Dify vs Flowise
Dify is the production-grade LLMOps platform for teams needing model management, observability, and governance. Flowise is the lightweight, developer-friendly visual builder for rapid prototyping with LangChain ecosystem compatibility. Choose based on whether you need a platform (Dify) or a toolkit (Flowise).
Dify3.5Flowise3.5
Data Tools
Quick Comparison
| Feature | Dify | Flowise |
|---|---|---|
| Ease of Setup: Flowise wins — single npm command vs Dify multi-service Docker stack | — | — |
| Enterprise Readiness: Dify wins — RBAC workspaces, annotation, observability built in | — | — |
| Ecosystem Breadth: Flowise wins — inherits 300+ LangChain components automatically | — | — |
| Production Operations: Dify wins — centralized model management, cost tracking, logging | — | — |
| Cost Efficiency: Flowise wins — $35/month Starter with 10,000 predictions vs $59/month for 5,000 credits | — | — |
| Licensing Flexibility: Flowise wins — MIT license vs Apache 2.0 | — | — |
Dify
- Ease of Setup: Flowise wins — single npm command vs Dify multi-service Docker stack:
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- Enterprise Readiness: Dify wins — RBAC workspaces, annotation, observability built in:
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- Ecosystem Breadth: Flowise wins — inherits 300+ LangChain components automatically:
- —
- Production Operations: Dify wins — centralized model management, cost tracking, logging:
- —
- Cost Efficiency: Flowise wins — $35/month Starter with 10,000 predictions vs $59/month for 5,000 credits:
- —
- Licensing Flexibility: Flowise wins — MIT license vs Apache 2.0:
- —
Flowise
- Ease of Setup: Flowise wins — single npm command vs Dify multi-service Docker stack:
- —
- Enterprise Readiness: Dify wins — RBAC workspaces, annotation, observability built in:
- —
- Ecosystem Breadth: Flowise wins — inherits 300+ LangChain components automatically:
- —
- Production Operations: Dify wins — centralized model management, cost tracking, logging:
- —
- Cost Efficiency: Flowise wins — $35/month Starter with 10,000 predictions vs $59/month for 5,000 credits:
- —
- Licensing Flexibility: Flowise wins — MIT license vs Apache 2.0:
- —
Interface Preview
Flowise
Feature Comparison
| Feature | Dify | Flowise |
|---|---|---|
| Core Capabilities | ||
| Visual Workflow Builder | Canvas editor with branching, loops, and conditionals | Drag-and-drop node editor built on LangChain components |
| LLM Provider Support | 100+ providers including OpenAI, Anthropic, Ollama | LangChain-supported models: OpenAI, Anthropic, HuggingFace, Replicate |
| RAG Pipeline | Built-in knowledge base with configurable chunking and hybrid search | Composable nodes: document loader + splitter + vector store + retrieval chain |
| Multi-Agent Orchestration | Native agentic workflows with tool calling and parallel execution | Agentflow for multi-agent coordination with distributed workflows |
| Custom Code Execution | Python sandbox within workflows | JavaScript function nodes within flows |
| Operations & Governance | ||
| Model Management | Centralized configuration with cost tracking and usage analytics | No centralized management; configured per-node |
| Observability | Built-in logging for token usage, latency, and error tracking | Basic execution logs; requires external tools like LangSmith |
| Annotation & Feedback | Built-in annotation queue for human review and quality scoring | No native annotation; requires external integration |
| Team Collaboration | Multi-workspace RBAC: 3 to 50 members depending on plan | Cloud Pro: 5+ users at $15/user/month; self-hosted unlimited |
| Deployment & Integration | ||
| API Deployment | Auto-generated REST API per app with API key auth | Auto-generated REST API per chatflow with API key and OAuth |
| Self-Hosted Deployment | Docker Compose or Kubernetes; requires PostgreSQL, Redis, vector DB | npm install or Docker; minimal dependencies beyond Node.js |
| License | Apache 2.0 | MIT |
| Marketplace / Templates | Official app marketplace with community templates | Community-shared chatflows via JSON import/export |
Core Capabilities
Visual Workflow Builder
DifyCanvas editor with branching, loops, and conditionals
FlowiseDrag-and-drop node editor built on LangChain components
