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Databricks vs PlanetScale

PlanetScale scores higher on the AgentReady, 64/100 against 52/100. They differ on 7 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.

PlanetScale. PlanetScale provides the world's fastest and most scalable cloud databases, offering fully-managed Vitess and PostgreSQL clusters with exceptional speed, reliability, and horizontal scalability through sharding.

Where Databricks is ahead

Databricks passes official python sdk, and PlanetScale does not. That is adopt, whether an agent can get a key and make its first successful call without a human in the loop.

Where PlanetScale is ahead

PlanetScale 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: authentication documented and pricing understandable. Databricks misses those.

And on adopt, no mandatory sales call, copyable quickstart and mcp integration available. Databricks misses those.

What neither does

Both fail openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, self-service signup, programmatic credential creation, free trial or free allowance, fast time to first request, 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. PlanetScale leads 100 to 87. Databricks misses mcp discoverable; PlanetScale misses nothing.

Understand. PlanetScale leads 46 to 31. Databricks misses openapi / spec quality, authentication documented, pricing understandable, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; PlanetScale misses openapi / spec quality, 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. PlanetScale 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; PlanetScale misses self-service signup, programmatic credential creation, free trial or free allowance, fast time to first request, official python sdk.

Operate. PlanetScale leads 56 to 53. Databricks misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; PlanetScale 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. PlanetScale starts at $5/mo.

Signal by signal

Which to pick

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

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