Dify 1.8 Deployment & Configuration Guide: Comparison with Coze, RagFlow, and N8N

Introduction

As one of the most popular open-source AI application development platforms today, Dify recently released version 1.8. Based on a hands-on walkthrough shared by a tech instructor on Bilibili, this article covers the deployment and configuration changes in Dify 1.8, the platform's core capabilities, and a comparison with similar tools (Coze, RagFlow, N8N) to help beginners quickly understand Dify's positioning and how to get started.

AI application development platforms (also known as LLMOps platforms) are a category of tools that have emerged over the past two years alongside the explosion of large language models (LLMs). Traditional AI development requires developers to have experience with deep learning frameworks (such as PyTorch and TensorFlow), along with handling complex tasks like model inference, prompt engineering, context management, and vector database integration. LLMOps platforms encapsulate these underlying capabilities into visual modules, allowing users to build AI applications through drag-and-drop and configuration. Dify is a representative open-source project in this space—its name derives from "Define yourself," emphasizing the philosophy of users autonomously defining their AI applications.

What's Changed in Dify 1.8 Deployment

Based on the instructor's hands-on testing, Dify 1.8 has no major functional changes, but there are differences in deployment methods and configuration workflows compared to previous versions. Users already familiar with the old deployment process should pay attention to adjustments in configuration files and environment variables.

Dify 1.8 Interface

Download and Installation

Dify 1.8 can be downloaded from the official GitHub repository—simply search for "Dify" to find the latest release. Since GitHub access can be unstable in China, users may need network tools or alternative channels to obtain the installation package.

Dify deployment is typically based on Docker Compose, a container orchestration technology that allows users to start multiple interdependent service containers (such as web frontend, API backend, PostgreSQL database, Redis cache, vector database, etc.) through a single YAML configuration file. The advantage of Docker containerization lies in environment isolation and consistency—whether on a local development machine or a cloud server, as long as the Docker engine is installed, you get an identical runtime environment. This also means that having Docker and Docker Compose correctly installed on your machine is a prerequisite for deploying Dify.

It's worth noting that the new version may present some inconsistencies with older versions during deployment, such as renamed configuration items or adjusted default parameters. Users are advised to carefully read the official Release Notes (the changelog document accompanying each version release that details new features, bug fixes, breaking changes, and migration steps) and migration guides before upgrading to avoid service startup failures due to configuration incompatibilities.

New Version Configuration Differences

What Is Dify: Five Application Types Explained

Dify's core positioning is as an AI application development platform—it enables users to quickly build various AI applications through a visual interface without writing code. This significantly lowers the barrier for non-technical users or teams looking to rapidly validate ideas.

Dify's Five Application Types

Dify currently supports five application types, offering broader capability coverage compared to Coze's two types (Azure-type and Email-type):

First three types: Basic application types for beginners, suitable for learning and simple scenario building
Workflow: An orchestration tool for complex business logic
Chatflow: Essentially a type of workflow, but specifically optimized for conversational scenarios

Dify Application Types

Although Workflow and Chatflow are categorized separately, their underlying logic is the same. Simply put: Workflow is better suited for automated task orchestration, while Chatflow is better suited for interactive conversational scenarios.

From a technical perspective, the Workflow concept originates from Business Process Management (BPM), with the core idea of breaking complex tasks into multiple sequentially executed nodes, each performing a specific function (such as calling an LLM, querying a database, executing code, conditional branching, etc.), connected by data flows. In Dify, the Workflow type is suitable for non-interactive scenarios like batch processing, scheduled tasks, and API triggers—for example, automatically generating weekly reports or batch-translating documents. Chatflow builds on Workflow by adding conversation state management mechanisms that maintain multi-turn dialogue context memory, support user intent recognition, and enable dynamic routing, making it more suitable for building intelligent customer service, knowledge Q&A, and other applications requiring sustained interaction. Both share the same node component library, but Chatflow additionally provides conversation-specific components like dialogue history injection and user input nodes.

For enterprise applications, these two types are the most commonly used and represent Dify's core competitive advantage over other platforms.

