n8n vs Make: Which Workflow Platform Wins?

The short answer

Choose n8n if you need self-hosting, custom code, and predictable per-execution pricing. Choose Make if you want a managed visual builder with the largest pre-built connector library. Both are strong at deterministic, repeatable automation. Once a workflow has to reason over messy inputs and produce a governed, repeatable result, a node graph starts to strain, and that calls for a different kind of tool.

What n8n actually is (and who it's for)

n8n is source-available workflow automation built around a node-based canvas. You wire triggers and actions together visually, and when the prebuilt nodes run out, you drop into JavaScript or Python in a code node. That escape hatch into real code is why developers reach for it. It runs as a free, self-hosted Community Edition or as managed n8n Cloud, and it bills by workflow execution rather than by step.

Hosting and pricing model

n8n's Community Edition is free to self-host, which matters for teams with data-residency rules or a hard cost ceiling. Its license is the Sustainable Use License, a fair-code model: the source is available, but it is not OSI open-source and commercial use has limits. On n8n Cloud, the Starter plan begins around EUR 20 per month billed annually for 2,500 executions, Pro around EUR 50 for 10,000, and Business at EUR 667 with SSO and Git-based version control. The unit that matters is the execution. One run of an entire workflow counts as a single execution, no matter how many nodes fire or how much data moves through.

Integrations and custom code

n8n ships with more than 1,000 integrations, plus generic HTTP and GraphQL nodes for anything without a prebuilt connector. Every plan, including the free self-hosted edition, lets you write JavaScript or Python inside a node and build custom nodes. For an engineering team, self-hosting plus real code is the entire appeal.

What Make actually is (and who it's for)

Make, formerly Integromat, is a cloud-first visual automation platform built around Scenarios and Modules. You assemble a scenario by dragging modules onto a canvas and connecting them, with no code for the common cases. It leans hard into the no-code experience and a very large app catalog, and it runs only in Make's cloud.

Visual builder and credit-based pricing

Make's Scenario Builder is the more approachable canvas, which is why ops and marketing teams pick it up quickly. Its pricing changed in a way worth knowing. On August 27, 2025, Make moved its billing unit from operations to credits. Each module action, such as adding a row to Google Sheets or reading a Gmail message, counts as one credit, and AI or complex modules can consume more. The Free plan includes 1,000 credits a month, Core starts at $12 per month for 10,000 credits, Pro at $21, and Teams at $38, with roughly 15% off on annual billing. Comparisons still quoting the old $9 operations pricing are out of date.

Integrations and AI agents

Make advertises more than 3,000 prebuilt app integrations, the larger catalog of the two. It has added Make AI Agents, currently in beta, which are goal-driven and shareable, along with Make Grid for governance across the AI and automation landscape. The agent tooling is simpler than n8n's LangChain-based nodes, which suits a no-code audience but gives less control to a team that wants to wire up retrieval or a self-hosted model.

Head-to-head comparison

Here is the same comparison in one view. Major appears as a separate row because it is a different category, an app layer that agents build on, not a third workflow tool you would swap in.

• n8n
• • Best for: Technical teams that want control and self-hosting
  • Pricing model: Per execution (one workflow run = 1 execution); Cloud from ~EUR20/mo, free self-hosted edition
  • Hosting: Self-hosted (Community Edition) or n8n Cloud
  • Integrations: 1,000+ plus HTTP/GraphQL
  • Custom code: JavaScript and Python in nodes, on every plan
  • AI capabilities: LangChain-based AI Agent nodes, RAG, self-hosted LLMs
  • Governance: Self-managed; SSO and Git on Business and up
• Make
• • Best for: Non-technical ops, marketing, and support teams
  • Pricing model: Per credit (one module run = 1 credit); Free 1,000 credits, Core from $12/mo
  • Hosting: Cloud-only (no self-hosting)
  • Integrations: 3,000+ prebuilt apps
  • Custom code: Limited; functions and HTTP, no full code runtime
  • AI capabilities: Make AI Agents (beta), Make Grid, AI Toolkit
  • Governance: Managed cloud; enterprise controls and Make Grid
• Major (separate category)
• • Best for: Non-deterministic work that needs reasoning plus governed, repeatable execution
  • Pricing model: Front-loaded then flat; reasoning happens once, repeatable work runs as code
  • Hosting: Managed platform with database, storage, and logs
  • Integrations: Connectors plus the apps the agent builds
  • Custom code: The agent writes the app; deterministic code holds the repeatable work
  • AI capabilities: Predictable agents that compile repeatable work into deterministic apps
  • Governance: Scoped credentials, RBAC, and audit at the point of action

