THE COMPARISON / A CLOSER LOOK
Dify vs n8n
Compare the listed plans, useful capabilities, and practical tradeoffs.
Prices and features reflect editorial records. Check vendor terms before you buy; documentary checks do not imply hands-on testing.
Dify
🟡Low CodeLLM application platform
Dify review covering visual LLM workflows, agents, RAG, self-hosting, verified cloud pricing, deployment tradeoffs, practical use cases, pros, and cons.
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Freen8n
🟡Low CodeAI Automation
Source-available workflow automation platform with native AI agent and LangChain nodes, popular for self-hosted AI workflows.
SOURCE-CHECK COVERAGE
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Starting Price
FreeFeature Comparison
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Dify - Pros & Cons
Pros
- ✓Combines workflow, knowledge, testing, and deployment
- ✓Open-source edition enables self-hosting
- ✓Multiple providers reduce model lock-in
- ✓Visual graphs aid cross-functional collaboration
Cons
- ✗Self-hosting shifts upgrades, security, backups, and scaling to customers
- ✗Message credits do not include every model or infrastructure cost
- ✗Large visual graphs become hard to review
- ✗RAG still requires retrieval evaluation and data governance
n8n - Pros & Cons
Pros
- ✓Self-hosted Community edition is free and runs anywhere — no per-task pricing tax
- ✓Native AI Agent node and LangChain blocks let you build real agents on a visual canvas
- ✓MCP Client node integrates n8n into the emerging MCP agent ecosystem
- ✓JavaScript code nodes provide escape hatches for anything the visual editor cannot express
Cons
- ✗Visual editor becomes noisy on workflows with dozens of branches and tool calls
- ✗Debugging long-running agent loops is still painful versus a real code IDE
- ✗Cloud tier execution caps can surprise heavy users — self-hosting is the practical path at scale
- ✗Sustainable Use License is fair-code, not OSI-approved open source, which some procurement teams flag
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