"DigitalOcean" Q2 2026 Earnings Call - AI Inference Flywheel Drives Record ARR and Profitable Hypergrowth

Operator: Hello, everyone. Thank you for joining us, and welcome to the DigitalOcean 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 one to raise your hand. To withdraw your question, press star one again. I will now hand the conference over to Radu Patrichi, Head of Investor Relations. Radu, please go ahead.

Radu Patrichi, Head of Investor Relations, DigitalOcean: 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 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.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: 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.

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. 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. 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. 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. 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.

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 Grok Build. 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, an advanced AI sandbox company, builds 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 scale 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, 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 native, 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.

Neo clouds 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, customers often bear that complexity. Inference providers serve tokens well, 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 agentic applications on our cloud. We will dive deeper into our AI-Native Cloud at our AI Builder Summit on October 13th in San Francisco, 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 dates, 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 use of cash by retiring approximately $472 million of our 2030 convertible notes, 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 offtake contracts with our top 25 customers representing only 20% of ARR in Q2. 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. 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.

Matt Steinfort, Chief Financial Officer, DigitalOcean: Thanks, Paddy. Good morning, everyone, and thanks for joining. As Paddy shared, Q2 was an outstanding quarter. I’m excited to take you through the results, provide further context on some of the actions we have taken, and provide some additional color on our updated outlook. Q2 revenue was $281 million, up 29% year-over-year above the high end of guidance. The outperformance was broad-based, led by growth from our highest spending customers and our expanding AI customer base. Our highest spending customers didn’t just keep growing, they accelerated. ARR from our 100,000-plus customers grew 98%, up from 37% in the second quarter of last year. Our 500,000-plus customer ARR grew 160%, up from 64%. Our $1 million-plus customer ARR grew 214%, up from 92%. Each of these highest spending customer cohorts is now growing more than twice as fast as it was a year ago.

We continued to gain meaningful traction with some of the most sophisticated AI natives. AI customer ARR reached $234 million, growing 212%. Critically, 85% of that ARR is non-bare metal. This traction is evident in the material commitments we secured during the quarter, which collectively increased remaining performance obligations to $894 million, up more than 12 times year-over-year, with a 3.7-year average life. While changes to RPO will be lumpy, these commitments add visibility, and we expect to secure more of them in the future. They have not, however, come at the expense of our broad customer diversification, as our top 25 customers represented only 20% of ARR in Q2, and this will only modestly increase as these deals ramp up. One quick note on key financial metrics.

Our business has changed dramatically over the last two years, with growth increasingly driven by our top customers and by emerging AI customers. Against that backdrop, net dollar retention, a strong indicator in the slow and steady growth SaaS world, has become a less useful measure of our performance. While our 102% NDR in Q2 is a three-year high, we’ll no longer highlight it as a key financial metric. Growth today is shaped far more by our highest spending in AI customers than by the NDR trend across our 680,000-plus customer base. Profitability remained strong in Q2. Adjusted EBITDA was $114 million, an adjusted EBITDA margin of 40%. GAAP operating income was $29 million, a 10% margin, and adjusted operating income was $67 million, a 24% margin. Non-GAAP diluted net income per share was $0.45. Adjusted free cash flow in the quarter was $61 million.

Trailing 12-month adjusted free cash flow was $175 million or 17% of revenue. As Paddy highlighted, we proactively strengthened our balance sheet, reducing our leverage with effectively no dilution and minimal use of cash. In July, we equitized $472 million of our 0% 2030 convertible senior notes. The underlying shares were both already reflected in our diluted share count and were highly likely to be converted given where our stock is trading. Yet the full principal value was also reflected in our net debt, reducing our leverage capacity. Through this proactive transaction, we retired more than half of our convertible debt, four years ahead of maturity, reduced net leverage, and did so with effectively no dilution and minimal use of cash, freeing up capacity to invest in further growth. Turning to guidance, we are raising our 2026 revenue outlook.

