DigitalOcean Holdings, Inc.

Hello, everyone. Thank you for joining us, and welcome to the DigitalOcean's second quarter 2026 earnings conference call. After today's prepared remarks, we will host a question and answer session. If you would like to ask a question, please press star 1 to raise your hand. To withdraw your question, press star 1 again. I will now hand the conference over to Radu Pritriki, head of investor relations. Radu, please go ahead.

Thank you, and good morning. Thank you all for joining us today to review DigitalOcean's second quarter 2026 results. Joining me on the call today are Paddy Srinivasan, our Chief Executive Officer, and Matt Steinfort, our Chief Financial Officer. For those of you following along, an accompanying slide presentation is available on the webcast. Before we begin, let me remind you that certain statements made on today's call may be considered forward-looking, which reflects 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 SEC filings 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. Reconciliations to the most comparable GAAP financial measures can be found in today's earnings press release, as well as in our investor presentation that outlines the discussion on today's call. A webcast of today's call is available in the IR section of our website. And with that, I turn the call over to Paddy.

Thank you, Radu. Good morning, everyone. And thank you for joining us today. We had an exceptional Q2. as we continue to accelerate growth in a disciplined way and I'm excited to share the highlights with all of you. Let me start with four key takeaways from the quarter. First, our growth rate continues to accelerate. As we previewed several weeks ago, Q2 was another strong quarter for DigitalOcean. We were above guidance on every key metric. We delivered 29% year-over-year revenue growth while continuing to have strong profitability. Second, our inference services, the collection of all non-bare metal inferencing capabilities on our AI native cloud is getting tremendous traction and grew almost 800% year over year. Launched in late April this year, our inference engine, which is a managed offering that includes serverless inference and related technologies, is off to a flying start with over 6,000 customers. including material inference workloads from some of the most sophisticated AI-native companies. Third, an AI-native flywheel is emerging, driving adoption across our full AI-native cloud with a new entry point through our inference engine. We're already seeing early signs of this flywheel. More than half of new AI customers added year-to-date had core cloud attached, We believe this flywheel will drive higher margin and stickier services, further increasing our ARR per megawatt and differentiating us from bare metal neoclouds. And finally, we continue to focus on disciplined execution and durable growth. While we continue to manage the same supply chain challenges that face the entire industry, we are delivering our new 2026 capacity on time and in some cases ahead of schedule. We secured an incremental 20 megawatts. We strengthened our balance sheet. We landed our first nine-figure annual commitment, revenue commitment, and we remain focused on responsible investment and generating attractive returns. With our meaningful progress and momentum, we are again raising our full year 2026 outlook. We now expect revenue growth of approximately 30% for the full year 2026 and to reach at least 35% growth by Q4 of 2026. While it is premature to give formal guidance for 2027, we are even more confident in our prior 2027 estimate of 50% plus revenue growth for the full year 2027. I'll now spend a few minutes drilling into each of these four key takeaways. First, we delivered record Q2 revenue performance and the top line continues to accelerate with demand well in excess of capacity. Q2 revenue was $281 million, up approximately 29% year-over-year, which is more than double our growth rate in the same period last year. We delivered a record $93 million in incremental ARR in Q2, The most incremental ARR in a quarter in the company's history and nearly tripled what we added in the same quarter last year. And we are doing all of this with strong profitability. We delivered 40% adjusted EBITDA margin, 24% adjusted operating income margin, and 17% trailing 12-month adjusted free cash flow margin in the quarter. We are driving this growth by continuing to deliver for our highest spending customers. ARR from 100K plus customers grew 98% year over year. And our 500K plus customer ARR grew 160%. And our million dollar plus customer ARR rose 214%. The higher spend the cohort has, the faster that cohort is growing. and this has been the case for eight quarters in a row. Our highest spending cohort is also becoming a much bigger portion of our business and a critical part of our growth engine, growing from 9% of total ARR a year ago to 23% in Q2. AI customer ARR reached 234 million, growing over 200% year over year. AI customers come to DigitalOcean for more than just capacity. They come to us for software and the capabilities that help them accelerate their business. 85% of AI customer ARR in the quarter came from inference services and core cloud, not from bare metal. Inference services are the fastest growing component of our AI customer ARR, growing close to 800% year over year. and now represent over 70% of our total AI customer ARR. We are a full stack cloud platform with software that AI native companies depend on to build, run and scale production AI. The second key takeaway is the growing traction of our inference engine. We launched our inference engine, which provides the right model at the right performance and price for every task, as a part of our AI native cloud in late April. Since then, over 6,000 customers have leveraged the inference engine, while customer count grew an average of close to 60% month over month, and the token volume increased 30x over the last 60 days. We have seen open weight models climb up from around 15% of total token volume following our April launch to close to 75% today, highlighting the importance of