Dify
What is Dify?
Dify is an AI application platform for product, engineering, and enterprise teams that builds agentic workflows, RAG pipelines, tools, and observability in one place. It includes drag-and-drop workflow design, native MCP integration, and publishing for apps or servers. The platform connects with OpenAI, Anthropic, Llama2, Azure OpenAI, Hugging Face, Replicate, GitHub, and Ollama. Plans run Sandbox free, Professional $59/month, and Team $159/month.
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At a glance
- Dify is best for product and engineering teams who need to ship AI apps and agents quickly.
- Sandbox Free; Professional $59; Team $159
What does Dify do?
Dify handles the full path from prompt to production by combining agentic workflows, RAG pipelines, tools, and observability in one platform. Teams can design complex natural-language flows with a drag-and-drop interface, connect systems through native MCP integration, and publish apps or servers without stitching together separate services. The result is a faster build cycle for chatbots, assistants, automation, and other AI applications. At scale, Dify is positioned for enterprise use: the site cites 141.6k GitHub stars, 800+ contributors, over a million applications, and deployment across 0+ countries and 0+ industries. Customer stories from Volvo Cars and Ricoh point to real operational use, including reducing cost and time to market and supporting 19,000+ employees across 20+ departments. The platform also shows scalable, stable, secure infrastructure, with enterprise deployment options and access controls for larger teams.
Why use Dify?
- It combines workflow design, retrieval, integrations, and observability so teams can avoid assembling a fragmented AI stack.
- Native MCP support lets teams bridge existing systems and publish reusable servers without rewriting core services.
- Enterprise deployment options and access controls support organizations that need on-premises, public cloud, or VPC flexibility.
- The platform is backed by a large open-source community, with 141.6k GitHub stars and 800+ contributors.
- Customer examples from Volvo Cars and Ricoh show it can support real internal and cross-department AI rollouts.
Who is Dify for?
- Product teams that need to prototype and launch AI applications without heavy setup.
- Engineering teams that want drag-and-drop workflows for complex LLM pipelines.
- Enterprise platform teams that need scalable deployment and access controls.
- Operations teams that want to automate support, HR, or internal knowledge workflows.
- Developers who need RAG, tools, and model connectivity in one place.
What are Dify's key features?
Agentic Workflow
Design agentic workflows that chain models, tools, and actions into production apps. Dify supports GitHub-backed development and Ollama models for local or self-hosted inference.
Sophisticated Workflow in Minutes
Assemble complex workflows quickly with reusable blocks and triggers. The platform is built for fast setup, helping teams move from idea to working AI app without heavy engineering.
Amplify with Any Global Large Language Models
Connect to OpenAI, Anthropic, Llama2, Azure OpenAI, Hugging Face, and Replicate to choose the right model for each task and avoid vendor lock-in.
Get Your Data LLM Ready with RAG
Prepare internal content for retrieval-augmented generation so agents answer from your own data. This helps keep responses grounded and useful for customer-facing or internal workflows.
Bridge Your Systems / Platforms with Native MCP Integration
Use native MCP integration to connect Dify with existing systems and platforms. That reduces custom glue code and makes it easier to automate across tools you already run.
Publish as an Universal MCP Server
Expose your app as a universal MCP server so other tools can call it through a standard interface. This makes reuse and integration simpler across teams and products.
Secure
Build and run AI apps with security controls suited for production use. Dify's GitHub presence at 141.6k stars and 800+ contributors signals active maintenance and broad adoption.
Connect and Automate
Connect workflows to GitHub and Ollama to automate development and model execution. This helps teams ship AI features faster while keeping control over code and inference.
What does Dify integrate with?
- GitHub
- Ollama
What are Dify's use cases?
Product teams launch AI apps
Product teams that need to prototype and launch AI applications without heavy setup use Dify to turn an idea into a working app fast. They rely on Sophisticated Workflow in Minutes and Launch Right Away to move from prompt testing to a production-ready experience without waiting on a long engineering cycle.
Engineering builds RAG pipelines
Engineering teams that want drag-and-drop workflows for complex LLM pipelines use Dify to assemble retrieval and orchestration in one place. They use Get Your Data LLM Ready with RAG and Agentic Workflow to connect knowledge sources, shape responses, and ship a more reliable assistant.
Operations automates internal support
Operations teams that want to automate support, HR, or internal knowledge workflows use Dify to route requests and answer common questions automatically. With Connect and Automate and Bridge Your Systems / Platforms with Native MCP Integration, they can reduce repetitive tickets and keep employees moving.
Developers connect models and tools
Developers who need RAG, tools, and model connectivity in one place use Dify to wire models, data, and external actions into a single build flow. Amplify with Any Global Large Language Models and Add Wings with Tools help them launch assistants that can actually do work.
How does Dify work?
- Connect your first data source or GitHub repository, then use Get Your Data LLM Ready with RAG to prepare documents, code, or knowledge for retrieval.
- Choose a starting template and shape the logic with Agentic Workflow, adding branches, prompts, and tool calls in the visual builder.
- Attach models through Amplify with Any Global Large Language Models, or point to Ollama when you want local model connectivity in the same workspace.
- Bridge your systems with Native MCP Integration, then use Add Wings with Tools to let the app query services and trigger actions automatically.
- Publish the app, monitor logs and trigger events, and keep improving with Secure controls and Connect and Automate as usage grows.
How much does Dify cost?
Sandbox
Free- Free
- Purchase through Cloud Marketplace
Professional
$59- Priority Document Processing
- 20,000 Trigger Events/month
- Unlimited Triggers/workflow
- Faster Workflow Execution
- 2,000 Annotation Quota Limits
- Unlimited Log History
- No Dify API Rate Limit
- Support OpenAI/Anthropic/Llama2/Azure OpenAI/Hugging Face/Replicate
Team
$159- 1 Team Workspace
- Support OpenAI/Anthropic/Llama2/Azure OpenAI/Hugging Face/Replicate
Frequently asked questions
What is Dify?
Dify is an AI application platform for product, engineering, and enterprise teams that builds agentic workflows, RAG pipelines, tools, and observability in one place. It includes drag-and-drop workflow design, native MCP integration, and publishing for apps or servers. The platform connects with OpenAI, Anthropic, Llama2, Azure OpenAI, Hugging Face, Replicate, GitHub, and Ollama. Plans run Sandbox free, Professional $59/month, and Team $159/month.
How much does Dify cost? Is it free?
Dify has a free plan, with paid tiers including Professional at $59, Team at $159.
What is Dify used for? Who is it for?
Dify is used for Agentic Workflow, Sophisticated Workflow in Minutes, and Amplify with Any Global Large Language Models. It's built for Product teams that need to prototype and launch AI applications without heavy setup, Engineering teams that want drag-and-drop workflows for complex LLM pipelines, and Enterprise platform teams that need scalable deployment and access controls.
Does Dify have an API and what does it integrate with?
Dify doesn't publish a public API. It integrates with GitHub, Ollama.
Editor's read
Check whether the Professional plan's 20,000 trigger events per month and 2,000 annotation quota limits match your expected workflow volume. If you need more than that, the Team plan's listed features should be reviewed before rollout.
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