Datadog in the AI stack

The business and what the filings show

Datadog sells monitoring and security software that engineering teams use to watch their applications and infrastructure, priced largely on usage. Over its latest four quarters it reported $4.0B of revenue, +32% on the year before, at an 80% gross margin and a 0% operating margin, with free cash flow of $1.2B and remaining performance obligations of $3.5B.

It is one of the few software companies whose filings tie growth directly to AI companies as customers. Its 10-K says larger customers in its “AI-native cohort”, a group that includes its largest customer, accounted for about seven percentage points of its year-over-year revenue growth in the quarter ended December 31, 2025. It also sells LLM Observability, which traces the inputs, outputs, errors and token usage of applications built on large language models.

That makes Datadog a second-order bet on AI adoption: as companies put AI applications into production, they need to monitor them. It also concentrates risk. The 10-K’s own example is that cloud-native customers in earlier periods rapidly increased their usage and then optimized it, and it warns AI-native customers may do the same, which is why growth outside its largest customers is the figure to watch.

How AI shows up in the numbers

AI shows up at Datadog twice: as revenue from AI-native companies running large, fast-growing workloads, and as products for monitoring AI applications in other companies. The first is already measurable in its growth and concentrated in a few customers; the second is the broader opportunity if AI applications spread through the enterprise.

For scale, Datadog’s revenue over the latest four quarters equals 1% of the $566B the five largest buyers spent on capital projects over their latest four quarters. Their capex rose +81% on the year before, while Datadog’s revenue grew +32%: slower than the spending, so it is capturing a smaller share of each new capex dollar, or more of its business sits outside the build-out.

How it talks about AI

In its latest earnings release, furnished to the SEC on August 6, 2026, Datadog mentioned AI 14 times in 608 words of narrative text, about 23.0 mentions per 1,000 words. Of its 5 sentences about AI, 1 included a number: a revenue figure, a growth rate, a customer count or another measurable result. Across the 8 releases we read, back to November 7, 2024, AI mentions per 1,000 words went from 0.0 to 23.0.

Specific claims are easier to check against the numbers than general enthusiasm, which is why the share of AI sentences that carry a figure is worth watching alongside how often AI is mentioned. The sentences below are quoted from the release as published, after removing safe-harbor text and company boilerplate.

• “Datadog Announces Second Quarter 2026 Financial Results August 6, 2026 Second quarter revenue grew 36% year-over-year to $1.12 billion Robust growth of larger customers, with about 4,720 $100k+ ARR customers, up from about 3,850 a year ago Launched AI-powered Bits Code, Bits Chat, and Bits Agent Builder for general…”
• “Our customers are building and deploying with AI, and they are using the Datadog platform to observe, secure, and act on their AI-enabled solutions.”
• “Acquired Adaptive ML, a frontier AI startup developing the world’s first Reinforcement Learning Operations platform, to accelerate Datadog AI Research’s investment in world models and agentic LLM post-training for observability—combining Datadog’s access to real-world infrastructure and security data with Adaptive M…”

Source: earnings release (Form 8-K, Exhibit 99.1), August 6, 2026.

The trend in the numbers

Since the start of 2023, Datadog’s largest quarter by revenue was Q2 2026 at $1.1B, and its highest gross margin was 82% in Q4 2023. The latest quarter set the revenue record for the period.

Quarter by quarter, Datadog’s revenue grew +28% in Q3 2025, +29% in Q4 2025, +32% in Q1 2026, +36% in Q2 2026 on the same quarter a year earlier. Growth has been accelerating.

Gross margin in the latest quarter was 79%, against 80% a year earlier, little changed. Operating margin moved from −4% to 0% over the same span. Over the latest four quarters it turned 31% of its revenue into operating cash flow and spent 1% of revenue on capital projects, against 1% a year earlier.

Where it sits in the stack

Datadog sits in the software and applications layer of the AI stack. It sits at the top of the stack, where revenue from end customers has to come from; its own spending flows to AI clouds.

Among the 27 companies in this layer on our map, it ranks number 14 by revenue and number 6 of 27 by revenue growth. Its gross margin of 80% compares with 78% for the layer as a whole, close to the layer average. The full layer analysis is on the software page.

Latest four quarters for 10-Q filers; latest fiscal year for 20-F filers. Companies under $100 million of revenue are left out.

Against the 27 US-listed companies in its layer, Datadog grew +32% over the latest year versus +19% for the layer combined, and converted 30% of revenue into free cash flow versus 33% for the layer. It is taking share of the layer’s revenue.

What has to go right, and what could go wrong

What has to go right. AI applications have to move into production across ordinary enterprises, not just AI-native companies, so Datadog’s AI revenue broadens beyond a handful of large customers.

What could go wrong. A large AI-native customer optimizing its usage or building its own tools would hit growth quickly, as the 10-K warns, and cheaper open-source monitoring could cap what enterprises pay for AI observability.

These describe the conditions the business depends on, as the filings show them. They are not a view on the stock.

Through the lens of Investing in AI

Rob May’s book offers six mental models for telling AI moats from traps. The one most useful for Datadog’s layer is “AI challenges traditional SaaS assumptions”: Per-seat pricing, zero marginal cost, smooth ARR curves, and automatic land-and-expand break down when software does work on your behalf. The mistake is not using SaaS instincts. It is using them automatically.

The question it asks of any company is: What work is this product actually doing, and how does pricing map to it? Applied to Datadog, the filings on this page are the place to start: the margin trend shows whether it holds scarce value or passes it through, the customer and financing disclosures show who it depends on, and the commitments show what it has promised before the revenue arrives.

Customers and concentration

US companies must disclose any customer that accounts for 10% or more of revenue, and foreign filers usually describe their largest customers. Concentration matters because a company’s growth is only as durable as the handful of buyers behind it, and in the AI stack those buyers are often the same few companies.

• “For example, in prior periods customers in our cloud-native cohort, and more recently larger customers in our AI-native cohort, which cohort includes our largest customer and represented approximately seven percentage points of our year-over-year revenue growth for the quarter ended December 31, 2025, have rapidly increased their usage of our product and then optimized or may in the future optimize their usage or fail to renew their subscriptions.” 10-K, February 18, 2026

Commitments and backlog

• Backlog. Datadog reports $3.5B of remaining performance obligations, 0.9 times its revenue over the latest four quarters: contracted revenue not yet earned.
• Purchase commitments. $1.4B of unconditional purchase obligations as of December 31, 2025 (10-K): what it has contracted to buy from its own suppliers.

What to watch

Datadog’s next quarterly report will cover the quarter ending around September 29, 2026. Large companies must file a 10-Q within 40 days of a quarter’s end and a 10-K within 60 days of the fiscal year’s end, so the new figures should arrive by about November 28, 2026 at the latest. This page, the layer pages and the next quarterly report all update from that filing.

• What the next filings say about the AI-native cohort’s share of growth, and any change in its largest customer’s usage.
• Revenue growth against the layer, with and without those customers where the company discloses it.
• Gross margin, which shows whether it is pricing its newer AI products profitably.
• Its place in the next quarterly AI Stack Report, which updates every company on the map from the new filings.

Figures from Datadog’s SEC filings, latest period ended June 30, 2026. Other companies: all company pages.