Flowise vs Dify
Detailed side-by-side comparison to help you choose the right tool
Flowise
🟡Low CodeAI app platform
Flowise supports visual flow builder, agent orchestration, api deployment for prototyping assistants and low-code agent workflows.
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FreeDify
Integrations
Open-source LLMOps platform for building AI agents, RAG pipelines, and chatbots through a visual workflow builder. Supports all major LLM providers, MCP protocol, and self-hosting under Apache 2.0.
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FreeFeature Comparison
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Flowise - Pros & Cons
Pros
- ✓Apache 2.0 self-hosting offers strong control and flexibility
- ✓Visual editing speeds up prototypes and stakeholder demos
- ✓Many connectors cover common model and retrieval stacks
- ✓Each flow can become an API rather than staying a diagram
Cons
- ✗Complex canvases can become hard to maintain
- ✗Self-hosters own authentication, upgrades, monitoring, and scaling
- ✗Breaking changes require version pinning and testing
- ✗Cloud execution limits can affect chatty or high-volume agents
Dify - Pros & Cons
Pros
- ✓Open-source with self-hosted option gives full control over data and removes vendor lock-in
- ✓Visual workflow builder makes agent design accessible to non-engineers while still supporting complex logic
- ✓MCP protocol support provides standardized tool integration as the ecosystem matures
- ✓Supports all major LLM providers out of the box with easy model swapping
- ✓Active community with 50,000+ GitHub stars and regular releases
- ✓Free self-hosted deployment with no feature restrictions
Cons
- ✗Cloud pricing is per-workspace, which gets expensive fast with multiple projects
- ✗200-credit sandbox barely scratches the surface for real evaluation
- ✗Visual builder hits a ceiling with very complex custom logic that's easier to express in code
- ✗Self-hosted deployment requires Docker infrastructure management and ongoing maintenance
- ✗Knowledge base features are solid but less flexible than dedicated RAG frameworks like LlamaIndex
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