Dify: a visual workshop for your AI applications

What you can build with Dify

Dify is developed by LangGenius, whose GitHub repository opened in 2023. From a web interface, you put together:

• chatbots that answer from your documents using RAG (upload PDFs, slides or pages, and Dify chunks and indexes them);
• AI agents that reason and call tools (plugins, MCP servers or your own APIs);
• visual workflows that chain model calls, conditions, knowledge-base lookups and requests to other services;
• single-shot text generators (product descriptions, summaries, translations).

Each app can be published as a web page, embedded in your site or called through its API. Dify can also expose an app as an MCP server, so a compatible assistant can use it as a tool. A plugin marketplace and app templates round things out.

Cloud or self-hosted

There are two ways to run it. Dify Cloud needs no installation: as of October 22, 2026, Sandbox is free, Professional costs $59 per month ($590 per year) and Team $159 per month ($1,590 per year), per workspace, with quotas on message credits, apps and documents. The Community edition runs on your own server with Docker Compose; it is free and your data stays with you, but maintenance is your job. Either way, model usage is billed by the model provider; the agent cost calculator helps you estimate it.

A word of caution for agencies: building a platform where each client gets their own Dify space counts as “multi-tenant” under the license and requires written permission.

Strengths and limits

Dify shines for assistants built on a document base: file ingestion, retrieval and conversation logs are built in, and the platform plugs into observability tools such as Langfuse, Opik and Arize Phoenix. It is a solid bridge between prototype and production.

The limits: Dify has fewer business-app connectors than a pure automation tool, and a complex workflow still requires understanding what the models are doing. Answer quality depends mostly on your documents and the model you pick.

Alternatives

Langflow takes a similar approach, leaning more toward Python developers. n8n is a better fit if your main need is moving data between apps. To code your agents end to end, a framework like LangGraph gives you more control.