Make vs n8n

When to pick Make

• You need breadth of integrations fast. Make offers 3,000+ pre-built app connectors — six times more than n8n’s 500+ nodes — so you’re far less likely to fall back to the HTTP module for common SaaS tools.
• Your users are non-technical. Make’s visual canvas with drag-and-drop routers, iterators, and aggregators is designed for operations managers and marketers who cannot write JavaScript. n8n assumes at least some developer fluency.
• You want a free cloud tier with no server setup. Make’s free plan includes 1,000 operations/month and 2 active scenarios with no credit card and no Docker environment to maintain — n8n has no free cloud option.

When to pick n8n

• You need code-level control inside workflows. n8n’s native JavaScript and Python code nodes let developers express arbitrary transformation logic, call internal libraries, or handle edge cases that no-code nodes cannot reach. Make has no equivalent.
• You need on-premises or cost-controlled hosting. The n8n Community Edition is free to self-host via Docker with no execution caps, making it the cheaper option at high volume. Make is cloud-only with a hard operations ceiling on every plan.
• You are building AI agents with traceable, debuggable pipelines. n8n’s step-by-step execution tracing, replay/mock data, and native AI evaluation tooling make debugging multi-agent and RAG workflows practical. Make’s agent builder is capable but offers less execution transparency.

Bottom line

Make and n8n target adjacent but distinct audiences. Make’s 3,000+ integrations, genuinely free cloud tier, and no-code canvas make it the faster choice for operations teams and marketers who need complex multi-path workflows without writing code — and at $9/month the Core plan is cheaper than n8n’s cheapest cloud tier. n8n is the stronger pick for developers and IT teams who need code nodes, self-hosted infrastructure, or on-premises data control: the free Community Edition removes the cost equation entirely at scale, and the transparent execution trace makes it the more productive platform for AI agent work. If your team cannot comfortably run a Docker container, choose Make. If they can, n8n is likely the better long-term investment.