n8n vs Zapier vs Make
All three connect systems without writing code and all three hit a ceiling. Where that ceiling sits, and what happens when you touch it, is the whole difference.
Key takeaways
Zapier has the largest connector catalogue and the highest price per operation. It wins when volume is low and your team time is worth more than the licence.
Make charges per operation on a cheaper model and allows visual branching. It is the middle ground for processes with real logic.
n8n can be self-hosted, so cost stops growing with volume and data never leaves your infrastructure. In exchange, somebody has to operate it.
All three break at the same place: error handling and retries. A flow that runs cleanly a thousand times and fails silently on the thousand and first is worse than no flow.
The three, on what decides
| Dimension | Zapier | Make | n8n |
|---|---|---|---|
| Pricing model | Per task, the most expensive | Per operation, cheaper | Per cloud execution, free self-hosted |
| Connectors | Largest catalogue | Broad | Broad, plus a generic HTTP node |
| Logic and branching | Limited | Good, visual | Full, with code inside the node |
| Self-hosting | No | No | Yes |
| Where your data lives | Their cloud | Their cloud | Wherever you decide |
| Learning curve | Gentle | Moderate | Moderate to steep |
| Typical ceiling | Cost as volume grows | Scenario complexity | Running the infrastructure |
Price per task decides, not the licence tier
All three look cheap while a flow runs a hundred times a month. The arithmetic changes at a hundred thousand. Zapier bills per task executed and every step of a flow counts as a task, so an eight-step scenario burns through a plan eight times faster than most buyers expect.
Make charges per operation at a lower rate, which makes it the natural middle ground. Self-hosted n8n breaks the link between volume and invoice: you pay for a server and the execution count stops mattering. In exchange, somebody has to patch, back up and watch that server.
The honest calculation includes operations time. A team with nobody who can maintain a container will spend more on n8n incidents than it saves on Zapier licences.
What changes when a step calls a model
All three can call a language model from a node, and all three do it about equally well. What none of them solves is what comes next: what happens when the model returns something that does not match the expected format, how much each run is costing, and how you verify that the answers are still correct.
A flow that classifies email with a model and writes the result to a CRM will eventually produce an invented category. Without output validation, that error lands in the CRM and spreads. Structural validation of the response (schema, required fields, allowed values) is the piece that separates an AI flow from an AI experiment.
The other blind spot is cost. None of the three shows how much you have spent with the model provider this month, and that number grows with volume far faster than the platform bill.
Four questions that settle it
- How many runs per month?Below a thousand, pick for comfort. Above ten thousand, the pricing model starts deciding on its own.
- Can the data leave your infrastructure?With sensitive data or a regulator involved, self-hosted n8n stops being a preference and becomes a requirement.
- Is there anyone to run a server?Self-hosting without someone to maintain it trades a predictable invoice for an unpredictable outage.
- What happens when a step fails?If the answer is "somebody notices", there is no error handling. You need an alert, a retry with backoff, and a queue of what went unprocessed.
When a flow passes fifteen steps, when the decision logic no longer fits in a condition node, or when the same scenario has been duplicated five times with tiny variations. At that point the visual editor stopped saving work and started hiding it.
The clearest signal is that nobody on the team will touch the scenario because nobody understands what it does. A versioned code file can be read, reviewed and tested; a canvas of sixty nodes cannot.
Frequently asked questions
Which is cheapest between n8n, Zapier and Make?
At low volume the difference is irrelevant and the right pick is whichever your team understands fastest, usually Zapier. At medium volume Make is clearly cheaper per operation. At high volume, self-hosted n8n is the only one that does not scale with usage, because you pay for a server rather than executions. The full calculation includes the time of whoever maintains that server.
Is n8n production-ready or only for prototypes?
It runs in production at real scale. The condition is treating it like any other service you own: backups, monitoring, version control for the flows, and a test environment separate from production. Without that, any platform is a prototype with users on top of it.
Can I automate with AI without any of the three?
Yes, and sometimes you should. If the process is a single flow, runs on a server you already have, and needs strict validation of what the model returns, a script with retries and alerts is easier to maintain than a visual scenario. Platforms win when there are many different processes and non-technical people who need to see what is happening.
What breaks first in an automated flow?
Expiring credentials and changes in the connected APIs. Almost no flow fails on its own logic: it fails because the system on the other side renamed a field, stopped accepting a format, or revoked a token. That is why monthly maintenance for an automation is reviewing announced deprecations and rotating credentials, not rewriting logic.