Teradata in the AI stack

The business and what the filings show

Teradata sells a data analytics platform to large enterprises, historically installed in customers’ own data centers and increasingly run in the public cloud. Over its latest four quarters it reported $1.7B of revenue, +1% on the year before, at a 61% gross margin and an 8% operating margin, with free cash flow of $736M, 44% of revenue.

It is midway through a move to the cloud: its 10-K reports public cloud annual recurring revenue up 15%, partly from on-premises customers migrating, while total revenue has barely grown. It also notes a resurgence of hybrid environments, as enterprises weigh on-premises against cloud deployment, and positions its platform for autonomous AI and agentic workloads on enterprise data.

Teradata is the incumbent-data-platform case, the opposite of Snowflake: an installed base in some of the largest companies, slow growth and solid cash generation. If enterprises keep sensitive data on their own premises and want AI run next to it, that installed base becomes an advantage; if AI workloads follow data into cloud platforms, Teradata has to win the migration rather than defend the base.

How AI shows up in the numbers

AI shows up at Teradata as a reason for customers to keep or expand analytics on its platform, on premises or in the cloud, and as AI features for working with that data. Its filings do not separate AI revenue, so cloud recurring revenue in its own reports and growth against the layer are the read.

For scale, Teradata’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 Teradata’s revenue grew +1%: 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 4, 2026, Teradata mentioned AI 3 times in 1,424 words of narrative text, about 2.1 mentions per 1,000 words. Of its 2 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 November 4, 2024, AI mentions per 1,000 words went from 0.0 to 2.1.

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.

• “We are pleased with our strong product innovation this quarter, highlighted by the launch of our Autonomous Knowledge Platform, bringing a powerful set of capabilities to help enterprises deploy agentic AI.”
• “See the foreign currency fluctuation schedule, which is used to determine revenue on a constant currency ("CC") basis, on the Investor Relations page of the Company’s website at investor.teradata.com The impact of currency on ARR is determined by calculating the prior period ending ARR using the current period end c…”

Source: earnings release (Form 8-K, Exhibit 99.1), August 4, 2026.

The trend in the numbers

Since the start of 2023, Teradata’s largest quarter by revenue was Q1 2023 at $476M, and its highest gross margin was 63% in Q1 2023. The latest quarter was below that peak.

Quarter by quarter, Teradata’s revenue grew −5% in Q3 2025, +3% in Q4 2025, +6% in Q1 2026, +0% in Q2 2026 on the same quarter a year earlier. Growth has been accelerating.

Gross margin in the latest quarter was 59%, against 56% a year earlier, so it is keeping more of each dollar as it grows. Operating margin moved from 6% to 12% over the same span. Over the latest four quarters it turned 45% 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

Teradata 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 23 by revenue and number 25 of 27 by revenue growth. Its gross margin of 61% 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, Teradata grew +1% over the latest year versus +19% for the layer combined, and converted 44% of revenue into free cash flow versus 33% for the layer. It is growing more slowly than its layer, so its share of the layer’s revenue is shrinking.

What has to go right, and what could go wrong

What has to go right. Enterprises have to run AI on governed data where it already sits, including on premises, and choose Teradata to do it, while the cloud migration grows faster than the legacy base declines.

What could go wrong. If AI workloads move to cloud data platforms and customers migrate away rather than to Teradata’s cloud, slow growth could turn into decline.

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 Teradata’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 Teradata, 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 accounts for more than 10% of the Company's revenue for any period presented.” 10-K, February 27, 2026

Commitments and backlog

• Backlog. Teradata reports $2.0B of remaining performance obligations, 1.2 times its revenue over the latest four quarters: contracted revenue not yet earned.

What to watch

Teradata’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.

• Public cloud recurring revenue in its 10-K and 10-Qs, the growth engine it is relying on.
• Total revenue growth against the layer.
• Free cash flow margin, which funds the transition.
• Its place in the next quarterly AI Stack Report, which updates every company on the map from the new filings.

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