DDOG

Snowflake drops 4% ahead of earnings and Datadog falls 6% with no catalyst, pointing to profit-taking on crowded high-beta software positions rather than company-specific news. IGV slides 3% while QQQ gains 0.2%, confirming traders are rotating out of software specifically rather than selling technology as a whole. Datadog's 65% and Snowflake's 46% YTD gains gave traders thick cushions to trim, making positioning the clearest driver of today's selloff. Just released. Our analysts combed the entire stock market and named the ten best stocks to buy right now. The report is free. Enter your email and see if any of your stocks made the cut. Software is the day's clearest sore spot at midday, with a handful of the year's biggest AI-era winners giving back ground even as the broader large-cap technology tape barely moves. That split is the actual story of the session, and it explains why several unrelated names are sinking together while the index stays quiet. The iShares Expanded Tech-Software Sector ETF (NASDAQ:IGV) is down 3% to $103.06, tracking software as a distinct slice of the market. Meanwhile, the Invesco QQQ Trust (NASDAQ:QQQ) is up 0.2% to $709.20, which leaves the NASDAQ 100 slightly higher on the session. That contrast tells the session's clearest story, since money is leaving software as a group rather than technology as a whole. Snowflake (NYSE:SNOW) stock is down 4% to $306.22 ahead of its fiscal Q2 2027 report scheduled for after today's close. Meanwhile, Datadog (NASDAQ:DDOG) shares are falling harder, down 6% to $211.29, with no earnings scheduled and no fresh company headline attached to the move. Cloudflare (NYSE:NET) stock is also down 4% to $273.09, rounding out a trio where the deepest cuts are landing on the highest-flying names in the space. Free Report, Just Released Did Any of Your Stocks Make the Top 10 List? It is an uncomfortable question, and there is now an answer to it. 24/7 Wall St has helped investors make money for over two decades, and our top analysts just finished ranking the definitive Top 10 Stocks To Buy Now. Not the ten biggest companies. Not the ten everyone is arguing about. The ten best stocks to buy right now. Open your account and look at what you own. Some of it you bought for a reason you could still defend today. Some of it you bought years ago for a reason you can no longer remember. The report is free. Put the ten next to what you own and find out which is which. Enter Your Email and See the Ten → Free from 24/7 Wall St. It lands in your inbox. Snowflake is confirmed to report fiscal Q2 2027 results after today's close, and that scheduled event is real and looming. Yet the pattern across the three tickers does not fit a straightforward earnings-nerves read, because Datadog and Cloudflare are not on the calendar today and are still moving lower in step with Snowflake. If nerves alone were the story, the two non-reporters would be somewhere near the flat line rather than leading the group down. No fresh company-specific headline explains today's declines in Datadog or Cloudflare, and the broader software fund is weakening at the same time. The cleaner explanation is a rotation out of high-multiple software rather than a narrative tied to any one ticker, and that framing lines up with what the ETF split is showing on the tape. Snowflake's late-day report is a coincidence of timing more than a driver of what has already happened this morning. The software group had rebuilt momentum coming into September after a strong August recovery, and today's action looks more like traders locking in profits than a change in the AI narrative that has powered the group all year. When the biggest decliners are also the biggest recent winners in a sector, positioning tends to explain more of the day than fundamentals do. That is the read most consistent with today's ticker-by-ticker picture across Snowflake, Datadog, and Cloudflare. Each of the three featured names is dropping further than the software fund itself, which is the fingerprint of the most expensive names in a sector being sold first. Snowflake stock was up 46% year to date (YTD) through Tuesday's close. Datadog stock was up 65% and Cloudflare stock was up 45% over the same window, giving each of them a thick cushion of prior gains for traders to trim into strength. Datadog's leading decline is the most instructive detail in the group today. With no report scheduled and no announcement circulating, the deepest cut is landing on a name with nothing on its own calendar to blame for the move. Traders trimming their exposure to the year's crowded winners looks like the simpler explanation, and profit taking of this shape typically hits the highest-beta software names before it spreads to steadier corners. Additionally, the QQQ's slightly-higher print today underscores that this is not a technology-wide flush. Large-cap tech is holding up while the software sleeve inside it is being sold down, which is what a targeted rotation looks like. That is rotation, not a sector-wide verdict on the AI trade that has driven names like Snowflake, Datadog, and Cloudflare to their current levels. Snowflake's fiscal Q2 2027 release and its conference call after today's close is the next scheduled event that can reset sentiment across the group. A clean report may steady IGV and pull the peer trade higher with it, and a softer one can extend today's move into the next session for Datadog and Cloudflare as well. Either outcome will be measured against a group already in a fragile spot. Traders can watch for whether IGV holds its recent range into the close, since the sector fund's behavior is doing more to explain today's action than any single company inside it. A finish below where the fund started the week would strengthen the rotation read and put more pressure on the peer group heading into the Snowflake report tonight. Investors weighing their exposure to the highest-multiple software names in IGV may want to lean toward moderate position sizes into tonight's report and keep dry powder for the reaction. The group's leaders have already moved sharply against their holders today, and the market shifted quickly enough to justify tighter risk controls on those positions. 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Interested in Snowflake Inc.? Here are five stocks we like better. MarketBeat's five most-upgraded stocks over the past 90 days are all software companies, including Snowflake, Okta, Datadog, CrowdStrike, and Palo Alto Networks. Analysts cite these companies' ability to directly monetize the AI boom, through consumption-based data platforms, AI agent identity security, and cloud observability, as the driver of upgrades. All five stocks have posted strong year-to-date gains and carry consensus Moderate Buy ratings, but several trade near price targets ahead of upcoming earnings that could test valuations. When analysts collectively raise their price targets and ratings on a single group of stocks, it pays to notice. Upgrades reflect where Wall Street's research desks see the earnings power and momentum heading next, and when they cluster tightly around one theme, that clustering could potentially mark the early stage of a leadership rotation. Right now, something worth flagging is happening on MarketBeat's most-upgraded stocks list: the five names drawing the heaviest analyst enthusiasm over the past 90 days are all software companies. Now, this is not entirely a coincidence. After a brutal stretch earlier in the year that saw software badly lag the AI hardware trade, the group has come roaring back. The iShares Expanded Tech-Software Sector ETF (BATS: IGV) has climbed about 9.65% over the past quarter, comfortably outpacing the broader S&P 500. This comes as capital has rotated out of the crowded, expensive semiconductor, memory, and neo-cloud names and into software companies that are now proving they can monetize AI in their own right. → Rocket Lab's Sell-Off Is Fading—Is It Finally Safe to Buy? The five names below sit at the center of that shift, and here is why analysts can't stop upgrading them. Snowflake (NYSE: SNOW) runs a cloud-based data platform that lets enterprises store, analyze, and share massive volumes of data across every major public cloud. It has become one of the purest ways to play a simple reality: artificial intelligence is only as good as the data feeding it, and analysts have taken note. → Travel + Leisure Goes Big—Is It Ready to Rally? The stock is up about 51% year-to-date, and the upgrades keep coming. Just last week, Truist raised its price target to $375, well above the current price, and the consensus rating among 40 analysts is Moderate Buy. And the overarching sentiment and theme driving the narrative is fairly straightforward. Snowflake's consumption-based model means that as customers build and run more AI workloads, they consume more Snowflake, turning the AI boom into a direct revenue tailwind. With earnings just around the corner, due Sept. 2, analysts have been confidently lifting targets and ratings ahead of what many expect to be another strong quarter. Okta (NASDAQ: OKTA) is the leading independent provider of identity and access management, the software that controls who can log in to what across an organization's digital footprint. Long viewed as a mature, steady grower, the company has found a fresh catalyst in the AI era, where securing not just human identities but autonomous AI agents has become a rapidly expanding need. → What Rising Delivery Forecasts Say About Rivian's Stock Prospects That narrative and organic, strategic shift, if you will, has powered the stock by almost 57% this year, and the analyst community has moved quickly, rating it as a consensus Moderate Buy. In just the past week alone, JPMorgan raised its price target to $165, and Cantor Fitzgerald reaffirmed its Buy rating. Morgan Stanley and KeyCorp reiterated their Overweight ratings on the stock, with both price targets implying over 27% upside. The company's improving profitability, paired with its emerging role in securing AI agents, has reframed what was once seen as a forgotten software name. The company is set to report its Q2 results on Aug. 26, so investors can expect another round of analyst actions. Heading into the print, the stock has spent several months consolidating in the upper band of its 52-week range, with $130 acting as critical support and $156 as the major breakout level. Datadog (NASDAQ: DDOG) provides cloud monitoring and observability, giving engineering teams real-time visibility into the health and performance of their applications and infrastructure. As enterprises pour money into AI and cloud workloads, the need to monitor those increasingly complex systems grows right alongside it, placing Datadog squarely in the flow. The stock has been a standout sector and industry performer, up about 73% year to date, and it carries the most attractive upside target in this group. The consensus rating across 45 analysts is Moderate Buy, with an average price target of $276.32, implying more than 17% upside from current levels. Datadog's Q2 2026 report on Aug. 6 actually beat expectations, and while the stock sold off on that print amid a stretched valuation, analysts have used the subsequent pullback to reiterate their conviction. The company reported earnings per share (EPS) of 65 cents, comfortably topping the estimate of 58 cents, while quarterly revenue grew 35.6% over the prior year, also beating estimates. CrowdStrike (NASDAQ: CRWD) is the dominant force in cloud-native cybersecurity, protecting endpoints, cloud workloads, and identities through its Falcon platform. It has become the enterprise standard in an era when AI is enabling attacks at machine speed, and that structural tailwind has kept analysts firmly in its corner. Shares are up almost 64% year to date, and the company has continued to draw positive analyst action, including recent target raises tied to its new partnership with Cerebras (NASDAQ: CBRS) to accelerate its AI security capabilities. The consensus across 50 analysts, the deepest coverage of this group, is Moderate Buy. One honest flag to note is that, after its powerful run, the stock has recently pulled back and now trades near the consensus price target, so much of the near-term optimism might already be priced in. But that structure could change soon, with earnings expected on Aug. 26 after the market closes. Expectations are running hot for CRWD, with the consensus estimate calling for an almost 26% year-over-year increase in earnings and about 23% growth in sales over the prior quarter. Palo Alto Networks (NASDAQ: PANW) is the largest pure-play cybersecurity company in the world, consolidating firewalls, cloud security, and AI-driven security operations into a single platform. Its platformization strategy, getting customers to adopt its entire security stack rather than piecemeal tools, has made it the default enterprise choice as companies race to secure their AI deployments. No name on this list has performed better in 2026, with the stock up an eye-catching 94% year to date as of Friday’s close. Palo Alto also has the strongest news sentiment score in the group, and it just launched a Frontier AI defense program to protect enterprise AI systems. The consensus across 48 analysts is Moderate Buy. As with several of its peers, the valuation is now rich and the stock trades close to its average target, so the burden shifts to its upcoming earnings report, expected around Sept. 1, to justify the enthusiasm. Institutional activity in Palo Alto has been rampant, with a whopping net inflow over the prior 12 months. Over the prior year, more than $51 billion in total institutional inflows have been recorded, versus just $4.4 billion in outflows. Ahead of the company's all-important Q2 report, its current institutional ownership percentage is nearly 80%. Five stocks, one sector, and a wave of analyst upgrades that is hard to ignore. What connects these names is a shared realization on Wall Street: software is no longer the laggard of the AI trade but an increasingly direct beneficiary of it, whether through consumption-based data platforms, identity security for AI agents, or observability for sprawling cloud systems. The upgrades reflect that shift in thinking. But the caution, as is often the case in this market, is valuation. Several of these names have run hard and now trade near their targets, with earnings reports looming that will separate the leaders from the laggards. However, one thing remains clear: when analysts move this decisively on an entire group, it is usually a signal worth respecting. The article "5 of the Most-Upgraded Stocks Over the Last Quarter Are All Software Names—Here's Why" was originally published by MarketBeat. View MarketBeat's top stocks for August 2026.

