What is particularly encouraging is that, as someone who tends to be conservative, we are also improving our free cash flow margin concurrently. This ability to grow revenue while maintaining strong free cash flow margins is very promising as we look ahead to the second half and how it positions us for 2026.

Your next question comes from the line of Jason Ader with William Blair.

I would like to get a breakdown of the business, specifically looking at the AI segment compared to the non-AI segment. I understand you've shared growth rates, but can you provide a rough estimate of the percentage of revenue coming from AI? I'm thinking it might be around 5% to 10%, but any additional details would be very helpful.

Jason, as you know, we don't provide specific breakdowns. Part of the reason is that we believe many of the AI capabilities will integrate with other offerings. Therefore, the impact of growth extends beyond just the AI-related products. You're in the right range; I would say AI is becoming a significant part of the business. While it remains small since we only launched it a year ago, it is accelerating and a reasonable estimate for its revenue contribution is valid. We anticipate that its role will become increasingly significant by 2026, but it will still be a minor component. The core cloud business remains robust and is expanding, and the AI segment acts as a strong complement to it. Additionally, it is driving our growth and attracting new customers that will further enhance our core cloud growth.

Okay. Great. And then just as a quick follow-up, is it fair to assume that the core cloud business grew at a similar rate in Q2 versus Q1 in that kind of low double digits? Is that accurate?

Yes. We still see momentum in the core cloud business. And while the NDR was a little bit lower in Q2 than it was in Q1, the revenue that we're getting from new customers is ahead of our plan and our expectations. We're doing a really good job there. And again, you got to remember, NDR is a little wonky lagging metric because what happened like the change in revenue from a year ago has as much impact as the change in revenue this year. So the core cloud business continues to accelerate. It's in that low double-digit growth rate and is improving.

So most of the upside then was from new customers, it sounds like.

Yes, correct. Yes. Because I mean with NDR coming down a little bit, the new customer acquisition plus the growth in AI offset the slight headwind from the NDR. But again, if you look at the incremental ARR, if you look at it on an exit run rate standpoint, there was a very good balance between the core business and AI. And so you both saw AI at its highest point, but there was still very good core cloud growth on an incremental ARR as well.

Your next question comes from the line of Josh Baer with Morgan Stanley.

Just wanted to confirm that in the net dollar retention rate, AI and ML revenue is not in that metric. Is that right?

That's correct, Josh. This situation will remain for some time. We've discussed this internally and during Investor Day, we indicated that it will eventually contribute to the net dollar retention, and we still believe that to be true. It will most likely apply to more stable inferencing workloads rather than temporary projects that a customer tests and then scales back. Considering the time lag for a customer to be counted in net dollar retention, they aren't counted in our core cloud until their 13th month. Therefore, if we start seeing inferencing workloads from notable customers now, it will take a year before they contribute to the net dollar retention. We are planning to include at least the inferencing aspect of AI eventually, but it definitely won't be in the upcoming quarters. So currently, net dollar retention does not consider AI.

Okay. Got it. Yes, I would think like especially now as it's scaling, but also you have more than 12 months, you talked about 100% growth off of the Q2 last year where there was AI revenue and it's all organic kind of missing piece to that NDR percentage just around that expansion from existing customers. I did want to ask you about the large deals. Like how we should be expecting the potential for large deals in the future? And then also for you, Matt, how you're thinking about it from a guidance perspective, assuming that would be a little bit lumpier or have longer sales cycles or it's just a new motion for you guys, how do you incorporate the potential for large deals in guidance?

The nature of large deals is a new area for us in terms of sales, business development, and forecasting. Our primary focus is on ensuring customer success and leveraging our technology advantage to attract, retain, and help these customers scale. My main priority, along with Bratin and Larry, is to clearly communicate our technology differentiation and ensure we have the right engineering expertise to support our customers. I'm encouraged by some early wins we've had and the opportunities in our pipeline related to these deals. However, it takes time to transition from securing a deal to scaling operations with actual traffic. We are currently working through this process with several customers. Looking ahead, we aim to improve our ability to predict outcomes, but I anticipate some variability in the beginning as our customers also adjust to this new approach. They can experience sudden changes due to updates to their models or software, particularly those in consumer and B2B AI. We are learning alongside them as they develop their business models and scale. I'll let Matt discuss how we will reflect these developments in our financials.

With that context, Josh, we will take a cautious approach to our forecasts, as you would anticipate from our historical performance. The positive aspect is that we recognize revenue as it is earned, rather than signing large agreements that activate immediately. We have insight into the progress and behavior of our customers. However, since this is a new initiative for both us and our clients, we will proceed conservatively regarding the inclusion of projected revenue from significant deals until we are confident that everything is on the right path, that we are experiencing growth, and that we can clearly see that growth. Therefore, you can expect us to remain conservative in how we incorporate any large deals into our forecasts.

