Dify: What It Is and How to Build AI Applications

What it is

Dify is an open-source application development platform created to help individuals and software teams build software powered by large language models (LLMs). The project draws its distinctive name from the phrase “Do It For You,” reflecting a core mission to eliminate the repetitive friction and heavy lifting typically involved in taking artificial intelligence ideas from concept to reality. Rather than requiring developers to piece together disjointed libraries, custom scripting, and fragmented infrastructure, Dify provides a unified, collaborative workspace designed to handle the core building blocks of modern AI applications.

A frequent challenge in artificial intelligence development is the gap between early experimentation and true production readiness. Builders frequently put together a prototype using temporary scripts and basic connectors, only to discover that turning that concept into a scalable, maintainable tool requires completely rewriting the application and rebuilding the underlying infrastructure. Dify is structured specifically to resolve this issue, allowing teams to move seamlessly from an initial prototype to a reliable production environment on the exact same stack.

To accommodate different operational requirements, Dify provides multiple deployment approaches. Teams can use the platform directly in the cloud, run it in a virtual private cloud (VPC), or host it locally within their own self-managed environments. Through this adaptable structure, Dify supports both solo creators assembling their first conversational tools and larger organizations building robust, enterprise-grade AI systems.

What it can do

Dify gathers a comprehensive set of development and management features into a single environment, covering every major stage of building and running an artificial intelligence product:

• Visual Workflow and Chatflow Canvas: At the center of the platform is a visual workspace where users can build, inspect, and evaluate multi-step AI workflows and interactive conversational flows (chatflows). By organizing logic across a visual canvas, developers can coordinate complex branching, manage data movement, and combine multiple tools without needing to write extensive glue code.
• Broad Model Integration: Dify features direct integration with hundreds of proprietary and open-source models supplied by dozens of inference providers and self-hosted model engines. The system supports leading model families such as GPT, Mistral, and Llama3, as well as any inference service compatible with the standard OpenAI API specification.
• Prompt IDE: Designing and optimizing prompts takes place inside a dedicated integrated development environment (IDE). This workspace allows users to author prompt templates, contrast how different models respond to the identical prompt, and add helpful interactive capabilities—such as text-to-speech functionality—directly to chat-based interfaces.
• Retrieval-Augmented Generation (RAG) Pipelines: Retrieval-Augmented Generation, often abbreviated as RAG, is a technique that supplies language models with external reference documents so they can generate grounded, factual answers. Dify delivers an end-to-end RAG pipeline that manages the entire lifecycle from document ingestion to indexing and retrieval. It provides built-in text extraction from common file types, including PDF documents and PowerPoint presentation decks (PPTs), making private knowledge simple to integrate into apps.
• Autonomous Agents and Tool Sandboxes: The platform supports autonomous agents that operate inside isolated sandboxes. In these secured environments, agents can execute command-line instructions, install needed software, and manipulate local files to accomplish open-ended goals. Users can describe the desired behavior of an agent, and the system assists in constructing it automatically. Furthermore, agents can be given custom skills and connected to tools from the Dify Marketplace, Model Context Protocol (MCP) servers, or custom external APIs. These agents can serve as standalone chat applications or function as individual execution steps within broader multi-stage workflows.
• LLMOps and Observability: Maintaining artificial intelligence applications in production requires ongoing oversight. Dify offers continuous operational monitoring and logging, allowing teams to analyze performance trends over time and inspect system logs. Using real-world operational traces and human annotations, operators can continually refine prompts, fine-tune models, and improve reference datasets. For advanced tracing and inspection, the platform integrates with observability tools such as Opik, Langfuse, and Arize Phoenix.
• Backend-as-a-Service (BaaS) and Developer APIs: Every workflow, agent, and application created within Dify comes equipped with corresponding REST APIs. This enables engineering teams to connect Dify’s reasoning engines straight into existing web applications, mobile software, or internal business services. Developers can also use the difyctl command-line interface to operate Dify applications directly from terminal sessions, shell scripts, automated continuous integration (CI) environments, or other automated agents. The platform can be extended further using custom plugins to add new models, tools, and integrations.

How to get started

Getting started with Dify can follow two primary paths: using the hosted cloud service or setting up a self-hosted instance on your own infrastructure.

For individuals who want to explore Dify without managing server infrastructure, the project provides Dify Cloud. This hosted service allows users to begin building applications immediately and includes a free Sandbox plan designed to help creators explore the platform’s core concepts right away.

For developers and organizations that prefer complete control over their software and data, Dify Community Edition can be self-hosted. The project recommends using Docker Compose as the most straightforward method to run the platform locally.

Before launching a self-hosted instance, verify that your machine has Docker installed alongside Docker Compose version 2.24.0 or later. To deploy the containers, open a terminal, navigate into the project’s repository directories, copy the sample environment configuration, and run the deployment command:

cd dify
cd docker
cp .env.example .env
docker compose up -d

Once the containers have finished launching, open a web browser and visit http://localhost/install to access the setup screen. From there, administrators can complete the initialization process and configure their workspace. If any obstacles arise during installation, the official documentation offers a comprehensive FAQ section to assist with troubleshooting. Additionally, developers who wish to contribute code or perform custom platform development can follow the documentation guide for deploying directly from source code.

Pricing and requirements

When planning a self-hosted installation of Dify, the host server must satisfy the project’s stated minimum system requirements:

• Processor: At least 2 CPU cores (CPU >= 2 Core)
• Memory: At least 4 GiB of RAM (RAM >= 4 GiB)
• Prerequisite Software: Docker and Docker Compose v2.24.0 or newer

In terms of licensing, the public GitHub repository is distributed under the Dify Open Source License, which is based on the Apache 2.0 license with additional conditions.

The platform is available across three primary operational editions: Dify Cloud, the self-hosted Dify Community Edition, and Dify Enterprise. Dify Cloud provides a free Sandbox plan to help new users explore the service immediately. For larger organizations with advanced governance and compliance needs, Dify Enterprise provides support for Single Sign-On (SSO), Role-Based Access Control (RBAC) security, dedicated self-hosting options, and formal Enterprise Support Service Level Agreements (SLAs).

Because specific tier pricing, usage allowances, and plan details are subject to updates, users should consult the official Dify pricing page for the latest plan terms and information.

Who it is for

Dify is designed for a broad spectrum of builders, engineering teams, and organizations seeking an integrated environment for generative AI:

• Software Engineers and Developers: Technical builders who need a comprehensive backend-as-a-service to handle prompt logic, document extraction, and model connections without having to write custom infrastructure code from scratch.
• Collaborative Product Teams: Groups of engineers, product managers, and domain specialists who benefit from an intuitive visual canvas where workflows and conversational chatflows can be designed, tested, and iterated together.
• Enterprise Organizations: Companies with strict governance, privacy, or infrastructure requirements that necessitate hosting software locally or inside a private virtual cloud (VPC), while taking advantage of role-based security and single sign-on.
• Builders of Autonomous Systems: Creators developing multi-step agentic workflows who require isolated sandboxes to execute code, alongside modular tools from marketplaces, external APIs, and Model Context Protocol servers.

Alternatives

Flowise is an open-source platform designed for building LLM applications and flows.

Langflow is an open-source visual tool for building and experimenting with AI pipelines.

Sources

• https://github.com/langgenius/dify
• https://docs.dify.ai/
• https://github.com/FlowiseAI/Flowise
• https://github.com/langflow-ai/langflow