DOCN

A month has gone by since the last earnings report for DigitalOcean Holdings, Inc. (DOCN). Shares have lost about 15.7% in that time frame, underperforming the S&P 500. Will the recent negative trend continue leading up to its next earnings release, or is DigitalOcean due for a breakout? Before we dive into how investors and analysts have reacted as of late, let's take a quick look at the latest earnings report in order to get a better handle on the important drivers. DigitalOcean posted second quarter 2026 non-GAAP earnings of 45 cents per share, which fell 23.7% year over year but topped the Zacks Consensus Estimate by 73.08%.Revenues increased 28.6% year over year to $281.18 million and beat the consensus mark by 1.23%. Annual Run-Rate Revenue (ARR) reached $1.125 billion, up 29%, while AI Customer ARR jumped 212% to $234 million. Growth was led by higher-spending customers. ARR from $1 million-plus customers reached $259 million, up 214% year over year and accounted for 23% of total ARR. ARR from $500,000-plus customers rose 160% to $291 million, while the $100,000-plus cohort increased 98% to $395 million.The expansion also strengthened contracted visibility. Remaining performance obligations climbed to $894 million from $71 million a year earlier, with $366 million expected to be recognized over the next 12 months. DigitalOcean also signed its first nine-figure annual customer commitments, extending weighted average contract life from 1.6 years to more than three years. Inference services grew 762% year over year, while 85% of AI customer ARR came from inference services and core cloud rather than bare metal. The Inference Engine attracted more than 6,000 customers after its late-April launch, and token volume increased roughly 30-fold over the prior 60 days.Product expansion supported that adoption. DOCN shipped more than 80 releases across its five-layer AI-Native Cloud since April. Roughly 70% of AI customers with at least $100,000 in ARR attached a core cloud product, indicating broader use of compute, storage, databases and orchestration alongside AI workloads. Gross profit increased to $154.66 million from $130.95 million, but gross margin declined to 55.0% from 59.9%. Total operating expenses rose to $125.29 million from $95.33 million. Research and development expense climbed to $57.5 million from $39.6 million, while sales and marketing rose to $22.6 million from $19.3 million. General and administrative expense increased to $45.2 million from $36.4 million.Adjusted EBITDA increased 26.9% to $113.56 million, while the margin edged down to 40% from 41%.GAAP operating income fell 17.5% to $29.37 million, with operating margin contracting to 10% from 16%. Adjusted operating income rose 9.3% to $67.48 million, though its margin declined to 24% from 28%. As of June 2026, cash and cash equivalents totaled $767.03 million compared with $741.5 million as of March 31, 2026.Net cash provided by operating activities rose 19.0% to $109.97 million, while the operating cash flow margin declined to 39% from 42%. Adjusted free cash flow increased 6.3% to $60.59 million, with the corresponding margin narrowing to 22% from 26%. For the third quarter of 2026, DigitalOcean expects revenues of $304 million-$307 million, representing 32%-34% growth. Adjusted EBITDA margin is projected to be 38%-39%, while non-GAAP earnings are expected to be between 28 cents and 30 cents per share.For 2026, DOCN raised revenue guidance to $1.170 billion-$1.180 billion from $1.130 billion-$1.145 billion. The company now expects 30%-31% revenue growth, a 38.5%-39.5% adjusted EBITDA margin, an 11%-13% adjusted free cash flow margin and non-GAAP earnings of $1.35-$1.40 per share. Management also expects revenue growth of at least 35% by the fourth quarter and reiterated confidence in more than 50% growth in 2027. Since the earnings release, investors have witnessed a upward trend in estimates revision. Currently, DigitalOcean has a subpar Growth Score of D, however its Momentum Score is doing a lot better with an A. However, the stock was allocated a grade of F on the value side, putting it in the bottom 20% quintile for value investors. Overall, the stock has an aggregate VGM Score of F. If you aren't focused on one strategy, this score is the one you should be interested in. Estimates have been trending upward for the stock, and the magnitude of these revisions looks promising. Notably, DigitalOcean has a Zacks Rank #3 (Hold). We expect an in-line return from the stock in the next few months. DigitalOcean belongs to the Zacks Internet - Software industry. Another stock from the same industry, Reddit Inc. (RDDT), has gained 1.8% over the past month. More than a month has passed since the company reported results for the quarter ended June 2026. Reddit Inc. reported revenues of $804.91 million in the last reported quarter, representing a year-over-year change of +61.1%. EPS of $1.25 for the same period compares with $0.45 a year ago. Reddit Inc. is expected to post earnings of $1.33 per share for the current quarter, representing a year-over-year change of +66.3%. Over the last 30 days, the Zacks Consensus Estimate has changed +1.7%. The overall direction and magnitude of estimate revisions translate into a Zacks Rank #3 (Hold) for Reddit Inc.. Also, the stock has a VGM Score of B. Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report DigitalOcean Holdings, Inc. (DOCN) : Free Stock Analysis Report Reddit Inc. (RDDT) : Free Stock Analysis Report This article originally published on Zacks Investment Research (zacks.com). 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DigitalOcean’s second quarter results were well received by the market, reflecting strong demand for its AI-native cloud platform and continued success in landing larger, high-value customers. Management credited the acceleration in revenue growth to traction with inference services, which grew nearly 800% year-over-year, and increasing adoption of the company’s full stack platform by sophisticated AI-native businesses. CEO Padmanabhan Srinivasan emphasized that the company’s differentiated software and integrated infrastructure enabled it to deliver value to customers beyond raw computing capacity, resulting in a broader and more durable revenue base. Is now the time to buy DOCN? Find out in our full research report (it’s free). Revenue: $281.2 million vs analyst estimates of $278.8 million (28.6% year-on-year growth, 0.9% beat) Adjusted EPS: $0.45 vs analyst estimates of $0.26 (72.9% beat) Adjusted EBITDA: $113.6 million vs analyst estimates of $105.7 million (40.4% margin, 7.4% beat) The company lifted its revenue guidance for the full year to $1.18 billion at the midpoint from $1.14 billion, a 3.3% increase Management raised its full-year Adjusted EPS guidance to $1.38 at the midpoint, a 19.6% increase Operating Margin: 10.4%, down from 16.3% in the same quarter last year Annual Recurring Revenue: $1.13 billion (28.6% year-on-year growth, beat) Billings: $327.9 million at quarter end, up 46.2% year on year Market Capitalization: $15.25 billion While we enjoy listening to the management’s commentary, our favorite part of earnings calls is the analyst questions. Those are unscripted and can often highlight topics that management teams would rather avoid or topics where the answer is complicated. Here is what has caught our attention. Gabriela Borges (Goldman Sachs) asked about scaling for larger customers, with CEO Padmanabhan Srinivasan emphasizing DigitalOcean’s history of global scale and new hires in sales and marketing to address sophisticated AI workloads. Jason Ader (William Blair) inquired about the impact of recent price increases on revenue growth, with CFO Matt Steinfort describing the effect as modest for Q2 and noting that new pricing is already reflected in forward guidance. Mark Zhang (Citi) questioned the adoption of additional platform layers by large customers, to which Srinivasan highlighted that most major AI customers are already integrating core cloud services beyond inference and that the company is focused on deepening these relationships. Wamsi Mohan (Bank of America) pressed for more color on 2027 growth outlook, with Steinfort stating it is too early for specifics but affirming that the current momentum and backlog suggest possible upside to their previous projections. Sanjit Singh (Morgan Stanley) explored drivers of revenue per megawatt, with Steinfort explaining that software integration, higher attach rates, and next-generation hardware will help maintain a premium over competitors despite industry mix shifts. In future quarters, the StockStory team will be watching (1) the pace of adoption and monetization for new AI-native services, especially inference and agentic workflows, (2) execution and fill rates on additional data center capacity coming online, and (3) the continued growth and diversification of large customer cohorts. Progress on integrating new platform features and maintaining strong margins will also be key signposts for sustained performance. DigitalOcean currently trades at $131, up from $127.17 just before the earnings. Is there an opportunity in the stock? Find out in our full research report (it’s free for active Edge members). ALSO WORTH WATCHING: Top 5 Momentum Stocks. The best time to own a great stock is when the market is finally noticing it. These aren’t just high-quality businesses. Something is happening with them right now. Elite fundamentals meet near-term momentum — both boxes checked at the same time. Find out which stocks our AI platform is flagging this week. See this week’s Strong Momentum stocks — FREE. Get Our Strong Momentum Stocks for Free HERE. Stocks that have made our list include now familiar names such as Nvidia (+1,460% between June 2020 and June 2025) as well as under-the-radar businesses like the once-small-cap company Exlservice (+271% between June 2020 and June 2025). Find your next big winner with StockStory today.

