n8n charges per full execution and can be self-hosted; Make charges per module action with a friendlier builder. Costs for short and loop-heavy workflows.
Both are visual automation builders that teams move to when they outgrow Zapier, and both now pitch AI agents. The differences come down to two questions. How do you want to pay: n8n counts each full run of a workflow as one execution, while Make charges a credit for every module action. And who will build and maintain the automations: n8n is designed for people comfortable with data structures and code, Make for a wider audience.
The meter decides which is cheaper, and the answer flips with the shape of the workflow.
| Workflow | n8n Cloud | Make |
|---|---|---|
| 10 steps, 1,000 runs a month | 1,000 executions: Starter, €20 a month yearly | About 10,000 credits: Core, $9 a month yearly |
| 10 steps, 4,000 runs a month | 4,000 executions: Pro, €50 a month yearly | About 40,000 credits: Core, $29 a month yearly |
| Loops over 50 records with 3 modules each, 1,000 runs a month | 1,000 executions: Starter, €20 a month yearly | About 150,000 credits: Core, $99 a month yearly |
| Any of the above, self-hosted | Server cost only | Not available |
n8n prices are in euros as shown on its pricing page; Make prices in US dollars. Both checked October 1, 2026.
For short and medium workflows, Make's Cloud is cheaper. Once a workflow loops over lists, calls many modules per record or runs long chains of AI steps, Make's credits multiply while n8n's execution count stays flat. Self-hosting changes the picture entirely: n8n's Community Edition costs nothing beyond the server, which is why high-volume technical teams often end up there.
Make is a cloud service only. n8n can run on your own servers with Docker or Kubernetes, reach internal databases and files, and keep data inside your network, which matters for regulated industries and companies with strict data policies. The free Community Edition is licensed under n8n's Sustainable Use License, which allows internal business use; reselling n8n as a service needs a commercial agreement. Paid self-hosted plans add SSO, multiple environments and Git-based version control.
n8n's editor is powerful and assumes you are comfortable with JSON, expressions and the occasional JavaScript or Python step. Debugging means reading data structures, and self-hosting means maintaining a server. That suits developers and technical operations teams, and it is why n8n handles unusual logic without workarounds.
Make's canvas is easier to read for non-developers. Routers branch, filters stop records, iterators loop and aggregators combine, all visible as bundles moving through the diagram, and its 3,000-plus app connectors cover most SaaS tools without touching an API. Building a first complex scenario still takes practice, but marketing and operations teams usually get there without engineering help.
The two behave very differently at the end of the allowance. On n8n Cloud, executions over the monthly limit fail immediately with an error and are not retried later, while workflows stay active, so every trigger after the cap produces a failed run until the monthly reset or an upgrade. On Make, scenarios stop, but incoming webhooks are queued up to your storage limit and polling scenarios search from their last successful run when credits return, and paid plans can buy 10,000 credits automatically before running out.
n8n has the deeper toolkit for builders: an AI Agent node with tools and memory, an MCP Server Trigger and MCP Client node, human approval before tool calls, evaluations and tracing in LangSmith or OpenTelemetry for self-hosted setups. Make offers AI Agents in beta, an AI Toolkit, its own AI provider on every plan, an MCP server and Maia, which builds scenarios from a conversation. For agents in production with code and observability, n8n is ahead; for agents a no-code team can maintain, Make is easier.
| If you | Choose | Why |
|---|---|---|
| Have no developer on the team | Make | Easier visual builder, 3,000+ connectors |
| Run short workflows on a small budget | Make | Core at $9 for 10,000 credits |
| Loop over lists or run long chains | n8n | One execution per run, however many steps |
| Need data to stay on your servers | n8n | Free self-hosted Community Edition |
| Build AI agents with code and tracing | n8n | Mature agent and MCP tooling |
| Want automations a non-developer can maintain | Make | Readable scenarios, AI Agents in beta |
This one is a tie because the right answer follows from your team and your workflows. Make is the better choice for non-technical teams and simpler automations at low cost; n8n is the better choice for technical teams, complex or high-volume workflows and anyone who wants to self-host. Teams still on Zapier should read Zapier vs Make and n8n vs Zapier.
Make for non-technical teams and short-to-medium workflows: its visual builder is easier and its Cloud prices are lower for simple automations. n8n for technical teams, loop-heavy or long workflows and AI agents: it charges per full execution, supports code, and can be self-hosted for free. Many teams choose by who will build and maintain the automations.
It depends on the workflow. A 10-step workflow run 1,000 times a month costs $9 a month on Make Core billed yearly and €20 on n8n Starter. A workflow that loops over 50 records with three modules each uses about 150,000 Make credits a month, $99 on Core, while n8n still counts 1,000 executions. Self-hosted n8n costs only the server.
No. Make runs only as a hosted cloud service. n8n's Community Edition is free to self-host, and its Business and Enterprise plans add features such as SSO, environments and Git for self-hosted deployments, which matters for teams whose data cannot leave their infrastructure.
On n8n Cloud, executions over the monthly limit fail immediately and are not retried, though workflows stay active. On Make, scenarios stop until you add credits, but incoming webhooks are queued and polling scenarios catch up from their last successful run. Both let you upgrade or add more capacity.
n8n is the more mature choice for technical teams: its AI Agent node, MCP server and client, human approval for tool calls, evaluations and tracing are built in. Make AI Agents are in beta, with Make's own AI provider on every plan and an MCP server, and suit teams that want agents inside a no-code builder.
Usually, yes. n8n assumes comfort with JSON, expressions and occasionally JavaScript or Python, and self-hosting adds server work. Make's canvas with routers, iterators and aggregators has its own learning curve, but non-developers typically get productive faster.