n8n vs Make: Which Workflow Tool Fits a Team That Wants to Own Its AI Automations

n8n and Make are both workflow automation tools: a trigger, a chain of steps that read and write other apps, and, in both, native steps that call a language model. They overlap on most of what a lean team needs. Where they differ is hosting, pricing unit, how much code you can put inside a step, and who on the team can maintain what gets built. For a team that intends to own its AI automations rather than rent them, those differences decide the choice.

Direct Answer

Choose n8n when the workflow will touch internal systems or private data, when a developer will maintain it, or when you want to run it on your own infrastructure. Choose Make when a non-developer will build and maintain it, the apps involved are all mainstream SaaS, and hosted is fine. Both handle the rule-plus-model workflows described in what is AI automation. The decision is about where the workflow lives in a year and who fixes it at 6pm, not about which tool can do more in the first hour.

Key Takeaways

• Same job, different ownership model. n8n can be self-hosted and coded; Make is hosted and visual.
• Pricing units differ. Compare current pricing pages against your real monthly run count.
• Both have model steps. The difference for AI builds is code inside steps and where data lives.
• Migration is a rebuild. Decide for the year, not the week.

How do n8n and Make compare?

The n8n documentation and the Make Help Center are the primary references for what each exposes today. Pricing changes; the n8n pricing and Make pricing pages are the only current answer to “which is cheaper”, and the answer depends on whether you count your own server and your own time.

Which matters more for an AI workflow: hosting or the builder?

Hosting, once the workflow reads private data. An enquiry-triage workflow that only touches a public form and a CRM is fine hosted anywhere. A document assistant that reads contracts, or an invoice extractor that reads supplier details, raises the question of where the data passes through. Self-hosted n8n keeps that on your infrastructure, with the model call as the only external hop. Make routes through Make. Neither is wrong; one of them is a conversation with whoever owns your data policy.

The builder matters more when the maintainer is not a developer. Make’s visual scenario is easier to read six months later for someone who built it without code. n8n’s node graph is readable too, but the moment a workflow needs a code node, it needs someone who can read that code. If that person is on the team, n8n’s flexibility is an asset. If not, it is a future outage.

How to choose between n8n and Make

1. List the systems the first three workflows will touch. Public SaaS only, or internal databases and private documents?
2. Name the maintainer. The person who fixes it when it breaks. Developer or not?
3. Decide the data question. Does anything in the workflow need to stay on your infrastructure?
4. Estimate monthly runs. Enquiries, invoices, tickets per month. Put that number against both pricing pages.
5. Build the same starter workflow in both. The enquiry-triage flow from AI workflows for small business takes an afternoon in either. The one the maintainer can read afterwards wins.
6. Choose for the year. The tool that fits workflow six, not workflow one.

What this comparison is not

It is not a verdict that one tool is better. They are built for different owners.

It is not a pricing answer. Pricing units differ and change; only the current pages against your own run count can answer it.

And it is not the only comparison. Zapier is the third common choice and has its own trade-offs, covered in Zapier vs Make.

Summary

n8n and Make both run the rule-plus-model workflows a lean team needs. n8n fits a developer-maintained build that touches internal systems or private data and may need to run on your own servers. Make fits a non-developer builder working with mainstream SaaS on a hosted service. Compare pricing on your own run count, name the maintainer, decide the data question, and choose for where the workflow lives in a year. The wider library is under AI automation; if you want the first workflow designed and built with you, that is AI prototypes and pilots.