Image source: The Motley Fool. Tuesday, Aug. 4, 2026 at 8:00 a.m. ET Chief Executive Officer - Padmanabhan Srinivasan Chief Financial Officer - Matt Steinfort Head of Investor Relations - Radu Patrichi Need a quote from a Motley Fool analyst? Email [email protected] Operator: Hello, everyone. Thank you for joining us, and welcome to the DigitalOcean Second Quarter 2026 Earnings Conference Call. [Operator Instructions] I will now hand the conference over to Radu Patrichi, Head of Investor Relations. Radu, please go ahead. Radu Patrichi: 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. And with that, I turn the call over to Patti. Padmanabhan Srinivasan: 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 4 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 server-less 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 are already seeing early signs of this flywheel. More than half of new AI customers added year-to-date have 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 9-figure annual commitment -- revenue commitments, 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 4 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 triple 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 $100,000-plus customers grew 98% year-over-year and our $500,000-plus customer ARR grew 160% and our $1 million-plus customer ARR rose 214%. The higher spend the cohort has, the faster that cohort is growing, and this has been the case for 8 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 batch for the most token consumption. Tokenmaxxing 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 valuemaxxing, 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 valumaxxing 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. For 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 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 run time 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 0 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 onetime 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 0 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 V4, GPT-5.6, OPUS 5, et cetera, with 14 day 0 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, 5 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 run times 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 3 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 run times 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 have 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 scaled agentic infrastructure platform is integrating DigitalOcean 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 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 $100,000 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 native 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 13 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 3 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 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. 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. Matt Steinfort: 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%. And 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 continue to gain meaningful traction with some of the most sophisticated AI natives. AI customer ARR reached $234 million, growing 212%. And 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 12x 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 2 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 3-year high, we'll no longer highlight it as a key financial metric. Growth today is shaped far more by our highest spending and AI customers than by the NDR trend across our 680,000-plus customer base. Profitability remained strong in Q2. Adjusted EBITDA was $114 million, and 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. And 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 4 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 to $307 million, representing 32% to 34% year-over-year growth. We project adjusted EBITDA margins of 38% to 39% and non-GAAP diluted net income per share of $0.28 to $0.30 on approximately 126.5 million weighted average fully diluted shares. For the full year 2026, we expect revenue of $1.17 billion to $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 to $1.40 and adjusted free cash flow margin of 11% to 13%, an increase to our prior guide. While it's premature to speak to 2027 guidance, the positive momentum we're 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%. We are now projecting to nearly double it again on an annual basis next year. And 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. Padmanabhan Srinivasan: 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 $1 million-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. And 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 4 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 and 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: [Operator Instructions] Your first question comes from the line of Gabriela Borges with Goldman Sachs. Gabriela Borges: I wanted 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 smaller customers, but 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. So twofold question for you. How are you meeting those demands of the larger scaled customers? And then I think just maybe 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. Padmanabhan Srinivasan: Thank you, Gabriela. It's a great question. We feel very confident in our ability to scale, given our track record, like we have 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 0.5 million paying customers along the way. So we feel very confident in our ability, and 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. Like right now, they are coming to us not just for capacity, as I mentioned. So they are not expecting some exotic bespoke hardware or network configuration. They're predominantly coming to us because of the richness of our AI native cloud. So 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. And 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. But 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. And they're in the process of scaling up our go-to-market muscle to help us address the next phase of our hyper growth. But this is something we feel very confident. We've been doing this for a number of years. And I'll let Matt answer the infrastructure question. But I think from a talent density perspective, both on core engineering and go-to-market, I feel really good. And we have demonstrated in the recent past, and that's why we keep talking about our $500,000 and $1 million customers and how that flywheel is spinning and has been doing it for about 8 quarters in a row now. Matt Steinfort: Yes. And 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, very sophisticated technical customers where their founders and leaders are often deeply, deeply technical. And 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, 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. And 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. So 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, and we're very encouraged by the progress we're making. Operator: Your next question comes from the line of Jason Ader with William Blair. Jason Ader: 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: Yes, Jason, good questions. On the pricing, we've -- 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 as we -- 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 in -- with a different customer that was willing to pay a higher price. So all of that pricing is included in the '26 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%. And it's a good setup for us in 2027 as well. On the equipment financing side, the -- 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 just simply -- and the LTM EBITDA and simply subtract the amount of debt we retired in the equitization, puts us at 0.7x net leverage. We're in a very, very good position to stay well below that 4x net leverage that we had articulated. And in fact, it should be well below that. And 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. So it was a great step for us, and our leverage is going to be very comfortably below that guideline that we had provided. Jason Ader: And just on the free cash flow. Matt Steinfort: Free cash flow -- sorry, Jason. Yes. No, it's great -- that's a great question. Yes, so free cash flow, as we said, would be 11% to 13% for the year. That's on an adjusted free cash flow basis, which is higher than what we had guided previously. And if you take all of the principal payments and everything, we'll still generate cash in 2026. So 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 payment. We will continue to generate cash in '26. Operator: Your next question comes from the line of Mark Zhang with Citi. Mark Zhang: So I wanted to actually dig in a little bit more into the 9-figure deals that you guys were able to sign this quarter. Number one, sort of wanted to get a sense of, I guess, like the inferencing and the core cloud -- these logos are committed for. And sort of what's the adoption of the other aspects of the 5-layer stack and monetization road map looks like going forward? Because I think like the go-to-market philosophy here is really looking for large deals that can make good sense that can continue to expand going forward. So I just want to get a sense of the opportunities from here as we go forward with the AI stack. Padmanabhan Srinivasan: Yes. Thank you, Mark. So 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. See some of our AI native cloud layers are still new, and that's why we have another version of our AI Builders Conference scheduled on October 13 to talk a little bit more about more specifically the managed agents layer of our platform. So 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? And 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. And immediately, they are 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. So 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. So we feel increasingly confident, and that's why I spent 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: Got it. That's very helpful. And then 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 -- I guess, 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 for the enterprise -- at the enterprise level going forward from here? Padmanabhan Srinivasan: Yes. Thanks, Mark. So the primary focus right now is to land very high-quality AI-native workloads, right, like the ones that we discussed on the call. And these are top-tier AI native companies. And as I described, just in the last 90 days for our inference engine, we've added over 6,000 customers. That is just incredible. I mean, think about it, right, 6,000 customers in 60 to 90 days. And a lot of that is still standing on the shoulders of our incredible world-class product-led growth motion. And we are tapping into the ecosystem at scale, whether it is OpenRouter or OpenClaw 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 enterprise AI native enterprise go-to-market motion. But 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 engagements and nailing that motion before we scale it. So in terms of investments, I don't see the investment scaling 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. Operator: [Operator Instructions] Your next question comes from the line of Wamsi Mohan with Bank of America. Wamsi Mohan: I appreciate the comment that it's still a bit premature for 2027. But if we 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. And I have a follow-up. Matt Steinfort: Yes, 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. And 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. So we felt it's premature to give a specific number. But clearly, the message is 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. So we're very bullish, and we expect there to be additional upside. We're just -- it's too early to put a number on it. And so we'll wait until later this year before we provide any more specifics around that. But all the indications are we're -- as you saw by the virtue of the fact that we increased our guidance for '26 and the exit rate, we're better positioned than we were just 90 days ago. It's a good conclusion. Wamsi Mohan: