Dify

What is Dify?

Dify is an open-source platform for building AI applications, agents, and workflows through a visual drag-and-drop interface. It targets developers, product teams, and non-technical builders who want to ship AI-powered features without writing complex orchestration code. With 137,000+ GitHub stars and over 800 contributors, Dify stands out from other agent platforms by combining a no-code builder with full self-hosting support and connections to hundreds of LLM providers.

Key Features

• Visual Workflow Builder: Design AI pipelines by dragging and dropping model calls, tools, and logic blocks on a canvas, then deploy them as APIs or standalone apps
• Multi-Model Support: Connect to hundreds of LLMs from OpenAI, Anthropic, Google, Mistral, Llama, and any OpenAI-compatible API, including local models through Ollama
• RAG Pipeline: Upload documents and connect knowledge bases so your apps can answer questions from your own data rather than relying solely on the model's training
• Agent Capabilities: Build autonomous agents using function calling or ReAct patterns that can reason, call tools, and loop through steps until a task is complete
• Prompt IDE: Test, compare, and refine prompts across different models with real outputs before deploying to production
• MCP Integration: Native support for Model Context Protocol, letting agents connect to external tools and data sources through a standardized interface
• Backend-as-a-Service APIs: Every workflow and agent you build is automatically available as an API endpoint for integration into existing products
• Self-Hosting with Docker: Deploy on your own infrastructure with Docker Compose (minimum 2 CPU cores, 4GB RAM) for full data control

Use Cases

• Product teams at startups: Build internal AI tools and customer-facing chatbots without hiring dedicated ML engineers, going from prototype to production in days
• Developers building RAG applications: Connect company documents, wikis, and databases to LLMs so teams can query internal knowledge through natural language
• Non-technical operators: Set up automated customer support workflows, content generation pipelines, and data processing agents using the visual builder
• Enterprise teams with data privacy requirements: Self-host the entire platform on private infrastructure while still getting the visual builder and model flexibility

Strengths and Weaknesses

Strengths:

• The visual workflow builder makes it possible to build and iterate on complex AI pipelines without writing orchestration code
• Model flexibility is genuine: switch between commercial APIs and self-hosted open-source models without changing your application logic
• Self-hosting option gives teams full control over data, which is rare among platforms with this level of polish
• Active open-source community with 137,000+ GitHub stars and steady contribution from 800+ developers
• The free Sandbox tier is functional enough for prototyping, and no credit card is required to start

Weaknesses:

• The Sandbox (free) tier is limited to 200 message credits and 5 apps, which runs out quickly during active development
• Paid plans start at $59/month per workspace, a significant jump from free that may not suit solo builders or hobbyists
• Documentation has gaps in places, particularly for advanced configurations, so users sometimes rely on community posts and GitHub issues
• Initial setup for self-hosted deployments can be more involved than the documentation suggests, especially with custom model providers

Pricing

• Sandbox (Free): 200 message credits, 5 apps, 50 documents, 50MB storage, 1 team member, 30-day log history
• Professional: $59/workspace/month (17% off annually). 5,000 message credits, 50 apps, 500 documents, 5GB storage, 3 team members, unlimited log history
• Team: $159/workspace/month (17% off annually). 10,000 message credits, 200 apps, 1,000 documents, 20GB storage, 50 team members
• Enterprise: Custom pricing, contact sales

Self-hosting the open-source version is free with no message credit limits. Cloud pricing is per workspace, not per user.

FAQ

Is Dify open source?

Yes. Dify is open source under a license based on Apache 2.0 with additional conditions. The full source code is available on GitHub at github.com/langgenius/dify, with 137,000+ stars.

Is Dify free?

Dify offers a free Sandbox tier with 200 message credits and 5 apps. The self-hosted open-source version is completely free with no usage limits. Paid cloud plans start at $59/month.

What LLMs does Dify support?

Dify connects to hundreds of models from dozens of providers, including OpenAI, Anthropic, Google, Mistral, Meta Llama, and any OpenAI-compatible API. Local models are supported through Ollama.

Can I self-host Dify?

Yes. Dify can be deployed on your own servers using Docker Compose with a minimum of 2 CPU cores and 4GB RAM. Kubernetes and cloud-specific deployment options (AWS, Azure, GCP) are also available.

How does Dify compare to LangFlow?

Both offer visual workflow builders for AI applications. Dify includes a built-in RAG pipeline, prompt IDE, and native agent capabilities out of the box, while LangFlow focuses more closely on LangChain component orchestration. Dify also has a managed cloud offering alongside its self-hosted option.

What can I build with Dify?

You can build chatbots, AI agents, RAG-powered Q&A systems, content generation pipelines, data processing workflows, and multi-step agentic applications. Every app can be deployed as a web interface or accessed through an API.