For the third quarter of 2026, we expect revenue of $304 million-$307 million, representing 32%-34% year-over-year growth. We project adjusted EBITDA margins of 38%-39% and non-GAAP diluted net income per share of $0.28-$0.30 on approximately 126.5 million weighted average fully diluted shares. For the full year 2026, we expect revenue of $1.17 billion-$1.18 billion, representing approximately 30.5% year-over-year growth with an exit growth rate of 35% or more in Q4. We expect adjusted EBITDA margins of approximately 39%, non-GAAP diluted EPS of $1.35-$1.40, an adjusted free cash flow margin of 11%-13%, an increase to our prior guide. While it’s premature to speak to 2027 guidance, the positive momentum we are generating and the higher projected exit growth rate give us even more confidence in our estimated 50%-plus growth for the full year 2027.

Before I turn it back to Paddy, let me put our progress in perspective. Revenue grew 14% year-over-year in the second quarter of last year. In a single year, we have doubled our growth rate to 29%, and we are now projecting to nearly double it again on an annual basis next year. We are delivering this growth with attractive margins, appropriate leverage, a strong and flexible balance sheet, and disciplined execution. With that, I’ll hand it back to Paddy.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: Thank you, Matt. Before we move to Q&A, let me recap what we shared today. First, growth continues to accelerate. Approximately 29% revenue growth, more than double the growth from a year ago. Record $93 million in incremental ARR. AI customers and million-dollar-plus customers each growing ARR more than 200%. We delivered this growth with strong profitability and free cash flow. Second, our inference services are getting tremendous traction. Inference services grew nearly 800% year-over-year. Token usage on our Inference Engine is compounding monthly, and open-weight models have climbed from 15% of token traffic to close to 75%. Open-weight model adoption leverages our strength, turning open models into fast, reliable, economical production tokens. Third, adoption of our Inference Engine is creating a growth flywheel. Inference is the entry point, and every layer a customer adopts improves their token price performance and pulls them deeper into the platform.

Leading AI builders like OpenCode, Vercel, and Daytona began spinning that flywheel. Because the entire cycle runs on infrastructure we own, it drives customers to higher margin and stickier products, increasing our potential ARR per megawatt. Finally, we remain disciplined in our execution, deploying planned capacity on or ahead of schedule, securing 20 megawatts of incremental capacity, delivering strong margins, and strengthening the balance sheet. Our momentum and solid execution enables us to raise our 2026 outlook and positions us for strong performance in 2027. Before I end my comments, let me connect these four key takeaways, because the connection is the real story. Software makes megawatts more valuable. Our software attracts high-quality AI native customers with insatiable demand. Those customers adopt more of the platform than just capacity, and that broader adoption increases what each megawatt earns, driving durable growth, higher margins, and cash flow in future years.

Strategy is becoming results are building momentum. Platform shifts like this come along once in a generation. Quarters like this one show that we are becoming both an enabler and a beneficiary of that shift. With that, let’s open it up for questions.

Operator: We will now begin the question and answer session. Please limit yourself to one question and one follow-up. If you would like to ask a question, please press star one to raise your hand. To withdraw your question, press star one again. We ask that you pick up your handset when asking a question to allow for optimum sound quality. If you are muted locally, please remember to unmute your device. Please stand by while we compile the Q&A roster. Your first question comes from the line of Gabriela Borges with Goldman Sachs. Your line is open. Please go ahead.

Gabriela Borges, Analyst, Goldman Sachs: Hi. Good morning. Thank you. Thank you for all the detail. I want to ask a little bit about DigitalOcean’s ability to scale. Paddy, to your point, the hyperscalers are optimized for large enterprises. DigitalOcean has historically been optimized for small customers, you’re actually landing these larger flagship customers that have larger commitments, have larger backlog deals, and require perhaps a different type of sales process, a different type of operational process. Two follow-up questions for you. How are you meeting those demands of the largest scaled customers? I think this may be partly for Matt, how are you thinking as you scale these larger chunks of megawatts, talk to us about some of the operational puts and takes to being able to get those megawatts online at the right time and up and running. Thank you so much.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: Thank you, Gabriela. Good morning. It’s a great question. We feel very confident in our ability to scale given our track record. We’ve been doing this at a global scale, running a cloud business, managing a global network of data centers for the last dozen plus years with hyperscaler SLAs and serving over a half a million paying customers along the way. We feel very confident in our ability, we are demonstrating that by bringing capacity on time and also before schedule. I always work backwards from the customers we are targeting and what they are coming to us for. Right now, they’re coming to us not just for capacity, as I mentioned. They’re not expecting some exotic bespoke hardware or network configuration. They’re predominantly coming to us because of the richness of our AI-Native Cloud.