Datadog’s Q2 results were met with a significant negative market reaction, despite the company delivering revenue and non-GAAP profit above Wall Street’s expectations. Leadership pointed to continued acceleration in both AI-native and traditional customer segments, as well as broad product adoption, as the main drivers of quarterly performance. CEO Olivier Pomel noted, “We continue to see healthy trends in customer demand,” emphasizing that the platform’s usage is expanding across a wide range of customers. However, management acknowledged that a usage reduction by Datadog’s largest customer impacted the quarter and has been factored into their risk assessment and outlook. Is now the time to buy DDOG? Find out in our full research report (it’s free). Revenue: $1.12 billion vs analyst estimates of $1.08 billion (35.6% year-on-year growth, 3.9% beat) Adjusted EPS: $0.65 vs analyst estimates of $0.58 (11.4% beat) Adjusted Operating Income: $257 million vs analyst estimates of $233.3 million (22.9% margin, 10.2% beat) The company lifted its revenue guidance for the full year to $4.46 billion at the midpoint from $4.32 billion, a 3.2% increase Management raised its full-year Adjusted EPS guidance to $2.52 at the midpoint, a 5% increase Operating Margin: 0.5%, up from -4.3% in the same quarter last year Customers: 4,720 customers paying more than $100,000 annually Annual Recurring Revenue: $4.71 billion (35.6% year-on-year growth, beat) Billings: $1.18 billion at quarter end, up 38.1% year on year Market Capitalization: $86.5 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. Sanjit Singh (Morgan Stanley): Asked about the largest customer’s contract renewal and its impact on guidance. CEO Olivier Pomel said usage decreased, so guidance was fully derisked, while CFO David Obstler explained this approach was consistent with prior methodology. Raimo Lenschow (Barclays): Inquired about observability needs for AI inference workloads. Pomel explained there are opportunities at every layer of the stack, noting customer focus has shifted from correctness to cost optimization as AI matures. Gabriela Borges (Goldman Sachs): Questioned how Datadog addresses CFO concerns about rising observability costs. Pomel highlighted Infinite Cardinality Metrics and evolving product packaging as responses to customer feedback. Michael Cikos (Needham): Asked about the ramp in new logo contributions. Obstler clarified that growth from new customers is compounding over time, driven by AI capabilities and broader platform pull-through. Ittai Kidron (Oppenheimer & Co.): Sought clarity on customer additions and Bits AI’s role in security automation. Obstler pointed to stable gross additions, while Pomel said Datadog is moving toward broader AI-driven security operations center (SOC) capabilities. In the coming quarters, our analyst team will focus on (1) sustained adoption and monetization of new AI observability and security products, (2) customer response to evolving product packaging and cost-management features, and (3) the impact of large enterprise and government wins on overall revenue growth. The pace of Bits AI adoption and progress in federal sector sales will also be important signposts for Datadog’s execution. Datadog currently trades at $240.45, down from $283.17 just before the earnings. At this price, is it a buy or sell? See for yourself in our full research report (it’s free for active Edge members). ONE MORE THING: Top 5 Growth Stocks. The biggest stock winners almost always had one thing in common before they ran. Revenue growing like crazy. Meta. CrowdStrike. Broadcom. Our AI flagged all three. They returned 315%, 314%, and 455%, respectively. Find out which 5 stocks it’s flagging this month — FREE. Get Our Top 5 Growth 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-micro-cap company Kadant (+214% between June 2020 and June 2025). Find your next big winner with StockStory today.

Image source: The Motley Fool. Thursday, Aug. 6, 2026 at 8:00 a.m. ET Senior Vice President of Investor Relations - Yuka Broderick Co-Founder and Chief Executive Officer - Olivier Pomel Chief Financial Officer - David Obstler Operator: Good day, and thank you for standing by. Welcome to the Q2 2026 Datadog Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded. I would now like to hand the conference over to your first speaker today, Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead. Yuka Broderick: Thank you, Lauren. Good morning, and thank you for joining us to review Datadog's second quarter 2026 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-Founder and CEO; and David Obstler, Datadog's CFO. During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the third quarter and the fiscal year 2026 and related notes and assumptions, our product capabilities and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will and similar expressions are intended to identify forward-looking statements or similar indications of future expectations. These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-Q for the quarter ended March 31, 2026. Additional information will be made available on our upcoming Form 10-Q for the fiscal quarter ending June 30, 2026, and other filings with the SEC. This information is also available on the Investor Relations section of our website, along with a replay of this call. We will discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com. With that, I'd like to turn the call over to Olivier. Olivier Pomel: Thanks, Yuka. Thank you all for joining us to go over our Q2 results. Let me begin with this quarter's business drivers. Our revenue growth in Q2 has accelerated across our customer base. On one hand, our AI native customer cohort continued to grow and diversify, both in the number of customers we serve and the scale of those customers. On the other hand, and as a great illustration of the breadth of trends across our business, revenue growth for our non-AI customers also accelerated again this quarter to the high 20s percent year-over-year, up from the mid-20s last quarter and 18% in the year ago quarter. Overall, we continue to see healthy trends in customer demand. Our broad base of customers, from the most nimble startups to the largest and most established enterprises, are all adopting AI. We think this is accelerating their usage of cloud and modern technologies, as well as their usage of the Datadog platform to observe, secure, and act on their cloud and AI workloads. Regarding our Q2 financial performance and key metrics, revenue was $1.12 billion, an increase of 36% year-over-year and above the high end of our guidance range. We ended Q2 with about 33,400 customers, up from about 31,400 a year ago. We also ended with about 4,720 customers with an ARR of $100,000 or more, up from about 3,850 a year ago. These customers generated about 91% of our ARR. And we generated free cash flow of $279 million with a free cash flow margin of 25%. Turning to product adoption. Our platform strategy continues to resonate in the market. For example, 58% of our customers now use 4 or more products, up from 52% a year ago. 37% of our customers use 6 or more products, up from 29% a year ago, and 13% of our customers use 10 or more products, up from 7% a year ago. We're landing more customers and delivering value across more products, our products are broadly delivering strong growth in usage and ARR. As an example, RUM, or Real User Monitoring, now exceeds $200 million in ARR and accelerated at its scale to over 50% growth year-over-year. Our customers are sending more user sessions and using RUM in conjunction with our newer Product Analytics to optimize their business outcomes. Moving on to R&D. We held our DASH user conference in June, where we announced over 100 exciting new products and features for our users. Let's go through some of the announcements. First, we expanded Bits AI to accelerate and automate the DevOps loop. This is the loop that goes from detection to investigation to remediation that engineers go through each time something breaks. At DASH, we announced a lot of new Bits capabilities for the DevOps loop. Bits can now create and maintain monitors, identify root causes within minutes of a negative signal, recommend and implement fixes, follow guardrails to add safety and controls, continuously learn and improve from prior incidents, and detect symptomatic behaviors early to repair infrastructure issues before they escalate. Second, we announced Bits AI products to address the development loop. This is the loop that goes from coding to delivery to evaluation that developers navigate to get code to production. For this loop, Bits Release now acts as an AI release validation agent, analyzing the impact of code changes, running end-to-end checks, and verifying production rollouts. Bits Code generates code fixes, guaranteeing every fix and reproduction behavior and Bits Testing also automates synthetic test generation and maintenance. Third, we expanded Datadog for AI, our products that observe, secure, and optimize the AI stack from end to end. Data Observability enables companies to trust the data being used by AI with lineage quality monitoring and jobs monitoring. Bits Data Analysis uses a rich data context to accurately answer business questions. And Agent Console provides visibility into AI agent usage, cost, and effectiveness. In Agent Observability, our patterns capability automatically clusters user interactions into behavior groups to identify quality or cost issues and Bits Evals handles the repetitive parts of the agent development loop in order to improve the outcomes of agents. Fourth, we are broadening our platform to ingest, correlate, analyze, and act on more data, whether on-prem or in the cloud. In Network Monitoring, we launched Network Path and Network Configuration Management to trace changes that cause complex network issues. Within Database Monitoring, Bits Database Optimizer now automatically simulates and evaluates the impact of AI-generated changes in order to optimize [ slow ] queries. In Log Management, federating logs enables users to query external data stores, including Databricks and ClickHouse. And With Bring Your Own Cloud, or BYOC, customers can now use the full Datadog experience on logs that are kept within their infrastructure. We've also announced that we're bringing BYOC to metrics and traces as well. In the digital experience space, Journey Monitoring automatically gives a single shared view for every critical user flow. And for custom metrics data, we introduced Infinite Cardinality Metrics, which allow our users to answer arbitrarily complex questions as they generate larger amounts of data with AI agents without incurring any extra costs. Finally, we launched a number of innovations to secure the AI stack and defend against a new class of AI-powered attacks. AI Guard agent discovery finds and maps every known and unknown custom agent so security teams can see what is protected and what is not. AI Guard for custom agents provides runtime protections to block attacks that can only be detected with real-time observability data. AI Guard for coding agents applies the same deep observability to block malicious skills and packages in code. We also announced Runtime Prioritization Engine to cut vulnerability noise by over 95%. And finally, we expanded Bits Security Analyst to run on [ non ] Datadog SIEMs so customers can benefit from the smarts and the learnings of a broad data set regardless of which SIEM they deploy. As we continue to innovate, we are being rightfully recognized by independent research. We are pleased to see that for the sixth year in a row, Datadog has been named a leader in the 2026 Gartner Magic Quadrant for Observability Platforms. Let's move on to sales and marketing and look at a few of the deals our GTM teams have closed in what has been a very strong quarter. First, we landed a 6-figure annualized deal with a Fortune 10 company. This company is expanding its e-commerce business, and they plan to use Datadog Log Management alongside 10 other Datadog products to improve customer experience and business outcomes. This win validates our [ expanded ] go-to-market approach to focus on the world's largest companies and win opportunities in the most complex environments. Next, we landed 7-figure annualized deals with two neuro labs. These AI labs are rapidly scaling their AI model training workloads and preparing for major product launches. By deploying observability using Datadog, they gain visibility across their training infrastructure and GPU fleets and can iterate faster on their AI models. They