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Databricks vs Zilliz

Databricks scores higher on the AgentReady, 52/100 against 49/100. They differ on 8 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.

Zilliz. Zilliz offers a fully managed Vector Lakebase powered by Milvus, unifying real-time vector search, lake-scale discovery, and AI data operations.

Where Databricks is ahead

Databricks passes llms-full.txt / full agent docs, and Zilliz 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. Zilliz misses it.

And on adopt, agent-compatible signup flow. Zilliz misses it.

Finally, on operate, agent compatibility verified. Zilliz misses it.

Where Zilliz is ahead

Zilliz passes authentication documented and 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: self-service signup and free trial or free allowance. Databricks misses those.

What neither does

Both fail mcp discoverable, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, 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. Databricks leads 87 to 80. Databricks misses mcp discoverable; Zilliz misses llms-full.txt / full agent docs, mcp discoverable.

Understand. Both sit at 31/100 here. Databricks misses openapi / spec quality, authentication documented, pricing understandable, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; Zilliz misses structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt. Zilliz leads 45 to 35. 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; Zilliz misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, fast time to first request, copyable quickstart, mcp integration available.

Operate is whether an agent can run against it in production and recover when a call fails. Databricks leads 53 to 39. Databricks misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; Zilliz misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified.

Pricing

Databricks does not publish a machine-readable starting price. Zilliz does not publish one and has a free tier.

Signal by signal

Which to pick

Signal counts are level here, so fit and price decide it. Full profiles: Databricks and Zilliz. Alternatives to each: Databricks, Zilliz.

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