In-depth review of n8n, Dify, and Coze: From 0 to 1, choose the right AI automation platform and avoid 99% of the pitfalls

n8n : Founded by German Jan Oberhauser in 2019, its original intention was to solve the problems of insufficient flexibility and high cost of automation tools such as Zapier. n8n adopts an open source model with the concept of "free, sustainable, open and pragmatic". It is committed to enabling different users to connect various applications through visualization and code dual modes, thereby automating complex processes. Its technical architecture is node-driven and has more than 400 pre-built nodes, basically covering mainstream applications such as Notion, Feishu, OpenAI, MySQL, etc. It also supports custom JavaScript/Python nodes and can splice out a variety of workflows. In addition, n8n is compatible with all platforms, whether it is local NAS/server or cloud Docker/Kubernetes deployment, so as to ensure the autonomy and controllability of data and meet the strict requirements of data compliance in industries such as finance and medical care. In terms of operation mode, business personnel can quickly build basic processes by dragging and dropping nodes, while developers can achieve deep customization with the help of code nodes, which well balances ease of use and scalability.

Dify : Founded in 2023 by Zhang Luyu, who was previously an entrepreneur from Tencent. Dify first proposed the concept of "LLMOps", with the goal of lowering the threshold for large model application development, positioning itself as an enterprise-level AI application development platform, using open source and supporting private deployment, focusing on the deep integration of large models and workflows, and striving to make the development of complex AI applications as simple as building blocks. In terms of technical architecture, Dify takes large models as its design concept, has built-in mainstream model interfaces such as OpenAI, DeepSeek, and Llama, and supports the RAG (retrieval enhanced generation) framework, which can access corporate documents with one click and generate intelligent knowledge bases. Its low-code workflow supports operations such as conditional branches, loops, and sub-processes. With API nodes, it can realize the linkage between model calls and external tools. For example, after a user asks a question, the model generates an answer and triggers the work order system. In addition, Dify also provides Backend-as-a-Service (BaaS), which has functions such as traffic monitoring, log analysis, and permission management, and is suitable for enterprise-level deployment in high-concurrency scenarios.

Coze : This is a zero-code platform launched by ByteDance in 2025. It is positioned as a "new generation of AI application development artifact" and focuses on "building a chatbot in 5 minutes". It is mainly aimed at individual developers with zero foundation and small and medium-sized teams, emphasizing the lightweight experience of "use it and go", and deeply connected to ByteDance ecosystems such as Douyin and Feishu. Its technical architecture is designed with dialogue priority as the core, and presets a variety of "intelligent body" templates such as customer service, assistant, and translation. Users can configure the dialogue process by dragging and dropping, and support multi-round dialogue memory and plug-in calls, such as weather query, express tracking and other functions. At the same time, Coze supports one-click publishing to platforms such as Douyin, WeChat, and Feishu, and comes with user management and payment functions, which is suitable for quick verification of MVP (minimum viable product).

2. Comparison of core advantages

n8n : One of its advantages is that it is open source and free, and data sovereignty is completely independent. The code is completely public on GitHub, and the number of stars has exceeded 620,000. Users can deploy it privately on the corporate intranet through Docker, which can ensure zero risk of sensitive data leakage. It is a must-have for scenarios with strict compliance requirements such as finance and medical care. In addition, the self-hosted model does not require any fees. Compared with Dify, which requires its own model API key (such as OpenAI's paid interface) and Coze Enterprise Edition, which has high fees, n8n is obviously a better choice for teams with limited budgets. Its second advantage is the super integration capabilities demonstrated by more than 400 nodes, which can cover a variety of applications from traditional databases (MySQL, PostgreSQL) to cloud services (AWS S3, Google Sheets), and even hardware devices (Arduino), truly realizing "cross-system data synchronization + business process automation." In actual cases, a cross-border e-commerce company used n8n to connect Shopify orders, logistics API, and Kingdee ERP, automatically completing the entire process of "order creation → inventory deduction → logistics order number return → financial accounting", reducing manual intervention by 80%, saving an average of more than 300 hours per month. Its third advantage lies in the dual-engine mode of code and visualization, which can adapt to different types of users. Non-technical users can easily build basic workflows such as "form submission → approval process → data archiving" by dragging and dropping nodes such as "HTTP request", "email notification", and "data filtering"; while developers can use JavaScript/Python nodes to write custom logic, such as data encryption, complex algorithms, etc., and even connect to their own microservices. For example, a technology company used n8n to achieve real-time linkage of "user behavior data → AI model analysis → personalized recommendation interface", shortening the development cycle by 60%. However, n8n also has some disadvantages, such as the relatively high learning threshold. Users need to understand API concepts and workflow logic, and novices usually need 3-5 days of system learning (but the official documents and domestic blogger "Crazy Brother AI" provide more detailed tutorials). In addition, there are few Chinese resources. If you want to understand its deep functions in depth, you need to refer to English documents or community cases.

