N8N vs. Dify: Process Automation and AI Native Application Selection

I. Product positioning and core competence differences

This paper will systematically compare and analyze N8N vs. Dify The key differences between these two tools, in terms of product positioning, technical features, and application scenarios.

N8N Is an open source tool focused on workflow automation . Its core ability is to achieve cross-system data integration and efficient flow, especially for building complex inter-system linkage logic. It can be considered that N8N plays the role of "connector of the digital world". The platform also shows good friendliness to the community contribution node and custom node development, which is convenient for technicians to expand.

Dify It is positioned as an AI native application development platform. It focuses on using Large Language Models (LLM) to build intelligent applications, empowering these applications with "intelligent decision-making" and "natural language understanding" capabilities. Therefore, Dify is more like a "plug-in brain for LLM", which is dedicated to simplifying and accelerating the implementation of LLM applications.

II. Design philosophy and modus operandi

• N8N A mature visual drag-and-drop node configuration interface is used. The platform has more than 400 built-in nodes, which cover common cross-system process automation needs, such as HTTP requests, database operations, and integration of various cloud services. The target users are developers or operation and maintenance personnel with certain technical background.
• Dify Dify also uses a process-based node orchestration model for application building. However, Dify's focus is on the configuration of AI-related task flows, such as Prompt engineering and management, knowledge base access and optimization, and RAG (Retrieval Enhancement Generation) Rapid configuration of processes.

While both ostensibly use "node-based visualization" interactions, Dify's workflows are essentially developed and optimized around AI reasoning and semantic processing tasks.

III. Technical architecture and scalability

N8N

• The core is based on Node.js implementation, and supports the use of JavaScript and Python scripts for functionality extension and custom development.
• Through built-in nodes or nodes developed by third parties, N8N can call external AI services such as OpenAI API, thus indirectly realizing access to AI capabilities.
• N8N focuses more on the realization and management of "automation script logic" in the traditional sense, such as data processing, format conversion, timing execution, and system interfacing.

Dify

• The platform is deeply integrated with many mainstream large language models, such as OpenAI's GPT family of models, DeepSeek models, and Alibaba's Thousand Questions series of models, among others.
• Dify natively supports core AI application features such as knowledge base building, RAG technology, fine-grained cue word configuration, contextual memory management, and multi-model on-demand switching. This reflects its "AI-native" design approach, such as integration with vector databases and potential support for Agentic AI workflows.
• Therefore, Dify is more suitable for developing applications with advanced AI functions such as semantic understanding, intelligent Q&A, and content generation.

In short, Dify is designed for AI-driven application scenarios, while N8N is more of a "process engine" driven by traditional logic.

IV. Comparison of application scenarios

Specific case studies:

• If the goal is to build a "cross-border e-commerce" intelligent customer service system that supports seven languages, it would be more efficient to utilize Dify's built-in multi-language LLM support and intelligence building capabilities.
• If the task is to "synchronize data between the ERP system and the back-end database, and set up alarm notification mechanisms based on specific conditions", then N8N's powerful system integration and task scheduling capabilities will be the first choice.

V. Deployment methods and open source strategies

• Both N8N and Dify support private deployments. This is critical for scenarios where systems need to be deployed on an organization's intranet to ensure data security and meet compliance requirements, all without direct access to the public network.
• Both offer open source versions that allow users and developers to download, deploy, and modify on their own; they also offer more comprehensive enterprise versions with commercial support to meet the needs of organizations of different sizes.

It's worth noting that N8N and Dify are not mutually exclusive tools, and in many complex scenarios, they can be used in combination to capitalize on their respective strengths. For example:

• Use N8N to build the back-end automated scheduling logic responsible for data ETL (Extract, Transform, Load) and task triggering.
• Use Dify to build user-oriented front-end intelligent interaction systems, such as processing user's natural language questions, intelligent content recommendation, etc., whose required data can be pre-processed and provided by N8N.

VI. Comparison of core features at a glance