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Activepieces vs Fly.io

Activepieces scores higher on the AgentReady, 63/100 against 53/100. They differ on 10 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Activepieces. AI automation platform that lets teams build agents and automations through chat, with enterprise-grade governance and 760+ integrations

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 Activepieces is ahead

Activepieces passes clear product positioning, 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: authentication documented. Fly.io misses it.

And on adopt, free trial or free allowance, fast time to first request and copyable quickstart. Fly.io misses those.

Finally, on operate, structured, predictable output, retry behavior documented and idempotency support. Fly.io misses those.

Where Fly.io is ahead

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

What neither does

Both fail llms-full.txt / full agent docs, structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, machine-readable errors, 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. Activepieces leads 93 to 80. Activepieces misses llms-full.txt / full agent docs; Fly.io misses clear product positioning, llms-full.txt / full agent docs.

Understand. Activepieces leads 31 to 23. Activepieces misses structured api reference, openapi / spec quality, 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. Fly.io leads 55 to 52. Activepieces misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, official typescript sdk, official python sdk; 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 is whether an agent can run against it in production and recover when a call fails. Activepieces leads 76 to 53. Activepieces misses machine-readable errors, rate-limit behavior predictable; Fly.io misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable.

Pricing

Activepieces does not publish a machine-readable starting price and has a free tier. Fly.io starts at $29/mo.

Signal by signal

Which to pick

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

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