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PlanetScale vs Riverqueue

PlanetScale scores higher on the AgentReady, 64/100 against 45/100. They differ on 12 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

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.

Riverqueue. Fast and reliable background jobs in Go.

Where PlanetScale is ahead

PlanetScale passes clear product positioning and mcp discoverable, and Riverqueue 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 and authentication documented. Riverqueue misses those.

And on adopt, agent-compatible signup flow and mcp integration available. Riverqueue misses those.

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

Where Riverqueue is ahead

Riverqueue passes limits / constraints documented, and PlanetScale 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: free trial or free allowance and official python sdk. PlanetScale misses those.

And on operate, retry behavior documented. PlanetScale misses it.

What neither does

Both fail openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, self-service signup, programmatic credential creation, fast time to first request, structured, predictable output, machine-readable errors, 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 is whether an agent can find the product at all without being told it exists. PlanetScale leads 100 to 73. PlanetScale misses nothing; Riverqueue misses clear product positioning, mcp discoverable.

Understand. PlanetScale leads 46 to 31. PlanetScale misses openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; Riverqueue misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented.

Adopt. PlanetScale leads 55 to 48. PlanetScale misses self-service signup, programmatic credential creation, free trial or free allowance, fast time to first request, official python sdk; Riverqueue misses self-service signup, agent-compatible signup flow, programmatic credential creation, fast time to first request, mcp integration available.

Operate is whether an agent can run against it in production and recover when a call fails. PlanetScale leads 56 to 29. PlanetScale misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; Riverqueue misses structured, predictable output, machine-readable errors, idempotency support, rate-limit behavior predictable, observable execution, agent compatibility verified.

Pricing

PlanetScale starts at $5/mo. Riverqueue does not publish one and has a free tier.

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: PlanetScale and Riverqueue. Alternatives to each: PlanetScale, Riverqueue.

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