Activepieces vs Relevance AI
An independent, review-free comparison compiled by the SaaSTracker editorial team. Both products are profiled in full, and neither can pay for placement here.
The short answer
Editorial assessmentActivepieces compared with Relevance AI
Relevance AI sells AI agents and agent teams as the primary product, with tools and integrations serving the agents. Activepieces sells automation infrastructure, with agents as one surface on top of it. The practical difference is what happens when the agent needs to be reliable: Activepieces lets you demote a probabilistic step into a deterministic flow, while Relevance keeps you in the agent paradigm. Relevance also has no self-hosted path. Teams that want an agent workforce start with Relevance; teams that want automations that occasionally think start with Activepieces.
Choose Activepieces if
Technically comfortable small teams and GTM engineers who have outgrown per-task automation pricing and want either a cheap credit-metered cloud or a self-hosted instance they fully control, with an escape hatch into TypeScript when a native integration does not exist.
Choose Relevance AI if
Revenue and operations teams that want AI agents doing repeatable research, enrichment, outreach, and triage work under supervision, and that have someone willing to treat agent building as an ongoing job rather than a one-week setup. Strongest where the work is judgment-heavy and unstructured, which is exactly where a rules-based automation tool stalls.
Side by side
13 attributes| Attribute | Activepieces | Relevance AI |
|---|---|---|
| Category | GTM Engineering | GTM Engineering |
| Starting price | Free on cloud (daily credit allowance, one user) or free forever self-hosted; Plus from $16 per month billed yearly (free plan available) | Free (200 Actions per month); Pro from $19 per month billed annually (free plan available) |
| Pricing model | Two tracks. The hosted cloud is metered in credits: one credit per flow run regardless of how many steps it contains, one per agentic action, and two to twenty per AI model call unless you bring your own model key. Seats are included up to a per-tier cap and charged beyond it. The self-hosted Community Edition is MIT licensed and free with no credit meter or run cap, but excludes agents, chat, projects, API access, and the team and admin features; paid self-hosted licenses restore those. | Two meters plus a plan fee. Actions are consumed one per tool run, including runs that fail or retry. Vendor Credits cover the underlying LLM and third-party model spend, passed through at wholesale with no markup, and can be bypassed entirely on paid plans by connecting your own OpenAI or Anthropic API keys. Plans set the included Actions, included Vendor Credits, number of build users, and number of projects. This structure took effect on 8 September 2025 and replaced the previous single-credit model; the Business plan was discontinued at the same time. |
| Free plan | Cloud Free: one user, a daily credit allowance that resets each day, with flows, agents, chat, tables, MCPs, and API access included. Self-hosted Community Edition: unlimited runs, no credit meter, but no agents, chat, projects, or API access. | 200 Actions per month, a one-time grant of 1,000 Vendor Credits, one build user, one project |
| Free trial | Free plan rather than a time-limited trial; the open-source edition can be run indefinitely | No time-limited trial; the free plan serves that purpose |
| Best for | Technically comfortable small teams and GTM engineers who have outgrown per-task automation pricing and want either a cheap credit-metered cloud or a self-hosted instance they fully control, with an escape hatch into TypeScript when a native integration does not exist. | Revenue and operations teams that want AI agents doing repeatable research, enrichment, outreach, and triage work under supervision, and that have someone willing to treat agent building as an ongoing job rather than a one-week setup. Strongest where the work is judgment-heavy and unstructured, which is exactly where a rules-based automation tool stalls. |
| Setup time | Cloud signup to first working flow is under an hour. Self-hosting is roughly 15 to 30 minutes on a fresh VPS with Docker and Postgres for someone who has deployed a Compose stack before, plus the usual work of a domain, TLS, and SMTP configuration for invitations and alerts. | An hour to sign up, clone a marketplace agent, and see it run. A week or two to get a first agent doing real work against real data, most of which is spent writing instructions, connecting integrations, and discovering the edge cases where the agent is confidently wrong. |
| Learning curve | Low for linear flows; the builder is deliberately close to Zapier in feel. The real curve is data mapping across steps and knowing when to stop fighting the visual layer and write a code step. Writing a custom piece assumes working TypeScript and npm familiarity, and the documentation for it is thinner than the task deserves. | Low to begin, steep in the middle. The visual tool builder is approachable for a non-engineer, but reliable agents require prompt discipline, structured output enforcement, and an appetite for reading traces. Multi-agent Workforces and custom API steps are a genuine technical exercise, not a no-code one. |
| Platforms | Web application (cloud), Self-hosted via Docker Compose, Kubernetes and Helm, One-click deploys on common VPS and PaaS providers | Web application, REST API, Model Context Protocol (MCP) server, Slack, Microsoft Teams, Android app, Embeddable tools and agents |
| Compliance | SOC 2 Type II, GDPR | SOC 2 Type II, GDPR |
| Founded | 2022 | 2020 |
| Headquarters | San Francisco, California | Sydney, Australia |
| Ownership | Independent, venture-backed (Y Combinator W2023) | Independent, venture-backed (OnSearch Pty Ltd trading as Relevance AI) |
Strengths and limitations
Activepieces
Strengths
- Genuinely open source under MIT, not source-available, so self-hosting carries no licensing ambiguity for commercial use.
- Credits meter flow runs rather than individual steps, which removes the incentive to keep automations artificially short.
- The TypeScript pieces framework makes writing a custom integration a normal development task rather than a platform hack.
- Flows, AI agents, a built-in table store, and MCP tool serving share one connection layer, so credentials are configured once.
Limitations
- The Community Edition withholds agents, chat, projects, API access, SSO, roles, and audit logs, so self-hosting for free means a materially smaller product than the cloud.
- Piece quality is uneven: the library is broad because it is community-contributed, and depth of action coverage and maintenance vary considerably between popular and obscure integrations.