are also using Bits AI to rapidly build monitors, dashboards, and alerts for deep observability context. Next, we landed a 7-figure annualized deal with a South American bank. This bank's fragmented legacy monitoring stack and manual triaging caused significant application downtime that was often called in by customers. By consolidating into Datadog with 11 products, this customer enables visibility from their mainframe all the way to their microservices and has already reduced mean time to resolution on live production incidents. They are adopting Cloud SIEM and Data Security and evaluating other Datadog security products to improve their security posture. Next, we signed a 7-figure annualized expansion for an 8-figure annualized deal with a Fortune 100 health insurance company. This customer's biggest pain point is to deliver great experience to their members throughout their care while protecting PII across dozens of business units. Datadog's HIPAA compliance and PII handling in RUM, Log Management, and Cloud SIEM allowed us to differentiate and win over competitive solutions. And Bits AI investigation is already speeding up incident resolution and reducing expensive escalations. This customer will expand to 19 Datadog products. Next, we signed a multiyear, over $30 million TCV deal with one of the world's largest online media companies. This customer chose to standardize on Datadog across its business, displacing 4 commercial and internal tools. Datadog also proved value beyond core observability with Product Analytics, CI Visibility, Data Observability, and Cloud Cost Management. This deal includes our largest win to date for Bring Your Own Cloud, displacing their legacy commercial logging tool at a petabyte scale. And finally, we signed a 9-figure renewal with a leading AI company. This longtime, very large customer uses 17 Datadog products to enable unified visibility on production workloads at a very large scale, albeit with a user reduction starting in Q3, which we considered in our guidance and which David will speak to. Before I turn it over to David for a financial review, let me offer a few words on our longer-term outlook. There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. but we now have an additional growth driver with AI as we help our customers deliver value with this transformative new technology. We are tremendously excited about our opportunities in AI. To summarize where we are and where we're going. First, AI is a tailwind for Datadog today as cloud consumption grows and drives more use of our platform. As of Q2, over 750 AI customers use Datadog to monitor and improve their tech stacks. When we look at the largest companies driving AI, all 10 of the top 10 AI leaders are Datadog customers. Beyond AI natives, we see AI activity growing across our broader customer base. We are also seeing signs of rapid growth in agentic activity with a number of MCP tool calls quadrupling again quarter-over-quarter and growing more than 22x when compared to Q4 2025. Second, we are delivering AI for Datadog to deliver more value and greater platform capability to our customers. This includes our Bits AI products, chat, investigation, detection, code, testing, release, and many, many others. Third, next-gen AI introduces new complexity and observability challenges. We are addressing this with what we call Datadog for AI to observe and secure the AI stack from end to end. This includes GPU Monitoring, Agent Observability, Agent Console, Data Observability, AI Guard, and many other products. Finally, our AI research team and our large volume of rich data using critical workflows enable us to conduct groundbreaking research. We have shown some of our work already with the second version of our time series model, Toto, in May. Toto version 2 was exciting for 2 reasons. First, we've shown it to be state-of-the-art on key benchmarks. But more importantly, we've demonstrated for the first time true scalability for time series models, allowing us to target the same improvement path language models have followed since 2020. So now beyond Toto, we are working on larger and more ambitious dedicated models, post-training models to power Bits AI and bringing other modalities beyond time series data into world models that we think can lead to a step change in capabilities for our customers. And we plan to accelerate these research efforts with the acquisitions of Adaptive ML, which will close in June. Because of all of that, now more than ever, we feel ideally positioned to have customers of every size and every industry, as well as all types of users, whether humans or AI agents, so they can transform, innovate, and drive value to AI and cloud adoption. And with that, I will turn it over to our CFO, David. David Obstler: Thanks, Olivier. Our Q2 revenue was $1.12 billion, up 36% year-over-year. Within that, our 11% quarter-over-quarter revenue growth is the highest since Q2 2022. And our quarter-over-quarter revenue added of $115 million is a record by a significant margin. We continue to see robust usage growth from existing customers as well as a strong ramp in our new customers. Revenue growth accelerated with our broad base of customers, excluding AI customers to the high 20s year-over-year, up from the mid-20s percent last quarter and 18% in the year ago quarter. We saw robust growth across our customer base with broad-based strength across customer size, spending bands and industries. Meanwhile, our AI customers continue to grow rapidly and diversify in the quarter. This 750 strong customer group includes a broad range of AI start-ups as it has in the past, but now also includes hyperscalers using Datadog for in-house AI labs. In Q2, this includes 31 customers spending more than $1 million annually, of which 8 customers spent more than $10 million annually. We also achieved strong new logo dollar bookings with particular strength in enterprise, where new logo annualized bookings more than doubled from a year ago. And we are seeing new logos ramping faster and contributing more to revenue growth. The portion of our year-over-year revenue growth that relates to new customers was about 30% in Q2, up from 25% in Q1. Geographically, we're performing well in all regions with growth acceleration across the regions. We see particular strength in the Americas as much of the AI activity is occurring in the U.S. as well as in addition, we are executing strongly in LatAm. Regarding retention metrics, our trailing 12-month net revenue retention percentage was in the low 120s, similar to last quarter, and churn remains low with gross revenue retention in the mid- to high 90s. We believe this metric highlights the mission-critical nature of our platform for our customers. Now moving on to our financial results. Billings were $1.18 billion, up 38% year-over-year. Remaining performance obligations, or RPO, was $3.47 billion, up 43% year-over-year. Current RPO grew about 40% year-over-year, and RPO duration increased year-over-year. As we previously mentioned, we continue to believe revenue is a better indication of our business trends than billing and RPO. Now let's review some of the key income statement results. Unless otherwise noted, all metrics are non-GAAP. We have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. Our Q2 gross profit was $892 million for gross margin of 79.6%. This compares to a gross margin of 80.2% last quarter and 80.9% in the year ago quarter. As we've discussed in the past, our gross margin varies from quarter-to-quarter with investments into innovations for our customers, offset by efficiency efforts. There's no change in our expectations for gross margin, which has been in the 80% plus or minus range historically. Q2 OpEx grew 26% year-over-year versus 31% last quarter and 36% in the year ago quarter. We held our DASH conference -- user conference in June, and as expected, the event cost about $15 million. Q2 operating income was $257 million for a 23% operating margin compared to 22% last quarter and 20% in the year ago quarter. Turning to our balance sheet and cash flow statements. We ended the quarter with $5 billion in cash, cash equivalents and marketable securities. Cash flow from operations was $316 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $279 million for a free cash flow margin of 25%. And now for our outlook for the third quarter and the fiscal year 2026. Our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on trends observed in recent months and imply conservatism on these growth trends. Regarding our largest customer, we have seen a usage reduction, which is incorporated in our Q3 and full year 2026 guidance. As Olivier noted, this customer has recently renewed with us. For the third quarter, we expect our revenue to be in the range of $1.135 billion to $1.145 billion, which represents a 28% to 29% year-over-year growth. Non-GAAP operating income is expected to be in the range of $260 million to $270 million, which implies an operating margin of 23% to 24%. And non-GAAP net income per share is expected to be in the $0.63 to $0.65 per share range based on approximately 378 million weighted average diluted shares outstanding. For the full fiscal year 2026, we expect revenue to be in the range of $4.45 billion to $4.47 billion, which represents a 30% year-over-year growth. Non-GAAP operating income is expected to be in the range of $1.01 billion to $1.03 billion, which implies an operating margin of 23%. And non-GAAP net income per share is expected to be in the range of $2.50 to $2.54 per share based on approximately 376 million average diluted shares outstanding. And for some additional notes on guidance, we expect net interest and other income for the fiscal year 2026 to be approximately $180 million. We expect cash taxes in 2026 to be about $30 million to $40 million. We continue to imply a 21% non-GAAP tax rate for 2026 and going forward. And finally, we expect CapEx and capitalized software together to be in the 4% to 5% of revenue range in fiscal 2026. Now finally, to summarize, we are pleased with our execution in Q2. Our investments in R&D and go-to-market are yielding positive results, and they position us well for continued execution. I want to thank all the Datadogs worldwide for their efforts. And with that, we'll open the call for questions. Operator, let's begin the Q&A. Operator: [Operator Instructions] Our first question comes from the line of Sanjit Singh with Morgan Stanley. Sanjit Singh: On the acceleration in revenue growth again this quarter. David, thank you for giving us the color on some of the guidance assumptions, particularly headed into Q3 with respect to the largest customer. I was wondering if you could share any additional details in terms of the new contract? Was it a similar duration? And in terms of the lower usage, is that a function of the customer getting lower unit price because of making a new commitment? Or is there some churn downsell that we're seeing through not only for Q3 but for the balance of the year? Olivier Pomel: Yes. So maybe I'll take this one. I think we -- so overall, we -- as usual, we don't want to comment too much on any specific customer because we also don't really control what's happening with any specific customer. We wanted to be transparent about this on the call because we did see a reduction in usage, and we took the liberty to fully derisk the guidance for the rest of the year with respect to that customer. And again, the reason for that is we don't control what's happening to a specific customer, but we do have a great amount of control on what's happening to everything else in the business, and the business is booming, and we don't want that to overshadow basically the acceleration we see pretty much everywhere else in the business. So the -- as we mentioned on the call, we renewed the customer. We -- it's a long-time customer using many of our products, but there's not a lot more we can share. David Obstler: Yes. I think just to get specific on the guidance, we last quarter and previous quarters said that we essentially have a level of commit, and we can derisk our guidance by using that. And then as you know, in most of our large customers, we have variability relating to the commit, so take that into consideration. Olivier Pomel: Yes. The last thing I will say because I know so it's on people's minds is if you back out our largest customer from our growth, you get pretty much the same growth rate as the rest of the business has been accelerating very steadily. Actually, we've seen, I think, now 5 quarters of continuous acceleration from the rest of the