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Databricks vs Electric Sql

Databricks scores higher on the AgentReady, 52/100 against 42/100. They differ on 11 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.

Electric Sql. Electric is an agent platform built on sync, providing managed agents, streams, and a sync engine for building multi-agent systems and local-first apps.

Where Databricks is ahead

Databricks passes clear canonical domain and clear product positioning, and Electric Sql 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. Electric Sql misses it.

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

Finally, on operate, observable execution and agent compatibility verified. Electric Sql misses those.

Where Electric Sql is ahead

Electric Sql 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: self-service signup, free trial or free allowance, fast time to first request and copyable quickstart. Databricks misses those.

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, no mandatory sales call, programmatic credential creation, 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 67. Databricks misses mcp discoverable; Electric Sql misses clear canonical domain, clear product positioning, mcp discoverable.

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

Adopt. Electric Sql 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; Electric Sql misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, mcp integration available.

Operate is whether an agent can run against it in production and recover when a call fails. Databricks leads 53 to 24. Databricks misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; Electric Sql misses 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. Electric Sql starts at $1 per 1M writes and has a 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 Electric Sql. Alternatives to each: Databricks, Electric Sql.

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