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

The one-line answer

Choose n8n if you have technical hands on the team and either want to self-host (data sovereignty, no execution quota), run complex many-step workflows where its billing model pays off, 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 in credits (formerly operations), and each module run in a scenario uses credits. 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 roughly 5 credits per run; on n8n it's 1 execution. Scale that across thousands of runs and the gap grows with every step you add. The worked example below puts numbers on it.

The catch is what you pay in the other direction. n8n Cloud Starter is $20 a month billed annually ($24 a month billed monthly) for 2,500 executions, and Pro is $50 a month billed annually ($60 a month billed monthly) for 10,000.^[1] Make Core is $12 a month billed annually ($16 a month billed monthly) for 10,000 credits.^[2] So for simple, low-step workflows at modest volume, Make is the cheaper cloud option on either billing period. Make also has a Free plan (1,000 credits a month, 2 active scenarios, 15-minute interval, no time limit, no credit card required); n8n has no free Cloud plan, only a 14-day Cloud trial (no card required) or the free self-hosted Community Edition. These are the US prices; n8n shows prices for the browsing location, so readers elsewhere see other currencies (n8n pricing in detail). The crossover is complexity: the more steps per workflow, the more n8n's per-execution model pulls ahead.

A worked cost comparison

Put real numbers on it. Take a 10-step workflow running 1,000 times a month. On Make, that's roughly 10 credits × 1,000 = 10,000 credits, exactly Core's monthly allowance, so you sit at Core's $12 a month billed annually ($16 a month billed monthly) with no headroom.^[2] On n8n, it's 1 execution × 1,000 = 1,000 executions, comfortably inside Cloud Starter at $20 a month billed annually ($24 a month billed monthly), or free on the self-hosted Community Edition (no SSO, projects or Git, and you run the server).^[1] At this level, Make is cheaper on the cloud sticker.

Now scale the same workflow to 5,000 runs a month. Make needs about 50,000 credits, so you move Core's credit slider to a larger tier, priced on Make's pricing page (Make pricing explained). n8n needs 5,000 executions: Cloud Pro covers it at $50 a month billed annually ($60 a month billed monthly) for 10,000 executions, or the free Community Edition runs it on a server you operate.^[1] The more steps per run and the higher the volume, the wider n8n's lead. Flip it around, though. For a 2-step workflow at low volume, Make Core at $12 a month billed annually ($16 a month billed monthly) beats n8n Starter's $20 a month billed annually ($24 a month billed monthly). 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, fair-code software with no execution quota, released under the Sustainable Use License; n8n says it is not open source under the OSI definition.^[3] 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. The free edition lacks SSO, projects, sharing, Git and environments, though; those need a paid self-hosted licence (Business from $800 a month billed annually, $960 a month billed monthly). Make is cloud-only. If running on your own 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 to run. You pay for and operate the server yourself, plus updates, backups and uptime monitoring, and scaling with queue mode needs Redis and Postgres. That is real operational overhead, and it lands on whoever owns the box. So the true comparison isn't a server bill against Make's subscription; it's your server plus engineering time against a managed cloud with zero maintenance. For a team with a capable engineer, that overhead is small 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 3,000+ apps out of the box, while n8n lists 500+ native integrations, plus any API through its HTTP Request node.^[4] 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 a community node or the HTTP request. Community nodes carry a condition: on n8n Cloud, owners and admins can install verified community nodes, while unverified ones need self-hosting and a security judgment call. Both platforms cover anything with an API through a generic HTTP module, so neither hits a hard wall. Make's breadth is still a genuine time-saver, especially for non-technical builders who'd rather not vet third-party 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-store support, local models via Ollama, and proper RAG workflow building. 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 AI Agents live inside the scenario builder and are still in beta. According to Betterclaw's comparison, they interpret input and choose tools within your workflow, take context from uploaded files without a RAG pipeline to build, can't operate outside a Make scenario, and 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. 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, closer to a flowchart on steroids, with a Code node in JavaScript or Python. The upside of that density is depth. On self-hosted n8n you can add JavaScript custom nodes and npm modules (n8n Cloud allows neither), and modifying the source is fine for internal use under the Sustainable Use License, but not for resale or white-labelling.^[3] That is why engineering-led teams often prefer it. 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 keeps the count flat as steps grow), or if your AI needs real RAG and multi-agent orchestration, n8n is the better and often cheaper tool. Check the licence first if you plan to put it inside a product you ship: letting your customers build or edit workflows needs n8n's separate embed agreement.

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, Make is the smoother, cheaper start. On price, Core at $12 a month billed annually beats n8n Starter's $20 a month billed annually. You also get 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 source-available automation projects, with over 200,000 GitHub stars (208k) and a forum of over 40,000 members, n8n says,^[4] contributing nodes, sharing self-hosting recipes, and reading 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. On support, n8n's email and dedicated SLA support are Enterprise-only; other plans use the forum, and support runs Monday to Friday, 9:00 to 17:00 Berlin or New York time.

In practice the split falls along predictable lines. Marketing, ops and agency teams, people who want outcomes rather than infrastructure, gravitate to Make: cloud-hosted, guided, template-rich, broad connectors. Engineering-led teams and privacy-sensitive or regulated orgs gravitate to n8n: self-hostable, 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.