MongoDB in the AI stack

The business and what the filings show

MongoDB makes a document database developers use to build applications, sold mostly as Atlas, a managed service on the major clouds, and priced largely on usage. Over its latest four quarters it reported $2.8B of revenue, +25% on the year before, at a 73% gross margin and a −1% operating margin, with free cash flow of $659M and remaining performance obligations of $1.5B.

Its AI pitch is that applications built on AI need a database that handles unstructured data and vector search alongside ordinary records, so developers do not have to bolt on a separate vector store. In February 2025 it acquired Voyage AI, a maker of embedding and retrieval models, to make that retrieval more accurate inside the database.

MongoDB is a test of whether AI applications become a large new workload for general-purpose databases or for specialized ones. If AI application development speeds up, usage-priced databases should see it early; if developers standardize on dedicated vector databases or the clouds’ own services, MongoDB’s growth would trail the AI cycle rather than ride it.

How AI shows up in the numbers

AI shows up at MongoDB as new applications built on Atlas that use its vector search and retrieval models, and as the usage those applications generate. Its filings do not separate AI workloads, so the read is Atlas-driven revenue growth and whether the company starts quantifying AI customers or usage in its releases.

For scale, MongoDB’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 MongoDB’s revenue grew +25%: 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 September 1, 2026, MongoDB mentioned AI 17 times in 1,739 words of narrative text, about 9.8 mentions per 1,000 words. Of its 8 sentences about AI, 0 included a number: a revenue figure, a growth rate, a customer count or another measurable result. Across the 8 releases we read, back to March 5, 2025, AI mentions per 1,000 words went from 6.8 to 9.8.

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.

• “This performance reflects the mission-critical role our platform plays for customers, with strength driven by core enterprise workloads and early momentum with AI use cases.”
• “Looking ahead, we're emerging as the intelligent data platform for the AI era—launching powerful retrieval innovations, simplifying how developers connect coding agents to their operational data on MongoDB, and reinforcing our position as the only modern data platform that extends seamlessly from self-managed to an…”

Source: earnings release (Form 8-K, Exhibit 99.1), September 1, 2026.

The trend in the numbers

Since the start of 2023, MongoDB’s largest quarter by revenue was Q2 2026 at $772M, and its highest gross margin was 75% in Q3 2023. The latest quarter set the revenue record for the period.

Quarter by quarter, MongoDB’s revenue grew +19% in Q3 2025, +27% in Q4 2025, +25% in Q1 2026, +30% in Q2 2026 on the same quarter a year earlier. Growth has been accelerating.

Gross margin in the latest quarter was 74%, against 71% a year earlier, so it is keeping more of each dollar as it grows. Operating margin moved from −11% to 4% over the same span. Over the latest four quarters it turned 24% of its revenue into operating cash flow and spent 0% of revenue on capital projects, against 1% a year earlier.

Where it sits in the stack

MongoDB 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 18 by revenue and number 8 of 27 by revenue growth. Its gross margin of 73% compares with 78% for the layer as a whole, so it keeps less of each dollar than 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, MongoDB grew +25% over the latest year versus +19% for the layer combined, and converted 24% 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. Developers building AI applications have to keep choosing a general-purpose database with built-in vector search over a separate vector store, and those applications have to reach production scale.

What could go wrong. Dedicated vector databases, the clouds’ own databases and Postgres extensions all compete for the same AI workloads, and an AI coding shift toward default cloud services could leave MongoDB growing more slowly than AI adoption.

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 MongoDB’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 MongoDB, 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.

• “No single customer represented more than 10% of our revenue in fiscal year 2026.” 10-K, March 11, 2026

Commitments and backlog

• Backlog. MongoDB reports $1.5B of remaining performance obligations, 0.5 times its revenue over the latest four quarters: contracted revenue not yet earned.
• Purchase commitments. $30M of unconditional purchase obligations as of July 31, 2026 (10-Q): what it has contracted to buy from its own suppliers.

What to watch

MongoDB’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.

• Revenue growth against the layer: a rise in AI application development should show in usage-priced databases.
• Gross margin as Atlas, which carries cloud hosting costs, takes a larger share of revenue.
• Whether its earnings releases put numbers on AI customers or workloads.
• Its place in the next quarterly AI Stack Report, which updates every company on the map from the new filings.

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