Make vs n8n (2026): Cloud Ease vs Self-Hosted Power — Which Wins

TL;DR — Make vs n8n is a fight between two genuinely powerful tools, decided by three questions: do you want to self-host, how many steps do your workflows have, and how deep does your AI need to go? n8n is open-source, self-hostable, bills per workflow run (dramatically cheaper for complex, many-step automations), and has pulled ahead on AI-native depth. Make is cloud-first, easier to learn, connects to far more apps out of the box, and is the faster way to ship an AI workflow a non-engineer can maintain. Developer + control + complex/AI-heavy: n8n. No-code polish + breadth + gentle cloud start: Make.

The one-line answer

Choose n8n if you have technical hands on the team and either want to self-host (data sovereignty, flat cost), run complex many-step workflows where its billing model saves you a fortune, or need deep AI (RAG, local models, multi-agent orchestration). Choose Make if you want the smoother no-code experience, the widest out-of-the-box app coverage, a cheaper cloud entry point, and don't want to own any infrastructure. Unlike the Make-vs-Zapier decision — which is mostly ease vs cost — this one is genuinely close on power; it turns on ownership, workflow shape, and how far your AI ambitions go.

The real difference: how they count usage

This is the crux, and it's the opposite of a rounding error. Make bills per operation — every module in a run counts. n8n bills per execution — one whole workflow run counts as one execution, no matter how many steps it contains.^[1] Put a five-step workflow next to itself: on Make that's ~5 operations per run; on n8n it's 1 execution. Scale that across thousands of runs and the gap is enormous — n8n can be 10–20× cheaper than Make for complex, multi-step automations.^[1]

The catch is what you pay in the other direction. n8n Cloud starts at $24/mo for 2,500 executions (Pro is $60/mo for 10,000),^[2] versus Make's $9/mo for 10,000 operations — so for simple, low-step workflows at modest volume, Make is actually the cheaper cloud option. The crossover is complexity: the more steps per workflow, the more n8n's per-execution model pulls ahead. Model your own shape before assuming either is "cheaper."

A worked cost comparison

Put real numbers on it. Take a 10-step workflow running 1,000 times a month. On Make, that's ~10 operations × 1,000 = 10,000 operations — exactly Core's monthly allowance, so you're at $9 but with no headroom.^[2] On n8n, it's 1 execution × 1,000 = 1,000 executions — comfortably inside the $24 Starter tier, or effectively free if self-hosted.^[2] At this level they're close, with Make cheaper on the cloud sticker.

Now scale the same workflow to 5,000 runs/month. Make jumps to 50,000 operations — five credit packs on top of Core. n8n needs 5,000 executions — the $60 Pro cloud tier covers it, or unlimited on a ~$7/mo self-hosted server.^[2] The more steps per run and the higher the volume, the wider n8n's lead. Flip it around, though — a 2-step workflow at low volume — and Make's $9 entry beats n8n's $24 comfortably. That's the whole decision in one line: step-count × volume is the dial, and it moves the answer.

Self-hosting: n8n's trump card (and its hidden bill)

Here's the option Make simply doesn't offer. n8n's Community Edition is free, open-source software with unlimited executions — you run it on your own server for roughly $5–10/mo in hosting, and self-hosting saves most teams $200–700/year versus cloud.^[2] More importantly for some, self-hosting means your automation data never leaves infrastructure you control — a real requirement for regulated industries, privacy-sensitive workloads, or anyone with a strict data-residency policy. Make is cloud-only; if "it must run on our servers" is on your list, the comparison ends here in n8n's favor.

Be honest about the trade, though, because "free software" isn't "free." A production self-hosted n8n typically wants Postgres, Redis and queue workers behind it, plus updates, backups and uptime monitoring — real operational overhead that lands on whoever owns the box.^[3] So the true comparison isn't "$7 server vs Make's $9"; it's "$7 server plus engineering time vs Make's $9 and zero maintenance." For a team with a capable engineer, that overhead is trivial and the savings are real. For a team without one, Make's managed cloud is the cheaper option once you price in the hours. Freedom with a maintenance bill attached.

Integrations: Make's quiet advantage

This one rarely makes the headline but decides more real projects than it should. Make connects to far more apps out of the box — on the order of 1,800+ native integrations — while n8n ships around 400+, extended by 500+ community-published nodes.^[3] For most teams that difference is invisible; the tools you use are covered on either. But when you need a mid-market SaaS with a native connector, Make is likelier to have it ready, whereas on n8n you may be reaching for the community node or the HTTP request. And the community-node route carries a caveat n8n itself flags: installing community nodes means running unverified third-party code on your instance, which requires self-hosting and a security judgment call.^[3] Both platforms cover "anything with an API" through a generic HTTP module, so neither hits a hard wall — but Make's out-of-the-box breadth is a genuine time-saver, especially for non-technical builders who'd rather not vet npm packages.

