NoCode Agents
n8n vs Make is the most common comparison for teams choosing a visual, canvas-based AI agent builder. Both let you build agents that reason, use tools, and trigger real workflows — but they differ sharply on pricing model, self-hosting, and how they show you what an agent is doing. This page breaks down every difference so you can pick the right one for your team.
Side-by-Side Comparison
| n8n | Make | |
|---|---|---|
| Pricing model | Per workflow execution (one full run counts once, regardless of steps) | Credits (most actions use 1 credit; some AI features use more) |
| Entry price | €20/mo (Starter, billed annually) | Free plan (1,000 credits/mo) |
| Free option | Self-hosted Community Edition | Free plan, 1,000 credits/month |
| Self-hosting | Yes (Community Edition on GitHub) | No |
| Agent transparency | Replay any run to see where it broke; set human approval for tool calls | Reasoning panel shows every agent decision step by step on the canvas |
| Integrations | 600+ native integrations plus your own workflows as tools | 3,500+ pre-built apps |
| Best for | Technical operators, agencies, EU-based teams, high-volume multi-step workflows | Visual thinkers, SMBs already on Make, teams mixing fixed logic with judgment steps |
How Much Does n8n Cost?
n8n’s cloud plans start at €20 per month billed annually for the Starter plan, according to n8n’s pricing page. That gets you 1 shared project, 5 concurrent executions, agents, MCP and AI nodes, and 1,600 Assistant credits per month.
The Pro plan is €50 per month billed annually and adds 3 shared projects, up to 50 concurrent executions, admin roles, workflow history, and up to 9,600 Assistant credits per month. Business is €667 per month billed annually and is self-hosted with SSO/LDAP, multiple environments, queue mode, and Git version control. Enterprise is contact-sales pricing.
All plans include unlimited users, unlimited workflows, and every integration. Pricing is based on monthly workflow executions — one run of a whole workflow counts once, no matter how many steps it contains. That makes multi-step agent workflows cheaper to predict than on per-step or per-credit tools.
A free self-hosted Community Edition is available on GitHub for teams that want to run n8n on their own infrastructure at no cost.
How Much Does Make Cost?
Make offers a free plan with 1,000 credits per month and 1 data store, according to Make’s pricing page. Paid plans — Core, Pro, and Teams — are credit-based, and extra credits can be bought in bundles of 1,000 or 10,000. Enterprise pricing is contact-sales.
Credits became Make’s billing unit on August 27. Most actions use 1 credit, though some AI features consume more. Router and error-handler modules do not consume credits, which helps keep costs down on complex scenarios.
If you run out of credits, your scenarios pause. Incoming webhooks are queued up to your plan’s webhook-queue limit, so you do not lose data — but your agents stop running until you add credits or the month resets.
Which Has Better AI Agent Features?
Both platforms treat AI agents as first-class building blocks inside a visual canvas, but they approach transparency differently.
n8n lets you drop an AI Agent node into any workflow, give it a model, memory, and tools, and mix it with deterministic steps. According to n8n’s AI agents page, agents get access to 600+ native integrations plus your own workflows as tools. You can replay any run to see exactly where it broke, and you can set which actions require human approval before they execute. n8n also lists 7,500+ community-built templates to start from.
Make builds agents in the same canvas as ordinary scenarios. Its standout feature is the Reasoning panel, which shows every decision the agent makes, step by step, directly on the canvas. According to Make’s AI agents page, agents connect to 3,500+ pre-built apps. Make’s own guidance is to use automation when a workflow just needs doing, and an AI agent when it needs judgment — for example, with unstructured text, messages, or documents. You can add manual approvals or stop the agent at specific points, and agents are reusable across workflows.
For pure auditability, n8n’s replay-and-approve model gives you more control. For at-a-glance understanding of what an agent is thinking, Make’s Reasoning panel is the clearest visualisation we list.
Which Is Better for Self-Hosting and Data Control?
n8n wins here, and it is not close. n8n offers a free Community Edition on GitHub that you can self-host with full access to agents, MCP, and AI nodes. Its Business plan (€667/mo billed annually) is also self-hosted and adds SSO/LDAP, multiple environments, queue mode, and Git version control.
For cloud users, n8n stores hosted data in the EU on servers in Frankfurt, Germany. That makes it a natural fit for EU-based companies with data residency requirements.
Make is cloud-only. There is no self-hosted option. If your organisation requires on-premise deployment or wants to audit the infrastructure your agents run on, Make cannot meet that requirement.
Which Is Better for Agencies?
n8n is the stronger pick for agencies. All plans include unlimited users, so you are not paying per seat as your team grows. The Pro plan adds admin roles and workflow history, which matter when multiple people touch client workflows. Execution-based pricing means a 10-step agent workflow costs the same as a 2-step one per run, making client billing more predictable.
Companies with fewer than 20 employees can apply for n8n’s Start-up Plan with 50% off Business, which is relevant for smaller agencies that need SSO and Git version control.
Make works for agencies already invested in its ecosystem, but credit-based pricing is harder to forecast across multiple clients with varying volumes. There is no self-hosting, so you cannot offer clients an on-premise option.
An illustrative scenario: a 5-person agency reselling a support bot to 10 clients would likely find n8n’s execution pricing and unlimited users easier to manage than Make’s credit metering across 10 separate scenario sets.
Which Is Better for Beginners?
Make has the edge for beginners. Its FAQ says no coding experience is needed, and the free plan with 1,000 credits per month lets you experiment without spending anything. The Reasoning panel makes it easy to understand what your agent is doing and why, which is valuable when you are still learning how agents behave.
n8n’s canvas is visual but assumes more technical comfort. Its own positioning targets technical operators. The Community Edition requires you to set up and maintain your own server, which is a barrier for non-technical users.
That said, neither tool is a chat-only “describe it and done” assistant. If you want an agent that works in Slack and email with no canvas at all, Lindy is worth a look. For more beginner-friendly options, see our guide to the best no-code AI agent builders.
When Should You Choose n8n Over Make?
Choose n8n when any of these apply:
- You need to self-host for compliance, cost, or control.
- You run high-volume, multi-step workflows and want execution-based pricing that stays flat per run.
- You need human approval gates on specific agent actions.
- You are an EU-based company that cares about data residency.
- You want unlimited users without per-seat pricing.
When Should You Choose Make Over n8n?
Choose Make when any of these apply:
- You want a free plan to start building without a credit card.
- You value the Reasoning panel for understanding agent decisions visually.
- You are already running Make scenarios and want to add agent judgment to existing workflows.
- You need 3,500+ pre-built app connectors.
- You prefer a fully managed cloud service with no infrastructure to maintain.
Bottom Line
n8n wins for technical teams, agencies, and anyone who needs self-hosting, execution-based pricing, or EU data residency. Make wins for visual thinkers and SMBs who want a free entry point, a Reasoning panel for agent transparency, and a fully managed cloud experience. Both are strong canvas-based agent builders — the right choice depends on whether control or simplicity matters more to you.