Make vs n8n: Which One Should You Build On

Make vs n8n is a narrower question than Zapier versus either, because both are node canvases aimed at people building real branching workflows rather than two-step zaps. The differences that matter are three: what a run costs, whether you can host it yourself, and how much code you are willing to write.

I build client systems on both. Here is the decision framework, and the migration plan if you are moving.

Make vs n8n at a glance#

The pricing difference that decides your bill#

This is the part that surprises people in month three.

Make charges per operation. Every module execution counts. A scenario with fifteen steps running a thousand times a month is fifteen thousand operations. The cost grows with how good your scenario is, which quietly penalises exactly the workflows worth building.

n8n charges per workflow execution on its cloud plans. One run is one unit, whether it has three nodes or thirty. Deepening a workflow is effectively free.

Self-hosted n8n removes the metered bill entirely and replaces it with a small server and your own maintenance hours. That trade is good above a few thousand runs a month and bad below it, because your hours are not free either.

The practical rule: for a handful of simple scenarios, Make's bill will be small and its ergonomics will save you time. Once your workflows have real branching and real volume, n8n gets cheaper as they get better.

Where Make is genuinely nicer#

The canvas. Cleaner, more legible, easier to hand to someone who did not build it.

Error handling. Configuring retries and error routes is more guided, which matters because it is the step people skip.

Module documentation. Consistently good, with clear examples of what each field expects.

Onboarding. A marketer with no engineering background gets further on their own, and that is not a small thing when they are the person who will maintain it.

If nobody technical will ever touch your automations and your data can live in a cloud, Make is a defensible choice and switching would waste a week.

Where n8n wins#

Self-hosting. The one that ends most enterprise conversations. Sensitive data never leaves your infrastructure, which turns a legal objection into a solved problem.

The code node. Every automation eventually meets the 5% no connector covers. Being able to write twelve lines of JavaScript instead of building a three-module workaround is worth a lot.

AI and agent workflows. Native LLM and agent nodes, and the branching to build something that decides what to do next rather than following a fixed path. How to build an AI agent covers that design, and n8n workflows covers the ones that pay for themselves.

Cost at volume. Not marginal once you are past a few thousand runs.

For the three-way comparison including the simplest option, Zapier vs n8n covers where each of the three sits.

The question that actually decides it#

Who maintains this in six months?

If the answer is a marketer with no technical support, Make's gentler canvas is worth more than n8n's ceiling. They will build more, break less and ask for help less often.

If the answer is someone technical, or the data cannot leave your building, or you are building AI workflows with real branching, n8n is the better home and the learning curve pays for itself within a quarter.

Everything else, including which demo felt smoother, is noise next to that question. AI workflow automation covers the ownership and runbook practices that keep either platform alive.

What neither of them fixes#

Both platforms make it easy to build something that nobody owns, and that is the failure mode I see most often in audits.

Credentials in workflows. Use the credential store, always. Exported workflows with embedded keys get shared, and then rotated keys break things nobody can trace.

No version history. Export your workflows somewhere durable. An instance that dies with no exports takes the work with it, and on cloud plans a deleted scenario is genuinely gone.

Undocumented logic. A canvas is readable, not self-explanatory. A one-line note on each workflow saying what it does and who owns it costs nothing and saves the person who inherits it.

No alert on failure. Both platforms can notify you. Neither does by default in a way you will notice, and a broken workflow that fails quietly is worse than no workflow, because the manual habit is already gone.

Whichever platform you pick, these four decide whether you still have a working system in a year. The tooling choice is secondary to them.

Migrating without wasting a month#

If you are moving, do it in this order.

1. List every live scenario with its monthly run count and what it is worth.
2. Kill the dead ones. In most audits a third are running for nobody.
3. Rebuild only the top five in the new platform, with the old ones still live.
4. Run both for two weeks and compare outputs, not screenshots.
5. Switch the trigger and keep the old version disabled for a month before deleting anything.

Never migrate everything at once, and never migrate the broken ones. Rebuild those properly or drop them.

The short version#

Make if a non-technical person owns automation and cloud hosting is acceptable. n8n if someone technical owns it, if you need code, if you are building AI agents, or if the data cannot leave your infrastructure. Decide by maintainer, then by pricing unit.

If you want the workflows designed around your actual process and your team trained to extend them, that is what my AI training packages cover, and the AI automation agency page is where I do this work for clients.