Dify

What it's good for

• 1

  Deploying an LLM app on a client's own infrastructure with no usage caps

• 2

  Building a RAG chatbot over internal docs with a visual workflow instead of custom code

• 3

  Prototyping an agent flow that non-engineers can read and adjust

• 4

  Standing up an internal AI tool without wiring the orchestration layer from scratch

• 5

  Giving a client a self-hosted alternative to a hosted agent platform for data reasons

Pricing

• Sandbox

  Cloud, limited usage for trials

  Free

• Professional

  For individual builders and small teams

  $59/mo

• Team

  Higher limits and collaboration

  $159/mo

• Enterprise

  SSO, support, scale

  Custom

• Community Edition

  Self-host, source-available, no usage limits

  Free

How to use it

Self-host the Community Edition with Docker Compose, or start on the cloud Sandbox to try it fast. Build your app in the visual editor by chaining nodes: prompt steps, knowledge retrieval (RAG), tool calls, and conditional logic. Upload documents to a knowledge base and Dify handles the chunking and embedding for retrieval. Expose the finished app as a chat widget or an API. For client work, run the Community Edition on their infrastructure so data stays put, and reserve the paid cloud tiers for cases where a managed host is fine.

Pros & cons

Frequently asked questions

• Is Dify free?

  The self-hosted Community Edition is free and has no usage limits, which is the main reason we use it. The cloud has a free Sandbox for trials, then paid tiers: Professional at $59/mo, Team at $159/mo, and Enterprise custom. So it is free if you self-host, and freemium if you use the managed cloud.

• Is Dify open source?

  Not in the strict sense. It is source-available: the base is Apache-2.0, but two conditions apply, since you cannot resell it as a managed multi-tenant service and you have to keep its logo. That is why we call it source-available and self-hostable rather than fully open source. In practice you can still read the code and self-host it freely for your own use.

• Dify vs n8n: which should I pick?

  n8n is a general automation platform with strong AI nodes, good when the job is mostly connecting apps and moving data with some AI mixed in. Dify is purpose-built for LLM apps, so RAG, prompt orchestration, and chat interfaces are first-class. If the center of gravity is an AI app or chatbot, Dify fits; if it is broad workflow automation across many services, n8n fits.

• Dify vs LangGraph: when do I need which?

  Dify is a visual, lower-ceiling platform you can self-host and hand to non-engineers. LangGraph is a code framework for building agents with full control over state and branching. Reach for Dify when a visual builder gets the job done and you want it deployable fast; reach for LangGraph when you need code-level control that a visual tool cannot express.

• What is Dify best for?

  Building LLM apps and agents you can deploy on your own (or a client's) infrastructure without writing the orchestration layer by hand. The self-hostable Community Edition with no usage limits makes it a strong fit for data-sensitive deployments and internal AI tools.

More

• Dify docs
• Dify pricing

Alternatives to Dify

Other tools we'd consider for the same job.

• n8n

  Open-source workflow automation with first-class LLM + vector-DB nodes. Self-host or use their cloud

• LangGraph

  Code-level framework for building multi-step, stateful LLM agents. The serious engineer's choice for agent orchestration

• CrewAI

  Role-based multi-agent framework. Spin up a planner-researcher-writer crew in ~30 lines of Python

Other Agents tools

• Vapi

  Developer-first platform for building voice AI agents that make and take phone calls. Usage-based, per-minute pricing

• Retell AI

  Voice-agent platform to build, test, and deploy phone agents. Per-minute pricing with generous free concurrency

• n8n

  Open-source workflow automation with first-class LLM + vector-DB nodes. Self-host or use their cloud

• LangGraph

  Code-level framework for building multi-step, stateful LLM agents. The serious engineer's choice for agent orchestration