Dify vs n8n (2026)
Comparing Dify and n8n is like comparing a chef's knife to a Swiss Army knife. They're designed for different jobs — but used together, they're even more powerful.
Quick Verdict
Choose Dify if...
- You're building AI chatbots or assistants
- You need a RAG knowledge base
- You want a ready-made chat UI
- Non-technical users need to use it
Choose n8n if...
- You need to automate business workflows
- You integrate 400+ different apps
- You want event-driven automation
- Low RAM / minimal resources
Best of both worlds: Many teams use n8n for business automation and Dify for the AI layer. n8n can call Dify's API to trigger LLM workflows when AI processing is needed.
Side-by-Side Comparison
| Feature | Dify | n8n |
|---|---|---|
| Primary Use | LLM app builder, RAG pipelines | Workflow automation, integrations |
| GitHub Stars | 134k+ | 48k+ |
| License | Apache 2.0 (with EE features) | Sustainable Use License |
| Self-host | Docker Compose (complex) | Single Docker container (simple) |
| Min RAM | 4 GB | 2 GB |
| Free Tier | Yes (5,000 credits) | Yes (community edition) |
| Cloud Pricing | $59/mo (Pro) | $20/mo (Starter) |
| AI / LLM Focus | Native (core feature) | Via HTTP / AI nodes |
| RAG Support | Built-in knowledge base | Via external tools only |
| Chatbot UI | Built-in, embeddable | Not included |
| Visual Editor | Workflow + prompt editor | Node-based workflow canvas |
| Integrations | 20+ LLM providers | 400+ apps and services |
Use Case Breakdown
When to use Dify
When to use n8n
Self-Hosting Comparison
Dify Self-Hosting
- 8 Docker containers via Compose
- Min 4 GB RAM (8 GB recommended)
- ~15 min to get running
- 50+ GB disk for storage/DB
- Updates via
docker compose pull
n8n Self-Hosting
- Single Docker container
- Min 2 GB RAM
- ~5 min to get running
- 10+ GB disk
- Updates via
docker pull
Using n8n and Dify Together
A powerful pattern: use n8n as the automation backbone and call Dify's API when AI processing is needed.
# Example: n8n HTTP node calling Dify API
POST https://your-dify.com/v1/chat-messages
Headers:
Authorization: Bearer YOUR_DIFY_APP_API_KEY
Content-Type: application/json
Body:
{
"inputs": {},
"query": "{{ $json.customer_message }}",
"response_mode": "blocking",
"conversation_id": "",
"user": "{{ $json.user_id }}"
}This lets you trigger Dify AI responses from any n8n trigger — new Zendesk ticket, incoming email, Slack message, webhook, or scheduled job.
Frequently Asked Questions
Can Dify and n8n be used together?
Yes — they complement each other well. Use n8n for event-driven automation and integrations, then trigger Dify workflows via Dify's REST API whenever LLM processing is needed.
Which is easier to self-host?
n8n is considerably simpler: a single Docker container on 2 GB RAM is all you need. Dify requires docker-compose with 8 services and at least 4 GB RAM to run reliably.
Does n8n support LLMs and AI?
n8n has HTTP nodes and basic AI agent nodes for calling LLM APIs, but it lacks Dify's RAG pipeline, knowledge base management, conversation history and embeddable chat UI.
How do the cloud prices compare?
n8n Starter is $20/mo (2,500 workflow executions). Dify Professional is $59/mo (1M message credits). For general workflow automation n8n is cheaper; for dedicated AI apps Dify provides more specialized value.