Elastic in the AI stack

The business and what the filings show

Elastic makes Elasticsearch, the open-source search and analytics engine, and sells it as a managed cloud service and as software customers run themselves, for search, observability and security. Over its latest four quarters it reported $1.8B of revenue, +16% on the year before, at a 76% gross margin and a −3% operating margin, with free cash flow of $349M. In its fiscal year to April 2026, its 10-K says Elastic Cloud revenue grew 22% and other subscriptions 14%.

Its AI relevance is retrieval. Applications built on large language models need to find the right documents before the model answers, and Elastic describes its platform as the world’s most downloaded open-source vector database, with the retrieval algorithms and model integrations developers use to build those applications. One channel partner accounted for 11% to 12% of its revenue in each of its last three fiscal years.

That puts Elastic in a crowded field: specialist vector databases, the clouds’ own search services and general-purpose databases such as MongoDB all offer vector search. Elastic’s advantage is that many companies already run it for search and logging; the question is whether AI workloads add to that installed base faster than alternatives take new projects.

How AI shows up in the numbers

AI shows up at Elastic as retrieval for AI applications, vector search on data already indexed in Elasticsearch, and as AI features in its observability and security products. Its filings do not separate AI revenue, so Elastic Cloud growth and how specific its earnings releases are about AI customers are the read.

For scale, Elastic’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 Elastic’s revenue grew +16%: 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 27, 2026, Elastic mentioned AI 14 times in 1,184 words of narrative text, about 11.8 mentions per 1,000 words. Of its 11 sentences about AI, 2 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 21, 2024, AI mentions per 1,000 words went from 5.5 to 11.8.

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.

• “Engaged with thousands of customers and partners at Black Hat and the RAISE AI Summit in Paris Share Repurchase Program In October 2025, Elastic announced a share repurchase program pursuant to which the Company may repurchase up to $500 million of the Company’s outstanding ordinary shares.”
• “AI is reshaping the enterprise technology stack, and organizations are making deliberate choices about where to build and how to observe and secure their applications and data.”
• “We enter the year with growing momentum across Search & AI, Security, and Observability and confidence in the trajectory of our business.”

Source: earnings release (Form 8-K, Exhibit 99.1), August 27, 2026.

The trend in the numbers

Since the start of 2023, Elastic’s largest quarter by revenue was Q2 2026 at $478M, and its highest gross margin was 77% in Q2 2025. The latest quarter set the revenue record for the period.

Quarter by quarter, Elastic’s revenue grew +16% in Q3 2025, +18% in Q4 2025, +16% in Q1 2026, +15% in Q2 2026 on the same quarter a year earlier. The pace has been steady.

Gross margin in the latest quarter was 74%, against 77% a year earlier, so more of each new dollar is passing through to costs. Operating margin moved from −2% to −5% over the same span. Over the latest four quarters it turned 20% of its revenue into operating cash flow and spent 0% of revenue on capital projects, against 0% a year earlier.

Where it sits in the stack

Elastic 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 20 by revenue and number 17 of 27 by revenue growth. Its gross margin of 76% 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, Elastic grew +16% over the latest year versus +19% for the layer combined, and converted 19% 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. Companies building AI applications have to choose the search engine they already run for retrieval, and those workloads have to grow into a meaningful share of Elastic Cloud.

What could go wrong. If AI retrieval consolidates onto the clouds’ services, specialist vector databases or the databases applications already use, Elastic could see AI as a feature it has to offer rather than a source of growth.

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 Elastic’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 Elastic, 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.

• “One customer, a channel partner, accounted for 11% of total revenue for the years ended April 30, 2026 and 2024, and 12% of total revenue for the year ended April 30, 2025.” 10-K, June 8, 2026

Commitments and backlog

• Backlog. Elastic reports $1.9B of remaining performance obligations, 1.0 times its revenue over the latest four quarters: contracted revenue not yet earned.
• Purchase commitments. $8M of unconditional purchase obligations as of April 30, 2026 (10-K): what it has contracted to buy from its own suppliers.

What to watch

Elastic’s next quarterly report will cover the quarter ending around October 30, 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 December 29, 2026 at the latest. This page, the layer pages and the next quarterly report all update from that filing.

• Elastic Cloud growth in the 10-K and 10-Qs, where AI retrieval workloads would land.
• Revenue growth against the layer.
• Operating margin, as it balances investment in AI products against profitability.
• Its place in the next quarterly AI Stack Report, which updates every company on the map from the new filings.

Figures from Elastic’s SEC filings, latest period ended July 31, 2026. Other companies: all company pages.