What is Dify?

Dify is an open‑source platform that enables users to build agentic workflows without code, allowing developers and non‑technical teams to design complex AI pipelines through a drag‑and‑drop interface. It supports retrieval‑augmented generation (RAG) pipelines, providing context‑aware responses by bridging external data sources to large language models.

The tool offers native integration with multiple LLMs, including OpenAI‑compatible APIs, Ollama, and other commercial models, and can be extended through a plugin marketplace. Built on an enterprise‑grade architecture, Dify includes observability, logging, and performance monitoring to track usage and optimize operations.

Its modular design allows seamless connection of third‑party tools, databases, and APIs, simplifying workflow composition for data engineers and AI specialists. Dify can expose services as a universal Machine‑Learning‑as‑a‑Service (MLP) server, enabling easy deployment across internal environments.

Dify on the web:

Dify details

Company: LangGenius, Inc.
Jurisdiction: Delaware, United States
Built for: Businesses and teams
Data residency: United States
Stated compliance: SOC 2, ISO 27001, CCPA

Compliance as stated in Dify's own published documentation.

Dify tech specs

Works with: OpenAI / GPT Anthropic / Claude Google / Gemini DeepSeek xAI / Grok Perplexity
Integrations: Slack Discord GitHub

Dify pricing

Freemium

Sandbox $0

Get started $59 per workspace/mo

Get started $159 per workspace/mo

Auto-renewal: Yes

Trial, refund and renewal terms as stated in Dify's own published documentation.

Verify on the official pricing page.

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Dify's key features

Drag-and-drop no-code workflow builder
RAG pipeline integration
Native MCP integration for systems
Universal MCP server publishing
Marketplace for advanced LLMs
Concurrent multi-prompt execution

Dify use cases

Build an automated customer support chatbot that dynamically retrieves answers from your internal knowledge base and calls external APIs, all with drag‑and‑drop workflow design and no coding required.
Create a retrieval‑augmented recommendation engine that pulls product data from a database, uses multiple LLMs to generate personalized suggestions, and monitors performance with Dify’s observability features.
Deploy a custom language model as a production‑grade MLP server, expose it through Dify’s API layer, and track latency, usage, and errors across teams using built‑in monitoring tools.