business, and we feel very good about the -- what we see in the market. Sanjit Singh: Yes. No, I appreciate the thought. Let's talk about maybe the rest of the business. What we've seen in the past couple of years sort of AI native sort of leading the charge. It sounds like the enterprises are getting on board with their AI initiatives. And so just in terms of like the enterprise AI app dev cycle, what does that look like for Datadog over the last couple of quarters? Olivier Pomel: We do see broad adoption, and we see it in two ways. One is we see it manifest itself in just more transformation, more cloud adoption, more workloads, more modernization from customers. And that's what drives the majority of the known AI customer acceleration. So we mentioned also we've seen continuous acceleration from customers that existed before AI and that are not majority AI businesses. And that's been pretty remarkable, like the acceleration that we gave the numbers on the call, but the acceleration since last year has been constant and very significant, and we -- it keeps happening as far as we can tell. So it's a very positive trend there. That's the first thing we see. The second thing we see is a very rapid increase in the usage of all of our AI-first surfaces. So that Would be the products that measure agents and LLMs, we see an explosion of traffic in terms of the LLM and tool calls we're getting. That would be the amount of calls we're getting to our MCP endpoints. So we see that explode completely over the past two quarters. Operator: Our next question comes from the line of Raimo Lenschow with Barclays. Raimo Lenschow: Perfect. Could I stay on that AI theme, please? At the moment, like if you think about the large customers, there's a lot of model training, et cetera. But if we broaden it out, inference is really becoming the bigger part. Can you talk a little bit about like how much more observability is needed? And I'm thinking there, if I do inference, I need to think about vector databases, I need to think guardrails, all of these agents are going to be in containers that need to be monitored, et cetera. So what do you see in real life at the moment in terms of if some people do more inference, how much more observability gets triggered by inference? Is that kind of an opportunity that we should probably pay more attention than at renewal? And I had one follow-up. Olivier Pomel: There's opportunity at every layer of the stack in inference. So we do think at the end of the day, inference will be the dominant workload. That's -- any time you train, you probably will want to infer more than you train as a rule of thumb. We see opportunity at the low level when it comes to the infrastructure, the GPUs and the consumption you have there. There's opportunities at the very top end when you measure what the agents are doing and whether you're getting the right outcomes and whether you're getting the right alignment. And there's opportunities that every layer in between, just looking at the LLM itself, just looking at the tool calls and the applications that are being called by the agents, like everything is an opportunity in there. We see growing adoption from the products we already have there. We mentioned our GPU monitoring product is actually getting quite a bit of usage in a number of neuro Labs and very AI-first types of customers. We're also seeing an explosion of volume in our agent monitoring product. And so we're well positioned there. But we think this market is going to change quite a bit and the procurations of customers, they also change over time. So for example, last year, our customers were mostly trying to validate correctness and validate that they were getting some form of outcomes that they could then scale up. I would say 3 to 6 months ago, the focus has moved quite a bit towards cost. Now customers were spending a lot on AI and they were wondering what to optimize cost. And I think we'll see some variations in the concerns over time as customers get further into the adoption and new products emerge for them. David Obstler: I just want to add that when you look at what we described as some of our deals in the quarter and you look down our description, you'll see that a number of them have the AI products included. And so that is indication that those large enterprises are using the platform and buying the AI products as well. Raimo Lenschow: Okay. Perfect. And then, David, one for you. It's like it's obviously -- you're always in a tough position if you have to guide and there's these large contracts. How did you do it historically? So did you always kind of put in the base level and then what happened happens? Or has that approach changed? Or I don't envy you on having to do this. David Obstler: No, we essentially use -- as we've talked about over the many years, we kind of use the inputs of what we see. And what we said, I think, in the last quarter or 2 is that we have certain base levels. As you know, we have a commitment and a usage model. And we've factored that in and providing our guidance. So our -- as we said in the prepared remarks, our methodology for guidance hasn't changed. We've always used those inputs and looked at the commitment and usage and doing that. Olivier Pomel: Yes. I mean the only thing I'd say is, in this case, we did chose to fully derisk our largest customer. And the reason for that is we don't want that to be an overhang on what is otherwise business that is accelerating and performing extremely well. So we extended that we have the same overall conservatism as we always do when we look at our numbers. But in this case, we also weighted this one a little bit differently. Operator: Our next question comes from the line of Gabriela Borges with GS. Gabriela Borges: I wanted to ask you both about one of our observations at DASH, which is the engineers love the pace of innovation. They talk very positively about the product. The CFOs love to complain a little bit about their Datadog bills. So my question for you is talk to us a little bit about how the CFO level conversations are evolving. Clearly, the ROI is there, but maybe give us a little bit more on where the budget is coming from. And something like Infinite Cardinality, is that now part of the conversation with CFOs in solving some of those very particular cardinality cost questions? Olivier Pomel: I mean, look, at a high level, there's only 2 reasons people buy software. It makes them more money or it saves them money. And anytime we sell, anytime we go out in a renewal, we go out an upsell, or we land a new customer, that's because we do one of those 2 things for them, and we always have to make that case. So I wouldn't say that's any different from what we've seen before. The -- what we do for our customers today, especially as they keep adopting AI, is we help them save a lot of the money they would spend on building, running operations or running AI agents. When we have a concern with customers, that's the one thing they kept mentioning is, Hey, how can you help me rein in my AI costs. This is growing very fast, and I don't have any control on it, and I don't know whether I'm reaching the right outcomes with that. And so that's one of the reasons we've invested in all those products we've mentioned earlier. And also we're seeing some of the great returns on that products already. In terms of Infinite Cardinality, that's -- I would say it's been one of the longest-standing source of frustration for customers, when sometimes they send more data or they send more fine-grained tags with their data, and they get some unpredictability on the bills because of that, because it increases the cardinality of the data we're getting. And we've solved that from a technical perspective and from a commercial perspective by packaging our metrics a little bit differently. And we think it's particularly important and relevant as customers are building more applications with AI and as they want to send basically more tags and more information and ask more complex questions and get more fine-grained answers to those questions. And so that fits well within their plans, basically. So we've got great feedback on that so far, but it's still early. If sometimes we'll get it right, sometimes we'll get it slightly wrong, and when we get it slightly wrong, we fix it. That's not different from what we've done in the past. Operator: Our next question comes from the line of Mike Cikos with Needham. Michael Cikos: I wanted to come back to the significant size of the lands that you had this quarter, and it's great to see the sustained traction, especially with those AI labs. But if I'm thinking about the 2 7-figure AI labs that you landed this quarter and then going to David's commentary around winning some of these in-house AI labs with the hyperscalers, are those one and the same here? Or are those 2 separate customers that were -- customer sets we're talking to? Olivier Pomel: These are different customers. The ones we mentioned on the new lands are neuro Labs. So these are companies that didn't exist a few years ago. And what's interesting about them on the use case there is that very often, we land customers when they go into production and they release products and they start serving their customers. In this case, these are customers we're getting as they are training models, and they're using us to observe and improve and optimize the training of the models. And so that's an exciting new area that was not really a business area for us a couple of years ago, and we've seen a number of new proof points around that. In addition to that, and we've mentioned in previous calls, we've also landed the AI lab or super intelligence labs of a number of hyperscalers. And I would say the workloads are similar in that it's largely training of the models, but the customers are a little bit different. These are very large companies that, in that case, previously had a lot of a lot of homegrown technology to observe and run workloads. Michael Cikos: Excellent. And for a follow-up, I know you had cited the new logos ramping more strongly than what we've seen historically. And correct me if I'm wrong, but I feel like that's a newer phenomenon that you guys are calling out this quarter. When I think about those new logos ramping, is that a function of pull-through where maybe some of these AI capabilities are pulling through the broader platform? Or is it vice versa? Anything you can do to help us think through what is creating that catalyst, if you will, when the new logos are contributing to the model? David Obstler: It's been happening and building up the number that we have in our Qs, which is the percent from customers of growth that we didn't have a year ago, -- that number, we said has gone from [ 25 ] to [ 30 ]. So this has been building, and we wanted to point that out because of that disclosure, indicating that the customers that we're landing that it's not only the new logos, but it's also the growth of the new logos that we've added over the last couple of years -- last year, sorry. So it's a compounding of that. Operator: Our next question comes from the line of Alex Zukin with Wolfe Research, LLC. Aleksandr Zukin: Oli, maybe first for you, just on the -- a lot of headlines around security over the course of the last few weeks, particularly AI breaking containment. And it occurs to me that with your positioning in observability and security increasingly, the notion of a Guardian model and development around that could meaningfully increase kind of your ambit on what you can do and achieve for clients, both AI natives and legacy. Can you maybe talk to what -- the increasing opportunity around this crossover in this AI age and what that means for Datadog? And then I've got a quick follow-up for David. Olivier Pomel: I mean, look, there's a complete switch in the way the security products need to work. So you can't wait basically for putting humans in the loop. You can't have the typical path when you have 12 or 15 different products that are going to aggregate signal, then you put that signal into a system and to prioritize them for humans, and humans will review them when they can. Like you need to integrate everything a lot more. You need to operate a lot closer to the application and to the infrastructure, and you need to have AI agents solve the issues first. So it's a complete rebuild for most of the industry. And I think it plays into our approach, which is to have an integrated platform and have all of the different data streams come directly from observability straight into the security agent and