open weight models in the AI-native ecosystem. This token growth is driven by strong demand from AI natives, not from individual users looking for a badge for the most token consumption. Token maxing was the industry's first instinct. Maximize usage. throw the largest frontier model at everything and let the bill compound. As workloads shifted from human-prompted to agent-driven, token consumption and cost exploded. For an AI-native company, tokens are both a source of value and COGS, so runaway costs are an existential threat to their unit economics. We believe that the market is shifting towards value-maxing, the right model at the right cost for every task. measured in business outcomes per dollar. This shift is a tailwind for us as we believe that value creation opportunities will expand from just whoever built the model to include whoever serves it the best. Open weight models make value maxing possible. Open weights let customers post-train on their own data and control their cost curve. Frontier quality open weight models at compelling cost performance characteristics have been a key adoption driver. Per analyst firm Artificial Analysis, today's best open models trail the frontier models by only a few percentage points and are over 70% of token volume per open router, the largest and most popular AI gateway. An open weight file is necessary but not sufficient for companies to own their intelligence. Turning open weights into fast, reliable, economical production tokens is a systems problem our inference engine solves. Continuous batching, quantization, KV cache optimization, speculative decoding, prompt caching, intelligent routing, and workload-aware scheduling, all engineered as one system on infrastructure we own. Like traditional open source software, The model may be free, but making it useful and serving it well is the product. Our inference engine is much more than an API endpoint to an open weight model. It has become a full production runtime solving today's most pressing needs. Our inference router optimizes requests in real time for quality, latency, and cost across our full open and frontier catalog. behind one unified API. Close to 1,400 inference customers actively use this feature to optimize dollars per unit of intelligence. Model synthesis, a new feature we just released, orchestrates a panel of models in parallel with the synthesizer merging their outputs, delivering frontier-grade quality at a fraction of frontier cost. Model evaluations let customers test any model against their own business data. Batch inference handles high-volume asynchronous workloads. Prompt caching cuts cost and latency with zero application changes. Server-side tools give agents web search, retrieval, and function calling natively inside inference requests with built-in access to knowledge bases and MCP servers. Together, these features turn model choice from a one-time decision into a dynamic, ongoing engineering and business decision. On our platform, open-weight models grew from roughly 15% of tokens following our initial launch to close to 75% today. And when Kimi K3, the largest open-weight model ever released, went live on July 27, we were the only full-stack cloud provider to be a launch partner, delivering day-zero access. Adoption has been incredible with over 400 net new customers just in the first week. Our model catalog now offers 75 plus open and closed source models through a single endpoint, including GLM 5.2, DeepSeq v4, GPT 5.6, Opus 5, et cetera, with 14 day zero launches since April of this year. Our third key takeaway is that our AI-native cloud is becoming a flywheel. Every layer a customer adopts pulls them into the next. In late April, we launched the DigitalOcean AI-native cloud, five fully integrated layers from silicon to inference to agents with open source support at every layer. Since then, we shipped more than 80 releases across all layers, demonstrating innovation across the platform. These releases included managed agent products like server-side tools, data and learning products like knowledge bases, the inference engine I just discussed, and cloud primitives like our new insights observability service. An integrated full-stack platform is foundational to AI builders because AI-native applications require far more than raw GPUs or just tokens. They need a production cloud designed around inference and agentic execution. Building and operating that cloud is hard. It requires deep engineering across data centers, silicon, networking, storage, Kubernetes, databases, model serving, routing, evaluations, agent runtimes, and much, much more. Our integrated platform eliminates this complexity for customers and a flywheel is emerging as these AI builders adopt it. Customers enter the platform through one of the three front doors, inference, agents or core compute. Most AI native customers first need inference with the right model at the right performance and the right price for every task. From there, inference graduates into agentic workflows. which use and generate data that requires databases, storage, knowledge bases, and observability. That generated data becomes raw material for learning, improving, and customizing the models. Agent runtimes and learning drive demand for compute, and because that compute runs on infrastructure we own and operate, every turn of the wheel improves our unit economics. Better price performance for customers, spur even more tokens, and the cycle accelerates. Adoption in each layer drives the next, and the effects compound. Inference is one entry point into a self-reinforcing cycle that pulls customers deeper into the platform and has been a leading indicator for full platform adoption. And this flywheel is already working. Let me give you some examples. OpenCode, a leading open-source AI coding agent with over 7.5 million monthly active developers, started by integrating with DigitalOcean Droplets to simplify agent development. Now, OpenCode is also using DigitalOcean's inference engine and AI-native cloud for its inference needs, including access to leading open-weight models. In addition to OpenCode, we've