n8n vs. Dify: Automation Platform vs. LLM App Builder — A Practical Comparison

Miasma Supply Chain Worm: Full Technical Breakdown of the Self-Replicating Credential Harvester

Comprehensive technical analysis of the Miasma (Shai-Hulud) self-replicating supply-chain worm: Phantom Gyp binding.gyp execution, AI coding agent persistence hooks, 73 Microsoft repositories compromised, 448+ artifacts across npm/PyPI, credential harvesting scope, and defensive countermeasures.

Disaster Recovery for Self-Hosted Infrastructure

A practical guide to backup strategies, DR planning, and recovery validation for small-to-medium self-hosted environments — because digital sovereignty includes recoverability.

Why Compare n8n and Dify?

On the surface, n8n and Dify look similar: both are open-source, both have visual workflow builders, both integrate with AI models. But they solve fundamentally different problems.

n8n is a general-purpose workflow automation platform — a self-hosted Zapier. It connects APIs, transforms data, and automates business processes. Its AI capabilities come from integrations with OpenAI, Anthropic, Hugging Face, and local LLMs via HTTP nodes.

Dify is an LLM application development platform. It provides a complete toolkit for building AI-powered apps: RAG pipelines, agent workflows, prompt management, and model routing. Its primary output is an AI application, not a business process.

This article helps you decide which tool fits your use case — and shows how to run both in production.

At a Glance

Architecture Comparison

n8n Architecture

n8n follows a traditional workflow engine pattern:

[Trigger] → [Node] → [Node] → ... → [Output]
    │          │         │
    ▼          ▼         ▼
  Webhook    HTTP      AI Model
  Schedule   DB Query  Transform

Every node is a step that processes data and passes it to the next node. The flow is sequential, deterministic, and debuggable. n8n shines at orchestrating APIs — it connects 400+ services (Slack, Google, GitHub, PostgreSQL, etc.) out of the box.

Production n8n requires PostgreSQL (persistence) and Redis (execution queue). The n8n Production Stack bundles all three in a single docker compose up.

Dify Architecture

Dify is built around LLM application concepts:

[User Input] → [Application] → [Orchestration Pipeline]
                                    │
                    ┌───────────────┼───────────────┐
                    ▼               ▼               ▼
              [LLM Node]      [Retrieval]      [Tool Call]
              (model +        (RAG from        (API tools,
               prompt)         vector store)     code runner)
                                    │
                                    ▼
                              [Response]

Dify's pipeline is designed for AI-native workflows. The orchestration graph includes:

• LLM Nodes: Call any model with custom prompts
• Knowledge Retrieval: RAG from uploaded documents (PDF, HTML, text)
• Tool Integration: Web search, code interpreter, custom API tools
• Variable Memory: Store and reference conversation context
• Conditional Branches: Route based on LLM output

Dify is not a Zapier replacement — it's a platform for building ChatGPT-like experiences, custom RAG bots, and AI agents.

Use Cases

When to Choose n8n

n8n is the right choice when your primary goal is process automation:

• Invoice processing: Watch email → extract attachment → save to Drive → create accounting entry
• CRM synchronization: New lead in Typeform → create contact in HubSpot → notify Sales in Slack
• DevOps workflows: GitHub PR merged → build Docker image → deploy to staging → notify team
• Data pipelines: Fetch API data → transform → insert into PostgreSQL → generate report
• AI-assisted workflows: New support ticket → classify with OpenAI → route to correct team → draft reply

When to Choose Dify

Dify is the right choice when your primary goal is AI application development:

• Customer support chatbot: RAG from knowledge base → classify intent → answer or escalate
• Document analysis: Upload PDF → chunk → embed → query with custom prompts → structured output
• AI writing assistant: Generate blog posts, emails, or reports with brand voice and context
• Data extraction pipeline: Ingest documents → extract structured data via LLM → validate → export
• Multi-agent system: Research agent → summarize → fact-check → format → deliver

The Overlap Zone

There is overlap, especially with n8n's AI nodes and Dify's workflow capabilities:

Production Deployment

Both tools are designed for Docker Compose deployment. Here's how they compare operationally:

Resource Requirements

Production Stacks

Both platforms have dedicated production stacks available:

• n8n Production Stack (€9.99): n8n + PostgreSQL + Redis, queue mode, execution pruning, production checklist
• Dify Production Stack (€12.99): Dify API + Worker + Web + PostgreSQL + Redis + Weaviate + Nginx, SSRF protection, sandboxed code execution

Each stack includes: docker-compose.yml, .env.example, config files, and a production checklist.

Key Operational Differences

n8n is simpler to operate:

• SQLite works for small deployments (though PostgreSQL is recommended for production)
• No vector store dependency
• Single process mode for low-traffic setups
• Well-documented health endpoints and metrics

Dify requires more operational care:

• Requires PostgreSQL, Redis, AND a vector database (Weaviate, Qdrant, or Milvus)
• Separate API and Worker processes need monitoring
• SSRF protection and sandbox need correct configuration
• Database migrations run at startup and can take 30+ seconds

Integration: Running Both Together

For teams building AI-powered automation, running n8n AND Dify together can be powerful:

• n8n handles the business logic: triggers, API integrations, data transformation, delivery
• Dify handles the AI logic: RAG, model routing, prompt management, knowledge retrieval
• They communicate via HTTP: n8n calls Dify's API for AI tasks; Dify calls n8n's webhooks for business actions

┌─────────────────────────────────────────────────────┐
│                    User Request                       │
└─────────────────────┬───────────────────────────────┘
                      │
                      ▼
┌──────────────────────────────────────────────┐
│               n8n (Orchestrator)               │
│  Trigger → Transform → Route → Deliver         │
│  Webhook → Schedule → Poll → WebSocket         │
└────┬──────────────────────────────────────┬───┘
     │                                      │
     ▼                                      ▼
┌──────────────┐                  ┌──────────────────┐
│ SaaS APIs    │                  │   Dify (AI)       │
│ Slack, Gmail │                  │ RAG, Agents,      │
│ HubSpot, DB  │                  │ Model Routing     │
└──────────────┘                  └──────────────────┘

Migration Paths

From n8n to Dify

You might consider Dify if:

• Your n8n workflows are 80% AI calls (classify, extract, summarize, generate)
• You need RAG capabilities but are hacking it together with HTTP nodes
• You're building user-facing AI features that need authentication and rate limiting

From Dify to n8n

You might consider n8n if:

• Your Dify workflows are 80% API orchestration (moving data between services)
• You need business automation triggers (schedules, email polling, webhooks from 400+ services)
• Your team is more process-oriented than AI-oriented

Summary

The honest answer: Most teams benefit from having both. n8n handles the plumbing — connecting systems, moving data, triggering actions. Dify handles the intelligence — understanding documents, making decisions, generating content. They're complementary, not competitive.

Related Resources

• n8n Production Stack — Production-ready n8n deployment
• Dify Production Stack on graphwiz.ai — Production-ready Dify deployment
• Self-Hosted AI Stack on graphwiz.ai — Ollama + Open WebUI + LiteLLM for local LLM inference
• Running n8n in Production — Deep dive on n8n production configuration