Okay. And then maybe, Paddy, just on the open weight models, you, I think, quoted that it's risen from roughly 15% to now 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 it was not batch, but I just want to make sure of that. And is the cloud core services attach any different between customers using closed versus open models? Padmanabhan Srinivasan: Yes. Thanks for the question, Wamsi. It's a great question. So I'll go from the reverse order. So there isn't any major difference in what these workloads are attaching based on whether they are open weight or closed source models. They are attaching the same kind of core cloud parameters. And 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. So if you -- 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. And I feel a lot of the traffic we are seeing is production traffic. And 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 the 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. So these are not cheap models to serve. And when you look at the cost per intelligence task, yes, it is cheaper than the frontier closed source models, but they're not cheaper by an order of magnitude. But 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. And as I mentioned, there's just a tremendous amount of demand that far exceeds our supply. So 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: [Operator Instructions] Your next question comes from the line of Sanjit Singh with Morgan Stanley. Sanjit Singh: I wanted to revisit the revenue per megawatt story at DigitalOcean. You guys have obviously been at a huge premium to the Neoclouds. The bare mix is obviously coming down. You guys have 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? Where do you think -- or what are some of the levers to drive support for revenue per megawatt over time? Matt Steinfort: That's a great question. So 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 more ARR per megawatt than our Neocloud 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 multiyear 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. So we expect that to increase as the mix of core cloud to an the attach rate increases. But we're also installing higher capacity equipment in the same megawatts going forward. So 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, but the revenue potential is also higher. So 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. Thomas Blakey: 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? And then maybe give an update to the megawatt cadence that you're looking at here in calendar '26. 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 '26 and if we're still looking for 25 megawatts in the second half. Matt Steinfort: 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. So we have 15 left in 1 facility, and it's -- we had said it would come on in the second half, and it's on track, and we expect that to come online as we had expected over the balance of the year. And... Padmanabhan Srinivasan: Yes. The first question was the pricing impact on the net new ARR. Matt Steinfort: Modest. Padmanabhan Srinivasan: Yes, 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. But as Matt mentioned previously, as some of these contracts roll out, we have been adjusting the prices to market levels for our existing contracts. But in terms of its impact in Q2 and the $93 million in net new ARR, it had very little impact on it. So I don't want the takeaway to be that's how we had a blowout quarter. That's not the case at all. Operator: Your next question comes from the line of Jackson Ader with KeyBanc. Jackson Ader: I was just curious about the -- what exactly is baked into the out-year outlook? Like if I think about all the activity that you guys signed or contracted in the second quarter and the impact either here on '26 or '27, 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 '27. And any incremental activity that happens in the next few months, like 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 that -- for the 2027 numbers? Matt Steinfort: That's a great question. And 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. And so 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 '27 impact standpoint, you're getting pretty late in the year this year to have a huge impact on -- from a capacity standpoint on the calendar year '27 just because data centers typically have kind of a year-ish from lease signature to when you're generating revenue. So you're getting to the point where you might impact the exit growth rate, but the full year calendar year revenue might not be as impacted. And 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. But 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. And all of that upside is not reflected in the prior estimate of 50% or more growth for next year. But 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. So we certainly believe there's upside. Operator: And your last question comes from the line of Radi Sultan with UBS. Radi Sultan: Just one quick one. your customers using your AMD deployment, speak to how you see the mix between NVIDIA GPUs and inked in. And then maybe Matt, the unit economics on a per megawatt basis for AMDs compared to NVIDIA GPUs? Padmanabhan Srinivasan: Yes, 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. But it is a mix of both, and we continue to keep pace with the innovation in this market. And we certainly don't want to discuss the unit economics of different hardware throughputs. And I would just stop at that because it's a good mix of different types of accelerators. And 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, right? Most of the state-of-the-art GLM-5.2 or K3, we run on all kinds of hardware. And 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 Rahu. Radu Patrichi: Great. Thank you, Paige. Thank you, everyone, for joining, and this concludes our second quarter earnings presentation and conference call. Apologies, we couldn't get to all your questions, but look forward to speaking to everyone later in the day on our follow-up calls. Padmanabhan Srinivasan: Thank you. Operator: This concludes today's call. Thank you for attending. You may now disconnect. Before you buy stock in DigitalOcean, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and DigitalOcean wasn’t one of them. The 10 stocks that made the cut are built for long-term growth and could produce monster returns in the coming years. 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As with all our articles, The Motley Fool does not assume any responsibility for your use of this content, and we strongly encourage you to do your own research, including listening to the call yourself and reading the company's SEC filings. Please see our Terms and Conditions for additional details, including our Obligatory Capitalized Disclaimers of Liability. The Motley Fool has positions in and recommends DigitalOcean. The Motley Fool has a disclosure policy. DigitalOcean (DOCN) Q2 2026 Earnings Call Transcript was originally published by The Motley Fool

DigitalOcean Holdings DOCN reported second-quarter 2026 non-GAAP earnings of 45 cents per share, beating the Zacks Consensus Estimate by 73.08%. The figure declined 23.7% year over year. Revenues increased 28.6% year over year to $281.2 million and surpassed the consensus estimate of $278 million by 1.23%. Growth reflected rising demand from high-spending and AI-native customers. Annual run-rate revenue (ARR) reached $1.125 billion, up 29% year over year. DigitalOcean Holdings, Inc. price-consensus-eps-surprise-chart | DigitalOcean Holdings, Inc. Quote The company added a record $93 million of incremental ARR during the reported quarter, up 191% year over year. Digital Native Enterprise customer ARR accounted for 67% of total ARR, up from 59% in the prior-year quarter.Growth accelerated across DigitalOcean’s largest customer groups. ARR from customers spending at annualized rates above $100,000, $500,000 and $1 million increased 98%, 160% and 214%, respectively. The $1 million-plus cohort generated $259 million in ARR and represented 23% of the total. AI Customer ARR surged 212% year over year to $234 million and accounted for 21% of company ARR. Importantly, 85% of AI Customer ARR came from inference services and core cloud rather than bare-metal infrastructure.Inference services ARR advanced 762% year over year, while core cloud ARR from AI customers rose 158%. Bare-metal ARR declined 20%, highlighting a shift toward software-rich services that integrate computing, storage, databases and model-serving capabilities. DigitalOcean launched its Inference Engine in late April. More than 6,000 customers used the service by the end of the quarter, with customer additions averaging nearly 60% month over month. Token volume increased 30-fold over the preceding 60 days.Open-weight models expanded from roughly 15% of token traffic following the launch to nearly 75%. The platform offers more than 75 open and closed models through one endpoint, while features include intelligent routing, prompt caching, model evaluations, batch inference and model synthesis. GAAP Gross margin contracted 490 basis points (bps) year over year to 55% in the second quarter of 2026.Research and development expenses jumped 45.1% year over year to $57.5 million, while sales and marketing costs increased 17% year over year to $22.6 million. General and administrative expenses jumped 24.2% year over year to $45.2 million.Adjusted EBITDA increased 27% year over year to $114 million, producing a 40% margin. Adjusted operating income rose 9.3% year over year to $67.5 million, with the margin reaching 24%, down 420 bps on a year-over-year basis. Cash and cash equivalents stood at $767 million on June 30. DigitalOcean secured another 20 megawatts of committed data-center capacity, bringing the total to approximately 155 megawatts. The new capacity is expected to come online in late 2027 and early 2028.Net cash provided by operating activities was $110 million, up from $92 million a year earlier. Adjusted free cash flow totaled $61 million compared with $57 million, while the adjusted free cash flow margin was 22%. For the third quarter of 2026, DigitalOcean expects revenues between $304 million and $307 million, indicating growth of 32-34%. Adjusted EBITDA margin is projected between 38% and 39%, while non-GAAP earnings are expected between 28 cents and 30 cents per share.For 2026, DOCN raised its revenue guidance to $1.17-$1.18 billion from $1.13-$1.145 billion. The updated range implies growth of 30-31%, with the fourth-quarter growth rate expected to reach at least 35%.The company now projects a 38.5-39.5% adjusted EBITDA margin and an 11-13% adjusted free cash flow margin. Non-GAAP earnings are anticipated between $1.35 and $1.40 per share, up from the prior outlook of $1.10-$1.20. DigitalOcean currently has a Zacks Rank #2 (Buy).Some other top-ranked stocks in the broader Zacks Computer and Technology sector that are set to report their quarterly results are Onto Innovation ONTO, Inuvo INUV and Kimball Electronics KE. Each of the three stocks sports a Zacks Rank #1 (Strong Buy) at present. You can see the complete list of today’s Zacks #1 Rank stocks here.Onto Innovation, Inuvo and Kimball Electronics are set to report their quarterly results on Aug. 6, 11 and 12, respectively. Year to date, shares of Kimball Electronics and Inuvo have dropped 3% and 54%, respectively, while Onto Innovation have jumped 85.5%. Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report DigitalOcean Holdings, Inc. (DOCN) : Free Stock Analysis Report Inuvo, Inc (INUV) : Free Stock Analysis Report Kimball Electronics, Inc. (KE) : Free Stock Analysis Report Onto Innovation Inc. (ONTO) : Free Stock Analysis Report This article originally published on Zacks Investment Research (zacks.com). Zacks Investment Research

