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Databricks vs Koyeb

Koyeb scores higher on the AgentReady, 57/100 against 52/100. They differ on 13 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.

Koyeb. Developer-friendly serverless platform designed to let businesses and developers easily deploy reliable and scalable applications globally, with high-performance infrastructure for AI inference, sandboxes, and microservices on CPUs, GPUs, and accelerators

Where Databricks is ahead

Databricks passes clear canonical domain, llms.txt published and llms-full.txt / full agent docs, and Koyeb 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. Koyeb misses it.

And on operate, agent compatibility verified. Koyeb misses it.

Where Koyeb is ahead

Koyeb passes mcp discoverable, and Databricks does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: pricing understandable. Databricks misses it.

And on adopt, self-service signup, no mandatory sales call, free trial or free allowance, fast time to first request, copyable quickstart and mcp integration available. Databricks misses those.

What neither does

Both fail openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, programmatic credential creation, 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; Koyeb misses clear canonical domain, llms.txt published, llms-full.txt / full agent docs.

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; Koyeb misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt is whether an agent can get a key and make its first successful call without a human in the loop. Koyeb leads 90 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; Koyeb misses programmatic credential creation.

Operate. Databricks leads 53 to 35. Databricks misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; Koyeb 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. Koyeb does not publish one and has a free tier.

Signal by signal

Which to pick

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

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