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Databricks vs Fauna

Databricks scores higher on the AgentReady, 52/100 against 20/100. They differ on 14 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Databricks. Databricks provides a Data + AI Platform to unify data, analytics and AI, enabling users to build and run apps, agents and AI on their data.

Fauna. Fauna is a distributed serverless database that combines document flexibility with relational power, automatic scaling, and zero operational overhead.

Where Databricks is ahead

Databricks passes clear canonical domain, clear product positioning, public docs discoverable, llms.txt published, llms-full.txt / full agent docs and machine-readable metadata, and Fauna does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: structured api reference. Fauna misses it.

And on adopt, agent-compatible signup flow and cli available. Fauna misses those.

Finally, on operate, canonical workflow succeeds, observable execution and agent compatibility verified. Fauna misses those.

Where Fauna is ahead

Fauna passes pricing understandable, and Databricks does not. That is understand, whether an agent can read the docs and work out how the API behaves before calling it.

It also holds adopt: free trial or free allowance. Databricks misses it.

What neither does

Both fail mcp discoverable, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, self-service signup, no mandatory sales call, programmatic credential creation, fast time to first request, copyable quickstart, mcp integration available, structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable. If your agent needs any of those, you will be building it yourself either way.

Score, pillar by pillar

The AgentReady splits into four pillars, scored separately, because a product can be easy to find and still impossible to adopt.

Discover is whether an agent can find the product at all without being told it exists. Databricks leads 87 to 33. Databricks misses mcp discoverable; Fauna misses clear canonical domain, clear product positioning, public docs discoverable, llms.txt published, llms-full.txt / full agent docs, mcp discoverable, machine-readable metadata.

Understand. Databricks leads 31 to 23. Databricks misses openapi / spec quality, authentication documented, pricing understandable, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; Fauna misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt. Databricks leads 35 to 25. Databricks misses self-service signup, no mandatory sales call, programmatic credential creation, free trial or free allowance, fast time to first request, copyable quickstart, mcp integration available; Fauna misses self-service signup, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, fast time to first request, copyable quickstart, cli available, mcp integration available.

Operate. Databricks leads 53 to 0. Databricks misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; Fauna misses canonical workflow succeeds, structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, observable execution, agent compatibility verified.

Pricing

Databricks does not publish a machine-readable starting price. Fauna starts at $150/mo with no free tier.

Signal by signal

Which to pick

Databricks clears more of the signals an agent needs, so it is the safer default for unattended use. Full profiles: Databricks and Fauna. Alternatives to each: Databricks, Fauna.

An agent can fetch this as data: POST /v1/compare {"slugs": ["databricks", "fauna"]}