Pricing

The real pricing question is the metering unit. n8n counts a full workflow run as one execution, so a 30-node workflow and a 3-node workflow cost the same per run, which makes spend easy to forecast. Make counts each module action as a credit, so cost scales with how much each scenario does, and AI modules cost more. For step-heavy workflows, n8n is usually cheaper and more predictable. For light, occasional automations, Make's free tier and low entry price are hard to beat.

Ease of use

Make wins the first afternoon. Its drag-and-drop Scenario Builder is more visual and more forgiving, and a non-technical user can ship something useful quickly. n8n exposes more of the underlying structure, including JSON, and rewards people comfortable reading it. The thirty-day hands-on reviews tend to agree: Make is faster to start, n8n is more capable once you push past the basics.

Integrations

Make's catalog is larger, more than 3,000 apps against n8n's 1,000-plus, so for long-tail SaaS tools it is more likely to have a ready-made module. n8n narrows the gap with first-class HTTP and GraphQL nodes and custom nodes, so a developer is rarely blocked, just doing a bit more wiring.

AI and agent capabilities

This is the widest gap. n8n's LangChain-based nodes support retrieval, memory, and self-hosted models, which is why teams doing real AI work gravitate there. Make's AI Agents are in beta and aimed at no-code users: goal-driven, shareable, and quick to stand up, with less control over the internals. If you are still deciding what an AI agent should do in your stack, both run the basic patterns; only n8n exposes the lower-level controls.

Security and compliance

Self-hosting is n8n's compliance story: keep data on your own infrastructure, which helps in regulated or data-sovereignty settings. Make is cloud-only, hosted on AWS in the EU and North America, with an enterprise on-prem Agent for data access rather than full self-hosting. Both offer SSO and audit on higher tiers. Either way, governing which workflow can act on which system, and proving it afterward, is something you still assemble yourself, which is the agent observability problem that grows with every new workflow.

When to choose n8n

Choose n8n when a technical team owns automation and wants control. You can self-host for data-residency or regulated contexts, handle complex branching and custom logic without fighting the tool, and keep execution pricing predictable as workflows get longer. For AI agents with retrieval or a self-hosted model, n8n gives you controls Make does not. If you want the wider field, see n8n alternatives, or n8n vs Zapier for the other common head-to-head.

When to choose Make

Choose Make when the people building automations are not engineers. Ops, marketing, and support teams reach value fastest with the visual builder, the 3,000-plus connector catalog covers most SaaS tools out of the box, and a fully managed cloud means nothing to host or patch. If your automations are light and your team values speed over control, Make is the easier yes.

The Major take

Both tools are excellent at deterministic, repeatable steps. The ceiling shows up in three places. A node graph struggles to govern non-deterministic reasoning, the kind of task where the right next step depends on judgment over a messy input. Usage cost climbs quietly as executions or credits scale, especially on AI modules. And credentials sprawl across workflows, so proving who can act on what becomes its own project.

Major takes a different path. Its agents build deterministic apps: the reasoning is front-loaded into code once, and then the repeatable work runs as software that holds its own state and behaves the same way every time. Two things follow. Every app an agent builds is reusable across the organization, so the next team starts from working software instead of a blank canvas. And RBAC and audit live at the point of action through a credential proxy, rather than bolted on later. For simple deterministic automation, n8n or Make is the right tool. For work that reasons over ambiguous inputs and has to stay governed and repeatable, the app layer is the stronger bet. Reason once. Run forever.

If your automation has outgrown a node graph, the kind that needs reasoning plus an audit trail, you can build it as a governed app on Major instead of stitching it across scenarios. The agent does the reasoning once and leaves behind software the whole org can run. Build your first governed workflow app on Major and see where the app layer fits next to your existing tools.

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