Closer than most comparisons admit. Both are node-based, both handle branching well, both are far cheaper than Zapier at volume. The differences that matter are code, hosting and how each one bills a run.
Pick Make if your team is non-technical, your scenarios are mostly moving data between mainstream SaaS apps, and you want the most readable visual canvas on the market. Make's data mapper is genuinely the nicest to work in.
Pick n8n if you want to write code inside a step, keep workflows in version control, self-host for cost or compliance reasons, or build error-handling that a client can rely on unattended.
Unlike n8n vs Zapier, this is not a gap in capability. It is a difference in philosophy: Make is a product for operators, n8n is a product for builders.
Skipping the features they both have. These are the deciding factors.
| n8n | Make | |
|---|---|---|
| Billing unit | Workflow execution — node count does not matter | Operation — roughly one per module run |
| Cost of a 15-module flow | One execution, or zero self-hosted | About 15 operations, every single run |
| Self-hosting | Yes, in your own infrastructure | No, cloud only |
| Custom code in a step | JavaScript and Python in a Code node | Limited to functions and a constrained custom-app path |
| Version control in Git | Workflow JSON exports cleanly | Blueprint export exists, but not a Git-native workflow |
| Visual readability of the canvas | Clear, but dense on large flows | Best in class — bubble routing is easy to follow |
| Data mapping experience | Expression-based, powerful, less hand-holding | Excellent visual mapper, great for non-developers |
| Pre-built app modules | Several hundred, plus HTTP for anything else | Larger and more polished catalogue |
| Error workflows and retries | Dedicated error workflow per workflow, per-node retries | Error handlers per route, less centralised |
| AI and LLM node maturity | Strong — agent, chain and vector-store nodes built in | Improving, more module-shaped than agent-shaped |
| Best fit | Engineering-owned automation, volume, compliance | Operations-owned automation, mainstream SaaS, clarity |
We build in n8n by default, because we hand clients systems they own outright: self-hosted, no per-operation meter, workflow JSON in a repository they keep. That matters more over three years than any single feature.
We recommend Make when the client's own operations team will be editing the automation weekly without us. In that scenario Make's canvas readability is worth more than n8n's ceiling, and pushing a non-technical team into expressions and JSON items is how automations quietly rot.
Check current tiers on n8n pricing and Make pricing before deciding — both change, but per-execution versus per-operation billing does not.
This trips people up more than any feature comparison. On Make, a scenario with a router, three branches, four HTTP calls and two CRM writes can consume ten or more operations on a single run. Multiply by lead volume and the bill scales with the complexity of your logic, not just the amount of work you do.
n8n bills the run, not the shape of the run. Adding a node to improve a workflow costs nothing extra, so you are never quietly penalised for building it properly. Self-hosted, the marginal cost of an execution is server capacity you already pay for.
The practical consequence: on Make, engineers optimise flows to use fewer modules, which usually means less validation, fewer safety checks and worse error handling. That is a bad incentive to design under.
Handing over a Make scenario to a marketing manager works. They can open it, follow the bubbles, see the sample data in each module and change a filter without breaking anything. We have watched clients do exactly that and keep a scenario healthy for a year with no developer involvement.
The equivalent handover in n8n needs documentation and at least one training session, because the first time someone hits an expression like {{ $json.body.email }} they stop. If the client's plan is self-maintenance, that friction is a real cost and we price it in.
There is no clean automated path in either direction, so treat it as a rebuild rather than a conversion. The good news is the logic transfers almost one to one: routers become IF and Switch nodes, iterators become Split In Batches, modules become nodes. In practice we rebuild the top three or four scenarios by operation consumption, run both in parallel for a week, then cut over. Everything below that usually turns out not to be worth moving.
n8n, once the workflow needs custom code, real error workflows or version control. Make's visual scenario builder is excellent at mapping data between many apps and is arguably nicer to read, but n8n lets you drop into JavaScript or Python at any node and store the whole workflow as JSON in Git.
Make charges operations, where roughly every module run in a scenario is one operation, so a ten-module scenario burns ten operations per run. n8n cloud charges per workflow execution regardless of node count, and self-hosted n8n has no per-run meter at all. Make is usually cheaper than Zapier and more expensive than n8n at volume.
No. Make is cloud-only. Self-hosting is the structural difference between the two: if you need data to stay inside your own infrastructure, n8n is the only one of the pair that can do it.
Make has a larger and generally more polished set of pre-built app modules, particularly for ecommerce and Google Workspace. n8n covers the major platforms and handles the rest with a generic HTTP node, which is more work but has no ceiling.
Slightly, for visual thinkers. Make's scenario canvas with its bubble routing is very readable and its data mapping panel is friendlier. n8n asks you to think in JSON items and expressions earlier, which pays off later but is a steeper first week.
Move if your operations bill is climbing faster than your revenue, if you need code in the middle of a flow, or if a client requires data residency. Stay on Make if your scenarios are stable, moderate volume and maintained by someone non-technical.
Free call, no pitch deck. We will model your operation count and execution volume and give you the honest recommendation.