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Databricks vs Fly.io

Fly.io scores higher on the AgentReady, 53/100 against 52/100. 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.

Fly.io. Fly.io provides real hardware-isolated Linux computers (called Sprites and Machines) for agents, evals, and tools, with a pay-as-you-go model where billing goes to zero when not in use.

Where Databricks is ahead

Databricks passes clear product positioning and llms-full.txt / full agent docs, and Fly.io 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. Fly.io misses it.

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

Where Fly.io is ahead

Fly.io 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 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, no mandatory sales call, programmatic credential creation, free trial or free allowance, fast time to first request, copyable quickstart, 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; Fly.io misses clear product positioning, 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; Fly.io 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. Fly.io leads 55 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; Fly.io misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, free trial or free allowance, fast time to first request, copyable quickstart.

Operate. Both sit at 53/100 here. Databricks misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; Fly.io misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable.

Pricing

Databricks does not publish a machine-readable starting price. Fly.io starts at $29/mo.

Signal by signal

Which to pick

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

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