n8n vs Zapier vs Make: which one should you use?
A side-by-side comparison of pricing models, logic limits, AI agent support, self-hosting, and migration cost, from an agency that builds on all three.
The honest answer is that all three work. Teams rarely fail because they picked the wrong platform; they fail because they picked one for a reason that had nothing to do with their actual workload, then discovered the mismatch six months and several thousand runs later.
So the useful question is not which tool is best. It is which constraint binds first for you: the monthly bill, the complexity of your logic, where your customer data is allowed to live, or who on your team can maintain the thing after launch. Each platform breaks on a different one of those.
Side by side
The comparison in one table
Feature lists rarely decide anything. These are the nine dimensions that actually change the outcome of a build.
| Zapier | Make | n8n | |
|---|---|---|---|
| Who it suits | Non-technical operators who want something live today | Ops people comfortable with a visual canvas and branching logic | Technical teams and agencies who want code-level control |
| Pricing model | Per task, counted for every step in every run | Per operation, cheaper per action than Zapier | Per workflow execution, or free when self-hosted |
| Cost at high volume | Rises fastest; multi-step workflows multiply the bill | Moderate; predictable but still volume-linked | Lowest; steps inside a run are effectively free |
| App integrations | Largest library by a wide margin | Broad, with strong depth on the apps it covers | Smaller library, but any HTTP API plus custom code closes the gap |
| Complex logic | Linear by default; branching feels bolted on | Excellent: routers, iterators, aggregators | Excellent, plus arbitrary JavaScript or Python in-line |
| AI and agents | AI steps and simple agent features | AI modules inside standard scenarios | Native AI agent, tool, and vector-store nodes |
| Self-hosting and data residency | Cloud only | Cloud only | Self-hostable on your own infrastructure |
| Error handling and debugging | Basic replays and task history | Detailed run inspector per module | Full execution logs, retries, and error workflows |
| Learning curve | Lowest | Moderate | Steepest, and the most rewarding once past it |
Verdict
Which platform fits your situation
Match the platform to the constraint that binds first, not to the longest feature list.
Choose Zapier when
- Nobody on the team writes code and nobody wants to.
- You need a niche SaaS connector that only Zapier supports.
- Volume is low and the workflows are three or four steps long.
- Speed to first working automation matters more than long-run cost.
Choose Make when
- Your processes branch, loop, or aggregate data before acting.
- You want a visual map of the logic that a non-developer can still read.
- Zapier's per-task pricing has started to sting on multi-step runs.
- You need solid data transformation without writing much code.
Choose n8n when
- Volume is high, or workflows are long enough that per-task billing hurts.
- Customer data must stay on infrastructure you control.
- You are building AI agents with tools, memory, and multi-step reasoning.
- You have, or can hire, someone technical to own the builds.
Pricing
How the billing models actually behave
This is where most teams get surprised, usually in month four.
- Zapier counts every step. A ten-step workflow run once is ten tasks, not one, which is why quotes based on 'number of automations' always miss.
- Make counts operations, which behaves similarly but at a lower unit price, so multi-step scenarios land noticeably cheaper.
- n8n counts the execution. Adding a fifteenth step to a workflow changes your bill by nothing at all.
- Self-hosted n8n moves the cost from a subscription to a server plus someone to maintain it. Below roughly a few thousand runs a month, cloud is usually still cheaper once you price that time honestly.
- The real cost of any platform is the rebuild you do when it cannot express your logic. Test the hardest workflow first, not the easiest.
A practical rule: estimate your monthly runs, multiply by the average number of steps, and price all three against that number before you commit. If the answer is under a few hundred runs a month, pick for ease of maintenance. Above that, pick for the billing model.
Migration
Moving between platforms without breaking operations
Migration is the part every comparison article skips, and it is the part that decides whether switching was worth it. There is no reliable one-click export between these platforms: a move is a rebuild, and rebuilds surface undocumented behaviour nobody remembered was there.
Budget for it honestly. A team with twenty live automations should expect a few weeks of overlap, not a weekend. Done in this order, nothing goes dark while you do it.
- 1
Inventory before you touch anything
List every live automation, what triggers it, what it writes to, and who complains when it breaks. Most teams find a third are dormant and can simply be retired.
- 2
Rebuild the expensive ones first
Sort by task consumption, not by importance. The handful of high-volume workflows usually account for the majority of the bill and deliver the whole saving on their own.
- 3
Run both in parallel
Keep the original live and write the new build to a staging destination for a week. Compare outputs row by row before cutting over.
- 4
Add error workflows on day one
A migrated automation that fails silently is worse than the manual process it replaced. Route failures to a channel a human actually watches.
- 5
Document and hand over
Every workflow needs a one-paragraph description, an owner, and a note on what to check when it breaks. This is what separates a system from a liability.
Our take
What we deploy, and why
For most client builds we default to n8n. Not out of loyalty, but because the workloads we are asked to automate are long, run often, and increasingly involve AI steps that need tools and memory. Under per-task billing those three characteristics compound into a bill that grows faster than the value it delivers.
We reach for Make when the client's team will own the workflows themselves and the logic is genuinely branchy, because its canvas is the easiest complex thing for a non-developer to read six months later. We reach for Zapier when a single niche connector exists nowhere else, or when a founder needs one automation working this afternoon.
None of that is a recommendation for your business until someone has looked at your run volume, your stack, and who will maintain the builds. That is a one-conversation exercise, and it is worth having before a platform gets chosen for you by whichever free trial you signed up for first.
FAQ
Common questions
Not sure which platform fits your stack?
Book a free automation audit. We look at your run volume, your tools, and who will maintain the builds, then tell you which platform to standardise on, even if the answer is the one you already have.
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