Dynatrace in the AI stack

The business and what the filings show

Dynatrace sells observability software, monitoring of applications, infrastructure and user experience, combined with application security and what its 10-K calls agentic AI operations. Over its latest four quarters it reported $2.1B of revenue, +18% on the year before, at an 81% gross margin and a 12% operating margin, with free cash flow of $571M and remaining performance obligations of $3.4B.

Its data sits in Grail, a data lakehouse that stores logs, traces, metrics and other telemetry together, and Dynatrace argues that one complete, current store of that data is what lets AI produce reliable answers and act automatically. It sells largely to big enterprises, often through partners: one channel partner accounted for 10% of revenue in each of its last two fiscal years, while no end customer reached 10%.

Dynatrace competes with Datadog for the same work but sells more to large, slower-moving enterprises, and is growing more slowly (+18% against Datadog’s +32%). AI helps it on two fronts, as more applications to monitor and as automation that makes monitoring cheaper to run; the risk is that the same AI makes it easier for customers to build or switch monitoring tools.

How AI shows up in the numbers

AI shows up at Dynatrace as automation inside its product, which finds root causes and can trigger fixes, and as demand for monitoring the AI applications its customers deploy. Its filings do not separate AI revenue, so growth against the layer and how specific its earnings releases are about AI results are the read.

For scale, Dynatrace’s revenue over the latest four quarters equals 0% 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 Dynatrace’s revenue grew +18%: 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 5, 2026, Dynatrace mentioned AI 4 times in 2,023 words of narrative text, about 2.0 mentions per 1,000 words. Of its 4 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 0.7 to 2.0.

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.

• “Demand continues to strengthen as enterprises expand cloud-native workloads and accelerate their AI initiatives.”
• “As software becomes increasingly AI-driven, customers are turning to Dynatrace for the deterministic answers, contextual analytics, and intelligent automation required to operate at scale, and we are increasingly confident in the opportunity ahead.”

Source: earnings release (Form 8-K, Exhibit 99.1), August 5, 2026.

The trend in the numbers

Since the start of 2023, Dynatrace’s largest quarter by revenue was Q2 2026 at $555M, and its highest gross margin was 82% in Q2 2025. The latest quarter set the revenue record for the period.

Quarter by quarter, Dynatrace’s revenue grew +18% in Q3 2025, +18% in Q4 2025, +19% in Q1 2026, +16% in Q2 2026 on the same quarter a year earlier. The pace has been steady.

Gross margin in the latest quarter was 81%, against 82% a year earlier, little changed. Operating margin moved from 13% to 13% over the same span. Over the latest four quarters it turned 29% of its revenue into operating cash flow and spent 1% of revenue on capital projects, against 2% a year earlier.

Where it sits in the stack

Dynatrace 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 19 by revenue and number 15 of 27 by revenue growth. Its gross margin of 81% 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, Dynatrace grew +18% over the latest year versus +19% for the layer combined, and converted 27% of revenue into free cash flow versus 33% for the layer. It is growing roughly in line with its layer.

What has to go right, and what could go wrong

What has to go right. Large enterprises have to want AI that diagnoses and fixes problems automatically, and trust Dynatrace’s data store as the basis for it, so AI raises what they spend on observability.

What could go wrong. If AI makes observability cheaper to build, or the clouds’ own monitoring becomes good enough, Dynatrace could face pricing pressure from the customers it sells to most.

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 Dynatrace’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 Dynatrace, 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 the years ended March 31, 2026 and 2025, one channel partner accounted for 10% of revenue.” 10-K, May 20, 2026
• “There were no end-customers that represented more than 10% of revenue for the years ended March 31, 2026, 2025, and 2024.” 10-K, May 20, 2026

Commitments and backlog

• Backlog. Dynatrace reports $3.4B of remaining performance obligations, 1.6 times its revenue over the latest four quarters: contracted revenue not yet earned.
• Purchase commitments. $4M of unconditional purchase obligations as of June 30, 2026 (10-Q): what it has contracted to buy from its own suppliers.

What to watch

Dynatrace’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.

• Revenue growth against the layer and against Datadog.
• Gross margin as more customers use Grail, which carries storage and compute costs.
• Channel concentration in the 10-K, since one partner accounts for a tenth of revenue.
• Its place in the next quarterly AI Stack Report, which updates every company on the map from the new filings.

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