have all that being integrated from end-to-end. So obviously, this is a field that's moving very fast. We see new classes of issues pretty much every week at this point. We are quite busy building that up, but we think it displays into our strength and into where we are basically already are and we're building for our security products. Aleksandr Zukin: Perfect. And then, David, maybe just for you. On the largest customer renewal, is there anything you can tell us around maybe just any changes around the duration or anything that makes this new contract maybe a little stickier in terms of the discounted rate card, the amount of products that they're able to kind of use for better value, anything that increases the conviction level around stickiness? David Obstler: I won't comment on this other than to say that most of our enterprise customers, as we talked about for a long time, have annual plus and then the pricing is generally volume-based pricing. So I would say, overall, our customers transact with us in that way. And then we have that level of commitment. And then as we talked about over a lot of years, then there's usage and then we transact. So similar to what we have with most of our larger enterprise customers. Oli, anything you want to add there? Olivier Pomel: No, I think there's a lot of continuity in that renewal. I think that's what you wanted to put it. Operator: Our next question comes from the line of Eric Heath with KeyBanc Capital Markets. Unknown Analyst: This is [ Tracy Prachef ] on for Eric Heath. I would love to get more color on your Q3 guide specifically. It seems like it's a little below your sequential levels of how you've guided your previous Q3s. I would love to just hear more about what trends you're seeing going into Q3 and maybe what some of the assumptions of the guide are. David Obstler: Yes. I think it's similar to the methodology. We take what we see and provide some conservatism. And I think we had mentioned in the script that we've been renewed our largest customer, but we've seen us declines relative to the previous quarter. We said that. So that's all taken into consideration in trying to develop a guidance that is consistent with the methodology of conservatism that we've used as a public company. Unknown Analyst: Got you. And if I could just ask one more for Oli. I'd love to just get your thoughts on the impact of diversification of AI model usage in your customers and what you're seeing there? Olivier Pomel: Well, we think it's great. Like there's a lot more options for customers to choose from in general. That creates --that opens up a lot of doors and opportunities for them. It also creates a lot of complexity, and we're here to help deal with that complexity. So for us, these are great opportunities. And by the way, we see -- like we've had that thesis since the early days of AI that we would not just end up with one or two big AI companies and everybody using them, the same way we didn't just end up with one or two big cloud companies and everybody just using software from them. Like the ecosystems are very, very rich. They have -- there are lots of providers. There are very large providers. There are smaller providers, and everything in between. And there's many compositions of those different systems that are used by any given customer. And so we think the same is going to happen in AI. We think also that the multiplication of models, and open source models in particular, opens the door to customers doing a lot more training on their own. And so that's a new market for us. We see some signs that we have a very good role to play there. And we're building towards that as well. So overall, I would say it's very positive for everyone. Operator: Our next question comes from the line of Koji Ikeda with Bank of America. Koji Ikeda: Just one for me here. I wanted to ask on Bits AI. All the commentary that you guys are saying on Bits AI and all the work that we've been doing intra-quarter, it sounds like Bits AI is really taking off for you guys. And so just thinking that Bits AI is going to be increasingly automating activities that historically has created observability workflows. I'm curious and really wonder how do you ensure that greater automation that might be driven by Bits AI doesn't eventually reduce the volume of activity that traditionally drove Datadog consumption? Olivier Pomel: Well, look, if we provide more value, we get more -- as I was saying earlier on the call, like we sell more software by helping customers make more money or save money or both. And I think if we can automate more and let them do more, we'll provide more value. That's as simple as that. I think the future of observability is not just observing, it's fixing. It's not waking up people in the middle of the night because something book, but fixing it for them. It's not letting people do damage control on the security incident because an attacker is in. It's preventing the attacker from getting in to start with by auto mediating issues, and we're very, very busy building all of that. And we're super confident that this will yield great business outcomes for us in the end. And that's what we see from customers in the market. Like when they use Bits AI, they use more of our product. They deploy more of it. They create more dashboards and alerts and everything else. They have more users inside of our product, like it's not a zero-sum game. Operator: Our next question comes from the line of Samik Chatterjee with JPMorgan. Samik Chatterjee: Maybe just on the non-AI part and the acceleration that you're seeing related to the non-AI part of the business. I just wanted to sort of get your thoughts on the sustainability and whether this acceleration that you're seeing is driven by some of the new customer logos that you're pointing out or more usage going up? And as CFOs get more sort of cautious around their budgets, do you see more sensitivity around non-AI eventually relative to some of the AI products and how they're doing at this point? And I have a quick follow-up. Olivier Pomel: So I mean from what we can tell, it's very broad-based. And it's largely driven by existing customers because that's the majority. Like when you think of what it takes to move that number, that's basically the majority of our business, we're not just going to move that with a few newer customers. It's largely driven by the existing customers. And it's driven by both increases in volume and because they are moving more and more close to the cloud and adoption of our newer products as they consolidate on to us. We think it's sustainable. For one thing, if you compare to what we have seen in the heady days of 2021 or the growth rates are accelerating, but they're still far below what we were seeing at that time. And so we don't create the same issue of customers having to digest very large increases multiple years in a row. I think in this case, we're very well within the range of sustainability. And as has been a theme in this call, remember like when customers adopt and they consolidate, they have an eye towards the financial side of the equation, basically, how much money are they going to make or save by doing that at the end. And we are very good at helping customers understand that and making that case and helping them save money at the end of the day. So we feel good about that. David Obstler: And I want to just add one thing, and we talked about this last quarter that some of this has to do with the investments that we're making in our platform and our product, but it also has to do with the investments that we're making in our go-to-market. We've successfully expanded quota capacity, the geography of it. And essentially, that's, as we talked about last quarter, providing returns. So that's also being a growth driver in our non-AI or enterprise type business. Olivier Pomel: That's right. And you see it also in our continuing investment there. So we keep investing in R&D, obviously, because we're shipping more products that are successfully being adopted and consolidated into -- by our large number of existing customers but we also are adding to our go-to-market teams. We're still not at the scale we want to be in terms of getting to all of the customers worldwide in all of the segments that are relevant to us. So we're investing as we see the returns of those investments. Samik Chatterjee: Got it. Got it. And for my quick follow-up here, you talked about the FedRAMP High certification last quarter. Just curious if there's anything to sort of update us on the pipeline and how -- if there's any momentum on that front on the pipeline yet. Olivier Pomel: Yes. Well, we're investing quite a bit in the buildup of our federal and government sales in general. And we see pipeline there. These are -- in general, these are not deals that happen overnight, but this is a very large market, and we see great traction there, and we're investing to take full advantage of it. A lot of that was a buildup to get to the right level of certification so we can deliver SaaS to various levels of government. And we've done quite a bit there. There's actually even more we're planning to do there. And -- but we're happy with the results so far. Operator: Our next question comes from the line of Howard Ma with Guggenheim Securities. Howard Ma: Great. Congrats on the strong quarter and the full year guidance raise. I have 2 questions. I'll just ask them together. The first is on Bits AI. I'm curious how adoption and contribution compares to the previous major feature expansions in the past. And then my other question is the $30 million TCV deal with the -- I think you guys said it's the largest online -- or sorry, one of the largest online media companies. I'm assuming this company did mostly DIY before. So if you could share some light on the decision-making process and if they're using multiple Datadog products and why now? That would be really helpful. L. Olivier Pomel: Yes. I'm sorry, I missed some part of your second question. Yuka Broderick: It was -- are they taking multiple products, I think, right, Howard? Howard Ma: Are they -- yes, the nature of the sale. Olivier Pomel: The nature of the sale. Howard Ma: Why now? Olivier Pomel: Yes, yes. So I mean I would say -- so first on Bits AI. Yes, and one thing that happened is Bits AI used to be fairly specific. It used to be dedicated to alerts. Like, Bits AI would pick up an alert and would run an investigation for you. Now the surface of contact is a lot wider with the customer. So Bits AI, you can access it through chat. You can, of course, still do the investigations, and we've done quite a bit more there. You can have Bits AI manage your monitoring and manage your detection for you. You can have it code for you. You can have it generate managed tests. Like there's all sorts of different use cases that we built into it that broaden the surface of contact, and we see a lot of adoption across all of those different areas. We also are changing the way we package it. So we have a new model with AI credits that we're rolling out just because the surface of contact is so much wider now than the specific feature. So we -- there's quite a bit that is going on there. The explosion of activity that I mentioned earlier about other parts of our other AI surfaces is happening also in Bits AI. So that's something we're looking forward to. So that's on that. On the second one, on the products that are being adopted in the sale, look, we typically land with two or more products that the balance we try to strike there is always to land enough of the platform without slowing down the deals too much. Because the more you try to do at once, the more stakeholders you get, and the longer it takes. And so we found that two products in general is a good land, and then we can expand from there. On the calls, we tend to mention a lot of consolidation deals because they tend to be the larger ones. Like if you land with 12 products, you're going to be larger than if you land with two in general. That's not the majority of the deals. The consolidation typically happens later than when we land, but this make for very interesting examples of what our customers are doing when they're consolidated on us all at once. Operator: Our next question comes from the line of Andrew Sherman with TD Cowen. Andrew Sherman: Congrats on the core growth acceleration. Oli, CPUs have had a renaissance lately driven by Agentic AI. It would be great to hear your thoughts on this topic, if it can be an incremental growth driver for your infrastructure monitoring. Have you seen any evidence of this