n8n vs Make (2026): Which Automation Power Tool Should You Choose?

Two power tools, one real difference

Unlike the beginner-first tools, both n8n and Make embrace complexity: routers, branches, iterators, error handling, the works. So this comparison isn't simple vs powerful. Both are powerful. The real difference is ownership and extensibility versus polish and breadth. n8n can run on your own servers and drop into real code; Make lists no self-hosting option and is no-code-first (its Make Code App runs JavaScript or Python steps), but its visual canvas is the most refined in the category and its catalog is far larger. Pick your axis. The choice follows.

Head-to-head, at a glance

Pricing — different meters, and it matters

At entry level the prices are close, but the meters differ. Make Core is $12 a month billed annually ($16 a month billed monthly) for 10,000 credits, while n8n Cloud Starter is $20 a month billed annually ($24 a month billed monthly) for 2,500 executions, and Pro $50 a month billed annually ($60 a month billed monthly) for 10,000^[1]. n8n's US pricing page shows USD and other locations see other currencies — see our n8n pricing guide.

• Make bills in credits (formerly operations): each module action inside a scenario counts as one credit, and code runs use 2 credits per second^[2]. An iterator over 200 items uses about 200 credits. Cost scales with how elaborate your scenarios are.
• n8n Cloud bills on executions: one whole workflow run, regardless of how many nodes it contains. A twenty-node run is one execution. Cost scales with how often you run, not how complex each run is.
• n8n self-hosted Community Edition has no execution quota, but you run the server, and it lacks SSO, projects, sharing and Git; the paid self-hosted Business licence has a 40,000-executions-a-month quota.

A concrete example. Take a scenario that processes a batch of 100 records each run (an iterator plus a few modules per item, say 4 credits per record). On Make that's ~400 credits per run; run it daily and you're at ~12,000 credits a month from one scenario, above Core's 10,000. On n8n Cloud, that same daily run is one execution per day — about 30 executions a month, trivially inside Starter's 2,500. That's the structural gap: Make meters the work inside each run; n8n meters the runs.

The flip side matters too. By our arithmetic, Make Core works out to $0.0012 per credit and n8n Starter to $0.008 per execution (Pro $0.005). Both on annual billing. So n8n Cloud is cheaper only when an average run uses more than about 4 to 7 credits on Make. High volumes of simple runs often cost less on Make. Elaborate, many-module runs favor n8n.

Ease of use and the visual experience — Make's edge

This is Make's clearest win. Its canvas is genuinely the most refined visual builder in the category: laying out a branching scenario, watching data flow module-to-module, and debugging visually is a pleasure, and non-developers who still need real branching get further on Make than on n8n. n8n's canvas is capable but more utilitarian and assumes more technical fluency. If you want power and polish without writing code, Make is the friendlier of the two power tools.

Code and extensibility — n8n's edge

Both run code: Make through its Code App (JavaScript or Python, billed per second), n8n through JavaScript or Python Code nodes and custom nodes you can build and share^[3]. The catch is hosting. Custom nodes and external npm modules are self-hosted only; on n8n Cloud the Code node can import only crypto and moment, and native Python imports no libraries. Self-hosted, n8n lets you bend a workflow to almost anything, which is the reason engineering teams pick it. On Cloud the gap with Make is narrower than the marketing suggests.

AI — n8n goes deeper

Both are investing in AI. Make offers AI Agents (in beta), Maia for building scenarios by conversation, and an MCP server. n8n goes further for builders: an AI Agent node on LangChain, first-class RAG with vector stores (Pinecone, Qdrant, Supabase), multi-agent patterns, and a choice of your own model key or, on Cloud Starter and Pro, Gateway credits. Self-hosted, you can keep data in-house. For agentic, data-grounded AI, n8n is ahead in our reading; for a straightforward AI step inside a beautiful visual flow, Make is perfectly capable. Our n8n AI playbook shows how deep the n8n side goes.

Self-hosting and data ownership — n8n only

Make lists no self-hosting; n8n's comparison page says Make is cloud-only, and Make's on-prem agent only connects its cloud to local systems. n8n can run entirely on your own infrastructure, so regulated data never leaves it — often the deciding factor for finance, healthcare and legal teams. Mind the edition: SSO (SAML and LDAP) needs the paid self-hosted Business licence, and n8n recommends self-hosting for expert users. n8n Cloud is hosted on Azure in the EU (Frankfurt); its AI features use sub-processors that process data in the US.

