Dify

What is Dify?

Dify is an open source LLM app development platform that lets you build, test, and ship AI applications from a visual interface. It combines a workflow canvas, retrieval-augmented generation (RAG), agent tooling, model management, and LLMOps observability in one system, so you can move from prototype to production without stitching those pieces together yourself.

What is Dify best for?

Dify suits teams that want to build production AI apps — chat assistants, RAG-backed support bots, internal copilots, and multi-step agents — without writing all the orchestration glue by hand. It fits both non-developers who work on the visual canvas and engineers who need the generated app exposed as a backend API. It is a strong choice when you want one platform covering building, model access, and monitoring rather than assembling separate libraries.

What can Dify do?

• Visual workflow builder — design, test, and orchestrate multi-step AI workflows on a drag-and-drop canvas.
• RAG pipeline — ingest PDFs, slides, and other documents, then chunk, embed, and retrieve them for grounded answers.
• Agent capabilities — build agents with LLM function calling or ReAct, using 50+ built-in tools plus your own.
• Broad model support — connect hundreds of proprietary and open models (GPT, Claude, Mistral, Llama, and any OpenAI-compatible API) across many inference providers.
• Prompt IDE — craft prompts and compare model outputs side by side before shipping.
• LLMOps observability — log requests, monitor performance, and improve apps from production data over time.
• Backend-as-a-Service APIs — embed any app you build into your own product through Dify’s APIs.

Where does Dify fall short?

Dify is broad rather than deep — for complex, highly custom logic you may still hit the ceiling of the visual builder and want code-first frameworks like LangChain. Its license is not standard Apache 2.0: it adds conditions that restrict offering Dify itself as a multi-tenant SaaS, so reselling a hosted Dify product needs a commercial agreement. And self-hosting only covers the platform — you still pay separately for model API calls or your own GPU inference, which is the larger cost in most production deployments.

Is Dify free?

Yes — the self-hosted Community Edition is free and open source, and you can run it with Docker Compose or Kubernetes at no license cost (you pay only for your server and model usage). Dify also offers a managed cloud with a free Sandbox tier (200 one-time message credits) and paid plans — Professional at $59/mo and Team at $159/mo — plus a custom Enterprise edition.

What does Dify replace?

Dify stands in for several commercial pieces at once. As a conversational AI and agent builder it overlaps with Dialogflow; its built-in logging and monitoring cover much of what a dedicated LLMOps service like LangSmith provides. Compared with open source peers, it sits alongside visual builders like Flowise and Langflow, but bundles RAG, model management, and observability more tightly than most.

FAQ

Is Dify open source? Yes. The source is on GitHub under the Dify Open Source License, based on Apache 2.0 with added conditions that mainly restrict offering Dify as a hosted multi-tenant SaaS. For self-hosting and building your own apps, it behaves like a permissive open source license.

Can I self-host Dify for free? Yes. The Community Edition is free to self-host via Docker Compose, source, or Kubernetes Helm charts. You only pay for your server and for the LLM API calls or GPU inference your apps make.

What do I need to run Dify? The documented minimum is 2 CPU cores and 4 GB RAM, plus Docker for the standard Compose deployment. You will also need API keys for at least one model provider, or a self-hosted model endpoint, for your apps to call.

Is Dify a good Dialogflow alternative? For teams that want to own their stack, yes — Dify builds conversational agents and assistants with RAG and tool use, runs on your own infrastructure, and works with any model, rather than locking you into one vendor’s cloud.