n8n vs Make: Which Free Automation Tool Wins?

n8n wins on cost and control; Make wins on polish and speed to first workflow

The short answer to n8n vs Make: choose n8n if you want to self-host for free, keep your data in your own environment, and build complex AI-driven logic without per-operation billing. Choose Make if you want a slick visual builder, a generous free cloud tier, and the fastest possible path from idea to running scenario without touching a server. Both are excellent; they optimise for different things.

Neither is a toy. Both connect hundreds of apps, both handle branching and error paths, and both have invested heavily in AI nodes over the last two years. The decision comes down to where you want the tool to live and how you prefer to pay.

Pricing and the meaning of free

This is where the two diverge most sharply, and where the word free needs a footnote.

• n8n is fair-code and free to self-host with no execution limits beyond your own hardware. Its paid cloud removes the hosting burden but is billed by executions and active workflows.
• Make has a real free cloud tier measured in operations per month. It is genuinely usable for light automation, but every step of every run consumes from that quota.
• The practical rule: high-volume or step-heavy workflows favour self-hosted n8n, where you pay for a server, not per operation. Low-volume workflows that you would rather not host favour Make's free cloud.

Self-hosting and data control

If your workflows touch customer records, internal documents, or anything subject to data-residency rules, self-hosting is a feature, not a chore. n8n was built for it: run it in Docker, keep every credential and payload inside your own boundary, and never send sensitive data through a third party's cloud. Make is cloud-only, which is simpler to operate but means your data transits their platform. For many teams that is perfectly fine; for regulated ones it can be a dealbreaker.

Ask one question early: does this data need to stay inside our walls? If yes, n8n's self-hosting settles the debate before any other feature does.

AI features in 2026

Both tools treat AI as first-class now. n8n ships dedicated nodes for LLM calls, agent-style loops, and vector stores, and its open architecture makes wiring a custom model straightforward. Make offers AI modules and tight integrations with the major model providers through its app catalogue, presented in its signature visual style. If you are building genuinely agentic workflows with tool use and memory, n8n's flexibility tends to pull ahead; if you want managed AI building blocks with minimal setup, Make is very comfortable.

Learning curve and team fit

Make is easier to pick up. Its scenario canvas is visual and forgiving, and a non-developer can build something useful in an hour. n8n is also visual but exposes more of the underlying mechanics — expressions, raw data, and the option to drop into code — which is more powerful and slightly steeper. The right fit often follows the team:

• A marketing or ops team that wants self-service automation without IT usually gets further, faster, with Make.
• A technical team that wants control, custom code, and no per-step billing usually prefers n8n.
• Plenty of organisations run both — Make for quick business automations, n8n for the heavy, sensitive, or AI-rich pipelines.

Reliability and error handling

An automation is only as good as its behaviour when something fails, and in 2026 both tools take this seriously. Make surfaces errors clearly in its scenario view and offers built-in retry and error-handling routes you wire visually. n8n gives you error workflows, granular retry settings, and — because you can self-host — full access to logs and the underlying execution data when you need to diagnose a stubborn problem. The practical difference is ownership: with self-hosted n8n you can inspect everything down to the raw payload, which is invaluable for complex pipelines, whereas Make keeps you inside its managed environment, which is simpler but less transparent. For mission-critical workflows, decide how much visibility you will want at two in the morning when a run fails, because that is when the difference becomes concrete rather than theoretical.

So which one wins?

There is no universal winner, only a winner for your constraints. Pick n8n for cost control at scale, data sovereignty, and complex AI logic. Pick Make for speed, polish, and a free cloud tier you do not have to maintain. The mistake is choosing on brand familiarity rather than on hosting, billing model, and the sensitivity of the data you are moving. Whichever you lean toward, build one real workflow in each before you commit — the free tiers exist precisely so you can feel the difference in your own hands rather than trusting a comparison table, and an hour spent prototyping settles the question faster than a day spent reading reviews.

Prefer it built and managed for you?

BSH Technologies builds and operates production automation on both n8n and Make, so the tool choice is made on your actual constraints rather than guesswork. We can self-host n8n inside your environment, design the workflows, and keep them healthy — or stand up Make scenarios where that fits better. To choose well and ship faster, talk to BSH Technologies or see our AI & automation services.