n8n vs Zapier vs Make
The right automation platform is decided by three things: execution volume, whether the logic needs branching and code, and whether your data can leave your infrastructure. Almost every other comparison criterion is noise.
Key Facts
- Focus
- n8n vs Zapier vs Make
- Category
- GTM Stack
- Defined outputs
- 5 deliverables
- Regions served
- India · United States · United Kingdom · UAE · Singapore
- Last reviewed
- 2026-09-10
You Will Choose on Ease of Setup and Pay for It on Volume.
Every team picks the platform that gets the first workflow running fastest, which is the correct decision at three workflows and the wrong one at thirty. Per-task pricing that is trivial at low volume becomes a meaningful line item once enrichment and sync workflows run continuously across a large account base, and by then migration means rebuilding everything under time pressure.
Per-task pricing scales with your success, so the platform gets more expensive precisely as it becomes more important.
Complex branching, loops and custom code are where hosted no-code tools become awkward or impossible.
Data residency requirements rule out cloud platforms entirely for some buyers, and this is usually discovered late.
Deciding on the Three Criteria That Actually Matter
Project Volume Twelve Months Out
We model executions at your expected account volume and workflow count, not today's. Costs that look equivalent at current scale often diverge by an order of magnitude at projected scale, and that projection is the single most useful input to the decision.
Assess Logic Complexity Honestly
Simple triggers and linear steps run fine anywhere. Nested conditionals, loops over collections, custom transformations and API calls needing bespoke authentication are where platforms separate sharply. We catalogue what your workflows genuinely need rather than what they currently do.
Check the Data Residency Constraint First
If you handle regulated data or sell to buyers who impose residency requirements, self-hosting may be the only viable option regardless of every other consideration. This is a binary gate and it is worth checking before any other evaluation, because it decides the question on its own.
Consider the Fourth Option
Sometimes the right answer is none of them: a small service in your own codebase, or logic that belongs in the warehouse rather than in an automation platform. We include that option honestly, and it is the recommendation more often than automation vendors would like.
Deliverables
- A twelve-month execution volume and cost projection across platforms
- A catalogue of your workflows' actual logic complexity requirements
- A data residency and compliance assessment gating the platform options
- A comparison including the build-it-yourself option
- A recommendation with a migration plan if you are changing platforms
Is This You?
Strong fit
- You are choosing an automation platform for revenue workflows.
- Your automation bill is growing faster than your automation value.
- You have data residency requirements and are unsure what they rule out.
Not a fit yet
- You need three simple integrations. Pick whichever is easiest and move on.
- You want us to endorse a decision already made. We report what the modelling shows.
What Will This Cost at Ten Times the Volume?
Model your automation costs at next year's account volume. If the number is uncomfortable, that is the conversation, and it is much cheaper to have now than after thirty workflows exist.
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Common Questions
Which is cheapest?
Self-hosted n8n at high volume, by a wide margin, though that excludes the hosting and maintenance effort which is real. Zapier is cheapest at genuinely low volume once you account for the time not spent operating infrastructure. The crossover point depends on your executions and your engineering capacity.
Is Make a good middle ground?
It is genuinely capable: better than Zapier at complex scenarios, cheaper per operation, and considerably easier to run than self-hosted n8n. For teams that need more than Zapier and have no appetite for infrastructure, it is frequently the pragmatic answer.
Can we mix platforms?
Technically yes and it usually becomes a maintenance problem. Debugging a failure that crosses two automation platforms is genuinely unpleasant. If you do split, split on a clear boundary, for instance internal data workflows on one and customer-facing triggers on another.
When should we write code instead?
When the logic is complex enough that the visual builder is fighting you, when you need real testing and version control, or when the same operation runs often enough that reliability matters more than editability. A well-tested small service beats a sprawling visual workflow at that point.
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