TL;DR — n8n and Make are both power tools — node-based platforms for complex, branching automation, not simple "when-this-then-that" apps. The split is about ownership and code versus polish and catalog. n8n is self-hostable (free, unlimited executions), code-extensible (JavaScript/Python) and has the deeper AI-agent stack. Make is a hosted, exceptionally polished visual canvas with a larger app catalog and a gentler on-ramp for non-developers — billed on operations that scale with how elaborate your scenarios get. If you want ownership, code and AI depth, n8n wins; if you want the most refined visual builder and widest catalog with no infrastructure, Make wins.
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 is hosted-only and no-code-first (with limited code steps), but its visual canvas is the most refined in the category and its catalog is far larger. Pick your axis and the choice follows.
Head-to-head, at a glance
| Dimension | n8n | Make |
|---|---|---|
| Hosting | Self-host (free) or Cloud | Hosted-only |
| Pricing model | Per execution (whole run) · free self-hosted | Per operation (each module run) |
| App catalog | 400+ native (+ HTTP/code) | 3,000+ — broader |
| Visual polish | Functional, developer-oriented | Best-in-class visual canvas |
| Custom code | JavaScript & Python, first-class | Limited code modules |
| AI | Agent framework, RAG, your own model | Growing AI modules |
| Data control | Full — self-host keeps data in-house | Data flows through Make's cloud |
| Best for | Ownership, code, deep AI, cost at scale | Polished no-code branching, widest catalog |
Make bills per operation, so elaborate scenarios (iterators, many modules) cost more; n8n bills per execution, so complexity is free and only run-frequency counts. The more elaborate your workflows, the more n8n's model — and free self-hosting — saves.
Both cost more than beginner tools for the depth they offer, but they meter differently.
- Make bills on operations — each module run inside a scenario consumes one. An elaborate scenario with iterators and many modules burns operations quickly, and an iterator over 200 items is 200 operations. 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 removes the meter entirely — free, unlimited executions, for the price of a server.
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 operations per record. On Make that's ~400 operations per run; run it daily and you're at ~12,000 operations a month from one scenario, climbing tiers as you add more. On n8n Cloud, that same daily run is one execution per day — about 30 executions a month, regardless of the 100 records or the modules — trivially inside the entry plan. Self-hosted, it's free. That's the structural gap: Make meters the work inside each run; n8n meters the runs.
The practical consequence: the more elaborate your workflows, the more Make's per-operation model costs relative to n8n's per-execution model — and self-hosted n8n is dramatically cheaper at any real scale. Make's price buys a polished managed experience; n8n's buys ownership and near-free scale if you'll run it yourself. Compare the detail in our n8n pricing guide.
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.
An iterator over 200 items is 200 operations on Make; the same loop is still one execution on n8n Cloud (and free self-hosted). For elaborate or high-volume workflows, that difference compounds into a real cost gap.
Make has code modules, but n8n treats code as first-class: JavaScript or Python nodes throughout, custom nodes you can build and share, and full self-hosting[2]. When a scenario needs logic neither tool's modules express, n8n lets you simply write it; on Make you're more often working within the module set. For engineering teams, that difference is the reason to pick n8n — you can bend it to anything.
Both are genuine power tools, so this isn't simple-vs-powerful. If you want to self-host, write code, or build deep AI, n8n. If you want the most refined visual canvas and the widest catalog with zero infrastructure, Make. Decide which axis matters most to your team and the choice follows.
Both are adding AI, and Make's AI modules are solid for dropping intelligence into a visual scenario. But n8n goes further: a real AI-Agent framework on LangChain, first-class RAG with vector stores (Pinecone, Qdrant, Supabase), multi-agent patterns, and your own model and key — so you control what runs and, self-hosted, keep data in-house. For building agentic, data-grounded AI, n8n is ahead; 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 is hosted-only; your data flows through its cloud. n8n can run entirely on your own infrastructure, so regulated or sensitive data never leaves it — often the deciding factor for finance, healthcare and legal teams, and the reason n8n's cost is so low at scale. If self-hosting or strict data residency is a hard requirement, n8n is the only one of the two that can meet it.
Catalog and maturity — Make leads on breadth
Make's 3,000+ app catalog is considerably larger than n8n's 400+, with more ready-made connectors for niche tools. n8n closes most of the gap with its HTTP 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 open-source community is enormous (175,000+ GitHub stars), 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.
- Anything touching sensitive or regulated data: n8n self-hosted — data stays in-house.
- High-volume or very elaborate workflows where cost matters: n8n — per-execution and free self-hosting beat per-operation at scale.
- A serious AI agent with retrieval over your own documents: n8n — deeper agent/RAG tooling, your own model.
- 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 cost or data control, you need real code or the deepest AI, or your workflows are elaborate/high-volume enough that per-operation pricing hurts[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 cost and control — Make's no-code-first model and hosted-only nature would constrain you.
- Team with elaborate, high-volume scenarios: n8n. Make's per-operation billing multiplies with complexity and volume; n8n's per-execution model (and free self-hosting) keeps elaborate workflows affordable.
- Privacy- or compliance-sensitive business: n8n self-hosted. Data stays on your infrastructure — Make, hosted-only, can't offer that, and it's often the deciding requirement regardless of polish.
- Team building serious AI agents: n8n. Its LangChain/RAG/agent tooling and your-own-model approach go well beyond Make's AI modules.
- 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 cost at scale 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, more AI-driven or more cost-sensitive over time leans toward n8n, whose ceiling and free self-hosting reward 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
Neither tool imports the other's workflows, so switching means 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; n8n gives you a free self-hosted edition or a 14-day Cloud trial. Building the same real workflow on both for an afternoon tells you more than any feature table — you'll feel immediately whether Make's polished canvas or n8n's code-and-ownership fits how your team works. And because n8n workflows export as JSON, starting there keeps your logic portable if you ever need to move.
Frequently asked questions
n8n vs Make — what's the core difference?+
n8n is open-source and self-hostable with execution-based (or free self-hosted) pricing and code flexibility; Make is a polished, fully hosted visual tool billed per operation. n8n optimizes for control, cost at scale, and developer power; Make for ease and a smooth hosted experience.
Is n8n cheaper than Make?+
At volume, usually yes — self-hosted n8n has no per-run fee, and even n8n cloud counts whole executions rather than per-operation, so complex workflows cost less. Make can be simpler and needs no server, so factor in the value of not maintaining infrastructure when comparing.
Can Make be self-hosted like n8n?+
No — Make is cloud-only. If self-hosting for data control or flat cost is a requirement, n8n is the option that offers it; that capability is a core reason teams pick n8n over Make.
Which is easier, n8n or Make?+
Make — its hosted visual canvas is more polished and needs no setup, while n8n is more technical and (self-hosted) requires running infrastructure. For a smoother experience choose Make; for control and code power, accept n8n's steeper curve.
Which should a developer pick, n8n or Make?+
Developers who want self-hosting, code nodes, and flat cost usually prefer n8n; those who'd rather skip infrastructure and want polish may still choose Make. If data ownership or heavy-volume economics drive the decision, n8n; if convenience does, Make.
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 the widest catalog and the most refined canvas. n8n is the best control-and-AI power tool: self-hostable, code-extensible, with deeper AI and near-free scale for teams that run their own infrastructure. 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.