also integrated DigitalOcean AI Native Cloud into other leading coding and agent building environments like OpenClaw, Codex, Hermes, and GrokBuild. When developers build there, our inference engine is already in their workflows, just one API call away. That opens the inference front door at ecosystem scale. Daytona. and Advanced AI Sandbox Company build secure elastic sandboxes for AI-generated code and autonomous agents on DigitalOcean. This is a textbook full-stack agentic workload running on our platform. Its workloads require GPU acceleration, isolated compute environments, fast deployment, storage, networking and orchestration all working together. Vercel, a scaled agentic infrastructure platform, is integrating DigitalOcean's inference engine into their AI gateway to provide their customers with dedicated AI platform capabilities. Another great example of this is OpenRouter, which is both an efficient customer acquisition channel and a platform through which we can dial up or down on-demand traffic to test, learn, and scale as we launch new models. We now serve more than 20 billion tokens per day on OpenRouter, up more than 330% over the last 60 days, with much of that traffic being generated from agents. These customers are examples of AI builders spinning our flywheel, and the flywheel does not stop at the first entry point. Every turn adds products to the stack we own, an integrated platform running on our own infrastructure spanning 20 global data centers. Owning the stack lowers our cost to serve, and that lower cost structure, combined with the emergence of high-quality, low-cost open-weight models, gives us better unit economics to serve our customers, which in turn enables us to win more customers. For AI natives, that advantage enables precisely what they value, better cost and performance on every workload, faster time to market, tight integration across inference, agents, data, and compute, and freedom from having to stitch together a myriad of services across vendors. This is clearly resonating with our customers as roughly 70% of AI customers having 100K or more ARR in Q2 have attached a core cloud product to their AI workloads, showing early evidence of this flywheel in action. This value proposition is very differentiated in the market. Hyperscalers optimize for frontier labs and large enterprises. Neoclouds have built strong GPU rental businesses for model training and are adding software mostly through acquisitions. Assembling capabilities is not the same as building an integrated platform, and customers often bear that complexity. Inference providers serve tokens well, but rent their GPUs with margins, stacked on margins, and leaving customers to stitch together inference, agents, data, and compute. Our approach is different. One purpose-built AI-native cloud tightly integrated from the ground up, enabling AI-natives to start and scale their agent-tech applications on our cloud. We will dive deeper into our AI-native cloud at our AI Builder Summit on October 13th in San Francisco and we hope to see you all there. Which brings me to our fourth and final takeaway that we remain disciplined in our execution and continue to focus on durable growth. This discipline is evident not only in our financial performance but also in our operational execution and in our responsible and profitable approach to growth. Driving growth approaching 30% in Q2 on a path to 50% plus next year requires focused execution. We remain on time and even a little bit ahead of our previously communicated schedule on all three of our new 2026 data centers. We launched our Richmond data center in Q1, our Kansas City data center in Q2, both ahead of target, and we remain on track for the second half launch of our Memphis data center. Beyond just hitting our launch date, we've been able to allocate the majority of the capacity to specific customers or to our highly in-demand token fleet before we launch these data centers. We also secured approximately 20 megawatts of additional capacity this quarter, which is targeted to come online over the last part of 2027 and into 2028. This brings total committed capacity to approximately 155 megawatts, the majority of which will be online by the end of 2027. We continue to actively pursue additional capacity to drive further growth and meet customer demand. Our discipline is also evident in the steps we took to strengthen our balance sheet. In July, we reduced our leverage with minimal dilution and Yusuf Kash by retiring approximately $472 million of our 2030 convertible nodes, creating additional capacity to cost-effectively finance our future investments. It is worth pausing on how different our profile is from many others in the AI infrastructure market. Number one, our growth is driven by a broad set of AI native companies rather than by a handful of large, bare-metal off-take contracts with our top 25 customers representing only 20% of ARR in Q2. Next, our largely consumption-based model gives us the flexibility to adapt to market conditions and shift capacity to where it is most valuable. This flexibility enabled us to increase list prices on numerous GPU fleets recently by approximately 30%. Third, we are profitable. with 40% adjusted EBITDA margins, 24% operating income margin, and 17% last 12 months adjusted free cash flow margin. And finally, we closely match our cash outflow with our revenue by financing equipment, efficiently funding our growth. There are very few companies with our combination of positive adjusted operating margins and projected growth of 50% plus. This is a generational opportunity and we will go after it responsibly, building a durable business on the foundation of our differentiated software and full-stack AI-native cloud platform. With this momentum continuing to build, we are again raising our 2026 outlook. For the full year 2026, we now expect revenue growth of approximately 30% with an exit growth rate of 35% or more by Q4. That trajectory and the incremental committed capacity we've added both clearly strengthen our conviction in 50% or more revenue growth in 2027. With that, I will turn it over to Matt.