yet? That's it for me. Olivier Pomel: Look, we do see an acceleration of consumption of our infrastructure products in general. So that's -- at a high level, we do see that across the customer base. I don't know that if we see specifically the CPUs that get attached to GPUs in the new build-out. I think a lot of it has more to do with the fact that the AI agents are largely spending a good amount of their time, like sometimes the majority of their time, coding tools. And tools are just applications that already existed, and those applications typically run on CPUs, and so we see quite a bit of that. Operator: Our next question comes from the line of Brad Reback with Stifel. Brad Reback: Oli, given your commentary around how strong the core is and that your largest customer was not additive to growth here in 2Q, should we assume that if we ex out the sequential downtick in that customer that the core guide would have been probably 300 or 400 basis points higher? Olivier Pomel: Well, I can't speculate. But what I will say is, look, the business overall is growing at the same rate. if you exclude that customer, as I said. And the business has been accelerating overall. So that's why we feel good, like when we look at whether we're getting the right returns and the right outcomes for our investments in R&D or investments in go-to-market and when we look at our pipelines and all of the signs we have about the business, we feel great about the business. It's a good time to be in business. David Obstler: Yes. I think we commented in the remarks that the non-AI has accelerated and the AI, excluding the largest customer continues. So I think we gave those trends in describing the business. Olivier Pomel: And of course, customers are growing a lot faster than non-AI. David Obstler: And that AI is growing. Yes, exactly. Operator: Our next question comes from the line of Ittai Kidron with Oppenheimer & Co. Ittai Kidron: Congrats on the great quarter. I wanted to ask about new customer additions. This probably was the weakest quarter I ever remember for you guys, especially in the quarter where you had DASH, where historically DASH has been an accelerant of new customer additions. Any color there would be great. David Obstler: Yes. Yes, I think we -- essentially, it's very similar to what we talked about before. Our gross customer additions continue to be strong and on trend line. and that's the vast majority of our revenues. We have at the very low end, the border between free and contract, and that has variability, very low effect on revenues. So if you -- that accounts, as we talked about in many quarters, that accounts for the variability of the customer count, and it really has to do with something that has very little effect on revenues. Olivier Pomel: Yes. When you look at the customers above certain thresholds, like whether it's above $1 million, above $100,000 1000 all of those are trending very well. Ittai Kidron: Very good. And then as a follow-up, Oli, for you, perhaps, I want to follow up on the questions around Bits, which sounds super interesting. I guess longer term and as you try to push deeper also into the security side of things, could this be evolving into a broader AI SOC automation kind of platform? Is that a reasonable direction to think that this is where it's going to go? Olivier Pomel: Well, there's definitely -- we're taking moves towards that, right? So we -- Initially we built the SIEM first for that, then we built the agent into the SIEM. So our Bits AI Security Analyst. And now we've actually separated the agent from our SIEM so customers can use it with other SIEMs. And we do that because the agent performs just so well, and it's been such a differentiator when we pitch the SIEM that we think we're limiting our sales market-wise if we just go after customers that want to re-platform their SIEM, and it can have a much broader appeal as an AI SOC. So we are definitely taking moves towards that. Operator: Our next question comes from the line of Andrew DeGasperi with BNP Paribas. Andrew DeGasperi: I just wanted to ask a question on the non-AI natives, specifically in terms of the growth that you saw in the quarter. I was wondering, did you see rising demand for the AI monitoring tool, particularly with open source tools being deployed across enterprises? Olivier Pomel: Sorry, I missed the second part of your question. David Obstler: I think you're asking about within that, the AI, what we used to call AI monitoring. LLM, et cetera, the growth trend there. Olivier Pomel: And look, the volume -- like there used to be very little volume a year ago. It started growing quite a bit into the second half of last year. And now it's been very rapidly accelerating over the past couple of quarters. So we've seen an explosion, basically, of the volume we're getting there. And we get more usage from different kinds of companies, so we definitely see that. We see it also across traditional companies and some more recent AI natives. So we see a little bit of both. I would say for that category, it's still super early. Like we expect the products to change quite a bit. We expect the usage, maybe also the packaging to change over time quite a bit. Andrew DeGasperi: Got it. Thank you. Olivier Pomel: All right. So I think that was the last question. So I want to thank all of you for attending the call today. I also want to, again, thank the teams, everyone at Datadog. I think everybody's been doing a fantastic job, both on the product side and the go-to-market side. I know we have a lot more lined up for the end of the year on the product side, and I know also we have very large and very happy pipelines to tend to on the go-to-market side. So I hope to talk to you again in a quarter. Thank you all. Operator: Thank you for your participation in today's conference. This does conclude the program. You may now disconnect. Before you buy stock in Datadog, 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 Datadog wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you’d have $403,337!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you’d have $1,334,946!* Now, it’s worth noting Stock Advisor’s total average return is 958% — a market-crushing outperformance compared to 214% for the S&P 500. Don't miss the latest top 10 list, available with Stock Advisor, and join an investing community built by individual investors for individual investors. See the 10 stocks » *Stock Advisor returns as of August 13, 2026. This article is a transcript of this conference call produced for The Motley Fool. While we strive for our Foolish Best, there may be errors, omissions, or inaccuracies in this transcript. 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 Datadog. The Motley Fool has a disclosure policy. Datadog (DDOG) Q2 2026 Earnings Call Transcript was originally published by The Motley Fool

(Bloomberg) -- The latest reason to worry about the stock market is quite the doozy: Earnings growth has been too strong. Most Read from Bloomberg Phoebe Gates Knew Phia Shopping App Took Credit for Sales It Didn’t Drive Trump Weighs Call for Capital Gains Tax Cuts as Midterm Boost Tata Sons Chairman to Step Down, Deepening Leadership Turmoil Five Takeaways From Zuckerberg’s 6,500-Word Manifesto on AI Epstein Victim Files Cleared for Release Over Maxwell’s Protest As the latest reporting season nears completion, all signs are indicating the second quarter was one of the best three-month periods in recent memory with profit growth running at more than 30%. The only problem? That torrid pace is unlikely to last. The consensus currently expects growth to fall below 20% in the first quarter of 2027 before moderating into the mid-teens for the full year, according to strategists at Bank of America Corp. While in isolation those rates are healthy from a historical standpoint, the market often has been less supportive when earnings growth decelerates from elevated levels. It’s a recipe that potentially could place next year’s stock market in the weakest phase for equities: When earnings-per-share growth is above trend but decelerating, the S&P 500’s median 12-month return is 6.7% with a hit rate of 72.3%, according to BofA. That compares with a median 14% return and a hit rate of 83.3% when EPS growth is above trend and accelerating. Still, the historical data set is very limited when it comes to the type of profit bonanza unfolding this year. BofA strategists led by Savita Subramanian expect growth to remain above 20% in the third and fourth quarters, which would mark four consecutive quarters above that level. Streaks like that have been rare, occurring only 10 times since 1936. The most recent examples have taken place after EPS recessions, the strategists said. Examples include Covid and the global financial crisis. And the growth rate is not the only standout statistic for the second quarter reporting season. S&P 500 Index profits are also heading toward one of their largest beats on record versus analysts’ estimates, according to Citadel Securities. Scott Rubner, head of equity and equity derivatives strategy at the firm, noted that companies are also driving the steepest earnings-estimate revision path in at least 26 years. “Importantly, this is not just an AI story,” Rubner wrote in a note published on Tuesday. “The macro debate remains complicated, but the message from corporate America is much simpler: earnings are better than expected, and by a wide margin.” Overall, 85.2% of companies exceeded Wall Street’s EPS expectations through Monday’s close, which is the highest percentage since 2021, data compiled by Bloomberg Intelligence show. Furthermore, only 10.8% of companies have failed to meet expectations, which is the lowest number in three decades. The S&P 500 gained 0.3% on Wednesday as investors cheered better than expected quarterly reports from companies including CoreWeave Inc. and Super Micro Computer Inc. The question now: Is this is as good as it gets? Ben Inker, co-head of asset allocation at GMO, said that earnings have been “extraordinary” in the second quarter. However, there was a difference between the artificial-intelligence space and the rest of the market. Much of the latter can have its good earnings attributed to a “cyclical upturn.” “If the upturn continues, it is very likely to push up inflation and interest rates, and if it falters, companies are likely to disappoint relative to upgraded forecasts,” said Inker. While Bespoke Investment Group’s analysis shows companies are boosting their growth expectations at one of the highest clips in the last 25 years, the firm is exercising caution and warning of extremes. The elevation in analysts’ expectations and companies’ own guidance boosts the likelihood that “pockets of excess will emerge,” according to Noah Weisberger, chief US equity strategist at BCA Research, though he added that low-teens earnings growth expectations for 2027 looks achievable. Yet with interest rates elevated and a large amount of equity supply set to hit the market when more AI companies go public, it’s risky time for earnings growth to peak. “The bond market remains our chief source of concern for equities, given stretched multiples and an IPO wave that still needs to be absorbed at current valuations,” said Weisberger. “At some point, investors will rightly choose not to pay peak multiples for peak earnings.” Potentially, investors are realizing the bar now may be too high for companies in the coming quarters. BofA strategist Jill Carey Hall noted that market reactions to earnings beats and growth have been somewhat more muted in comparison to prior quarters, suggesting that “a lot of the good news has been priced in.” Western Digital Corp., Datadog Inc., Sandisk Corp. and DaVita Inc. all beat on the top and bottom lines but sold off. Indeed, Bloomberg Intelligence data has shown companies that have beaten on revenue, earnings, or both have on average seen flat one-day excess returns. And misses have triggered steeper selloffs. “Investors already were kind of positioning for this good news and strong earnings,” said Carey Hall. “Then once the stocks beat that, that reward isn’t really transpiring to be as much as you normally would see.” --With assistance from Geoffrey Morgan. (Updates with details throughout.) Most Read from Bloomberg Businessweek ICE Arrests Are Pushing Immigrant Families Deeper Into Poverty Supercharged by Social Media, the GLP-1 Boom Is Warping Teen Psyches Suno Says AI Is the Future of Music. Record Labels Say It’s Theft With EV Sales Slowing, Hybrid Cars Are Hot Again Lululemon Is At War With Itself ©2026 Bloomberg L.P.

