DigitalOcean Holdings, Inc. 2026 Q2 Earnings Call

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 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 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. 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 Cloud flywheel is emerging, driving adoption across our full AI-Native Cloud with a new entry point through our Inference Engine. We are 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 neo clouds. 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 revenue commitments. 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. 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 triple what we added in the same quarter last year. 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. Our 500K plus customer ARR grew 160%. 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 build 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 cost, 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 from 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 OpenRouter, 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. When Kimi K3, the largest open weight model ever released, went live on July 27th, 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, DeepSeek-V2, 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.