AI: n8n has gone deeper, Make ships faster

Here's where our read has shifted, and we'd rather be straight about it than flatter the product we score well. Through 2026, n8n has pulled ahead on AI-native depth. It offers native LangChain integration, an AI Agent node for autonomous tool use, vector-database support, local models via Ollama, and proper RAG workflow building — roughly 70 dedicated AI/LLM nodes in total.^[4] If you're building sophisticated multi-agent systems, doing retrieval over your own documents, or keeping models on your own hardware for privacy, n8n is the more capable tool today.

Make's answer is different in kind. Its next-generation AI Agents live right inside the scenario builder, interpret input and choose tools within your workflow, take context from uploaded files with no RAG pipeline to build, and are shareable across a team.^[3] The honest caveats: they're still in beta, they can't operate outside a Make scenario, and they don't do native RAG. So the trade is real and clean: n8n is deeper; Make is faster to ship an AI-enhanced workflow that a non-engineer can actually maintain. Which matters more is a question about your team, not the tools — and it's the whole subject of our Make AI Playbook.

Ease vs power

Both are powerful; the difference is the on-ramp. Make leans into no-code polish — a visual canvas where each module describes what it does, so you can infer your way forward. n8n takes a more technical, node-based approach ("a flowchart on steroids") and its nodes often lack in-app help text, leaving you to infer meaning from names alone.^[1] The upside of that density is depth: n8n supports JavaScript custom nodes, dynamic API calls and source-level extension, which is why teams building automation into their own product often prefer it.^[1] Rule of thumb by job: if a non-developer will maintain the automations, Make; if an engineer owns them, n8n's ceiling is higher.

Head-to-head, at a glance

When to choose n8n

Choose n8n when control, complexity or AI depth dominates. If you must self-host for data sovereignty, if you have a developer who'll live in it, if your workflows are genuinely many-step and high-volume (where per-execution billing saves 10–20×), or if your AI needs real RAG and multi-agent orchestration, n8n is the better and often far cheaper tool. It's also the natural pick if you're embedding automation into a product you ship.

When to choose Make

Choose Make when ease, breadth and cloud-first simplicity matter more than ownership. If you don't want to run servers, if non-technical teammates will build and maintain automations, if you value the widest out-of-the-box app coverage, or if your workflows are simple-to-moderate at modest volume (where $9 Core beats n8n's $24 entry), Make is the smoother, cheaper start — with a no-code experience and a ship-fast AI layer n8n doesn't match. The orientation still rewards a technical hand, but the guided canvas and deep template library mean a determined non-technical builder can get real work done.

Ecosystem, templates and who actually picks which

The tools' communities reflect their audiences, and that matters more than it looks. Make leans on a large template library and no-code community — you'll usually find a pre-built scenario close to what you want and adapt it, which shortens the learning curve for non-developers. n8n's strength is a developer ecosystem: it's one of the fastest-growing open-source automation projects, with 45,000+ GitHub stars and a 40,000+ member forum,^[4] contributing nodes, sharing self-hosting recipes, and letting you read or extend the source when a connector doesn't do quite what you need. One optimizes for "someone already built this"; the other for "I can build or fix anything." (Worth knowing on support: n8n's dedicated technical help is reserved for higher tiers, with forum-first support otherwise and email mainly on European business hours.)^[4]

In practice the split falls along predictable lines. Marketing, ops and agency teams — people who want outcomes, not infrastructure — gravitate to Make: cloud-hosted, guided, template-rich, broad connectors. Engineering-led teams, product companies embedding automation, and privacy-sensitive or regulated orgs gravitate to n8n: self-hostable, source-extensible, cheap at complexity, deep on AI. If you sit in the middle, the tie-breaker is usually who will own and maintain the automations day to day — a non-technical owner tips it to Make, an engineer tips it to n8n.

Switching and migration

As with any two automation platforms, moving between them is a rebuild, not an import — but the node/module mental model carries over cleanly, so it's mechanical. If you're leaving Make for n8n, the usual driver is cost on complex workflows, deeper AI, or a self-hosting mandate, so rebuild your heaviest, most step-dense scenario first — that's where n8n's execution model pays off immediately. Going the other way (n8n → Make), you're usually trading some cost for no-code ease and broader connectors, so start with the workflow your non-technical team most needs to own. Either way, keep both running in parallel until each rebuilt workflow is validated with a real-data test.

The verdict

Neither wins outright — they're built for different owners. n8n is the better tool for developers, self-hosters, and anyone running complex or deeply-AI workflows at volume, and both its per-execution pricing and its AI-native depth are genuine structural advantages there. Make is the better tool for no-code teams who want a polished cloud experience, the widest app coverage, and a cheap, fast start — and it's the quicker route to an AI workflow a non-engineer can keep running. Our own scoring rates Make highly for exactly that audience (the full Make review has the details), while acknowledging plainly that a technical team optimizing complex-automation cost or deep AI will often be right to pick n8n. Decide on the three questions up top — self-host or not, simple or complex, shallow or deep AI — and the answer is usually obvious.