LLM Provider Support
Dify100+ providers including OpenAI, Anthropic, Ollama
FlowiseLangChain-supported models: OpenAI, Anthropic, HuggingFace, Replicate
RAG Pipeline
DifyBuilt-in knowledge base with configurable chunking and hybrid search
FlowiseComposable nodes: document loader + splitter + vector store + retrieval chain
Multi-Agent Orchestration
DifyNative agentic workflows with tool calling and parallel execution
FlowiseAgentflow for multi-agent coordination with distributed workflows
Custom Code Execution
DifyPython sandbox within workflows
FlowiseJavaScript function nodes within flows
Operations & Governance
Model Management
DifyCentralized configuration with cost tracking and usage analytics
FlowiseNo centralized management; configured per-node
Observability
DifyBuilt-in logging for token usage, latency, and error tracking
FlowiseBasic execution logs; requires external tools like LangSmith
Annotation & Feedback
DifyBuilt-in annotation queue for human review and quality scoring
FlowiseNo native annotation; requires external integration
Team Collaboration
DifyMulti-workspace RBAC: 3 to 50 members depending on plan
FlowiseCloud Pro: 5+ users at $15/user/month; self-hosted unlimited
Deployment & Integration
API Deployment
DifyAuto-generated REST API per app with API key auth
FlowiseAuto-generated REST API per chatflow with API key and OAuth
Self-Hosted Deployment
DifyDocker Compose or Kubernetes; requires PostgreSQL, Redis, vector DB
Flowisenpm install or Docker; minimal dependencies beyond Node.js
License
DifyApache 2.0
FlowiseMIT
Marketplace / Templates
DifyOfficial app marketplace with community templates
FlowiseCommunity-shared chatflows via JSON import/export
Our Verdict
Dify is the production-grade LLMOps platform for teams needing model management, observability, and governance. Flowise is the lightweight, developer-friendly visual builder for rapid prototyping with LangChain ecosystem compatibility. Choose based on whether you need a platform (Dify) or a toolkit (Flowise).
When to Choose Each
Choose Dify if:
Choose Dify for teams of 5+ members managing multiple AI applications in production, where centralized model management, observability dashboards, and RBAC workspace governance justify the $59-$159/month per workspace cost.
Choose Flowise if:
Choose Flowise for solo developers or small teams prioritizing rapid prototyping, LangChain ecosystem compatibility, and cost efficiency — the $35/month Starter plan with 10,000 predictions and MIT license offers the lowest barrier to entry.
Choose Dify if:
Choose Dify when you need built-in annotation, human-in-the-loop feedback loops, and structured quality improvement workflows for production RAG applications.
Choose Flowise if:
Choose Flowise when you need maximum component flexibility — its LangChain foundation provides 300+ integrations including vector stores, document loaders, and memory systems that update with the LangChain ecosystem.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
Can Dify and Flowise both run entirely self-hosted without cloud dependencies?
Yes, both platforms offer fully self-hosted deployment with no mandatory cloud connectivity. Dify requires Docker Compose or Kubernetes with PostgreSQL, Redis, and a vector database. Flowise runs with npm install or Docker with minimal dependencies. Dify is Apache 2.0 licensed; Flowise is MIT licensed.
How do Dify and Flowise compare for building RAG applications?
Dify provides a managed knowledge base with built-in document ingestion and hybrid search. Flowise uses composable LangChain nodes for document loading, splitting, vector storage, and retrieval — more flexible but requiring more configuration.
What happens if I outgrow cloud plans — is migration to self-hosted straightforward?
Flowise cloud flows export as JSON and import directly into self-hosted instances. Dify migration is more involved due to different database schemas between cloud and self-hosted versions, requiring re-upload of knowledge base documents.
Which platform has better community support and ecosystem momentum?
Dify has strong GitHub traction with bi-weekly releases and an official marketplace. Flowise benefits from the broader LangChain ecosystem where community integrations are automatically available. Both have active Discord communities.