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·작성자 AI Resource Hub Team

AI 워크플로 오케스트레이션: Dify, LangFlow, Flowise 등

복잡한 AI 파이프라인을 설계, 배포 및 관리하기 위한 로우코드 AI 워크플로 빌더 비교.

Why Orchestrate AI Workflows?

Building AI applications involves chaining prompts, tools, APIs, and logic. Workflow orchestration platforms make this visual and maintainable.

Dify

  • Type: Open-source with cloud option.
  • Strengths: Full-featured — RAG, agents, workflows, prompt management.
  • Best for: Teams wanting an all-in-one platform.
  • Pricing: Free self-hosted, cloud from $59/month.

LangFlow

  • Type: Open-source, Python-based.
  • Strengths: Visual builder for LangChain pipelines.
  • Best for: Developers already using LangChain.
  • Pricing: Free self-hosted.

Flowise

  • Type: Open-source, Node.js-based.
  • Strengths: Drag-and-drop LLM flow builder.
  • Best for: Quick prototyping of chatbots and RAG.
  • Pricing: Free self-hosted.

n8n + AI Nodes

  • Type: Open-source workflow automation.
  • Strengths: Connect AI to 300+ integrations.
  • Best for: Business automation with AI steps.
  • Pricing: Free self-hosted, cloud from €20/month.

Comparison

| Feature | Dify | LangFlow | Flowise | n8n |

|---------|------|----------|---------|-----|

| Visual editor | ✓ | ✓ | ✓ | ✓ |

| RAG built-in | ✓ | ✓ | ✓ | ✗ |

| Agent support | ✓ | ✓ | ✓ | ✓ |

| API deployment | ✓ | ✓ | ✓ | ✓ |

| Self-hosted | ✓ | ✓ | ✓ | ✓ |

When to Use Code Instead

  • Complex business logic that's hard to express visually.
  • Performance-critical paths where overhead matters.
  • Custom model training or fine-tuning pipelines.
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