Make.com vs n8n vs Zapier: Which Automation Platform Should You Use?

I have built client automations on all three. They are not interchangeable, and the usual comparison articles get this wrong by scoring them on feature counts. What actually matters is which failure modes you can live with and who maintains the thing after it is built.

The short version

• Zapier, when the workflow is genuinely simple, you are already paying for it, and nobody technical will ever touch it.
• Make.com, the default for most businesses. Branching, error handling and iteration at a sane price.
• n8n, when data cannot leave your infrastructure, when volume makes per-task pricing painful, or when you need real code inside the workflow.

Zapier

Zapier's strength is that it is the easiest to start with and has the widest app catalogue. If your need is “when a form is submitted, add a row and send an email,” Zapier does it in four minutes and you never think about it again.

Where it breaks down is complexity. Multi-step logic with conditional branches gets expensive and awkward fast. Its pricing counts every task, including steps in a branch you did not take, so a workflow with filters can burn allowance on runs that do nothing useful.

Error handling is the bigger issue. Zapier will happily keep failing quietly unless you have explicitly set up alerting, and a silent integration failure is worse than no integration, you build a process on the assumption it is running, and it has not been for three weeks.

Use it when: the workflow is under five steps, has no branching, and the cost of an occasional silent failure is low.

Make.com

Make is where I build most client work. The visual scenario builder handles branching, routing, iteration over arrays and error handlers properly, and its pricing counts operations rather than tasks in a way that usually works out cheaper at moderate complexity.

The thing that actually matters: Make lets you attach error handlers to individual modules and choose what happens, retry, ignore, route to a fallback, or break and notify. That is the difference between an automation you can trust and one you have to babysit.

The learning curve is real. Data mapping between modules, especially with nested arrays, takes some time to get comfortable with. Non-technical clients can usually read a Make scenario but should not be editing one unsupervised.

Use it when: the workflow has real logic in it, you need to know when something breaks, and the data can live in a cloud service.

n8n

n8n's advantage is that you can self-host it. For clients with GDPR obligations, data residency requirements, or contractual restrictions on where customer data goes, that is not a nice-to-have, it is the only option that passes review.

It is also the only one of the three with sane economics at high volume. Self-hosted, you pay for a server rather than per execution, which changes the maths completely once you are processing thousands of items a day.

You can drop real JavaScript into a node, which means no workarounds when you need a transformation the visual tools do not cover.

The cost is operational. Self-hosting means you own uptime, updates and backups. If nobody on your side can maintain a container, the cloud version removes that burden but also removes the data residency advantage that made you pick n8n.

Use it when: data residency matters, volume is high, or you need code inside the workflow.

How I actually choose

Four questions, in order:

1. Where can the data legally live? If the answer is “only our infrastructure,” the decision is made: n8n, self-hosted.
2. How bad is a silent failure? If a missed run costs a customer, you need proper error handling, which rules out simple Zapier setups.
3. What is the monthly volume? Past a few thousand operations, self-hosted n8n usually wins on cost.
4. Who maintains it in six months? If it is a non-technical team with no support, bias toward the simplest thing that works.

That last question is the one people skip, and it is the one that decides whether an automation still works a year later.

What none of them fix

Choosing a platform is the easy part. The failure mode I see most often is not a bad tool choice, it is automating a process nobody had properly examined first.

If your lead handoff is chaotic because two teams disagree about who owns a stage, automating it produces a faster, more reliable version of the same confusion. The tool did its job. The process was the problem.

So before any of this: watch the process run, end to end, with the people who actually do it. Ask what they do when it goes wrong, because that exception path is usually where the real work lives and it never appears in the diagram.

That is the whole reason I audit before building. The process I use is written up here.

Where AI fits

All three now have AI nodes, and they are useful for a narrow set of steps: classifying an unstructured email, extracting fields from a document, summarising a thread, deciding routing from context.

They are not useful for steps where a rule would do. If you can write down the condition, write it down, a deterministic filter is faster, cheaper and will not surprise you next Tuesday. Putting a language model in the middle of a workflow that does not need judgement just adds latency, cost and a new failure mode.

The good version: deterministic logic for everything you can specify, an AI step only where genuine judgement is required, and a human review step wherever being wrong is expensive.