AI Platform Comparison: Dify vs Coze vs RagFlow vs N8N

Among the many AI application building tools available, how do you choose the platform that best fits your needs? The instructor provided a clear enterprise-level priority ranking:

Priority	Tool	Recommendation Rationale
🥇 First	Dify	Most comprehensive features, best usability
🥈 Second	Coze	Easy to get started, mature ecosystem
Reference	RagFlow / N8N	Each has strengths but doesn't match the top two overall

Platform Comparison Analysis

Why Dify Ranks First

The instructor believes that compared to international tools like RagFlow and N8N, Dify and Coze—as platforms that grew out of the Chinese market—have three significant advantages:

High feature completeness: Supports more capabilities than international counterparts, covering everything from simple chatbots to complex enterprise workflows
Excellent usability: The most user-friendly among all similar tools, allowing even complete beginners to get started quickly
Strong localization support: Smoother deployment and usage within the Chinese network environment, with a more active community ecosystem

RagFlow and N8N: Their Respective Strengths

Of course, tool selection ultimately depends on specific business scenarios. If your needs focus on RAG (Retrieval-Augmented Generation), RagFlow may be more specialized. RAG is one of the most critical technical architectures in enterprise AI applications today. Its basic principle is: when a user asks a question, the system first retrieves the most relevant document fragments from an external knowledge base (typically stored in a vector database), then feeds these fragments as context along with the user's question into the large language model, which generates answers based on real data. This approach effectively addresses two major LLM pain points—knowledge cutoff date limitations and hallucination (the model fabricating non-existent information). RagFlow has deeply optimized document parsing (supporting complex formats like PDF, Word, Excel), intelligent chunking, and hybrid retrieval (keyword retrieval + semantic vector retrieval). If your core scenario is enterprise knowledge base Q&A, RagFlow deserves serious consideration.

If you need complex automated workflow integrations, N8N has a richer connector ecosystem. N8N is an open-source workflow automation platform originating from Europe, positioned as an open-source alternative to Zapier or Make (formerly Integromat). It features over 400 pre-built third-party service connectors (called Nodes) that can chain together tools like Gmail, Slack, Notion, databases, and CRM systems for automation. With the AI wave, N8N has also integrated LLM calls, vector database operations, and other AI nodes, but its essence remains a general-purpose automation tool rather than a platform specifically designed for AI applications. Therefore, in AI-specific capabilities like prompt management, model switching, and conversation experience optimization, N8N falls short of Dify and Coze.

However, from the perspective of overall capability and beginner-friendliness, Dify is indeed the optimal choice at present.

Getting Started with Dify: Advice for Beginners

For users new to AI application development, here's a recommended learning path:

Step 1: Understand the Platform's Positioning

Dify is not a development framework that requires you to write code—it's a visual building platform. What you need to understand is the logic of AI applications (input → processing → output), not programming syntax.

Step 2: Start with Basic Application Types

Begin by practicing with Dify's first three basic application types to understand fundamental concepts like prompt configuration, model selection, and parameter tuning.

It's important to note that prompt configuration is one of the most critical skills when using AI application platforms. Prompt engineering refers to carefully designing the instruction text fed to large language models to guide them toward desired outputs. Common prompt techniques include: role assignment (having the model act as a specific expert), few-shot learning (providing several input-output examples), and Chain-of-Thought (requiring the model to reason step by step). Model parameter tuning involves key parameters such as Temperature (controls output randomness—lower values produce more deterministic results), Top-P (nucleus sampling, controlling the probability range of candidate tokens), and Max Tokens (maximum output length). In Dify, all these configurations can be completed through the visual interface, allowing users to experiment repeatedly without writing code to find the parameter combination best suited to their business scenario.

Step 3: Advanced Workflow Building

Once you've mastered the basics, try building Workflow and Chatflow type applications. These are the most commonly used capabilities in enterprise scenarios and represent the core functionality that demonstrates Dify's value.

Step 4: Project-Driven Learning

While theoretical learning is important, the most effective way to learn AI application development is through project-driven practice. Find a real business scenario (such as intelligent customer service, document Q&A, or a data analysis assistant) and go through the complete process from requirements analysis to application deployment.

Conclusion

Although Dify 1.8 doesn't bring revolutionary functional updates, the deployment and configuration optimizations show that the team is continuously polishing the product experience. As one of the most capable AI application development platforms available today, Dify is particularly well-suited for enterprises and individual developers looking to quickly deploy AI applications. Whether you're a complete beginner or a technical professional with some experience, Dify offers a sufficiently low entry barrier and a sufficiently high capability ceiling, making it well worth investing time to learn in depth.