Interested in DigitalOcean Holdings, Inc.? Here are five stocks we like better. DigitalOcean’s revenue rose 29% year over year to $281 million in Q2 2026, exceeding guidance. Management raised its full-year outlook to approximately 30.5% growth, with fourth-quarter growth expected to reach at least 35%. Growth was driven by large customers and AI offerings: AI customer ARR surged 212% to $234 million, while ARR from customers spending at least $1 million increased 214%. Inference services grew nearly 800% year over year, and more than 6,000 customers have used the company’s Inference Engine since its April launch. DigitalOcean maintained strong profitability, reporting a 40% adjusted EBITDA margin and $61 million in adjusted free cash flow. The company is expanding data-center capacity, with approximately 155 megawatts committed, to meet demand that management said continues to exceed available supply. Catching the AI Wave: DigitalOcean Reels in AI Whales DigitalOcean (NYSE:DOCN) reported second-quarter 2026 revenue of $281 million, up 29% from a year earlier and above the high end of its guidance, as growth accelerated among its largest customers and AI-focused offerings gained adoption. Chief Executive Officer Paddy Srinivasan said the company added a record $93 million in annual recurring revenue during the quarter, nearly triple the incremental ARR reported in the year-earlier period. The company also raised its full-year outlook, citing demand that it said continues to exceed available capacity. → SpaceX’s First Earnings Report Could Decide Whether Shorts or Bulls Have Control 3 Tech ETFs That Could Bounce Back After the AI Selloff “We delivered 29% year-over-year revenue growth while continuing to have strong profitability,” Srinivasan said. He added that DigitalOcean expects roughly 30% revenue growth for the full year and at least 35% growth in the fourth quarter. DigitalOcean said ARR from customers spending at least $100,000 annually rose 98% year over year. ARR from customers spending at least $500,000 increased 160%, while ARR from customers spending $1 million or more climbed 214%. → 3 Drone Stocks That Should Soar After the Summer Slump DigitalOcean’s AI Surge: How Far Can This Rally Go? The company’s largest customer cohort represented 23% of total ARR in the second quarter, compared with 9% a year earlier. AI customer ARR reached $234 million, rising 212% year over year, according to Chief Financial Officer Matt Steinfort. DigitalOcean said 85% of AI customer ARR came from inference services and Core Cloud products rather than Bare Metal offerings. Inference services grew nearly 800% year over year and accounted for more than 70% of total AI customer ARR, Srinivasan said. → Why Rare Earth Processing Could Be the Real 2027 Opportunity The company’s remaining performance obligations rose to $894 million, more than 12 times the prior-year level, with an average duration of 3.7 years. Steinfort said those commitments were secured from a range of customers and that the company’s top 25 customers accounted for 20% of ARR during the quarter. DigitalOcean also said it would no longer emphasize net dollar retention as a key metric. NDR reached 102% in the quarter, a three-year high, but Steinfort said the measure has become less representative of the company’s business as growth increasingly comes from its biggest customers and newer AI customers. DigitalOcean launched its Inference Engine in late April, offering managed serverless inference and related technologies. Srinivasan said more than 6,000 customers have used the service since launch, with customer count growing by nearly 60% on average each month. Token volume increased 30-fold over the past 60 days, he said. Open-weight models accounted for about 15% of token volume shortly after the launch and had grown to nearly 75% by the time of the call. The company said it now offers more than 75 open- and closed-source models through a single endpoint and has completed 14 day-zero model launches since April. Srinivasan said DigitalOcean is seeing a shift from customers seeking the greatest possible token consumption toward optimizing the quality, latency and cost of AI workloads. He described the company’s Inference Engine as a production runtime that integrates functions including routing, model evaluations, batch inference, prompt caching and server-side tools for AI agents. The company said its Inference Router, which adjusts requests across open and frontier models based on quality, latency and cost, had nearly 1,400 active customers. DigitalOcean also highlighted its launch-partner status for Kimi K3, stating that the model brought more than 400 net new customers in its first week on the platform. Management said it is seeking to build an “AI-Native Cloud” platform that connects inference, agents, data products and core infrastructure. More than half of new AI customers added year to date had attached a Core Cloud product, Srinivasan said. Among AI customers with at least $100,000 in ARR, roughly 70% had attached a Core Cloud product in the second quarter. The company cited customers and ecosystem relationships including OpenCode, Daytona, Vercel and OpenRouter. DigitalOcean said it serves more than 20 billion tokens per day through OpenRouter, up more than 330% over the prior 60 days. DigitalOcean launched data centers in Richmond during the first quarter and Kansas City during the second quarter, both ahead of schedule, management said. The company remains on track to open its Memphis data center in the second half of 2026. It also secured about 20 megawatts of additional capacity expected to come online in late 2027 and 2028. Total committed capacity is now approximately 155 megawatts, with the majority expected to be online by the end of 2027. Management said the company has 15 megawatts remaining to bring online this year. During the quarter, DigitalOcean increased list prices on several GPU fleets by approximately 30%. Steinfort said the effect on second-quarter incremental ARR was modest, while pricing actions were included in the company’s outlook for the remainder of 2026. Adjusted EBITDA was $114 million, representing a 40% margin. GAAP operating income was $29 million, or a 10% margin, while adjusted operating income was $67 million, or a 24% margin. Non-GAAP diluted earnings per share were $0.45, and adjusted free cash flow totaled $61 million. Trailing 12-month adjusted free cash flow was $175 million, equal to 17% of revenue. For the full year, DigitalOcean expects adjusted free cash flow margin of 11% to 13%. In July, the company retired approximately $472 million of its 0% convertible senior notes due in 2030. Steinfort said the transaction reduced leverage with minimal cash use and effectively no dilution because the underlying shares had already been reflected in diluted share calculations. Third-quarter revenue is projected at $304 million to $307 million, representing 32% to 34% year-over-year growth. Third-quarter adjusted EBITDA margin is expected to be 38% to 39%. Third-quarter non-GAAP diluted EPS is forecast at $0.28 to $0.30. Full-year revenue is expected to be $1.17 billion to $1.18 billion, representing approximately 30.5% growth. Full-year adjusted EBITDA margin is projected at about 39%, with non-GAAP diluted EPS of $1.35 to $1.40. Management did not provide formal 2027 guidance but reiterated greater confidence in its previous expectation for revenue growth of more than 50% next year. Steinfort said the timing of future data-center capacity additions remains an important variable in determining the company’s 2027 results. DigitalOcean Holdings, Inc is a cloud infrastructure provider that focuses on simplicity, performance and developer experience. The company offers a range of cloud services designed to help software developers, startups and small- to medium-sized businesses deploy, manage and scale applications. Its flagship offering, Droplets, provides virtual private servers that can be configured with various CPU, memory and storage options. In addition to compute instances, DigitalOcean's platform includes managed Kubernetes, scalable object and block storage, managed databases, load balancers and networking capabilities such as Virtual Private Cloud (VPC) and Floating IPs. Founded in 2011 and headquartered in New York City, DigitalOcean was created with the goal of making cloud computing more accessible to individual developers and smaller teams. This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest reporting and unbiased coverage. Please send any questions or comments about this story to [email protected]. The article "DigitalOcean Q2 Earnings Call Highlights" was originally published by MarketBeat. View MarketBeat's top stocks for August 2026.

Our analysts just identified a stock with the potential to be the next Nvidia. Tell us how you invest and we'll show you why it's our #1 pick. Tap here. Revenue growth accelerated to 29% year-over-year, driven by a record $93 million in incremental ARR and a 214% increase in spend from $1 million-plus customers. Management attributes the growth to a 'valuemaxxing' market shift, where AI-native companies prioritize the right model at the right cost over simply maximizing token consumption. The AI-native flywheel is emerging as a primary growth engine, with 70% of high-spend AI customers attaching core cloud products like databases and storage to their AI workloads. Inference services grew nearly 800% year-over-year, serving as a high-margin entry point that pulls customers deeper into the integrated full-stack platform. Open weight models now represent 75% of token volume, up from 15% at launch, validating the strategy of providing a production runtime for open-source intelligence. Operational discipline enabled the early launch of Richmond and Kansas City data centers, with capacity largely pre-allocated to specific customers or the internal token fleet. Strategic pricing adjustments, including a 30% increase on certain GPU fleets, reflect high demand and the flexibility of the consumption-based business model. Full-year 2026 revenue guidance raised to approximately 30% growth, with an expected exit growth rate of 35% or more by the fourth quarter. Management expressed increased conviction in achieving 50% plus revenue growth for the full year 2027, supported by current momentum and incremental capacity. Secured 20 megawatts of additional capacity for late 2027 and 2028, bringing total committed capacity to approximately 155 megawatts. Future growth assumes the continued transition of AI workloads from human-prompted to agent-driven, which increases demand for integrated data and compute services. Guidance reflects a disciplined investment approach, aiming to maintain 39% adjusted EBITDA margins while scaling the go-to-market and engineering teams. Retired $472 million of 2030 convertible notes in July to reduce leverage and free up capacity for future growth investments with minimal cash impact. Remaining Performance Obligations (RPO) surged to $894 million, up over 12x year-over-year, providing significantly improved revenue visibility. Management noted that while supply chain challenges persist across the industry, they are currently meeting or exceeding deployment timelines. Net Dollar Retention (NDR) will no longer be highlighted as a key metric, as management believes it no longer accurately reflects the growth driven by high-spend AI cohorts. One stock. Nvidia-level potential. 30M+ investors trust Moby to find it first. Get the pick. Tap here. Management is scaling through 'forward deployed engineering' rather than just traditional sales, working side-by-side with technical founders on complex workloads. The company is leveraging partnerships with top-tier data center operators and chip manufacturers to manage implementation schedules despite industry-wide supply constraints. The 30% list price increase had a 'modest' impact on Q2 results but is fully baked into the raised 2026 guidance and 2027 outlook. Pricing power is supported by the ability to reallocate capacity from lower-value users to the high-demand 'token factory' or higher-paying customers. Incremental ARR per megawatt is expected to increase over time as customers adopt higher-layer services beyond bare metal GPUs. Higher token throughput from newer hardware generations (NVIDIA/AMD) and increased core cloud attach rates are the primary levers for improving megawatt productivity. Management confirmed that the majority of token traffic is production-grade rather than proof-of-concept, driven by sophisticated agentic applications. The 'Model Synthesis' feature orchestrates a panel of models in parallel, merging their outputs to deliver frontier-grade quality and optimized intelligence at a fraction of the cost of frontier models.

