Datadog Posts Its Strongest Quarter Since 2022 as Non-AI Business Accelerates for a Fifth Straight Quarter, Even as Largest Customer Pulls Back
Q2 2026 earnings call, reported August 6, 2026
Datadog delivered its best quarter in four years, with revenue climbing 36% year-over-year to $1.12 billion, beating the high end of guidance and marking the largest sequential revenue add in company history at $115 million. Yet the headline number obscured the more important story: even after stripping out a usage pullback from its single largest customer, the core business is compounding at a rate management says has now accelerated for five consecutive quarters, a trend few software companies of Datadog's scale can claim in this environment.
The Non-AI Business Is Quietly the Bigger Story
The most consequential data point in the print had nothing to do with AI natives. Revenue growth from Datadog's non-AI customer base accelerated to the high 20s percent year-over-year, up from the mid-20s last quarter and just 18% a year ago. CEO Olivier Pomel was explicit about why this matters: "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. We've seen now five quarters of continuous acceleration from the rest of the business." CFO David Obstler attributed part of this to expanded go-to-market capacity and geographic reach that are "providing returns," alongside broader cloud migration and modernization tailwinds that AI adoption appears to be accelerating even among companies not typically thought of as AI-native.
Largest Customer Renews, But at Lower Usage
Datadog disclosed a usage reduction from its largest customer, a 9-figure renewal with what it described as a leading AI company using 17 Datadog products. Management fully "derisked" both Q3 and full-year guidance against this customer, a decision Pomel framed as deliberate: "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 the acceleration we see pretty much everywhere else." Neither Pomel nor Obstler would detail whether the usage decline reflects pricing renegotiation or workload reduction, and analysts pressed repeatedly without getting specifics on contract duration or stickiness terms. The résultant Q3 guide of 28% to 29% year-over-year growth landed below typical sequential patterns, prompting KeyBanc's proxy analyst to question the conservatism embedded in the outlook.
AI Customer Cohort Diversifying Beyond Startups
Datadog now counts over 750 AI customers, including all of the top 10 AI leaders globally, with 31 customers spending more than $1 million annually and 8 spending over $10 million. Notably, the cohort has broadened to include hyperscalers running in-house AI labs, a distinct customer set from the neuro labs Datadog has historically served. The company landed 7-figure annualized deals with two AI labs this quarter specifically for model training observability, a use case Pomel called "not really a business area for us a couple of years ago." Agentic activity is scaling even faster: MCP tool calls quadrupled again quarter-over-quarter and are up more than 22 times versus Q4 2025, an inflection point that management believes signals a structural shift toward inference-driven observability spend.
Bits AI Broadens From Point Feature to Platform Surface
Datadog's AI agent product, Bits AI, has evolved substantially from its original scope. As Pomel explained, "Bits AI used to be fairly specific, dedicated to alerts. Now the surface of contact is a lot wider with the customer" spanning chat, investigation, monitor creation, code generation, test automation, and release validation. The company is shifting to an AI credits pricing model to reflect this broader usage surface. When pressed on whether automation could cannibalize the consumption-based revenue model, Pomel pushed back firmly: "When they use Bits AI, they use more of our product. They deploy more of it. They create more dashboards and alerts. It's not a zero-sum game." Investors should watch this dynamic closely over coming quarters, as it remains an open question whether agentic automation ultimately expands or compresses the observability data volumes Datadog monetizes.
Security Positioning Shifts Toward AI SOC Ambitions
Datadog is repositioning its security stack for an AI-native threat environment, decoupling its Bits AI Security Analyst from its own SIEM so it can run atop competing SIEM products. Pomel described this as a deliberate market-expansion move: "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." The company also launched AI Guard products aimed at agent discovery and runtime protection against prompt-injection-style attacks, alongside a Runtime Prioritization Engine designed to cut vulnerability alert noise by over 95%. Pomel characterized the shift bluntly: "There's a complete switch in the way security products need to work. You can't wait for putting humans in the loop... it's a complete rebuild for most of the industry."
Enterprise Land-and-Expand Economics Improving
New logo annualized bookings in enterprise more than doubled year-over-year, and new customers are ramping to revenue faster than in prior cohorts, contributing about 30% of year-over-year revenue growth in Q2, up from 25% in Q1. Net revenue retention held in the low 120s and gross retention stayed in the mid-to-high 90s. Management cited several large expansion deals as evidence of platform consolidation economics, including an 8-figure Fortune 100 health insurer expanding to 19 products and a $30 million-plus multiyear deal with a major online media company displacing four competing tools, including its largest Bring Your Own Cloud win to date at petabyte scale.
Guidance and Margins
Full-year revenue guidance sits at $4.45 billion to $4.47 billion, implying 30% growth, with non-GAAP operating margin guided to 23%. Gross margin came in at 79.6% for the quarter, down slightly from 80.2% in Q1 and 80.9% a year ago, which Obstler attributed to ongoing innovation investment rather than a structural shift, reiterating the company's long-standing 80%-plus target range. Free cash flow margin was 25% on $279 million of free cash flow, with $5 billion of cash on the balance sheet giving Datadog ample room to continue funding R&D, including the recently closed Adaptive ML acquisition aimed at accelerating post-training model research for Bits AI.