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📚Complete Guide

Dify Tutorial: Get Started in 5 Minutes [2026]

Master Dify with our step-by-step tutorial, detailed feature walkthrough, and expert tips.

Get Started with Dify →Full Review ↗
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Getting Started with Dify

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Define your first Dify use case and success metric. Connect a foundation model and configure credentials. Attach retrieval/tools and set guardrails for execution. Run evaluation datasets to benchmark quality and latency. Deploy with monitoring, alerts, and iterative improvement loops.

💡 Quick Start: Follow these 1 steps in order to get up and running with Dify quickly.

🔍 Dify Features Deep Dive

Explore the key features that make Dify powerful for automation & workflows workflows.

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❓ Frequently Asked Questions

Is Dify free and open source?

Yes. Dify is released under an open-source license and can be self-hosted at no cost using Docker Compose or Kubernetes. The team also offers a managed cloud service with paid tiers for users who prefer not to manage infrastructure, plus enterprise plans with SSO, advanced RBAC, and SLA support.

Which LLMs and model providers does Dify support?

Dify is model-agnostic and supports hundreds of providers including OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, Mistral, Cohere, DeepSeek, Qwen, and Llama. It also integrates with locally hosted runtimes such as Ollama, vLLM, LocalAI, and Xinference, allowing fully on-premise deployments.

How does Dify compare to LangChain or LangGraph?

LangChain and LangGraph are code-first Python libraries for building LLM applications, while Dify is a complete platform that wraps similar capabilities behind a visual builder, hosted UI, RAG engine, and observability layer. Teams that want full programmatic control may prefer LangGraph; teams that want a deployable product with less boilerplate typically prefer Dify.

Can Dify handle Retrieval-Augmented Generation (RAG)?

Yes. Dify includes a built-in knowledge base feature that ingests PDFs, Word documents, web pages, and structured data, then handles chunking, embedding, vector storage, hybrid search, and reranking. Knowledge bases can be attached to any chatbot, agent, or workflow without external infrastructure.

Is Dify suitable for production deployments?

Yes. Dify exposes every application as a REST API, supports horizontal scaling on Kubernetes, and includes logging, prompt versioning, and analytics for production monitoring. Many companies run customer-facing chatbots and internal copilots on Dify, though teams with strict compliance needs typically choose self-hosted or enterprise tiers.

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Ready to Get Started?

Now that you know how to use Dify, it's time to put this knowledge into practice.

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Try It Out

Sign up and follow the tutorial steps

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Start Using Dify Today

Follow our tutorial and master this powerful automation & workflows tool in minutes.

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Tutorial updated March 2026