DigitalOcean Holdings, Inc. (DOCN) came out with quarterly earnings of $0.45 per share, beating the Zacks Consensus Estimate of $0.26 per share. This compares to earnings of $0.59 per share a year ago. These figures are adjusted for non-recurring items. This quarterly report represents an earnings surprise of +73.08%. A quarter ago, it was expected that this company would post earnings of $0.27 per share when it actually produced earnings of $0.44, delivering a surprise of +62.96%. Over the last four quarters, the company has surpassed consensus EPS estimates four times. DigitalOcean, which belongs to the Zacks Internet - Software industry, posted revenues of $281.18 million for the quarter ended June 2026, surpassing the Zacks Consensus Estimate by 1.23%. This compares to year-ago revenues of $218.7 million. The company has topped consensus revenue estimates four times over the last four quarters. The sustainability of the stock's immediate price movement based on the recently-released numbers and future earnings expectations will mostly depend on management's commentary on the earnings call. DigitalOcean shares have added about 164.3% since the beginning of the year versus the S&P 500's gain of 11%. While DigitalOcean has outperformed the market so far this year, the question that comes to investors' minds is: what's next for the stock? There are no easy answers to this key question, but one reliable measure that can help investors address this is the company's earnings outlook. Not only does this include current consensus earnings expectations for the coming quarter(s), but also how these expectations have changed lately. Empirical research shows a strong correlation between near-term stock movements and trends in earnings estimate revisions. Investors can track such revisions by themselves or rely on a tried-and-tested rating tool like the Zacks Rank, which has an impressive track record of harnessing the power of earnings estimate revisions. Ahead of this earnings release, the estimate revisions trend for DigitalOcean was favorable. While the magnitude and direction of estimate revisions could change following the company's just-released earnings report, the current status translates into a Zacks Rank #2 (Buy) for the stock. So, the shares are expected to outperform the market in the near future. You can see the complete list of today's Zacks #1 Rank (Strong Buy) stocks here. It will be interesting to see how estimates for the coming quarters and the current fiscal year change in the days ahead. The current consensus EPS estimate is $0.28 on $300.65 million in revenues for the coming quarter and $1.25 on $1.16 billion in revenues for the current fiscal year. Investors should be mindful of the fact that the outlook for the industry can have a material impact on the performance of the stock as well. In terms of the Zacks Industry Rank, Internet - Software is currently in the bottom 40% of the 250 plus Zacks industries. Our research shows that the top 50% of the Zacks-ranked industries outperform the bottom 50% by a factor of more than 2 to 1. HubSpot (HUBS), another stock in the same industry, has yet to report results for the quarter ended June 2026. The results are expected to be released on August 5. This cloud-based marketing and sales software platform is expected to post quarterly earnings of $3.02 per share in its upcoming report, which represents a year-over-year change of +37.9%. The consensus EPS estimate for the quarter has remained unchanged over the last 30 days. HubSpot's revenues are expected to be $897.82 million, up 18% from the year-ago quarter. Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report DigitalOcean Holdings, Inc. (DOCN) : Free Stock Analysis Report HubSpot, Inc. (HUBS) : Free Stock Analysis Report This article originally published on Zacks Investment Research (zacks.com). Zacks Investment Research

This article first appeared on GuruFocus. Revenue: Q2 revenue was $281 million, up approximately 29% year-over-year, above the high end of guidance. Adjusted EBITDA: $114 million, representing a 40% adjusted EBITDA margin. Adjusted Operating Income: $67 million, a 24% margin. GAAP Operating Income: $29 million, a 10% margin. Non-GAAP Diluted EPS: $0.45 per share. Adjusted Free Cash Flow: $61 million in the quarter; trailing 12-month adjusted free cash flow was $175 million, or 17% of revenue. Incremental ARR: Record $93 million added in Q2, nearly triple the amount added in the same quarter last year. AI Customer ARR: Reached $234 million, growing over 200% year-over-year. Inference Services Growth: Grew almost 800% year-over-year, representing over 70% of total AI customer ARR. Customer Cohort ARR Growth: ARR from $100,000+ customers grew 98%, $500,000+ customers grew 160%, and $1 million+ customers grew 214% year-over-year. Remaining Performance Obligations (RPO): Increased to $894 million, up more than 12 times year-over-year with a 3.7-year average life. Net Dollar Retention (NDR): 102% in Q2, a three-year high. Q3 2026 Guidance: Revenue expected between $304 million and $307 million, representing 32% to 34% year-over-year growth; adjusted EBITDA margins of 38% to 39%; non-GAAP diluted EPS of $0.28 to $0.30. Full Year 2026 Guidance: Revenue expected between $1.17 billion and $1.18 billion, representing approximately 30.5% year-over-year growth; adjusted EBITDA margins of approximately 39%; non-GAAP diluted EPS of $1.35 to $1.40; adjusted free cash flow margin of 11% to 13%. Warning! GuruFocus has detected 6 Warning Sign with DOCN. Is DOCN fairly valued? Test your thesis with our free DCF calculator. Release Date: August 04, 2026 For the complete transcript of the earnings call, please refer to the full earnings call transcript. Revenue growth accelerated to 29% year-over-year in Q2, more than double the growth rate from the same period last year, with record incremental ARR of $93 million. Inference services grew nearly 800% year-over-year, with the inference engine gaining over 6,000 customers and token volume increasing 30x in 60 days. AI customer ARR reached $234 million, growing over 200% year-over-year, with 85% of that ARR coming from non-bare metal services, indicating a shift to higher-margin offerings. The company raised its full-year 2026 revenue outlook to approximately 30% growth, with an exit growth rate of 35% or more in Q4, and expressed increased confidence in 50%+ growth for 2027. Strengthened balance sheet by retiring $472 million of convertible notes with minimal dilution, reducing net leverage to 0.7 times, and secured 20 megawatts of additional capacity, bringing total committed capacity to 155 megawatts. Net dollar retention (NDR) was only 102%, a three-year high but still low, and the company has stopped highlighting it as a key metric, indicating challenges in expanding existing customer spend. Supply chain challenges persist across the industry, which could impact the timely delivery of future capacity and growth plans. The company's growth is increasingly dependent on a small number of large customers, with the top 25 customers representing 20% of ARR, posing concentration risk. Adjusted EBITDA margin is expected to decline to 38-39% in Q3 and approximately 39% for the full year, down from 40% in Q2, due to investments in capacity and go-to-market. The company acknowledged that it is premature to provide formal 2027 guidance, and the 50%+ growth estimate is subject to significant uncertainty regarding data center timing and capacity availability. Q: Given the strong performance, should we assume there is further upside to the 2027 revenue growth estimate of 50%+ compared to what was thought 90 days ago?A: Matt Steinfort (CFO) confirmed that this is the appropriate conclusion. While it is premature to give a specific number due to the timing of data center implementations, the company is exiting 2026 at a much higher growth rate, has a strong RPO backlog, and is landing bigger customers. All indications point to additional upside, and the company is better positioned than it was 90 days ago. Q: How is DigitalOcean meeting the demands of larger flagship customers with bigger commitments, and what are the operational puts and takes to getting megawatts online on time?A: Paddy Srinivasan (CEO) stated that the company is confident in its ability to scale given its track record of running a global cloud business. Customers are coming for the AI-native cloud software, not just capacity, and the pace of innovation is high. The company has added a forward-deployed engineering organization and new go-to-market leadership (CRO Kevin Van Gundy and CMO Leo) to scale up. Matt Steinfort (CFO) added that the customers are sophisticated technical founders who appreciate the engineering talent, and the company has strong partnerships with data center operators and chip manufacturers to manage supply chain challenges. Q: Can you provide specifics on the impact of pricing on Q2 revenue growth and the updated outlook, and where do you expect net leverage and free cash flow to be for 2026?A: Matt Steinfort (CFO) explained that the ~30% list price increase on GPU fleets had a modest impact on Q2 results but is baked into the 2026 guidance, contributing to the higher exit growth rate. On the balance sheet, the equitization of $472 million in convertible notes puts pro forma net leverage at 0.7 times, well below the 4 times guideline. The company expects to be free cash flow positive on any metric in 2026, with adjusted free cash flow margin guided to 11%-13%. Q: Can you dig into the nine-figure deals signed this quarter and the adoption of the five-layer stack? What are the early plans of the new CRO, and should we expect more investment in enterprise go-to-market?A: Paddy Srinivasan (CEO) noted that over 70% of significant AI customers added this year are already using core cloud products, showing early evidence of the flywheel. These are production agentic workloads that need more than just GPUs or tokens, requiring databases, storage, and orchestration. On go-to-market, the focus is on landing high-quality AI-native workloads with a product-led growth motion and ecosystem hooks. The