Dify vs n8n: Which Automation Platform Should You Self-Host?
Dify and n8n both use visual workflows, but they solve different problems. Dify is an application platform for large-language-model products: prompts, retrieval, agents, model routing, and an end-user interface. n8n is a general automation engine that connects APIs, databases, schedules, and business systems. The right choice depends less on the canvas and more on what the workflow produces.
The short answer
Choose Dify when the result is an AI application that users interact with. Choose n8n when the result is a background business process that moves data or triggers actions across systems. Use both when n8n should orchestrate business events and Dify should provide the AI application layer.
| Decision | Dify | n8n |
|---|---|---|
| Primary job | Build and operate LLM applications | Automate work across systems |
| Best interface | Chat, agent, workflow, or generated API | Event-driven and scheduled workflow |
| Integrations | Models, vector stores, knowledge, AI tools | Broad SaaS, database, and API connectors |
| State | Conversations, prompts, knowledge, model output | Executions, credentials, workflow data |
| Typical user | AI product team | Operations or platform team |
Where Dify is stronger
Dify treats prompts and models as first-class configuration. It includes model-provider routing, retrieval-augmented generation, document ingestion, agent tools, evaluation, and application publishing. That removes substantial plumbing when a team needs a support assistant, internal search tool, or structured generation workflow.
The trade-off is operational breadth. Dify runs several services and data stores, and its visual workflow is intentionally centered on AI. It can call ordinary APIs, but it is not a replacement for a broad integration platform.
Where n8n is stronger
n8n has a large connector catalog and flexible control flow. It is well suited to scheduled reports, webhook processing, CRM synchronization, approval flows, data cleanup, and glue code that would otherwise become scattered scripts.
n8n includes AI nodes and can call model providers, but an AI-facing product usually needs more work around prompt versions, knowledge ingestion, conversation state, and user experience. Its fair-code license should also be reviewed for commercial redistribution scenarios.
Self-hosting footprint
A basic n8n deployment can run as one application with PostgreSQL. Queue mode adds Redis and workers when executions need to scale. A production Dify deployment includes the API, web interface, workers, sandbox, PostgreSQL, Redis, and a vector store. Dify therefore asks for more memory and more components from the start.
For either platform:
- pin container versions rather than using
latest; - keep the application behind TLS and an identity layer;
- store credentials in a dedicated secret system;
- back up PostgreSQL and test a restore;
- restrict outbound network access where possible;
- review workflow exports before sharing them because they may expose internal structure.
A useful combined architecture
Use n8n to receive a ticket webhook, collect customer context, and enforce business rules. Send the prepared context to a Dify application for retrieval and response drafting. Return the draft to n8n for human approval and delivery. Each platform then owns the part it models best.
Avoid duplicating orchestration across both products. Decide where retries, audit history, and final status live. A single owner for each workflow boundary makes failures easier to diagnose.
Recommendation
If your first requirement mentions prompts, agents, knowledge bases, or model evaluation, start with Dify. If it mentions integrations, schedules, webhooks, or moving records between systems, start with n8n. A combined deployment is justified only when the AI application and the business automation are independently valuable.