Dify 1.8 Complete Guide: Deployment and Building AI Applications

What Is Dify: An Enterprise-Grade AI Application Development Platform That Requires No Coding

Dify is a development platform purpose-built for creating AI applications. Contrary to what many people might expect at first glance, building AI applications with Dify requires virtually no coding — which is one of the key reasons it has gained rapid adoption among enterprises.

In short, Dify is a visual platform that lets you build AI applications quickly. It supports five main application types:

• Chatbot: Conversational AI interaction applications
• Text Generation: Content generation driven by prompts
• Agent: AI agents capable of autonomous decision-making
• Workflow: Automated task orchestration
• Chatflow: Workflow with built-in conversational capabilities

By comparison, some similar tools (such as Coze) support fewer application types, typically covering only Agents and email-based applications.

You might not have noticed that Workflow and Chatflow are essentially the same category — both are workflows — but Dify separates them to serve different use cases. This design strikes a balance between flexibility and accessibility for new users.

Why Choose Dify Over Other AI Orchestration Tools?

The major AI application orchestration tools on the market today include Dify, Coze, RagFlow, and n8n. In real-world enterprise deployments, Dify consistently ranks first in recommended priority, with Coze coming in second.

The Competitive Edge of Domestic AI Development Platforms

Compared to foreign counterparts, domestic platforms like Dify have matured considerably. Looking at two key dimensions:

• Feature coverage: Dify supports significantly more features than many foreign competitors, spanning knowledge bases, tool integrations, and complex workflow orchestration.
• Usability and accessibility: In terms of user experience and friendliness toward beginners, Dify leads the industry.

In other words, once you've mastered Dify, you can largely set aside most similar tools — there's little need to invest time learning them separately.

Deploying Dify 1.8.0 with Docker: A Practical Walkthrough

This guide is based on the latest Dify 1.8.0 release. Compared to older versions, the deployment process has been significantly streamlined, eliminating much of the tedious extra configuration.

Local Deployment in Three Steps

Here is the complete local deployment process:

Step 1: Navigate to the directory and extract the archive

cd into the Dify directory and extract the installation package.

Step 2: Enter the Docker directory

Once extracted, navigate into the docker folder within the project.

Step 3: Rename the environment variable file

You'll find a .env.example file in the directory. Simply rename it to .env. This file already contains all the required environment variables.

Key note: In older versions, you had to manually perform additional environment configuration. In version 1.8.0, almost no extra configuration is needed — just rename the file and you're ready to go.

Step 4: Launch all services with a single command

Run the following command to start all containers:

docker compose up -d

Image Size and Download Speed

Regarding resource usage, the total Docker image size for version 1.8.0 is approximately 6 GB, staying well under 10 GB — so disk space requirements are modest and easily handled by a standard development machine.

In terms of download speed, switching to updated image mirrors has noticeably improved pull times compared to earlier versions. All images can typically be downloaded and the service fully started within just a few minutes. This release also fixes numerous bugs present in older versions, resulting in better overall stability.

A Tour of the Interface Modules After Login

Once the service is running, you can access Dify's web interface by visiting the address of your deployment node and logging in.

The post-login interface is broadly consistent with earlier versions and includes the following core modules:

Module	Description
Explore	Browse and experience community templates and sample applications
Studio	The central area for creating and managing AI applications
Knowledge	Build the data foundation for RAG (Retrieval-Augmented Generation)
Tools	Manage external tools and plugins that can be called by your applications

Creating Your First AI Application

In the Studio, click Create Blank App to start building an AI application from scratch. You can choose any of the five application types mentioned above and quickly prototype something based on your actual needs.

Dify Learning Path: From Beginner to Production

For those just getting started with AI application development, the following learning path is recommended:

1. Set up your environment: Get the local Docker deployment running and confirm the environment is working.
2. Basic applications: Start with simple chatbots and text generation apps to understand fundamental prompt orchestration logic.
3. Advanced orchestration: Dive deeper into Workflow and Chatflow to master complex multi-node, multi-branch orchestration.
4. Production deployment: Combine knowledge bases and tool integrations to build complete AI applications that work in real business scenarios.

As a mature and user-friendly domestic AI application platform, Dify — paired with the simplified deployment process introduced in version 1.8.0 — has become the go-to choice for individual developers and enterprises looking to rapidly validate AI ideas. Whether you're a complete beginner or an experienced developer, Dify makes it efficient to integrate AI capabilities into real-world business workflows.