Make vs. n8n: Which automation platform fits your team?
Make suits teams that want to build app workflows visually in a managed cloud. n8n suits teams that prioritize technical extensibility or running the platform themselves. Both support AI workflows. The key cost question is whether you pay for individual actions and AI usage or complete workflow executions.
Sources checked on 13.10.2026. Provider information and our editorial assessment; no independent performance test. Check plans, model usage and permissions in your specific account.
Facts checked: 2026-10-13 · Translated: 2026-10-18
MakeView profile →
n8nView profile →Key differences at a glance
Swipe horizontally to see all tools in the comparison table.
| Comparison | Make | n8n |
|---|---|---|
| SME score | 90/100 | 87/100 |
| Pricing model | Credits per action; AI features may consume credits dynamically based on tokens and other factors. Connecting your own model adds provider fees.Make: Credits and AI usage ↗ | Cloud plans charge by workflow execution: a complete run counts as one execution, regardless of the number of steps. Check model usage and Assistant credits separately.n8n: Executions and credits ↗ |
| Hosting | Managed cloud platform; check data flows and contract terms for each connected app.Make: Plans ↗ | Cloud or self-hosting. Running it yourself requires servers, updates, security and backups, so it still involves costs and work.n8n: Self-hosting ↗ |
| AI and control | Make AI Agents extend scenarios. Give them a clearly limited task and check model access, tools and usage.Make AI Agents ↗ | AI processes can be combined with workflows, custom connections and evaluation features. Assistant helps with setup; access and preview status depend on the plan.n8n: AI features ↗ |
| A useful pilot | Build the same real workflow with test data: retrieve data, check it, create a draft and require approval before external changes. Measure running costs and maintenance time. | Build the same real workflow with test data: retrieve data, check it, create a draft and require approval before external changes. Measure running costs and maintenance time. |
Choose by your situation
Business teams want a visual start
Make is a good fit when teams want to build app connections and multi-step workflows in a visual interface.
Open Make profile →Technical control or self-hosting matters
n8n is stronger when developer involvement, flexible logic and control over hosting matter more than the easiest start.
Open n8n profile →Your first automation project
Start with a clearly measurable workflow and check error handling, maintenance and costs before a wider rollout.
View scoring methodology →Other options to consider
94/100Zapier
Zapier now bundles its agent features as an AI-by-Zapier step directly in the Zap editor. This lets you combine them with triggers, filters, paths and approvals; usage is billed in tasks.
86/100Happycapy
Happycapy is an agent computer in your browser. You describe a task and agents complete it in an isolated environment, from research and slides to a finished email. No setup or programming knowledge is required.
85/100Gumloop
Gumloop: Visual AI flows for document, data, and team automations.
Questions before choosing
Is Make easier than n8n?
In our existing assessment, Make focuses more on getting started visually. n8n requires more technical understanding for complex requirements.
Does self-hosting automatically make n8n compliant with data protection rules?
No. Self-hosting can increase control, but you still need to check data flows, configuration, contracts and the specific use case.
Was this comparison tested separately?
No. This page summarizes the existing tool profiles as an editorial guide to help you decide.