Our next question comes from the line of James Fish with Piper Sandler.

You keep using the word conservative here. But on the guide side, we haven't seen this level of second half step-up in some time, really going back to the pandemic. And you guys deserve credit here doing $32 million of net new ARR organic. But can you just walk us through the linearity you are seeing, what you're expecting from some of the newer solutions in the second half to raise guidance by this much? And any of the other moving parts that help you bridge this kind of larger-than-normal step-up here? Because if I look at this and say you book similar kind of to slightly better net new ARR in the sort of $30 million to $35 million range over the next 2 quarters, it really doesn't leave much wiggle room based on how you guys are defining ARR versus revenue now.

I appreciate your question, Jim. In the previous quarter, we chose not to raise our guidance after outperforming in Q1, as we were uncertain about the macro environment. Now that we have a complete quarter behind us, we feel optimistic about our visibility with core customers. We also have the advantage of exceeding our targets in both the first and second quarters. We have confidence in various aspects of our performance, including strong revenue from new customers and stability in our monthly growth. We're seeing increased traffic and better conversion rates from our customer base, which indicates a solid and lasting improvement. Additionally, we've ramped up our migration initiatives, completing around 70 migrations this quarter, and we have a steady pipeline for this process. Our AI projects are gaining traction as well. Overall, the combination of these factors provides us with the assurance to raise our guidance, even without fully accounting for the potential of large deals. We believe there are additional opportunities for growth in the year ahead.

Got it. And then, Paddy, maybe for you, can you talk about what you're seeing on sort of the GPU pricing dynamic as it seemed like across the space, pricing came down a little bit and how you're thinking about the ability to repurpose any GPUs that kind of migrate from customer to customer or what you're seeing in terms of utilization at this point across the GPU side?

Thank you, Jim. The utilization is very strong. We are operating efficiently with our GPU fleets, regardless of the GPU generation. As we intensify our focus on inferencing, we gain flexibility in how we allocate machines. Our inferencing customers typically focus more on price performance than just the raw throughput of a specific GPU generation. For instance, if we have 100 units of GPUs from the current generation, and we can provide the same price performance with 90 units from the next generation, the customer is satisfied as long as it remains within the same GPU family without requiring any reengineering. Therefore, we are reaching a stage where price performance outweighs considerations of price or performance alone. This flexibility allows us to allocate different GPU families across our inferencing workload customers. This will become increasingly significant as we scale up our customers across various regions and operate in multiple data centers. There are many new factors to address, but the pricing dynamics in training workloads differ considerably from those we see in the primarily inferencing-driven segment.

We have time for one more question, and that question comes from the line of Brad Reback with Stifel.

Matt, as we think about gross margin for the back half of the year as the revenue mix maybe shifts a little bit and you continue to invest in CapEx. How should we think about the trajectory? And then heading into next year as you lap the change in useful life, what type of impact should we expect then?

The gross margins are expected to remain relatively consistent at current levels for the remainder of this year. Although the AI business is growing rapidly, it still constitutes a small portion of our overall business, so it will not significantly affect gross margins. Looking ahead to next year, while we are not prepared to provide guidance, we anticipate a modest headwind to gross margins due to AI's growth. However, the majority of our business will maintain the same high margins we currently have. We are also focused on driving efficiencies in our core business, including bandwidth optimization and our longer-term data center optimization strategy. Therefore, we are confident in our ability to sustain healthy gross margins at present levels. If AI becomes a significantly larger part of our business in the future, we will provide more visibility on that, and at that time, a slight margin pressure may be observed. For now, we expect gross margins to stay around current levels for the rest of the year.

Your next question comes from the line of Mark Zhang with Citi.

Maybe just want to dig a little bit more into the RPO performance, very nice to see. But can you give us a sense of maybe the characteristics here? What are the average deal sizes, contract durations? And I just wanted to confirm that AI was the leading contributor here? Or you saw good contribution from Core Cloud as well?

I was starting to discuss the increase in remaining performance obligations, which came from both core cloud and AI, not just AI alone. There are indeed some AI deals included. The average duration, which I believe I mentioned for Q1, is around 19 months. Typically, the length of these deals ranges from 1 to 2 years, as this is a relatively new approach for us. It's encouraging that we're attracting customers who value the ability to engage in straightforward consumption with us and are willing to commit to a minimum revenue level over a certain period. This reflects our product innovation and improvements in the core cloud, as well as customers' confidence in our ability to meet their needs. Paddy, do you have anything to add?