Interested in Dynatrace, Inc.? Here are five stocks we like better. Dynatrace exceeded Q1 guidance: ARR rose 17% year over year to $2.14 billion, while revenue reached $555 million and non-GAAP EPS was $0.48. Net new ARR increased 66%, supported by record new-logo growth and larger enterprise deals. Logs and AI are accelerating platform consumption. Log management grew more than 100% to nearly $200 million in annualized consumption, while more than 1,000 customers now monitor AI workloads and over 800 use Dynatrace agentic capabilities. The company raised its revenue and EPS outlook but maintained ARR growth guidance. Fiscal 2027 revenue growth is now expected at 14.5%–15%, EPS at $1.97–$1.99, and operating margin up to 29.75%; CFO Jim Benson plans to retire by the end of fiscal 2027. Datadog Soars, Dynatrace Slumps: Gap Widens in AI Agent Stocks Dynatrace (NYSE:DT) said its first-quarter fiscal 2027 results exceeded the high end of its guidance, supported by record new-logo growth, expanding platform consumption and continued demand for observability tools as enterprises deploy more artificial intelligence workloads. Total annual recurring revenue, or ARR, reached $2.14 billion, up 17% year over year in constant currency. Net new ARR was $85 million, an increase of 66% from the prior-year quarter. Excluding the $13 million contribution from the BindPlane acquisition, organic net new ARR was $73 million, representing 41% growth. → Meta’s Earnings Drop Shows Wall Street Wants More Than Ad Growth 3 Stocks Flashing Rare Buy Signals After the Market's Wildest Month Chief Executive Officer Rick McConnell said the quarter reinforced management’s confidence that Dynatrace can accelerate ARR growth during fiscal 2027. The company cited enterprise demand for end-to-end observability, improving go-to-market execution and increasing complexity in customer technology environments as contributors to the performance. Total revenue was $555 million, while subscription revenue was $530 million. Both measures increased 15% year over year in constant currency and were 100 basis points above the high end of Dynatrace’s guidance, according to Chief Financial Officer Jim Benson. → Sandisk Just Delivered a Blowout Quarter—Here's Why the Stock Is Falling DTE’s Stargate Deal Turns Power Into Profits Non-GAAP operating margin was 29%, also exceeding the company’s guidance by 100 basis points. Non-GAAP net income totaled $140 million, or $0.48 per diluted share, which was $0.03 above the high end of the company’s outlook. Dynatrace generated $309 million in adjusted free cash flow during the first quarter. The company updated its free-cash-flow definition to exclude restructuring, acquisition-related and other non-recurring cash expenses. On a trailing 12-month basis, adjusted free cash flow was $579 million, or 28% of revenue, including a 500-basis-point effect from cash taxes. → 4 Oil and Gas ETF Plays as Prices Stay Sky-High The company added 122 new logos during the quarter. Average land size was nearly $285,000, helping drive more than 160% growth in new-logo ARR. Benson said the results reflected a go-to-market strategy that increasingly targets strategic and enterprise accounts, as well as demand from customers seeking to consolidate fragmented monitoring tools onto a single platform. Average ARR per customer rose to more than $500,000. Gross retention remained in the mid-90% range, while trailing-12-month net retention was 110%. Log management remained Dynatrace’s fastest-growing product category, growing more than 100% and reaching nearly $200 million in annualized consumption. The company had surpassed $100 million in annualized log consumption two quarters earlier. Benson said BindPlane, which supports OpenTelemetry data collection, was performing ahead of plan and would help accelerate the logs business. BindPlane contributed $13 million of ARR in the first quarter and is included in Dynatrace’s reported log-consumption figure. Management also emphasized AI as a driver of platform usage and potential monetization. McConnell said AI workloads generate significantly more telemetry, including logs, traces and metrics, than prior workloads. Dynatrace sees three AI-related revenue opportunities: increased consumption from AI workloads, demand for AI observability capabilities, and usage of Dynatrace’s own AI functions and agents through its Dynatrace Platform Subscription, or DPS, model. More than 1,000 customers now use Dynatrace to observe AI and large-language-model workloads in production, up from about 850 in the preceding quarter. More than 800 customers are using Dynatrace agentic capabilities for autonomous operations, up from about 500 in the prior quarter. Consumption growth among customers in those AI cohorts is 1.5 times that of customers outside the cohort, McConnell said. Dynatrace estimated that the AI observability market will exceed $10 billion by 2030 and grow at more than 50% annually. McConnell said the opportunity is expected to develop over time rather than rapidly displace the company’s core end-to-end observability business. The company also highlighted Bluebox, a new offering intended for AI-first development teams. McConnell said Bluebox provides coding agents with context from live systems before software changes are released and can identify root causes and return evidence-backed fixes after deployment, while keeping developers in control. Dynatrace maintained its full-year constant-currency ARR growth outlook of 15.5% to 16.5%. Benson said the company expects foreign exchange to reduce reported ARR by $14 million and revenue by $4 million, reflecting an incremental currency headwind of $23 million to ARR and $19 million to revenue compared with prior assumptions. For fiscal 2027, Dynatrace raised its constant-currency total revenue and subscription revenue growth outlook by 25 basis points at the midpoint. It now expects both measures to grow 14.5% to 15% year over year. Full-year non-GAAP operating margin is expected to reach up to 29.75%. Non-GAAP earnings per diluted share are projected at $1.97 to $1.99, up $0.04 at the midpoint. Adjusted free-cash-flow margin guidance was maintained at 26.5%. Second-quarter revenue and subscription revenue growth are expected to be 15% to 16%. Second-quarter non-GAAP operating margin is projected at 29.5% to 30%, with non-GAAP EPS of $0.48 to $0.49. Benson said the company expects its DPS renewals to be weighted toward the second half of the fiscal year, with roughly 70% of annual resets occurring during that period. If consumption trends continue, he said management expects improved expansion activity and a possible net-retention-rate inflection in the back half. Dynatrace repurchased 7.1 million shares for $275 million during the quarter, compared with $224 million in the prior quarter. Benson said the stepped-up repurchase activity reflected management’s confidence in the company’s operating momentum, long-term growth prospects and cash-flow outlook. McConnell also said Benson plans to retire by the end of fiscal 2027. Dynatrace plans to conduct a search for a successor, and McConnell said he expects a smooth transition. “We are pleased with our strong start to fiscal 2027 and remain confident that we are on the right track to accelerate ARR growth,” Benson said. Dynatrace is a global software intelligence company specializing in application performance management (APM), cloud infrastructure monitoring, and digital experience management. Its flagship offering, the Dynatrace Software Intelligence Platform, leverages artificial intelligence to provide real-time observability across distributed environments, including on-premises data centers, private clouds, public clouds and hybrid deployments. Organizations rely on Dynatrace to detect anomalies, troubleshoot performance issues and optimize end-user experiences through automated root-cause analysis powered by the company's engine, Davis. The Dynatrace platform comprises modules for full-stack application monitoring, digital experience monitoring, infrastructure monitoring and business analytics. 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 "Dynatrace Q1 Earnings Call Highlights" was originally published by MarketBeat. View MarketBeat's top stocks for August 2026.

