n8n vs Make vs Zapier: choosing an automation platform

They all do the same core thing

At heart, Zapier, Make, and n8n all do the same job: connect the apps you use and run automated workflows between them, so data moves and tasks happen without a person copying and pasting. A trigger fires, steps run, things get done.

Because the core overlaps, the choice is not really about capability for simple cases, all three handle those. It is about how complex your workflows get, how much volume you run, how much control you need, and what it costs you at scale.

Zapier: fastest to start, easiest to govern

Zapier is the most approachable of the three. The interface is built for non-technical users, the library of pre-built integrations is the largest, and you can have a useful automation running in minutes. For many teams, that is exactly the point.

Zapier fits when:

• Non-technical people own the automations and need to build without help.
• Your workflows are mostly linear: this happens, then that happens.
• Volume is modest, so per-task pricing stays reasonable.
• Breadth of app support matters more than deep customisation.

Make: more power for visual builders

Make (formerly Integromat) keeps a visual, drag-and-connect interface but gives you considerably more power: branching, iteration, data transformation, and a clearer view of complex flows. It is the natural step up when Zapier starts to feel constraining but you still want to build visually rather than in code.

Make fits when:

• Workflows need branching, loops, and real data manipulation.
• You want to see the whole flow as a diagram and reason about it visually.
• Volume is high enough that Make's pricing model works out cheaper than per-task billing.
• You have someone reasonably technical, but not necessarily a developer, owning automations.

n8n: control, extensibility, and the best economics

n8n is the most powerful and the most flexible of the three, and it is the one we most often build serious enterprise automation on. It is open-source, can be self-hosted, supports custom code inside workflows, and is not priced per task, which changes the economics dramatically at volume.

n8n fits when:

• You run high volumes where per-task pricing would be punishing.
• You need to self-host for data residency, security, or cost control.
• Workflows need custom logic, custom code, or integrations that do not exist off the shelf.
• You want to own your automation infrastructure rather than rent it.

The pricing trap nobody warns you about

Here is the pattern that bites teams: per-task pricing is cheap when you have ten automations and brutal when you have a hundred running thousands of times a day. The platform that was the obvious choice at the prototype becomes the most expensive line item once automation actually takes hold.

This is why we tell teams to plan for where they will be in a year. If automation is going to become core to how you operate, and done right it will, the volume curve bends sharply upward. A platform whose cost scales with usage can quietly become a five-figure monthly bill, while a self-hosted n8n instance runs many times the volume for the cost of a server.

A simple way to choose

You do not have to agonise over this. The decision tracks closely to who will own the automations and how much you will run.

• Non-technical owner, simple flows, low volume: Zapier.
• Visual builder, complex flows, growing volume: Make.
• High volume, custom logic, or you need to self-host: n8n.
• Building automation as core infrastructure for the long term: n8n, almost always.

It is rarely all-or-nothing

Plenty of organisations run more than one. A marketing team might keep simple Zapier flows it owns end-to-end, while the core operational automations, the ones that run thousands of times a day and touch sensitive data, live in a self-hosted n8n instance the technical team controls.

The mistake is not using more than one tool; it is defaulting to whatever you started with as your needs outgrow it. Pick deliberately for each job, watch the volume and the bill, and be willing to move the high-volume work to the platform whose economics actually fit it.