Dify is an open-source LLM application development platform that has quietly become one of the most starred AI projects on GitHub, accumulating over 50,000 stars since its 2023 launch. The platform occupies a unique position in the AI tooling landscape: it's more powerful than pure no-code tools but more accessible than pure developer frameworks, offering a visual workflow builder that generates production-ready LLM applications.
The name comes from "Define and Yield" — the core idea being that you define an LLM application's behavior visually and Dify yields a deployable application, complete with API endpoints, a web interface, and monitoring. This makes it particularly attractive for teams that need to move quickly while maintaining control over their infrastructure.
Key Features#
Visual Workflow Orchestration Dify's canvas-based workflow editor lets you build multi-step LLM pipelines by connecting nodes: LLM calls, code execution, document retrieval, API requests, conditional branching, and output formatting. The visual representation makes complex pipelines understandable and maintainable, even when the underlying logic is sophisticated.
Advanced RAG Pipeline Dify includes one of the most complete RAG implementations available in an open-source platform. You can upload PDFs, web pages, Notion pages, and database content into knowledge bases, configure chunking strategies, choose embedding models, and build retrieval workflows with hybrid search (vector + keyword). The RAG quality is competitive with purpose-built tools.
Agent Capabilities Dify supports ReAct-based agents with tool use. Agents can call web search, execute code, query databases, call external APIs, and interact with knowledge bases. You can build conversational agents that maintain session memory and make multi-step decisions to complete tasks.
Multi-Model Support One of Dify's standout features is seamless multi-model integration. Within a single application, different workflow steps can use different models — GPT-4o for complex reasoning, GPT-3.5 Turbo for classification, Claude for long-document analysis, local Ollama models for private data. Switching providers requires changing a dropdown, not rewriting code.
Built-in Application Deployment Every application built in Dify ships with an API endpoint, a shareable web chat interface, and an embeddable widget. You can publish an AI chatbot directly from Dify without setting up web servers, authentication, or API infrastructure.
Pricing#
Self-Hosted (Open Source): Free, unlimited usage. Requires Docker or Kubernetes setup. Community support via GitHub.
Dify Cloud:
- Sandbox: Free — 200 OpenAI message credits, limited features, for exploration
- Professional (~$59/month): 5,000 message credits/month, custom branding, priority support
- Team (~$159/month): 10,000 message credits, team collaboration, higher API limits
- Enterprise: Custom pricing with SSO, audit logs, SLA, dedicated support
Most technical teams self-host Dify on a $20–50/month VPS for essentially unlimited usage.
Who It's For#
Dify is the right choice for:
- Full-stack developers who want a visual workflow builder without sacrificing the ability to customize and self-host
- AI teams at startups that need to ship RAG applications and chatbots quickly without building the full stack from scratch
- Organizations with data privacy requirements that need to keep LLM application data on their own infrastructure
- Teams evaluating multiple LLM providers who need easy model switching within a single application
It is less suitable for non-technical users who can't manage Docker deployments, for organizations needing enterprise support SLAs without the budget for the enterprise plan, or for highly complex agent systems requiring fine-grained programmatic control.
Strengths#
Best-in-class open-source RAG. Dify's knowledge base and RAG pipeline are genuinely excellent — comparable to or better than many paid RAG platforms. The combination of chunking controls, hybrid search, and retrieval quality makes it a top choice for document-heavy applications.
Production-ready out of the box. Dify generates API endpoints, web interfaces, and monitoring dashboards automatically. Shipping a working chatbot to end users takes hours, not days.
Active development and community. The Dify team ships significant features regularly, and the GitHub community is large and active. The ecosystem of tutorials, templates, and integrations is growing rapidly.
Self-hosting cost efficiency. For high-volume applications, self-hosting Dify on your own infrastructure eliminates per-message pricing. You pay for compute once and get unlimited application calls.
Limitations#
Self-hosting complexity. Running Dify in production requires Docker Compose or Kubernetes knowledge, database management (PostgreSQL, Redis, vector DB), and operational expertise. This is a non-trivial overhead for smaller teams.
Limited programmatic extensibility. Complex business logic that doesn't fit Dify's workflow nodes requires coding custom extensions. Heavy customization can become awkward compared to working directly with LangChain or LangGraph.
Credits evaporate quickly on the free cloud tier. The Sandbox plan's 200 message credits are consumed in minutes of testing. Teams evaluating the cloud product will quickly need to upgrade or switch to self-hosting.
Related Resources#
Browse the full AI Agent Tools Directory for additional no-code and open-source platform options.
Compare open-source builders: Flowise profile for a lower-complexity alternative, and LangChain profile for the developer framework approach.
Comparisons: Dify vs Flowise: Open-Source LLM App Platform Comparison and Dify vs LangChain: When to Use Each Approach.
For tutorials and examples, see Building a RAG Chatbot with Dify and AI Agent Knowledge Base Applications: Real-World Examples.