Activepieces vs Porter
Activepieces scores higher on the AgentReady, 63/100 against 55/100. They differ on 8 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
Porter. Porter is a platform as a service (PaaS) that runs in your own cloud.
Where Activepieces is ahead
Activepieces passes clear product positioning, and Porter 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. Porter misses it.
And on operate, structured, predictable output, idempotency support and agent compatibility verified. Porter misses those.
Where Porter is ahead
Porter passes llms-full.txt / full agent docs, and Activepieces does not. That is discover, whether an agent can find the product at all without being told it exists.
It also holds adopt: official typescript sdk and official python sdk. Activepieces misses those.
What neither does
Both fail 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 87. Activepieces misses llms-full.txt / full agent docs; Porter misses clear product positioning.
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; Porter misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.
Adopt. Porter leads 70 to 52. Activepieces misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, official typescript sdk, official python sdk; Porter misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation.
Operate is whether an agent can run against it in production and recover when a call fails. Activepieces leads 76 to 41. Activepieces misses machine-readable errors, rate-limit behavior predictable; Porter misses structured, predictable output, machine-readable errors, idempotency support, rate-limit behavior predictable, agent compatibility verified.
Pricing
Activepieces does not publish a machine-readable starting price and has a free tier. Porter starts at $6/mo with no free tier.
| Activepieces plans | Porter plans |
|---|---|
| - | Standard $6/mo GB RAM ($0.009 per hour), $13/mo vCPU ($0.019 per hour) |
Signal by signal
| Signal | Activepieces | Porter |
|---|---|---|
| AgentReady | 63 | 55 |
| Discovery | 93 | 87 |
| Understanding | 31 | 23 |
| Adoption | 52 | 70 |
| Operability | 76 | 41 |
| Public API | Yes | Yes |
| MCP server | Yes | Yes |
| OpenAPI spec | Unknown | Unknown |
| CLI | Yes | Yes |
| llms.txt | Yes | Yes |
| Self-serve signup | Yes | Yes |
| Free tier | Yes | No |
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 Porter. Alternatives to each: Activepieces, Porter.
An agent can fetch this as data: POST /v1/compare {"slugs": ["activepieces", "porter"]}