n8n vs Make: Choosing an Automation Platform

Key takeaways

1. Hosting model differs fundamentally: Make is cloud-only; n8n can be self-hosted or cloud, which changes the cost and data-residency conversation.
2. Code flexibility differs: n8n's Code node runs real JavaScript or Python inline; Make's logic is built from its visual modules and a more limited function library.
3. Pricing scales differently: Make bills by "operations" (each module execution); n8n's self-hosted tier bills by workflow executions, which can be materially cheaper at high volume.
4. Neither is categorically better: the right choice depends on team skill, hosting constraints, and how much custom logic the workflow needs.
5. Migration cost is real: moving a mature workflow between platforms means rebuilding logic, not just re-pointing credentials.

Hosting and cost: who owns the infrastructure?

Make is a managed SaaS platform only — there is no self-hosted option. That means zero infrastructure to operate, but also means every execution runs on Make's cloud, billed per operation, and data passes through Make's servers by default.

n8n ships as both a managed cloud product and an open-source, self-hostable application. Self-hosting shifts responsibility: you operate the database, manage updates, and handle uptime, but you also control where data lives and can run workflows without a per-operation cloud bill. For a team already running infrastructure, self-hosted n8n can be materially cheaper at volume. For a team with no infrastructure operations capacity, that responsibility is a real cost, not a saving.

How much custom logic can each platform actually run?

Make's automation logic is assembled from prebuilt modules, routers, filters, and a limited set of built-in functions. Complex conditional logic is possible but gets visually dense fast, and there is no general-purpose scripting environment inside a Make scenario.

n8n includes a Code node that runs arbitrary JavaScript (and, via a separate node, Python) directly inside the workflow — full control over data transformation, custom API signing, recursive logic, or calling an npm package. This matters specifically for AI agent workflows: validating a model's structured output against a JSON schema, computing an idempotency key, or handling a multi-step retrieval pipeline is straightforward in a Code node and awkward to express in a purely visual builder.

// n8n Code node — not expressible as a single Make module
const key = crypto.createHash("sha256")
  .update(`${tenantId}:${operation}:${sourceId}`)
  .digest("hex");
return [{ json: { idempotencyKey: key } }];

Node and integration ecosystem

If the integration you need already exists as a well-maintained Make module, that module is often faster to configure than the equivalent n8n HTTP Request setup. If the integration is obscure, internal, or requires custom auth handling, n8n's Code node and HTTP Request node give more direct control.

When is Make the better call?

Make tends to win for a small team without engineering capacity that needs a working integration quickly, uses well-supported SaaS tools end to end, and does not need self-hosting for compliance or cost reasons. A marketing team connecting a form tool to a CRM and a Slack notification is a reasonable Make use case.

When does n8n's control matter more?

n8n tends to win once a workflow needs custom validation logic, connects to internal or unusual APIs, must run on infrastructure the business controls for compliance or cost reasons, or coordinates AI agents with tool calls, retrieval, and structured-output validation. Production automation carrying financial transactions, PII, or AI-agent decisions benefits from the auditability of code you can read line by line, and from hosting you control.