From a platform innovation perspective, our pace of innovation, as I described, is just staggering with over a major release every business day and sometimes multiple. Our engineering talent is absolutely world-class, and we aggressively keep adding to it. To augment that engineering talent, we have also stood up a forward deployed engineering organization to work with some of our larger, more sophisticated customers with demanding workloads to ensure that they’re getting the right price performance, throughput, accuracy combination. Most of our core software doesn’t have to be really customized to meet their needs. From a go-to-market point of view, we just added Kevin Van Gundy as our CRO, who comes with tremendous experience working in the digital and now AI native ecosystem. We added Leo as our CMO, who brings a wealth of marketing experience from Google Cloud and Oracle Cloud.

They’re in the process of scaling up our go-to-market muscle to help us address the next phase of our hypergrowth. This is something we feel very confident. We’ve been doing this for a number of years

I’ll let Matt answer the infrastructure question, I think from a talent density perspective, both on core engineering and go-to-market, I feel really good. We have demonstrated in the recent past, and that’s why we keep talking about our 500K and million-dollar customers and how that flywheel is spinning and has been doing it for about eight quarters in a row now.

Matt Steinfort, Chief Financial Officer, DigitalOcean: I would just add to that, Gabriela, that the customers that we’re dealing with, while they’re bigger, these aren’t your traditional kind of brick-and-mortar enterprise companies. These are very sophisticated technical customers, where their founders and leaders are often deeply technical. They very much appreciate the depth and the breadth of the engineering talent that we have and our ability to work with them, as Paddy said, which I think uniquely and very well positions us to be able to meet their needs. From an infrastructure standpoint, as you’ve seen, we’re working with some of the top data center operators in the industry that are very familiar with and experienced bringing up capacity. We’ve got a deep and talented team that works alongside of them. We have great partnerships with the leading chip manufacturers.

We’ve got a great supply chain with a diversified set of OEMs that are all global. We’ve been able to manage the implementation schedules and turn up capacity, despite some of the challenges that everyone faces in the industry. We’ve been able to do that on time and meet the requirements that these large customers have put in front of us, and we’re very encouraged by the partnership we have with those customers. We’re doing a lot of joint development already. I think it’s more than just turning up infrastructure. It’s having engineers working side by side with these very sophisticated and talented customers, and we’re bringing really strong talent to bear. We’re very encouraged by the progress we’re making.

Gabriela Borges, Analyst, Goldman Sachs: That’s really good stuff. Thank you.

Operator: Your next question comes from the line of Jackson Ader with William Blair. Your line is open. Please go ahead.

Jackson Ader, Analyst, William Blair: Yeah, thanks. Good morning. Two questions. First, just if you could provide any specifics on the impact of pricing on the revenue growth in Q2 and then for the updated outlook. That’s the first question. The second question on equipment financing, Matt, for 2026, where do you expect net leverage to be at year-end, and could you provide any specific guidance on the free cash flow for the year, including all the leases?

Matt Steinfort, Chief Financial Officer, DigitalOcean: Jason, good questions. On the pricing, as you saw, we increased our list price on a number of GPU generations by about 30% a while ago. A lot of that pricing we had already been, I’d say, upgrading. Given the short kind of contract duration for some of our customers, we had already been upgrading their prices and increasing their prices upon renewal, or in some cases, pulling capacity back from a customer that we thought we have a better use for it, either the capacity in our token factory or with a different customer that was willing to pay a higher price. All of that pricing is included in the 2026 guide, and it’s part of how we went from saying we’re going to exit the year around 30% to now exiting it at around 35%. It’s a good setup for us in 2027 as well.

