n8n vs Activepieces

The one question that decides this

The real question is not "which one replaces Zapier better?" because both explicitly replace Zapier. The question is: do you want the mature, ecosystem-heavy, custom-code automation platform that has been the default open-source answer for years (n8n), or the newer, MIT-licensed, AI-first competitor that is aggressively rethinking the automation surface (Activepieces)?

What each one is actually trying to be

n8n: the mature automation platform with the biggest ecosystem gravity well

n8n is the gorilla in the room. With 202k GitHub stars, 60k forks, and a description that reads "Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations," n8n is not just a Zapier replacement — it is trying to be the automation infrastructure itself. The platform has been around since 2019, has accumulated massive community content, and a company (n8n GmbH) whose entire business model is keeping the hosted version running while the source-available code stays free for self-hosters.

That is not a low-commitment tool. n8n is the bet you make when automation is a core part of how your company operates, not a side project you automate once and forget.

Activepieces: the MIT-licensed, AI-first competitor

Activepieces is newer and smaller, but its positioning is interesting. Its GitHub description says: "AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents." That is a lot of AI keywords, but the actual product is a very capable no-code automation platform — and it is MIT-licensed, which is a genuinely meaningful difference from n8n's Sustainable Use license.

Activepieces is betting that the next generation of automation will be AI-first (MCP servers, AI agents, LLM-powered workflows) rather than integration-first (connecting 400+ SaaS APIs with nodes). The tagline "AI-first, no-code workflow automation" matches the product: you can build AI agents, connect MCP servers, and do AI-driven automation in a way that feels native to the tool, not bolted on.

Head-to-head: the metrics

n8n's GitHub repo, n8n-io/n8n, shows 202,118 stars, 60,330 forks, 1,075 open issues, a Sustainable Use license, and a last push before 2026-08-21. It is written primarily in TypeScript. Those numbers are massive for any open-source tool, let alone an automation platform. The fork count alone tells you there is a vast ecosystem of self-hosters, extenders, and people who want to customize the platform.

Activepieces' GitHub repo, activepieces/activepieces, shows 24,002 stars, 4,086 forks, 486 open issues, an MIT license, and a last push on 2026-08-20. It is written primarily in TypeScript. 24k stars is a very healthy number for a project that has been around for a fraction of the time n8n has. The open issue count of 486 is proportional to the project size — not a red flag, just the tax of having a real user base. The MIT license is a genuine advantage if you care about licensing terms, because n8n's Sustainable Use license restricts certain commercial uses.

The license difference is the most under-discussed aspect of this comparison. n8n's "Sustainable Use License" (formerly "Fair-code") is source-available, not open source by the OSI definition. Activepieces is MIT, which is a standard OSI-approved open-source license. If your company's legal team cares about this distinction, Activepieces is the safer bet. If you are a self-hoster who just wants to run automation internally, the license difference is mostly academic.

Self-hosting

Both tools support self-host and official-cloud, with a deploy difficulty of 2 out of 5. So on the basic hosting axis, neither has a meaningful advantage. Both can be Docker-compose deployed, both have official cloud options, and both are approachable for a team that manages its own servers.

n8n's official SaaS price is $20/month, while Activepieces is $25/month. The difference is negligible — pricing is not the decision factor here.

What matters more is the self-hosting experience. n8n has been self-hosted by thousands of teams for years, so there are more community tutorials, Docker Compose examples, and troubleshooting threads. Activepieces is newer but its Docker setup is clean and well-documented. Neither is a "bring Kubernetes and a priest" situation.

Community signals

n8n has 1 Hacker News mention in the last 90 days, with a top all-time story from 2019 at 728 points and 196 comments. Activepieces has 2 Hacker News mentions in the last 90 days, with a top all-time story from 2024 at 485 points and 218 comments. The HN data is sparse for both, but Activepieces' launch story got significant attention for a younger project.

n8n's overwhelming GitHub presence is its main community signal. 202k stars is not something you can ignore. That level of attention means more integrations, more community nodes, more blog posts, more Stack Overflow answers, and more people who can help you when something breaks. Activepieces has a smaller but more focused community, and the MIT license attracts developers who care about open-source purity.

What we'd do

Pick n8n if you want the biggest ecosystem, the most integrations, the most community support, and you are building automation as infrastructure for your company. The 202k stars and 60k forks are not noise — they mean this tool has been battle-tested by thousands of teams. The Sustainable Use license is a practical constraint only if you plan to resell or embed n8n commercially.

Pick Activepieces if you want a clean MIT license, an AI-first approach that feels more native than bolted-on, and a tool that is innovating rather than accumulating. Activepieces is the interesting bet if you are building AI agents, MCP-based workflows, or automation that is more AI-driven than API-driven. It is also the safer choice if your legal team cares about OSI-approved licenses.

If this is for a small team doing straightforward automation, either tool will work. The real differentiator is your licensing posture and whether you want the 800-pound gorilla or the MIT-licensed upstart with a sharper AI focus.