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Cerbos vs Scalekit

Scalekit scores higher on the AgentReady, 65/100 against 46/100. 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

Cerbos. Authorization management platform for applications, APIs, AI agents, MCP servers, services, and workloads.

Scalekit. Authentication and authorization platform for AI agents and SaaS applications, providing OAuth flows, token management, and tool calling infrastructure

Where Cerbos is ahead

Cerbos passes clear product positioning, and Scalekit does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds adopt: official python sdk. Scalekit misses it.

Where Scalekit is ahead

Scalekit passes clear canonical domain, and Cerbos 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, authentication documented and pricing understandable. Cerbos misses those.

And on adopt, self-service signup, no mandatory sales call, free trial or free allowance, fast time to first request and cli available. Cerbos misses those.

Finally, on operate, agent compatibility verified. Cerbos misses it.

What neither does

Both fail openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, agent-compatible signup flow, 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. Cerbos leads 93 to 87. Cerbos misses clear canonical domain; Scalekit misses clear product positioning.

Understand is whether an agent can read the docs and work out how the API behaves before calling it. Scalekit leads 46 to 15. Cerbos misses structured api reference, openapi / spec quality, authentication documented, pricing understandable, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; Scalekit misses openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt. Scalekit leads 70 to 40. Cerbos misses self-service signup, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, free trial or free allowance, fast time to first request, cli available; Scalekit misses agent-compatible signup flow, programmatic credential creation, official python sdk.

Operate. Scalekit leads 56 to 35. Cerbos misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified; Scalekit misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable.

Pricing

Cerbos does not publish a machine-readable starting price. Scalekit starts at $0/mo and has a free tier.

Signal by signal

Which to pick

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

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