Palantir Technologies (NASDAQ:PLTR), an AI-powered decision software provider, closed at $172.01, up 10.32%. Today’s gains built on its post-earnings strength and Bank of America’s (NYSE:BAC) upbeat view, while investors are watching further upside after the quarter and commercial AI demand.Trading volume reached 76.2 million shares, coming in about 75% above its three-month average of 43.5 million shares. Palantir Technologies IPO'd in 2020 and has grown 1,711% since going public. The S&P 500 (SNPINDEX:^GSPC) closed at 7,758, up 0.62%, while the Nasdaq Composite (NASDAQINDEX:^IXIC) finished at 26,690, up 1.30%. Among enterprise software and AI-driven data analytics peers, Snowflake (NYSE:SNOW) closed at $330.49, up 3.93%, and Datadog (NASDAQ:DDOG) closed at $233.93, up 2.02%, a sign that software investors are still weighing AI demand and spending trends. The AI data analytics software company was one of the big gainers in software names today. Momentum in Palantir shares began after its stellar earnings report earlier in the week. Investors are embracing software names like Palantir that are leveraging artificial intelligence and machine learning to provide insights and solutions for complex data sets. Another catalyst for today’s surge was Bank of America reaffirming its “Buy” rating for Palantir and raising its price target to $255, indicating a nearly 50% gain from the closing price. The firm emphasized Palantir's competitive edge through its strong AI strategy, growing commercial activities, and solid government relationships. Analyst Mariana Perez Mora remarked that the company's success is driven by its effective AI approach, which helps clients achieve meaningful outcomes and is bolstered by strong partnerships. Before you buy stock in Palantir Technologies, 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 Palantir Technologies wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you’d have $397,405!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you’d have $1,344,091!* Now, it’s worth noting Stock Advisor’s total average return is 953% — a market-crushing outperformance compared to 214% for the S&P 500. Don't miss the latest top 10 list, available with Stock Advisor, and join an investing community built by individual investors for individual investors. See the 10 stocks » *Stock Advisor returns as of August 7, 2026. Bank of America is an advertising partner of Motley Fool Money. Howard Smith has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Datadog, Palantir Technologies, and Snowflake. The Motley Fool has a disclosure policy. Stock Market Today, Aug. 7: Palantir Surges on Bank of America Upbeat View After Strong Earnings was originally published by The Motley Fool

Datadog DDOG reported second-quarter 2026 non-GAAP earnings per share (EPS) of 65 cents, which increased 41.3% from the year-ago quarter and exceeded the company's guidance of 57-59 cents. The figure beat the Zacks Consensus Estimate by 12.07%.The company's revenues of $1.121 billion rose 36% year over year and surpassed the prior guided range of $1.07-$1.08 billion. The figure beat the consensus mark by 3.85%. Quarter-over-quarter revenue growth of 11% marked the strongest sequential pace since the second quarter of 2022, with the company adding $115 million in sequential revenues, a record for Datadog. Revenue growth among non-AI customers also accelerated to the high-20% range year over year, up from the mid-20% range in the first quarter. Datadog, Inc. price-consensus-eps-surprise-chart | Datadog, Inc. Quote The company ended the second quarter with approximately 33,400 customers, up 6.4% from about 31,400 in the prior-year period.In the quarter under review, Datadog had about 4,720 customers with an Annualized Run Rate (ARR) of $100,000 or more, up 22.6% from about 3,850 in the year-ago quarter. These customers generated about 91% of total ARR, up from 89% a year ago.As of the end of the second quarter, 58% of customers used four or more products, up from 52% in the year-ago period. Furthermore, 37% of customers used six or more products, up from 29% a year ago, while 22% used eight or more products, up from 14%, and 13% used 10 or more products, up from 7% in the prior-year quarter. Datadog reported a trailing 12-month net revenue retention rate in the low-120% range in the second quarter, similar to the first quarter and up from about 120% in the year-ago period, while gross revenue retention remained in the mid-to-high 90% range.New logo annualized bookings in the enterprise segment more than doubled year over year, and new customers continued to ramp faster, contributing about 30% of year-over-year revenue growth, up from 25% in the first quarter. Real User Monitoring surpassed $200 million in ARR and accelerated to more than 50% growth year over year at that scale.Datadog's AI-native customer cohort continued to expand, with more than 750 AI-related customers as of the second quarter, including all of the top 10 AI leaders. The number of Model Context Protocol tool calls on the platform quadrupled sequentially and grew more than 22 times versus the fourth quarter of 2025. Management also disclosed that its largest customer reduced usage entering the third quarter, a development that has been incorporated into the company's third-quarter and full-year 2026 guidance; the customer renewed its contract with Datadog during the quarter. In the second quarter, non-GAAP gross profit increased 33.3% year over year, reaching $892.3 million. Datadog's non-GAAP gross margin was 79.6%, contracting from 80.9% in the year-ago quarter, primarily reflecting continued investment in new product innovation.Research & development expenses on a non-GAAP basis grew 23.5% year over year to $325.1 million. Research & development, as a percentage of revenues, contracted roughly 290 basis points to 29%.Sales and marketing expenses on a non-GAAP basis rose 30.2% year over year to $260.4 million. Sales and marketing expenses, as a percentage of revenues, contracted nearly 100 basis points to 23.2%.General & administrative expenses on a non-GAAP basis increased 18.8% year over year, reaching $49.8 million in the reported quarter. General and administrative expenses, as a percentage of revenues, contracted roughly 60 basis points to 4.4%.Datadog reported a non-GAAP operating income of $257 million, up 56.6% year over year. Its non-GAAP operating margin expanded to 23%, up from 20% in the prior-year quarter. As of June 30, 2026, Datadog had cash, cash equivalents and marketable securities of $5 billion, up 4.8% from $4.8 billion as of March 31, 2026.Operating cash flow was $316 million in the reported quarter, which declined from $335 million in the previous quarter but increased 57.9% year over year. Free cash flow during the quarter was $278.7 million compared with $291 million in the prior quarter and $165.4 million in the year-ago quarter, marking a 68.6% year-over-year increase, with a free cash flow margin of 25% compared with 20% a year ago.Billings totaled $1.18 billion in the quarter, up 38% year over year, while remaining performance obligations were $3.47 billion, up 43% year over year. Current remaining performance obligations grew about 40% year over year. For the third quarter of 2026, Datadog anticipates revenues between $1.135 billion and $1.145 billion, representing 28-29% year-over-year growth. Non-GAAP operating income is expected in the range of $260-$270 million, implying an operating margin of 23-24%. Non-GAAP EPS is expected in the range of 63-65 cents.For fiscal 2026, Datadog anticipates revenues between $4.45 billion and $4.47 billion, suggesting about 30% year-over-year growth. Non-GAAP operating income is expected in the range of $1.01-$1.03 billion, implying an operating margin of about 23%. Non-GAAP EPS is projected to be between $2.50 and $2.54.Management noted that the full-year guidance already reflects the expected usage reduction from its largest customer; excluding that customer, the underlying business has shown five consecutive quarters of accelerating growth. Datadog currently carries a Zacks Rank #2 (Buy).Kimball Electronics KE, Quantum QMCO and Lumentum LITE are among the top-ranked stocks that investors can consider in the broader Zacks Computer and Technology sector. Currently, Kimball Electronics sports a Zacks Rank #1 (Strong Buy), while Quantum and Lumentum carry a Zacks Rank #2 (Buy) each. You can see the complete list of today’s Zacks #1 Rank stocks here. Kimball Electronics shares have inched up 1.8% in the past six months. KE is scheduled to report its fiscal fourth-quarter 2026 results on Aug. 13. Quantum's shares have surged 96% in the past six months. QMCO is scheduled to report its fiscal first-quarter 2027 results on Aug. 10, 2026. Lumentum shares have gained 48.9% in the past six months. LITE is slated to report its fiscal fourth-quarter 2026 results on Aug. 11. 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 Datadog, Inc. (DDOG) : Free Stock Analysis Report Lumentum Holdings Inc. (LITE) : Free Stock Analysis Report Kimball Electronics, Inc. (KE) : Free Stock Analysis Report Quantum Corporation (QMCO) : Free Stock Analysis Report This article originally published on Zacks Investment Research (zacks.com). Zacks Investment Research

Atlassian (NASDAQ:TEAM) was indicated more than 30% higher in pre-market trading on Friday after delivering fiscal fourth-quarter results that comfortably exceeded Wall Street expectations, prompting investors to rethink concerns that artificial intelligence could undermine traditional enterprise software providers. The company reported quarterly revenue of $1.77 billion, up 28% from a year earlier and well ahead of analysts’ forecasts of $1.66 billion. The earnings release is being viewed as more than just a company-specific success. Enterprise software stocks have faced heavy selling this year amid fears that AI would replace many workplace productivity and collaboration platforms. Atlassian’s performance is now being interpreted as evidence that AI can enhance, rather than disrupt, software businesses. Analysts suggested the results could also improve sentiment toward peers including Datadog (NASDAQ:DDOG) and Snowflake (NASDAQ:SNOW), both of which may benefit from a broader sector re-rating. Adjusted earnings reached $1.87 per share, comfortably exceeding the consensus estimate of $1.50. Cloud revenue climbed 31% year over year to $1.21 billion and accounted for 68.7% of total revenue, compared with 67.0% a year earlier. Remaining performance obligations increased 44% to $4.8 billion, providing strong visibility over future revenue. Atlassian also returned to GAAP operating profitability for the first time in more than two years, delivering a 12% operating margin. The company’s Rovo AI platform emerged as one of the standout drivers during the quarter. Management said more than 80% of Fortune 500 companies now use Rovo, while Rovo-assisted actions increased 50% compared with the previous quarter. Users of the platform completed 20% more Jira tasks and created 25% more Confluence pages than customers not using the AI tools. CEO Mike Cannon-Brookes described the company’s competitive advantage by saying, “in the AI era, context is the edge.” He also demonstrated confidence in Atlassian’s outlook by announcing plans to purchase up to $250 million worth of the company’s shares. The earnings report prompted a wave of positive analyst reactions. Bank of America upgraded Atlassian to Buy from Neutral and lifted its price target to $175 from $105, describing the company as “an AI beneficiary rather than AI victim.” The bank highlighted Atlassian’s Teamwork Graph as a key competitive advantage that could prove difficult for rivals to replicate. Mizuho’s Jordan Klein was equally enthusiastic, writing, “TEAM would be my GAME CHANGER stock of the day and key name to watch.” He added, “28% rev growth crushed Street at 20%, core Cloud growth accelerated, and the big risk of initial FY27 growth guide now defanged and was better. Best yet is CEO buying $250M of stock, and new AI related products gaining serious traction. WHY I THINK STOCK GETS CHASED & GOES HIGHER: its still super cheap for the growth: 5x EV/Sales even up 33% and 16x EV/FCF.” Atlassian’s results arrive as investors reassess how artificial intelligence will affect the software industry. Earlier this week, Shopify delivered stronger-than-expected results that also suggested AI is supporting business growth rather than replacing software platforms. That contrasted with earlier concerns following results from ServiceNow and IBM, which had intensified fears of AI-driven disruption across the sector. HSBC had previously argued that enterprise software companies “will not be threatened by AI” and that depressed valuations presented an attractive buying opportunity. Atlassian’s latest performance is likely to reinforce that view. Before Friday’s rally, Atlassian shares had fallen around 32% since the start of the year. At the indicated pre-market price of approximately $144.61, the stock was on course to reach its highest level in roughly seven months after closing at $110.17 on Thursday. Despite the upbeat quarter, investors will continue to examine the company’s fiscal 2027 outlook. Management forecast annual revenue growth of 13%, well below the 28% growth reported for the latest quarter, as its Data Center business is expected to decline 17% while customers continue migrating to cloud-based services. The combination of slower forward guidance and the release of the U.S. July employment report ahead of Friday’s opening bell could contribute to heightened volatility as the market assesses Atlassian’s longer-term outlook. Atlassian stock price