Catalog and maturity — Make leads on breadth

Make's 3,000+ app catalog is considerably larger than n8n's 500+ native integrations, with more ready-made connectors for niche tools. n8n closes most of the gap with its HTTP Request node and code, but Make already has a polished module versus n8n needs a small build is a real, everyday difference. Both are mature, well-documented platforms with active communities. n8n's is large (over 200,000 GitHub stars, 208k by n8n's count), while Make's is a large, hosted-SaaS user base.

When each wins — by scenario

• An elaborate, multi-branch scenario built by a non-developer who wants a beautiful visual canvas: Make.
• A workflow that needs custom code or logic the modules don't express: n8n, self-hosted if you need npm modules.
• Anything touching sensitive or regulated data: n8n self-hosted. Data stays in-house.
• Very elaborate workflows (many modules or iterators per run): n8n — it bills a whole run as one execution.
• High volumes of simple one-to-three-module runs: often Make, since a credit per action costs less than an execution.
• A serious AI agent with retrieval over your own documents: n8n — deeper agent/RAG tooling.
• A niche app only Make supports natively, with zero build: Make. Catalog breadth is the tie-breaker.

Which should you pick?

Choose Make if: you want the most polished visual builder, you have non-developers who still need real branching, you value a larger catalog with ready-made connectors, and you're happy on a hosted platform. Choose n8n if: you have technical capacity, you want to self-host for data control, you need real code or the deepest AI, or your runs are elaborate enough that a credit per module action adds up^[1]. Both are excellent power tools. The decision is ownership-and-code (n8n) versus polish-and-breadth (Make).

By team profile — the honest pick

• Non-technical team that wants power without code: Make. Its polished visual canvas lets non-developers build serious branching logic, and the larger catalog means fewer build steps. n8n would demand more technical fluency than you have on hand.
• Engineering or technical team: n8n. You'll use the code nodes, want the AI depth, and likely prefer self-hosting for control and npm access. Make's no-code-first, hosted model would constrain you.
• Team with elaborate, many-module scenarios: n8n. Make's credits multiply with every module action; n8n's per-execution model keeps elaborate workflows affordable. For high volumes of simple runs, price both first.
• Privacy- or compliance-sensitive business: n8n self-hosted. Data stays on your infrastructure, which Make doesn't offer; budget for the Business licence if you need SSO.
• Team building serious AI agents: n8n. Its LangChain/RAG/agent tooling goes deeper than Make's AI Agents, which are still in beta.
• Team that values a beautiful, low-friction building experience above all: Make. If the day-to-day joy of building and debugging visually matters most and you don't need code or self-hosting, Make is the nicer tool to live in.

The pattern: Make wins on visual polish and catalog breadth for teams happy on a hosted platform; n8n wins on code, AI depth, data ownership and complex runs for technical teams. Both are genuine power tools. Your team's profile, not a feature war, decides it. And if you're on the fence, weigh the trajectory: a team that will grow more technical or more AI-driven over time leans toward n8n, whose ceiling rewards that direction; a team that will stay firmly no-code and values the visual building experience above all else is happier staying on Make. The honest tie-breaker is rarely a feature. It's whether you'll want to write code and own your infrastructure a year from now, or keep everything visual and fully managed for you.

A note on trying before you commit

We found no tool from either vendor that imports the other's workflows, so plan on rebuilding by hand. Choose deliberately. The low-risk way to decide: build your two or three most important scenarios on each. Make has a free tier to prototype on (1,000 credits a month, 2 active scenarios); n8n gives you a free self-hosted edition or a 14-day Cloud trial (no card, 1,000 executions), and there is no free Cloud plan. Building the same real workflow on both for an afternoon tells you more than any feature table. And because n8n workflows export as JSON, starting there keeps your logic portable if you ever need to move.

The bottom line

n8n vs Make isn't simple vs powerful. It's two powerful tools optimized for different priorities. Make is the best hosted, visual, no-code power tool, with a larger catalog than n8n and the most refined canvas. n8n is the best control-and-AI power tool: self-hostable, code-extensible, with deeper AI and a meter that ignores how many steps a run takes. If ownership, code and AI matter most, n8n is the pick; if visual polish and catalog breadth matter most, Make is. For the full evaluation, see our n8n review; to weigh cost, the pricing guide breaks down both meters.