- Documentation thins out fast beyond the common path; writing custom pieces, complex data mapping, and production scaling are under-documented relative to the effort they take.
- The Node.js execution engine is not competitive on compute-heavy work with scripting platforms built on faster runtimes.
Relevance AI
Strengths
- The agent abstraction is genuinely well built: instructions, scoped tools, knowledge, approval gates, and escalation rules are all first-class rather than bolted on.
- Tracing and cost visibility are better than most competitors, showing per-step model calls and per-run cost, which turns debugging from guesswork into reading a log.
- The two-meter pricing separates platform cost from model cost honestly, and bring-your-own-keys removes the markup that most agent platforms quietly charge.
- Approval gates and escalation are the right primitives for letting an agent touch a CRM or an inbox without an incident.
Limitations
- Cost is hard to forecast. Actions are charged per tool run including failures and retries, and a multi-agent Workforce fans out into far more runs than a first-time buyer estimates.
- The public pricing page shows only Enterprise as of August 2026; Free, Pro, and Team still exist but you have to dig through documentation to find their terms, which is a deliberate move away from self-serve buyers.
- The Pro to Team step is punishing, roughly $19 to $234 per month for under three times the Actions, and there is no longer a Business tier in between since it was retired in September 2025.
- Governance essentials are Enterprise-gated: evals, agent performance observability, audit logs, SSO, RBAC, and Salesforce, Snowflake, and Zendesk triggers are all out of reach of a self-serve buyer.
Pricing compared
Activepieces
Two tracks. The hosted cloud is metered in credits: one credit per flow run regardless of how many steps it contains, one per agentic action, and two to twenty per AI model call unless you bring your own model key. Seats are included up to a per-tier cap and charged beyond it. The self-hosted Community Edition is MIT licensed and free with no credit meter or run cap, but excludes agents, chat, projects, API access, and the team and admin features; paid self-hosted licenses restore those.
- Free (cloud)$0
- Plus$16
- Team$166
- UltimateCustom
- Community Edition (self-hosted)$0
Judged per dollar, Activepieces is among the strongest offers in automation, provided you fit one of its two shapes. If you self-host, the marginal cost of an additional flow or an additional million runs is the hardware, which is a fundamentally better curve than any per-task vendor can offer; the bill for a busy small business is a VPS, not a subscription. If you use the cloud, one credit per run rather than per step means complex flows stop being punished, and $16 a month buys a workload that would cost several times that on task-metered competitors. What you give up is polish and breadth: fewer templates, community-maintained pieces of uneven depth, and thinner documentation once you leave the common path. The Team tier at $166 is the least compelling point in the range, since it is a ten-times step for projects, SSO, and roles that many small teams do not need.
Relevance AI
Two meters plus a plan fee. Actions are consumed one per tool run, including runs that fail or retry. Vendor Credits cover the underlying LLM and third-party model spend, passed through at wholesale with no markup, and can be bypassed entirely on paid plans by connecting your own OpenAI or Anthropic API keys. Plans set the included Actions, included Vendor Credits, number of build users, and number of projects. This structure took effect on 8 September 2025 and replaced the previous single-credit model; the Business plan was discontinued at the same time.
- Free$0
- Pro$19
- Team$234
- EnterpriseCustom
Priced against what it replaces, the Pro plan is cheap: $19 a month plus metered model spend for work that would otherwise be a contractor's afternoon. The problem is the shape of the curve above it. Team at $234 a month gives under three times the Actions for more than ten times the price, and the governance features that make agents safe to leave running (evals, observability, audit logs, SSO) are not purchasable at any published price. A small business gets real value from the free and Pro tiers for supervised, human-in-the-loop work. A company that wants agents operating unattended on customer data is buying Enterprise, and should assume a five-figure annual commitment and a procurement cycle.
Editorial verdict on each
Activepieces
Activepieces is the cleanest answer available to a specific frustration: automation priced per task, running on someone else's server, with no way to write a real integration when one is missing. It is MIT licensed rather than source-available, meters cloud usage per run rather than per step, and self-hosts on a Docker stack that a small business can afford to run indefinitely. The pieces framework turns a missing integration from a dead end into a day of TypeScript. The costs are equally clear. The free self-hosted edition is deliberately smaller than the cloud, holding back agents, projects, and API access, so open source here does not mean feature-complete. Piece quality varies because the library is community-built, documentation thins out exactly where self-hosting teams need it most, and the price step from $16 to $166 leaves no middle ground for a team that wants SSO without 25 seats. Buy it if someone on the team is comfortable with Docker and TypeScript, and the automation volume is high enough that per-task billing has become an engineering problem. Stay on a hosted, task-metered tool if it is not.
Read the full Activepieces profileRelevance AI
Relevance AI is one of the better-engineered agent platforms available to a small company, and the parts that matter most for production use, tracing with per-step cost, approval gates, escalation rules, a job queue with retries, and an honest two-meter bill, are more mature here than in most of its peers. The friction is commercial rather than technical. Pricing jumps from $19 to $234 per month with under three times the Actions, the Business tier that used to bridge that gap was retired in September 2025, and the governance features that make agents safe to leave running unattended (evals, observability, audit logs, SSO) are all locked behind a quote-only Enterprise plan. The public pricing page now advertises nothing else, which tells you where the company's attention has gone. Buy the Free or Pro plan for supervised, human-in-the-loop work where a person reviews output, and expect real value from it. Do not plan an unattended, customer-data-touching deployment on a self-serve plan, because the tools to prove it is behaving are not sold at that price.
Read the full Relevance AI profileActivepieces profile last reviewed 2026-08-23; Relevance AI last reviewed 2026-08-23. Pricing is compiled from public sources and can change without notice. See our methodology.