n8n vs Make: an honest comparison (2026)

Make bills per operation. An operation is one module doing one thing to one bundle of data - so a scenario that fetches 100 records and updates each one consumes roughly 100 operations, not one. Iterating over a list is where Make budgets actually go.

n8n bills per execution: one workflow run, one execution, regardless of how many items pass through it. A workflow that processes 100 records in a batch is one execution.

For record-by-record work - syncing a table, enriching a list, processing a feed - this difference is large and consistently favours n8n. For scenarios that handle one item per run, the two models are much closer and the price comparison depends on the plan tier rather than the architecture.

The visual editor is better. Make's canvas, with its circular modules and inline data preview, is more legible than n8n's for medium-sized scenarios, and its error-path visualisation is genuinely good. People who find n8n's canvas cluttered are not wrong.

Data mapping is friendlier. Make's mapping panel shows you the available fields from every upstream module and lets you drag them in. n8n's expression editor is more powerful and less approachable, and beginners spend real time confused about item structure.

Built-in scheduling and error handling are more polished out of the box, and the entry-level paid tier is cheap for what it includes.

It is fully hosted with no operational burden - the same argument that favours Zapier applies here.

Self-hosting. Make has no self-hosted option at all. If data residency, air-gapped operation or unlimited volume on your own hardware matters, this decides it.

Code when you need it. n8n's Code node runs JavaScript or Python over the whole item set. Make has functions and a limited custom-code capability on higher tiers, but n8n treats writing code as normal rather than as an escape hatch.

Per-execution billing on high-item workflows, as above.

AI and agent tooling. n8n has invested heavily in AI Agent nodes, tool calling and vector stores; Make has AI modules but the agent-building experience is less developed.

Version control and portability. An n8n workflow is a JSON file you can diff, commit and move between instances. Make scenarios are exportable as blueprints, but the workflow-as-a-file model is more natural in n8n and is why a template ecosystem exists there at all.

Choose Make if the priority is a pleasant visual builder for a team that is not going to write code, the workflows are mostly single-item, and hosting it yourself is not on the table.

Choose n8n if you process lists and batches, want to self-host, expect to write some code, or are building AI agents.

If you are undecided and technical, the deciding factor is usually the iteration question: sketch your busiest scenario and count how many modules would run per record. If the answer is 'a lot, times a lot of records', n8n's model will cost less and the difference will grow.