On the equipment financing side, we continue to get access to very attractive rates and have ample capacity to fund the growth over the committed capacity that we’ve taken down. If you look at the pro forma net leverage, just take the Q2 balance sheet and the LTM EBITDA, and simply subtract the amount of debt we retired in the equitization. It puts us at 0.7 times net leverage. We’re in very good position to stay well below that four times net leverage that we had articulated, and in fact, it should be well below that. That’s part of why we did that. We’re now sitting with an incredibly strong and flexible balance sheet. We have the ability to take on incremental equipment financing and equipment-related borrowing capacity and fuel our growth.

It was a great step for us, and our leverage is going to be very comfortably below that guideline that we had provided.

Jackson Ader, Analyst, William Blair: Just on the free cash flow for-

Matt Steinfort, Chief Financial Officer, DigitalOcean: Oh, free cash flow. Sorry, Jackson. That’s a great question. Free cash flow, as we said, would be 11%-13% for the year. That’s an adjusted free cash flow basis, which is higher than what we had guided previously. If you take all of the principal payments and everything, we’ll still generate cash in 2026. We expect to be free cash flow positive on any metric that you use, whether it’s adjusted free cash flow or take complete cash generation and take out the principal payments. We will continue to generate cash in 2026.

Jackson Ader, Analyst, William Blair: Thank you.

Operator: Your next question comes from the line of Mark Zhang with Citi. Your line is open. Please go ahead.

Mark Zhang, Analyst, Citi: Hey, good morning team. Thanks for taking the question. Wanted to actually dig in a little bit more into the nine-figure deals that you guys were able to sign this quarter. Number 1, wanted to get a sense of, I guess, the inferencing and the core cloud projects that these logos are committed for. What’s the adoptions of the other aspects of the 5-layer stack amongst nation roadmap looks like going forward? I think the go-to-market philosophy here is really looking for large deals that can make good sense, that can continue to expand going forward. I just want to get a sense of the opportunities from here as we go forward with the AI stack. Thanks.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: Yeah. Thank you, Mark. In terms of the larger deals, and pretty much any deal that we are talking about these days, I think we had a couple of different stats that I used in the prepared remarks. Over 70% of AI customers that we added this year at any significant scale are already using some aspects of the core cloud. Some of our AI-Native Cloud layers are still new, and that’s why we have another version of our AI Builder Summit scheduled on October 13th to talk a little bit more about more specifically the managed agents layer of our platform. If you take a step back and think about these types of workloads landing in our platform, they typically land on one of the 3 front doors that I talked about. Right?

The front door is really, really important because that’s the dominant use case for which any of these sophisticated workloads are coming to us for. Immediately, they’re attaching some part of our other layers of the cloud, whether it is databases or storage or orchestration. In many cases, it’s a combination of all of the above and gives us more confidence that they’re coming to us not just for tokens, not just for capacity, but they’re coming to us appreciating the value of the full platform because these workloads, they’re not proof of concept. They are building agentic applications from the ground up. By the nature of these agentic applications, they need far more than just GPUs or tokens. They need a place where they can do some post-training. They need a place where they can store memory and context.

They need a place where they can run agents in secure sandboxes. They need a way to orchestrate these agents. We feel increasingly confident, and that’s why I spend so much time talking about the flywheel of the more we can get these AI-Native workloads to consume more aspects of our platform, we feel really good about the durability of the revenue, durability of these workloads scaling up on our platform, and the early results are really, really encouraging given the attach that we are seeing on the platform.

