n8n vs Dify: Workflow Automation or AI App Builder?
Comparing n8n and Dify — two open-source platforms that look similar but solve fundamentally different problems.
n8n and Dify keep showing up in the same "best AI tools" lists, which confuses people into thinking they're alternatives. They're not. They solve different problems, and the confusion costs developers real time picking the wrong one.
n8n is a workflow automation platform. It connects your existing services and automates processes between them. Dify is an AI application builder. It gives you a visual canvas for designing LLM-powered apps with RAG, agents, and workflows.
They overlap on one thing: AI agent workflows. That overlap is where the confusion lives. Let me clear it up.
Quick Comparison
| Tool | Best For | Pricing |
|---|---|---|
| n8n TOP PICK | Automating business processes where AI is one step in a larger pipeline | Free / Open Source (Cloud from $24/month) |
| Dify | Building AI-native applications: chatbots, knowledge bases, RAG systems, and AI-powered tools | Free / Open Source (Cloud from $59/month) |
1. n8n
Open-source workflow automation platform for technical teams. Visual builder with 500+ integrations, code access in every node, and AI agent capabilities. 182k+ GitHub stars.
Pros
- + 500+ native integrations with SaaS tools
- + Write JavaScript or Python in any workflow node
- + Dead-simple self-hosting: single Docker container + Postgres
- + AI agent nodes with tool calling and memory
- + See input/output data at every step for debugging
- + Active community: 182k+ GitHub stars, 200k+ members
Cons
- - AI capabilities are add-on features, not the core focus
- - No built-in RAG pipeline — requires manual node configuration
- - Workflow version control is JSON-based, not Git-native
- - No automated testing for workflows
- - AI agent documentation is less mature than automation docs
Best for: Automating business processes where AI is one step in a larger pipeline. Ideal for connecting services, webhook processing, data sync, and scheduled jobs. Self-hosting is trivially easy.
Pricing: Free / Open Source (Cloud from $24/month)
2. Dify
Open-source platform for building LLM-powered applications. Visual workflow builder, built-in RAG, agent orchestration, and model-agnostic design. 136k+ GitHub stars.
Pros
- + Purpose-built for AI: RAG, agents, workflows, chatbots
- + Built-in RAG pipeline: upload docs, configure chunking, done
- + Model-agnostic: switch LLM providers with a dropdown
- + Every app deploys as an API endpoint automatically
- + Built-in observability: token usage, latency, cost per request
- + Non-developers can manage prompts and AI logic visually
Cons
- - Cannot automate non-AI workflows (no Slack, CRM, email integrations)
- - Complex self-hosting: 7+ services (API, worker, web, Postgres, Redis, vector DB, sandbox)
- - Visual canvas gets unwieldy with 30+ nodes
- - Workflow lock-in: no export to Python or code-based frameworks
- - Smaller integration ecosystem compared to n8n
- - Cloud pricing is higher than n8n
Best for: Building AI-native applications: chatbots, knowledge bases, RAG systems, and AI-powered tools. Best when the AI IS the product, not a feature of a larger system.
Pricing: Free / Open Source (Cloud from $59/month)
Verdict
Stop asking "n8n or Dify?" Start asking "Am I automating a process or building an AI app?"
Choose n8n if you're connecting services, automating business processes, and AI is just one step in a larger pipeline. The self-hosting simplicity (single container) and 500+ integrations make it the automation default.
Choose Dify if the AI IS the product. Chatbots, knowledge bases, RAG-powered tools, AI agents. Dify's visual builder and built-in RAG pipeline save weeks of development time.
Use both when n8n handles the automation layer and Dify powers the AI features. n8n calls Dify's API as one step in a broader workflow. They complement each other.