company will fortify its enterprise motion with forward-deployed engineering and technical sales reps, but will not scale investment soon; it is about quality of engagement before scaling. Q: Given the premium to neo clouds, is the revenue per megawatt story still declining as AI mix increases, or are there levers to drive it up?A: Matt Steinfort (CFO) clarified that the decline was from when the company was a general-purpose cloud. Now, incremental ARR per megawatt is expected to increase because the company offers higher-layer services beyond bare metal, sells to a broader customer base, and attaches core cloud services. Additionally, newer GPU generations have higher token throughput capacity, increasing revenue potential per megawatt. Q: What was the pricing impact on the record net new ARR of $93 million, and can you update the megawatt cadence for the second half of 2026?A: Paddy Srinivasan (CEO) stated that pricing had a very modest impact on Q2 net new ARR; the blowout quarter was not driven by price increases. Matt Steinfort (CFO) confirmed that 15 megawatts remain to be launched in the second half of 2026, on track with the previously communicated schedule. Q: Is the 2027 outlook based only on the 155 megawatts of committed capacity, and is any incremental activity upside?A: Matt Steinfort (CFO) confirmed that the outlook is based on already communicated capacity. Incremental capacity or deals would be upside, but it is getting late to impact full-year 2027 revenue due to the ~1-year lead time for data centers. The company is active in the market for incremental capacity and believes there is upside, but it is too early to provide a precise number. Q: How do you see the mix between NVIDIA and AMD GPUs, and how do their unit economics compare on a per-megawatt basis?A: Paddy Srinivasan (CEO) declined to provide specific details on hardware mix or unit economics for competitive reasons. He noted the company has a healthy mix of accelerators and is becoming hardware-agnostic due to its software optimization layer, which allows it to run state-of-the-art models on various hardware. Q: How much of the open-weight model token usage is recurring production traffic versus batch inference, and is core cloud attachment different between open and closed model users?A: Paddy Srinivasan (CEO) stated that there is no major difference in attachment between open-weight and closed-source model users. Most sophisticated production workloads are a combination of both. The traffic is overwhelmingly production traffic, not proof-of-concept. The adoption of open-weight models is creating a Jevons paradox, with demand far exceeding supply. Q: What is the impact of the inference engine and AI-native cloud on the company's ability to scale and differentiate from hyperscalers and neo clouds?A: Paddy Srinivasan (CEO) highlighted that the inference engine has over 6,000 customers since its late April launch, with token volume up 30x in 60 days. Open-weight models have grown from 15% to 75% of token volume. The AI-native flywheel is driving adoption across the full stack, with more than half of new AI customers having core cloud attached. This differentiates DigitalOcean from bare metal neo clouds and positions it as a full-stack platform for AI builders. For the complete transcript of the earnings call, please refer to the full earnings call transcript.

Raising 2026 revenue outlook RPO increased to $894 million, up 12x from a year ago Q2 2026 Revenue of $281 million grew 29% year-over-year Million+ Dollar Customer ARR grew 214% year-over-year to $259 million AI Customer ARR grew 212% year-over-year to $234 million Record $93 million in incremental ARR BROOMFIELD, Colo., August 04, 2026--(BUSINESS WIRE)--DigitalOcean Holdings, Inc. (NYSE: DOCN), the AI-Native Cloud purpose-built for inference and agentic workloads, today announced results for its second quarter ended June 30, 2026. "Our growth rate is accelerating, as revenue grew 29% year-over-year, more than double our growth rate a year ago," said Paddy Srinivasan, CEO of DigitalOcean. "The acceleration is coming from our highest spending customers and sophisticated AI Natives, and we are now beginning to land nine-figure annual commitments. Early Inference Engine customers drove their total token consumption up approximately 30x in the last 60-days, and 85% of our AI customer ARR now comes from inference and core cloud rather than bare metal. Just as important is how we are growing: attractive margins, positive free cash flow, capacity delivered on or ahead of schedule, and a stronger balance sheet. Our customer momentum and early product traction give us confidence to raise our 2026 revenue outlook to approximately 30%, reaching 35% or more by Q4 2026, and strengthen our conviction in our ability to exceed 50% growth in 2027." Second Quarter 2026 Financial Highlights(1): Revenue was $281 million, an increase of 29%. Annual Run-Rate Revenue ("ARR") ended the quarter at $1,125 million, an increase of 29%. AI Customer ARR was $234 million, an increase of 212%. Record $93 million of incremental ARR added during the quarter, an increase of 191%. Net income attributable to common stockholders was $35 million, a decrease of 4%, and net income margin was 13%. Operating income was $29 million, a decrease of 18%, and operating income margin was 10%. Adjusted operating income was $67 million, an increase of 9%, and adjusted operating income margin was 24%. Adjusted EBITDA was $114 million, an increase of 27%, and adjusted EBITDA margin was 40%. Diluted net income per share was $0.29 and non-GAAP diluted net income per share was $0.45. Net cash from operating activities increased to $110 million at a 39% margin, from $92 million at a 42% margin in the second quarter of 2025. Adjusted free cash flow increased to $61 million at a 22% margin, from $57 million at a 26% margin in the second quarter of 2025. Cash and cash equivalents was $767 million as of June 30, 2026. Remaining Performance Obligation ("RPO")(2) was $894 million, of which, $366 million is expected to be recognized over the next 12 months. RPO was $71 million in the second quarter of 2025. Second Quarter 2026 Operational Highlights(1): Launched Inference Engine as part of AI-Native Cloud. Shipped more than 80 product releases since April. Signed first nine-figure annual customer commitments with leading AI-Natives, extending weighted average contract life from 1.6 years to over 3 years. Secured an incremental 20 MW of committed data center capacity expected to come online in 2027 and 2028, bringing total committed capacity to approximately 155 MW, with additional capacity actively being pursued. Added to the Russell 1000 Index, recognition of a business that has scaled with discipline, pairing durable growth with consistent execution. The number of $100K+ Customers(3) grew 9%, while the revenue from these customers, which now represents 35% of total revenue, grew 98%. The number of $500K+ and $1M+ Customers grew 35% and 73%, respectively. Revenue from these customers, which now represents 26% and 23% of total revenue, grew 160% and 214%, respectively. Recent Developments: Repurchased approximately $472 million of our 0.00% Convertible Senior Notes due 2030, funded by a concurrent registered direct offering, reducing leverage with minimal cash usage and minimal dilution, with issued shares offset by the retired notes and an intended repurchase of approximately 500,000 shares. Financial Outlook: DigitalOcean is initiating guidance for the third quarter ending September 30, 2026 as follows: Total revenue of $304 to $307 million, up 32% to 34% year-over-year. Adjusted EBITDA margin of 38% to 39%. Non-GAAP diluted net income per share of $0.28 to $0.30. Fully diluted weighted average shares outstanding of approximately 126 to 127 million shares. For the full year 2026, we now expect: Total revenue of $1.170 to $1.180 billion, up 30% to 31% year-over-year. Adjusted EBITDA margin of 38.5% to 39.5%. Adjusted free cash flow margin in the range of 11% to 13% of revenue. Non-GAAP diluted net income per share of $1.35 to $1.40. Fully diluted weighted average shares outstanding of approximately 122 to 123 million shares. A reconciliation of non-GAAP outlook measures to corresponding GAAP measures is not available on a forward-looking basis without unreasonable effort due to the uncertainty regarding, and the potential variability of, expenses that may be incurred in the future. For example, stock-based compensation expense-related charges are impacted by the timing of employee stock transactions, the future fair market value of our common stock, and our future hiring and retention needs, all of which are difficult to predict and subject to constant change. Accordingly, a reconciliation is not available without unreasonable effort and we are unable to assess the probable significance of the unavailable information, although it is important to note that these factors could be material to our results computed in accordance with GAAP. The financial guidance presented in this release are estimates based on information available to management as of the date of this release. There can be no assurance that our actual results will not differ from the financial guidance presented in this release. Conference Call Information: DigitalOcean will host a conference call today, August 4, 2026, at 8:00 a.m. ET to review its results. The conference call and presentation can be accessed by registering for the webcast at https://events.q4inc.com/attendee/684389800. A live webcast and replay of the conference call in addition to the presentation can be accessed from the DigitalOcean investor relations website at investors.digitalocean.com. About DigitalOcean DigitalOcean (NYSE: DOCN) is the AI-Native Cloud, purpose-built for inference and agentic workloads. Its five-layer integrated platform, spanning GPU and CPU infrastructure, core cloud, inference, data, and managed agent orchestration, is open throughout with no vendor lock-in, giving builders everything they need to start fast, scale production AI workloads, and improve unit economics. More than 680,000 customers