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Complete pricing guide for Dify. Compare all plans, analyze costs, and find the perfect tier for your needs.
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View Full Features →Yes. Dify is released under an open-source license and can be self-hosted at no cost using Docker Compose or Kubernetes. The team also offers a managed cloud service with paid tiers for users who prefer not to manage infrastructure, plus enterprise plans with SSO, advanced RBAC, and SLA support.
Dify is model-agnostic and supports hundreds of providers including OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, Mistral, Cohere, DeepSeek, Qwen, and Llama. It also integrates with locally hosted runtimes such as Ollama, vLLM, LocalAI, and Xinference, allowing fully on-premise deployments.
LangChain and LangGraph are code-first Python libraries for building LLM applications, while Dify is a complete platform that wraps similar capabilities behind a visual builder, hosted UI, RAG engine, and observability layer. Teams that want full programmatic control may prefer LangGraph; teams that want a deployable product with less boilerplate typically prefer Dify.
Yes. Dify includes a built-in knowledge base feature that ingests PDFs, Word documents, web pages, and structured data, then handles chunking, embedding, vector storage, hybrid search, and reranking. Knowledge bases can be attached to any chatbot, agent, or workflow without external infrastructure.
Yes. Dify exposes every application as a REST API, supports horizontal scaling on Kubernetes, and includes logging, prompt versioning, and analytics for production monitoring. Many companies run customer-facing chatbots and internal copilots on Dify, though teams with strict compliance needs typically choose self-hosted or enterprise tiers.
Leading multi-agent framework and platform for building, deploying, and monitoring collaborative AI agent teams.
Compare Pricing →Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.
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Compare Pricing →SDK for integrating cutting-edge LLM technology into applications, with support for building AI agents and connecting model capabilities into existing app workflows.
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