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Dify

Dify

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Dify is an open-source visual platform for building AI workflows, agents and RAG applications, with free self-hosting and a hosted free tier.

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ℹ️Tool Info
💳 PRICING
Freemium
•Sandbox (Free): 200 message credits, 1 team member, 5 apps, 50 knowledge documents, 50MB storage
•Professional: $59 per workspace per month (or lower with annual billing) with 5,000 message credits, 3 team members, 50 apps, 5GB storage
•Team: Higher tier around $159 per workspace per month with more credits and collaboration features
•Enterprise: Custom pricing with advanced security, compliance and dedicated support
•Fully free open-source self-hosted Community Edition available
🌐 INDIA SUPPORT
IN Yes (100% accessible in India via standard web browsers or local Docker installation.)
🕒 LAST UPDATED
26 Aug 2026
💡What is Dify?

Dify is an open-source visual AI application development platform engineered for orchestrating LLM workflows, RAG engines, and autonomous AI agents. Designed for developers, startups, and enterprise teams, Dify combines prompt engineering, visual workflow building, vector database management, and operational monitoring in one interface. Dify offers 100% free open-source self-hosting via Docker alongside a hosted cloud free tier, allowing users to build production-ready AI chatbots, internal knowledge assistants, and automated AI agents with complete data control.

⚡Dify's key features
✓Visual LLM workflow orchestrator connecting AI models, APIs, and data nodes
✓100% free open-source self-hosting architecture deployable via Docker and GitHub
✓Native RAG pipeline supporting vector embeddings, document parsing, and hybrid search
✓AI agent framework supporting function calling, tool usage, and multi-step reasoning
✓Multi-model support integrating OpenAI, Claude, Llama, Ollama, and local models
✓Instant web application deployment generating shareable chatbot pages and widgets
✓REST API and SDK generation exposing visual workflows as scalable backend endpoints
✓Prompt engineering canvas with version control and side-by-side model comparison
✓Observability dashboard logging token usage, latency, and user interaction analytics
✓Enterprise team workspace with fine-grained permission controls and data privacy
🎯Use Cases
→Building private internal company chatbots that answer questions from internal PDFs
→Creating autonomous AI agents with web search and API execution capabilities
→Deploying customer-facing AI chatbot widgets on websites with custom branding
→Building backend LLM processing pipelines exposed as REST APIs for mobile apps
→Running 100% private offline AI workflows using local open-source LLMs
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⚖️Pros & Cons
✅ PROS
  • 100% free open-source self-hosting provides complete data privacy and zero vendor lock-in
  • Comprehensive LLM orchestrator combining RAG, AI agents, and visual workflows
  • Supports both local open-source models (via Ollama) and commercial AI APIs
  • Instantly deploys shareable chatbot widgets and REST API backend endpoints
  • Active open-source community on GitHub with rapid feature updates and plugins
❌ CONS
  • Self-hosting requires technical knowledge of Docker, server deployment, and SSL
  • Cloud hosted free tier limits message credits before requiring plan upgrades
  • Connecting complex local vector databases requires basic backend setup
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👥Who Is It For?
•Developers
•Startups
•AI engineers
•Enterprise teams
•Privacy-conscious companies
•Researchers
•Technical founders
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