Workflow & Productivity · Reviewed decision
dify vs n8n
Choose the platform that makes your actual workflow simpler to operate, including failures and model charges.
Start with Dify for an AI application whose workflow and knowledge are central. Start with n8n for an integration-led automation containing AI steps. Prototype the entire path before treating either as a replacement for the other.
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
Cost notes and limits →Visit n8n →Decision guide
Start with the application or the integration job
Dify's documented starting point is an AI application, with workflow and knowledge features and published access. n8n's is an automation workflow connecting steps and services, including AI. Both can cover parts of the same job; count the integration and operations work you would still own.
Dify
Consider it for a knowledge-backed AI application that you will publish and maintain as an application.
Limit: Cloud limits, model charges and self-hosting are separate costs. The repository license adds conditions concerning multi-tenant service and frontend branding; read the terms for your use.
Product documentation ↗n8n
Consider it when triggers, service integrations, data mapping and recovery organize the workflow, with AI as one or more steps.
Limit: Cloud plans and self-hosted operations differ. The Sustainable Use License restricts use, and enterprise files have separate terms; source availability does not make every use unrestricted.
Product documentation ↗How to decide: Use the supplied fictional support events with stubbed destinations. Check classification, duplicate handling, missing data, provider failure and recovery. Record hosting, workflow usage, model charges and manual maintenance. Do not connect the fixture to a real mailbox or CRM.
Reproducible evaluation
Fictional JSON support events and expected stub outputs test workflow correctness. They do not establish connector coverage, uptime or production security.
Vendor results: not run.
- Route the fictional support events to a stub queue and retain the original event ID. Expected: Known requests reach the expected queue; the missing-field event is held for review. Result: not run.
- Replay the same event twice. Expected: One intended downstream action, with duplicate handling visible in the run record. Result: not run.
- Make the stub model step fail once, then retry. Expected: A visible failure and recoverable retry without silently dropping or duplicating the action. Result: not run.
Download the original synthetic fixturesBlank run record · CSV
Record the tool/version, configuration, date, exact output and applicable billing evidence for each actual run. Use fictional data; remove account names, tokens and other private details from evidence. A small fixture batch cannot establish a general speed, cost or reliability winner.
Catalogue facts and editorial fit notes are different kinds of evidence. A repository push is not a release, a primary code language is not a list of supported user languages, and stars are not a quality score. How we review.
Side by side
| Spec | dify | n8n |
|---|---|---|
| Type | Platform | Platform |
| Licensing record | Source available | Source available |
| Pricing | Community hosting/model costs; Cloud plans | Self-host costs; Cloud execution allowances |
| GitHub stars | 159,588 | 208,629 |
| Repository language | TypeScript | TypeScript |
| Software license | Source available | Source available |
| Recorded license | Not recorded | Not recorded |
| Repository push | Oct 2026 | Oct 2026 |
Key differences
- dify has 160k recorded GitHub stars; n8n has 209k. Stars measure repository interest, not users or task performance.
About n8n
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
Full n8n profile →Frequently asked
- Does self-hosting make either platform free to operate?
- No. Hosting, maintenance and model or service usage can still cost money. Their repository licenses also have conditions; examine the actual deployment and intended use.
- Can n8n build AI workflows too?
- Yes. Its documentation includes AI workflows. The decision is which application and integration model fits your job, not whether only one platform supports AI.