and millions of developers globally trust DigitalOcean to build, ship, and scale their applications. Learn more at digitalocean.com. Forward-Looking Statements This release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, regarding our expected future performance, including but not limited to statements in the section titled "Financial Outlook" and the quotations of our CEO. The forward-looking statements contained in this release and the accompanying earnings call referenced in this release are subject to known and unknown risks, uncertainties, assumptions, and other factors that may cause actual results or outcomes to be materially different from any future results or outcomes expressed or implied by the forward-looking statements. These risks, uncertainties, assumptions, and other factors include, but are not limited to: (1) fluctuations in our financial results make it difficult to project future results; (2) our ability to sustain profitability in the future; (3) our ability to expand usage of our platform by existing customers and/or attract new customers and/or retain existing customers; (4) the speed at which the market for our platform and solutions develops; (5) the success of the development and use of our artificial intelligence and machine learning ("AI/ML") product offerings or use of third-party AI/ML-based tools; (6) our ability to release updates and new features to our platform and adapt and respond effectively to rapidly changing technology or customer needs; (7) our ability to control costs, including our operating expenses, and the timing of payment for expenses; (8) the amount and timing of non-cash expenses, including stock-based compensation, goodwill impairments and other non-cash charges; (9) breaches in our security measures allowing unauthorized access to our platform, our data, or our customers’ data; (10) the competitive markets in which we participate; (11) our ability to effectively integrate and retain new members of our executive leadership team and senior management; (12) the effects of acquisitions and their integration; (13) general market, political, economic, and business conditions, including changes in trade policies, such as trade wars, tariffs and other restrictions or the threat of such actions; (14) the impact of new accounting pronouncements; (15) our ability to control fraudulent registrations and usage of our platform, reduce bad debt and lessen capacity constraints on our data centers, servers and equipment; (16) our customers’ ability to have continued and unimpeded access to our platform, including as a result of evolving laws and industry standards; and (17) our plans with respect to accelerating investments in data centers and GPU capacity. Further information on these and additional risks, uncertainties, assumptions and other factors that could cause actual results or outcomes to differ materially from those included in or contemplated by the forward-looking statements contained in this release are included under the caption "Risk Factors" and elsewhere in our Annual Report on Form 10-K for the year ended December 31, 2025 and subsequent filings and reports we make with the SEC. We operate in a very competitive and rapidly changing environment. New risks and uncertainties emerge from time to time, and it is not possible for us to predict all risks and uncertainties that could have an impact on the forward-looking statements contained in this release. The results, events and circumstances reflected in the forward-looking statements may not be achieved or occur. The forward-looking statements made in this release relate only to events as of the date on which the statements are made. We assume no obligation to, and do not currently intend to, update any such forward-looking statements after the date of this release, except as required by law. About Non-GAAP Financial Measures To supplement our consolidated financial statements, which are prepared and presented in accordance with generally accepted accounting principles in the United States, or GAAP, we provide investors with non-GAAP financial measures including: (i) adjusted operating income and adjusted operating income margin, (ii) adjusted EBITDA and adjusted EBITDA margin and (iii) non-GAAP net income and non-GAAP diluted net income per share. These measures are presented for supplemental informational purposes only, have limitations as analytical tools and should not be considered in isolation or as a substitute for financial information presented in accordance with GAAP. We believe that adjusted operating income margin and adjusted EBITDA, when taken together with our GAAP financial results, provide meaningful supplemental information regarding our operating performance (including our long-term performance in the case of adjusted operating income) and facilitate internal comparisons of our historical operating performance on a more consistent basis by excluding certain items that may not be indicative of our business, results of operations or outlook. In particular, we believe that the use of adjusted operating income and adjusted EBITDA is helpful to our investors as they are measures used by management in assessing the health of our business, evaluating our operating performance, and for internal planning and forecasting purposes. We believe non-GAAP net income and non-GAAP diluted net income per share provides our management and investors consistency and comparability with our past financial performance and facilitates period-to-period comparisons of operations, as this metric generally eliminates the effects of unusual or non-recurring items from period to period for reasons unrelated to overall operating performance. Our calculations of each of these measures may differ from the calculations of measures with the same or similar titles by other companies and therefore comparability may be limited. Because of these limitations, when evaluating our performance, you should consider each of these non-GAAP financial measures alongside other financial performance measures, including the most directly comparable financial measure calculated in accordance with GAAP and our other GAAP results. A reconciliation of each of our non-GAAP financial measures to the most directly comparable financial measure calculated in accordance with GAAP is set forth in the tables in the section "Reconciliation of GAAP to Non-GAAP Data." Adjusted Operating Income and Adjusted Operating Income Margin We define adjusted operating income as operating income, adjusted to exclude stock-based compensation, amortization of acquired intangible assets, acquisition related compensation, acquisition and integration related costs, restructuring and other charges, restructuring related charges, impairment of certain long-lived assets and other charges. We define adjusted operating income margin as adjusted operating income as a percentage of revenue. Adjusted EBITDA and Adjusted EBITDA Margin We define adjusted EBITDA as net income attributable to common stockholders, adjusted to exclude depreciation and amortization, stock-based compensation, interest expense, acquisition related compensation, acquisition and integration related costs, income tax expense (benefit), restructuring and other charges, restructuring related charges, impairment of certain long-lived assets, interest income and other income, net, (gain) loss on extinguishment of debt, net, and other charges. We define adjusted EBITDA margin as adjusted EBITDA as a percentage of revenue. Non-GAAP Net Income and Non-GAAP Diluted Net Income Per Share We define non-GAAP net income as net income attributable to common stockholders, excluding stock-based compensation, acquisition related compensation, amortization of acquired intangibles, acquisition and integration related costs, restructuring and other charges, restructuring related charges, impairment of certain long-lived assets, (gain) loss on extinguishment of debt, net, and other charges. In addition to these exclusions, we subtract an assumed non-GAAP provision for income taxes to calculate non-GAAP net income that excludes the current period income tax benefit (expense). We utilize a fixed long-term projected tax rate in our computation of the non-GAAP income tax provision in order to provide better consistency across reporting periods. We define non-GAAP diluted net income per share as non-GAAP net income divided by the weighted-average diluted shares outstanding, which includes the potentially dilutive effect of our stock options, RSUs, PRSUs, and Convertible Notes and, beginning in the first quarter of 2026, excludes the in-the-money portion of our 2030 Convertible Notes as they are covered by our capped call transactions, which are expected to mitigate the dilutive effect of our 2030 Convertible Notes. Adjusted Free Cash Flow and Adjusted Free Cash Flow Margin Adjusted free cash flow is a non-GAAP financial measure that we define as net cash provided by operating activities less purchases of property and equipment, capitalized internal-use software costs, purchase of intangible assets, and excluding cash paid for restructuring and other charges, acquisition related compensation, restructuring related charges, and acquisition and integration related costs. Adjusted free cash flow margin is calculated as adjusted free cash flow divided by total revenue. We believe that adjusted free cash flow and adjusted free cash flow margin are useful indicators of liquidity that provide information to management and investors about the amount of cash generated from our core operations that can be used for strategic initiatives, including investing in our business and selectively pursuing acquisitions and strategic investments. We further believe that historical and future trends in adjusted free cash flow and adjusted free cash flow margin, even if negative, provide useful information about the amount of net cash provided by operating activities that is available (or not available) to be used for strategic initiatives. Adjusted free cash flow and adjusted free cash flow margin exclude acquisitions of equipment under financing arrangements, finance