Mark Zhang, Analyst, Citi: Got it. Thanks for that, Paddy. That’s very helpful. Maybe just a quick follow-up. You also mentioned that with the new CRO, Kevin, coming in, you guys are certainly in the process of scaling up the go-to-market muscles. Can you just maybe give a sense of what the early preview of what Kevin’s plans are for the go-to-market organization? Should we expect more investments into sales and marketing and go-to-market at the enterprise level going forward from here? Thank you.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: Thanks, Mark. The primary focus right now is to land very high-quality AI native workloads, right? Like the ones that we discussed on the call. These are top-tier AI native companies. As I described, just in the last 90 days for our Inference Engine, we’ve added over 6,000 customers. That is just incredible. Think about it, right? 6,000 customers in 60 to 90 days. A lot of that is still standing on the shoulders of our incredible world-class product-led growth motion. We are tapping into the ecosystem at scale, whether it is OpenRouter or OpenCode or Hermes Agent. We are getting customers from all kinds of ecosystem hooks, and that will continue. In terms of very specifically the human-based sales, yes, we will fortify our AI native enterprise go-to-market motion. Again, here it is about nailing that motion with forward deployed engineering.

It is nailing that motion with enterprise sales reps that know how to go and qualify these opportunities and hold their own with very technical founding teams rather than scaling it. We will scale it eventually, but right now it is all about quality of engagement and nailing that motion before we scale it. In terms of investments, I don’t see the investment scalings anytime soon. It is all about getting the right quality of engineering-oriented technical sales to enable us to attract and expand these AI native workloads.

Mark Zhang, Analyst, Citi: Perfect. Thank you, Paddy.

Operator: As a reminder, if you would like to ask a question, press star one to raise your hand, and please limit yourself to one question and one follow-up. Your next question comes from the line of Wamsi Mohan with Bank of America. Your line is open. Please go ahead.

Wamsi Mohan, Analyst, Bank of America: Yes. Thank you. I appreciate the comment that it’s still a bit premature for 2027. If you look at your performance here, which has been really strong, RPO, the timing, and on time or even earlier ramp of your data centers, your comments on token usage, higher exit rate for 2026, and put all of these together, should we not assume directionally that there is further upside to 2027 than what you thought 90 days ago? Any color there would be helpful. I have a follow-up.

Matt Steinfort, Chief Financial Officer, DigitalOcean: Yeah. Wamsi, I think that’s the appropriate conclusion. The challenge for us is the revenue growth is so predicated on the specific timing of data center implementations and the turn-on of capacity. We’re sitting here in August, and there’s still a fair bit of moving parts in terms of the dates and times for next year. We felt it’s premature to give a specific number. Clearly, the message is, well, we’re exiting the year a lot faster growth than what we had said we were. We’ve got tons of RPO, and we’re landing bigger customers. We’re very bullish, and we expect there to be additional upside. It’s too early to put a number on it. We’ll wait until later this year before we provide any more specifics around that.

All the indications are we’re, as you saw by virtue of the fact that we increased our guidance for 2026 and the exit rate, we’re better positioned than we were just 90 days ago. It’s a good conclusion.

Wamsi Mohan, Analyst, Bank of America: Okay. Thanks, Matt. Maybe Paddy, just on the open-weight models, you I think quoted that it’s risen from roughly 15% and now we’re nearly 75% of token volume since launch. How much of that usage is recurring production traffic versus maybe some batch inference where you have some discounts? I think you mentioned that was not batch, but I just want to make sure of that. Is the cloud core services attach any different between customers using closed versus open models? Thank you so much.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: Thanks for the question, Wamsi. It’s a great question. I’ll go from the reverse order. There isn’t any major difference in what these workloads are attaching based on whether they’re open-weight or closed-source models. They are attaching the same kind of core cloud primitives. One pattern that we are observing is most sophisticated production workloads are now becoming a combination of open-weight and closed models. It is almost always a fusion, or that’s why we released this new feature called Model Synthesis, where we can actually do the heavy lifting on behalf of the customer, where we run the same query in parallel across to multiple models and synthesize the results using a synthesizer rather than the customer having to stitch together these kinds of infrastructure plumbing technologies. Going back to the first part of your question, are these production workloads? Absolutely, yes.

