The short answer
For a small business that wants something running this week, Zapier or Make. For an automation that runs thousands of times a day, or that needs a real code step in the middle, n8n. The tool is rarely the reason an automation fails; an unclear rule is.
- Pick Zapier if you want it working today, the flow is simple and the volume is low.
- Pick Make if the flow branches and the data needs reshaping, and someone will look at it again.
- Pick n8n if the volume is high, you need a real code step, or you would rather host it yourself.
How do they compare?
This is the comparison I run through with a client before choosing.
| What matters | n8n | Make | Zapier |
|---|---|---|---|
| Where it runs | Their cloud, or your own server | Their cloud | Their cloud |
| How you pay | By workflow executions on their cloud, nothing extra if you host it | Per operation, including steps inside a scenario | Per task, per run |
| How many apps it connects | Fewer, plus anything with an API | A large library | The largest library |
| Code in the middle | A full code node, no limits | Limited scripting | A code step with limits |
| Branching and reshaping data | Good, and readable once you know it | The best of the three | Simple cases only |
| Who can maintain it | Someone technical | Someone patient | Almost anyone |
| Moving away later | Workflows are files you keep | Scenarios can be exported | Setups live inside Zapier |
Where does each one hurt?
Every tool has the same complaint from the same kind of user. Knowing it in advance saves you the migration.
- Zapier: the bill grows faster than the business, and multi-step logic gets awkward.
- Make: the visual map becomes hard to read once a scenario has forty modules, and operations are easy to spend without noticing.
- n8n: someone has to look after it, especially if you host it yourself, and non-technical colleagues will not edit it happily.
Where does AI fit in?
All three can call a model, and that is where most of my recent work has been. The useful pattern is not a chatbot: it is a narrow job, like sorting an enquiry, summarising a document or drafting a reply for a person to approve.
I keep the model step inside written limits: what it may decide, what it may never send without a human, and a log of what it was given and what it returned. That is what makes an AI step survivable in a real business.
When none of them is the right tool
An automation tool is for moving data between things that already exist. It is the wrong choice when the automation is the product, when the logic is genuinely complex, or when the volume makes a per-run fee silly.
At that point the honest option is a small piece of software with a database, which is also easier to test and cheaper to run. I would rather tell you that on the first call than build something you outgrow in three months.
What to remember
- Start with the rule you want automated, not with the tool.
- Count the runs before you pick, because that is what the bill follows.
- Put AI steps behind written limits and a human approval.
- If the automation is the product, build software instead.
Sources
Primary documentation, for anyone who wants to check the details.