leases, and our future contractual commitments. Additionally, adjusted free cash flow does not represent the residual cash flow available for discretionary expenses given our debt obligations and the total increase or decrease in our cash balance for a given period. Unlevered Adjusted Free Cash Flow and Unlevered Adjusted Free Cash Flow Margin Unlevered adjusted free cash flow is a non-GAAP financial measure that we define as adjusted free cash flow excluding cash paid for interest and interest income. Unlevered adjusted free cash flow margin is calculated as unlevered adjusted free cash flow divided by total revenue. We believe that unlevered adjusted free cash flow and unlevered adjusted free cash flow margin provide additional information to adjusted free cash flow about our liquidity and, measured over time, enable management and investors to monitor the underlying business’ growth pattern and ability to generate cash. We further believe that unlevered adjusted free cash flow is an important metric, as it provides a clear view of our cash generation before the impact of financing decisions and many investors and analysts use unlevered adjusted free cash flow as the basis of their enterprise value calculations as they assess the value of our business. Unlevered adjusted free cash flow and unlevered adjusted free cash flow margin exclude certain charges that will be settled in cash, such as interest paid to service our debt and equipment financing obligations. Additionally, unlevered adjusted free cash flow does not represent the residual cash flow available for discretionary expenses given our debt obligations and the total increase or decrease in our cash balance for a given period. Key Business Metrics: We utilize the key metrics set forth below to help us evaluate our business and growth, identify trends, formulate financial projections and make strategic decisions. Customers We calculate customer count as the average number of customers as of the last day of the month for each month in the most recent quarter. Customers are classified in the following categories based on the amount of their spend in a given month and individual customers may fall within different categories within a reporting period (customer spend in a month in whole dollars): Digital Native Enterprise Customers: users that spend more than $500 in a month. $100K+ Customers: users that spend more than $8,333 in a month. $500K+ Customers: users that spend more than $41,667 in a month. $1M+ Customers: users that spend more than $83,333 in a month. ARR We calculate ARR by multiplying total revenue for the most recent quarter by four. AI Customer ARR We calculate AI Customer ARR by multiplying total AI Customer Revenue for the most recent quarter by four. AI Customer Revenue is defined as the total revenue generated from customers who utilize one or more of our AI/ML offerings, inclusive of their revenue from our IaaS and PaaS/SaaS offerings during the period. Other Metrics: Remaining Performance Obligation Remaining performance obligation ("RPO") represents commitments in customer contracts for future services that have not yet been recognized in the condensed consolidated financial statements. RPO is not necessarily indicative of future revenue growth because it does not account for the timing of customers’ consumption or their usage beyond their contracted capacity. Additionally, RPO may increase when customers transition from usage-based to commitment-based agreements, which does not always reflect incremental revenue growth. RPO is influenced by a number of factors, including the timing and size of renewals, the timing and size of purchases of additional capacity and average contract term. Due to these factors, it is important to review RPO in conjunction with revenue and other financial metrics contained in this release and elsewhere in our Annual Report on Form 10-K for the year ended December 31, 2025 and subsequent filings and reports we make with the SEC. View source version on businesswire.com: https://www.businesswire.com/news/home/20260803098656/en/ Contacts Investor Contact [email protected] Media Contact [email protected]

The market expects Definitive Healthcare Corp. (DH) to deliver a year-over-year decline in earnings on lower revenues when it reports results for the quarter ended June 2026. This widely-known consensus outlook is important in assessing the company's earnings picture, but a powerful factor that might influence its near-term stock price is how the actual results compare to these estimates. The earnings report might help the stock move higher if these key numbers are better than expectations. On the other hand, if they miss, the stock may move lower. While management's discussion of business conditions on the earnings call will mostly determine the sustainability of the immediate price change and future earnings expectations, it's worth having a handicapping insight into the odds of a positive EPS surprise. This company is expected to post quarterly earnings of $0.04 per share in its upcoming report, which represents a year-over-year change of -42.9%. Revenues are expected to be $55.76 million, down 8.2% from the year-ago quarter. The consensus EPS estimate for the quarter has been revised 44.44% lower over the last 30 days to the current level. This is essentially a reflection of how the covering analysts have collectively reassessed their initial estimates over this period. Investors should keep in mind that the direction of estimate revisions by each of the covering analysts may not always get reflected in the aggregate change. Price, Consensus and EPS Surprise Estimate revisions ahead of a company's earnings release offer clues to the business conditions for the period whose results are coming out. Our proprietary surprise prediction model -- the Zacks Earnings ESP (Expected Surprise Prediction) -- has this insight at its core. The Zacks Earnings ESP compares the Most Accurate Estimate to the Zacks Consensus Estimate for the quarter; the Most Accurate Estimate is a more recent version of the Zacks Consensus EPS estimate. The idea here is that analysts revising their estimates right before an earnings release have the latest information, which could potentially be more accurate than what they and others contributing to the consensus had predicted earlier. Thus, a positive or negative Earnings ESP reading theoretically indicates the likely deviation of the actual earnings from the consensus estimate. However, the model's predictive power is significant for positive ESP readings only. A positive Earnings ESP is a strong predictor of an earnings beat, particularly when combined with a Zacks Rank #1 (Strong Buy), 2 (Buy) or 3 (Hold). Our research shows that stocks with this combination produce a positive surprise nearly 70% of the time, and a solid Zacks Rank actually increases the predictive power of Earnings ESP. Please note that a negative Earnings ESP reading is not indicative of an earnings miss. Our research shows that it is difficult to predict an earnings beat with any degree of confidence for stocks with negative Earnings ESP readings and/or Zacks Rank of 4 (Sell) or 5 (Strong Sell). For Definitive Healthcare, the Most Accurate Estimate is the same as the Zacks Consensus Estimate, suggesting that there are no recent analyst views which differ from what have been considered to derive the consensus estimate. This has resulted in an Earnings ESP of 0%. On the other hand, the stock currently carries a Zacks Rank of #3. So, this combination makes it difficult to conclusively predict that Definitive Healthcare will beat the consensus EPS estimate. While calculating estimates for a company's future earnings, analysts often consider to what extent it has been able to match past consensus estimates. So, it's worth taking a look at the surprise history for gauging its influence on the upcoming number. For the last reported quarter, it was expected that Definitive Healthcare would post earnings of $0.03 per share when it actually produced earnings of $0.06, delivering a surprise of +100.00%. Over the last four quarters, the company has beaten consensus EPS estimates three times. An earnings beat or miss may not be the sole basis for a stock moving higher or lower. Many stocks end up losing ground despite an earnings beat due to other factors that disappoint investors. Similarly, unforeseen catalysts help a number of stocks gain despite an earnings miss. That said, betting on stocks that are expected to beat earnings expectations does increase the odds of success. This is why it's worth checking a company's Earnings ESP and Zacks Rank ahead of its quarterly release. Make sure to utilize our Earnings ESP Filter to uncover the best stocks to buy or sell before they've reported. Definitive Healthcare doesn't appear a compelling earnings-beat candidate. However, investors should pay attention to other factors too for betting on this stock or staying away from it ahead of its earnings release. Another stock from the Zacks Internet - Software industry, DigitalOcean Holdings, Inc. (DOCN), is soon expected to post earnings of $0.26 per share for the quarter ended June 2026. This estimate indicates a year-over-year change of -55.9%. Revenues for the quarter are expected to be $277.77 million, up 27% from the year-ago quarter. Over the last 30 days, the consensus EPS estimate for DigitalOcean has been revised 8.1% up to the current level. Nevertheless, the company now has an Earnings ESP of -7.69%, reflecting a lower Most Accurate Estimate. When combined with a Zacks Rank of #2 (Buy), this Earnings ESP makes it difficult to conclusively predict that DigitalOcean will beat the consensus EPS estimate. The company beat consensus EPS estimates in each of the trailing four quarters. Stay on top of upcoming earnings announcements with the Zacks Earnings Calendar. Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report Definitive Healthcare Corp. (DH) : Free Stock Analysis Report DigitalOcean Holdings, Inc. (DOCN) : Free Stock Analysis Report This article originally published on Zacks Investment Research (zacks.com). Zacks Investment Research