I can’t put an exact number on this, but you can see from the combination of the throughput, latency, accuracy that these companies are demanding, it’s very easy to find out whether they are running a production workload or some internal proof of concept. I feel a lot of the traffic we are seeing is production traffic. As the open-weight models pick up in traffic, cost is an important factor, but it is not the only factor because as you see some of these sophisticated mixture of experts models like a K3 or the about-to-be-released Qwen 3.8, for example. These are 2.8 trillion, 2.4 trillion parameter models. These are very big, bulky models with active parameter count, like 140 billion, I believe, was the K3 model. These are not cheap models to serve.

When you look at the cost per intelligence task, yes, it is cheaper than the frontier closed-source models, they’re not cheaper by an order of magnitude. It is creating surely a Jevons paradox of the more open-weight models at a reasonable cost performance that we are starting to see, the adoption is just going through the roof. As I mentioned, there’s just a tremendous amount of demand that far exceeds our supply. I feel very good about these production workloads, whether it is in coding or generative media or business workflows. These are production workloads that are scaling, and they have insatiable demand on our systems.

Operator: In the interest of time, we ask that analysts only ask one question. Your next question comes from the line of Sanjit Singh with Morgan Stanley. Your line is open. Please go ahead.

Sanjit Singh, Analyst, Morgan Stanley: Yeah. Thank you for taking the question. Matt, I wanted to revisit the revenue per megawatt story at DigitalOcean, because obviously they’re not a huge premium to the neo clouds. The bare metal mix is obviously coming down. You guys had previously said that as the AI mix starts to increase, the revenue per megawatt will come down a bit from its current levels. Is that still the right thinking given we have the Inference Engine, given the success with attaching to the cloud portfolio? What are some of the levers to drive support for revenue per megawatt over time?

Matt Steinfort, Chief Financial Officer, DigitalOcean: That’s a great question. We expect the incremental ARR that we get per megawatt to increase over time. The decline that you described is from when we were a general purpose cloud Generating north of $22 million in ARR per megawatt without much AI. As we add incremental megawatts, we’re adding, I think, more ARR per megawatt than our neo-cloud peers because we offer higher layer services beyond just bare metal, because we sell to a broader customer base that isn’t a single customer with a multi-year commitment that’s going to drive pricing and margins down, and because we offer a core cloud and CPU services that we attach to those AI workloads. We expect that to increase as the mix of core cloud to, and the attach rate increases. We’re also installing higher capacity equipment in the same megawatts going forward.

As you see the generations of NVIDIA and AMD increasing their token throughput capabilities, it gives us more revenue potential. The costs are certainly higher per megawatt as well, the revenue potential is also higher. We expect it to be a combination of more attach, higher and more mix of inference services beyond just the GPU as a service, and the higher token capacity of the equipment we’re putting in. All of those will contribute to increasing our ARR per megawatt on an incremental basis.

Operator: Your next question comes from the line of Tom Blakey with Cantor. Your line is open. Go ahead.

Tom Blakey, Analyst, Cantor: Hi, thanks for taking my question. I think it’s maybe a dovetail off of Sanjit’s question. Could you just talk about maybe the pricing impact to this very strong ARR number, the net new ARR number that you reported this quarter, then maybe give an update to the megawatt cadence that you’re looking at here in calendar 2026, as you mentioned you’re a little bit ahead of plan. I’m just wondering if there was any details you can give us about 2Q 2026 and if we’re still looking for 25 megawatts in the second half. Thank you.

Matt Steinfort, Chief Financial Officer, DigitalOcean: Just to answer the latter part, we’ve got 15 megawatts that are left. We announced that the 10-megawatt Kansas City facility was launched already. We have 15 left in one facility, and we had said it would come on in the second half, and it’s on track. We expect that to come online as we had expected over the balance of the year.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: Yeah, the first question was the pricing impact on the net new ARR.