No, I think you nailed it, Matt. Yes, it is definitely a combination of both our core cloud as well as AI. So there's not. This is not just reflective of just one giant, huge deal or anything like that.

Got it. And then just maybe a quick follow-up. Just on capital allocation. It seems like you guys have been stepping up on share repurchases since, I guess, end of last year. But now with the authorization going down to about $3 million, what's sort of the thought process around just capital allocation going forward?

Yes. Our capital allocation, we actually reduced the amount of repurchases that we've been doing over the last 2 years. We did almost $500 million in 2023, and then across 2024 and into 2025, it was only $140 million. Our primary objective at the moment, and we articulated this in Investor Day, is it's all about organic growth and investing to drive organic growth. But then secondly, and as important, is we're committed to making sure that we've taken care of the balance sheet, and we've addressed the outstanding convert. And we've said that we're going to do that by the end of this year, and we started that process with our $800 million bank facility, $500 million of that is a term loan. And so we're dialing back the share repurchases just so that we can make sure that we take care of those first 2 objectives. And as soon as we take care of those 2 objectives, the first one will be ongoing, but the second being taken care of, the outstanding convert, then we'll go back to a, let's say, a reasonable level of share repurchases that are targeted at offsetting dilution. So it's, I think, priority 1 is organic growth. Priority 2 is take care of the convert. And priority 3 is use the repurchases to offset dilution. And right now, priorities 1 and 2 are the bigger focus for the next quarter or so.

Your next question comes from the line of Thomas Blakey with Cantor.

Congratulations on the results. I have a clarification regarding Jason Ader's earlier question. Matt, did you mention that the core cloud saw acceleration in the second quarter? Also, concerning the core AI, which is now organically growing over 100%, what kind of impact did it have on NDR, if any? It seems logical that there would be some effect from these customers purchasing additional services on the platform, and I would be interested to know what impact that had on that metric.

On the second part of your question, many of the AI customers that are approaching us are new customers, particularly in the infrastructure area of AI. They are not yet purchasing a significant amount of products on the core cloud side. Even if they were, they haven't been with us long enough to contribute to net dollar retention. Therefore, there isn't much impact from that currently. The benefits will be seen in the future, which I believe you're rightly highlighting. Could you please repeat the first part of your question?

Yes. I think you said earlier on the call to a question that core cloud kind of excluding AI/ML accelerated. And I just wanted to make sure I heard that correctly.

The year-over-year growth rates for the core cloud continue to improve. When you examine metrics like NDR, it reflects the change in revenue last year compared to this year, which involves several lagging components. The incremental ARR and overall ARR growth of the core business are also continuing to accelerate.

Your next question comes from the line of Wamsi Mohan with Bank of America.

Yes. I guess, firstly, on your AI customers, are you seeing higher volatility or churn in that customer base? And just to clarify, is the penetration of these customers, how would you categorize that between maybe learners, builders, scalers in your traditional way of thinking about the customers? Where are these in their journey? And any thoughts around graduation rates on these customers?

Yes, great question, Wamsi. It's good to hear from you. It's a completely different approach to customer acquisition. We don't categorize them as testers, learners, builders, or scalers because they usually don’t follow that journey on our platform. Many of these customers are early-stage startups. However, we are observing significant traction on the inferencing side. These customers, in their development, have made progress in securing funding and achieving product-market fit and customer traction. They are coming to us with scaling inferencing needs, which indicates they have found product-market fit and now have an audience willing to pay for those needs. We're noticing a shift from the test and leave approach we saw last year on the training side. Now, as we focus more on inferencing, these customers engage with us, stay, expand, and utilize different parts of our offerings. It's a very distinct lifecycle we are witnessing in this area.

Okay. Great. And if I could follow up quickly with Matt. On the growth CapEx side, any incremental thoughts over here? I know you said organic investments and driving organic growth is sort of highest priority. So relative to your comments that you made last quarter, how should we be thinking about the growth CapEx profile over the next few quarters or into next year?

Yes. Several points come to mind. Firstly, we have raised our guidance for free cash flow margins, and we are optimistic about this in relation to our projected growth rates. As mentioned last quarter, if we identify an opportunity to accelerate growth beyond our previously stated target of 18% to 20% by 2027, we will certainly pursue it. We have a variety of tools at our disposal to achieve this in a manner that is both capital-efficient and cash flow efficient. Therefore, we remain very confident that we can increase our revenue while preserving appealing free cash flow margins.

And ladies and gentlemen, that does conclude our question-and-answer session, and it does conclude today's conference call. Thank you for your participation, and you may now disconnect.