Matt Steinfort, Chief Financial Officer, DigitalOcean: It’s modest.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: It’s very modest in Q2, and, as Matt already answered, it is baked into our guidance for the rest of the year. It’s not what you might imagine right off the bat because we raised the list prices across the board for on-demand and spot instances. As Matt mentioned previously, as some of these contracts roll out, we have been adjusting the prices to market levels for our existing contracts. In terms of its impact in Q2 and the $93 million in net new ARR, it had very little impact on it. I don’t want the takeaway to be that, oh, that’s how we had a blowout quarter. That’s not the case at all.

Tom Blakey, Analyst, Cantor: Thank you. Bye.

Operator: Your next question comes from the line of Jackson Ader with KeyBanc. Your line is open. Please go ahead.

Jackson Ader, Analyst, KeyBanc: Great. Thank you. Morning, guys. I was just curious about what exactly is baked into the out year outlooks. If I think about all the activity that you guys signed or contracted in the second quarter and the impact either here on 2026 or 2027. If I just think about forward guidance, is it right to think that, okay, we’re at 155 megawatts, the majority online by the end of 2027, and any incremental activity that happens in the next few months, that is all incremental to the expectations for 2027? Or do you guys have certainly line of sight into a bunch of activity that’s coming down the line, and so that is also factored into what you’re expecting for the 2027 numbers? Thank you.

Matt Steinfort, Chief Financial Officer, DigitalOcean: Jackson, that’s a great question. What you’ll observe about us is we’re very good, I think, at having measured and appropriately conservative outlook based on what we’ve already communicated in terms of capacity. It’s a good observation that were we to add incremental capacity, and were we to add incremental deals beyond what we’ve articulated, that there would be upside. From a 2027 impact standpoint, you’re getting pretty late in the year, this year, to have a huge impact from a capacity standpoint on the calendar year 2027, just because data centers typically have kind of a year-ish from lease signature to when you’re generating revenue. You’re getting to the point where you might impact the exit growth rate, but full year calendar year revenue might not be as impacted. That’s part of why we’re saying we’re not going to provide formal guidance right now.

There’s just a lot of moving parts. What you and what Wamsi also highlighted is clearly we’ve got a ton of momentum. We’ve made a great amount of progress in just a quarter, all of that upside is not reflected in the prior estimate of 50% or more growth for next year. It’s too early for us to put a precise number on it other than, hey, we’re exiting the year at a much higher growth rate. We’ve got a very strong RPO backlog. We’re very active in the market looking for incremental capacity. We certainly believe there’s upside.

Operator: Your last question comes from the line of Redi Sultan with UBS. Your line is open. Please go ahead.

Redi Sultan, Analyst, UBS: Awesome. Thanks, guys. Thank you for squeezing me in. Maybe just one quick one. More of your customers using your AMD deployment. Speak to how you see the mix between NVIDIA GPUs in your engine and maybe Matt. As for the unit economics on a per megawatt basis for AMD GPUs compared to NVIDIA GPUs. Thank you.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: Yeah. Hi, Redi. We have a good, healthy mix of different types of accelerators in our farm. For obvious and competitive reasons, we don’t get into the details of what we use to host what type of models and things like that. It is a mix of both, and we continue to keep pace with the innovation in this market. We certainly don’t want to discuss the unit economics of different hardware throughputs. I would just stop at that because it’s a good mix of different types of accelerators. As you can imagine, we are really good at taking whatever hardware is available based on the capacity we have and running the state-of-the-art models. Like most of the state-of-the-art GLM-5.2 or K3, we run on all kinds of hardware.

That is the beauty of the software optimization layer that we continue to build and refine, where we are almost becoming hardware agnostic.

Operator: We have reached the end of our Q&A session. I will now hand the call back to Radu.

Radu Patrichi, Head of Investor Relations, DigitalOcean: Great. Thank you, Paige. Thank you, everyone, for joining. This concludes our second quarter earnings presentation and conference call. Apologies we couldn’t get to all your questions, look forward to speaking to everyone later in the day in our follow-up calls.

Paddy Srinivasan, Chief Executive Officer, DigitalOcean: Thank you.

Operator: This concludes today’s call. Thank you for attending. You may now disconnect.