Dify : One of its advantages is the full-link support of LLMOps, which can effectively lower the threshold for AI development. It has built-in functions such as model parameter configuration, prompt engineering optimization, and performance monitoring. It can call large models to complete tasks such as text generation, image recognition, and code review without manually writing complex code. Its RAG capability is also outstanding. It can upload corporate documents (such as PDF, Excel, Markdown, etc.) with one click, automatically generate vector indexes, and realize "document content question and answer + business process triggering". For example, a law firm used Dify to build a contract review robot, which increased the review efficiency by 90% and the accuracy of risk clause identification by 95%. Its second advantage is that it has complete enterprise-level functions and is suitable for production environments. It supports dynamic switching of models according to business scenarios, such as using DeepSeek to handle Chinese customer service during the day and GPT-4 to handle English consultations at night. Load balancing can ensure high concurrency stability. At the same time, it also provides API gateways, operation audits, data encryption and other functions to meet compliance requirements such as GDPR and Level 3 security protection, and is suitable for governments and financial institutions to deploy intelligent customer service, risk control and other systems. The third advantage is the balance between low code and high scalability. Through the drag-and-drop combination of "model call node + tool node", the closed loop of "user input → model processing → database operation → message notification" can be quickly realized without writing complex glue code. In addition, it supports access to custom models and external tools. For example, an e-commerce team used Dify to develop a "product description generation + SEO optimization" tool, which combined with internal product library data increased the generation efficiency by 5 times. However, Dify also has some disadvantages, such as high model call costs and reliance on third-party API payment interfaces such as OpenAI. Large-scale use may cause costs to soar. For non-technical users, it is necessary to understand concepts such as "vector database" and "model tuning". The entry threshold is higher than Coze, but lower than n8n.

Coze : One of its advantages is its extremely simple operation, which enables AI applications to be launched in 5 minutes. It provides more than 100 pre-made templates, such as customer service robots, WeChat assistants, English training, etc., without writing any code. Development can be completed by "configuring the dialogue process + adding plug-ins", which truly realizes "building AI agents with zero code". For example, a college student used the Coze template to build a "course schedule query robot", connected to the school's academic system API, and launched it within 30 minutes and published it to the WeChat public account. The number of users in the first week exceeded 2000 +, and even zero technical foundation can easily realize application monetization. Its second advantage is that it is deeply bound to the Byte ecosystem and traffic monetization is convenient. It supports one-click deployment to Byte products such as Douyin, Feishu, and Toutiao, and comes with user management, paid Q&A and other functions, which is suitable for individual developers to quickly reach a large number of users. In addition, it is deeply connected with Douyin e-commerce, Feishu documents, etc. For example, a self-media used Coze to build a "Douyin comment area smart reply robot", which automatically identifies keywords and guides users to jump to product links, and the conversion rate increased by 30%. The third advantage is that the free version is relatively complete and the cost of trial and error is extremely low. It provides 100,000 model calls, a basic knowledge base (6,000 Tokens), and basic workflows, which can meet 90% of lightweight scenario requirements, such as personal assistants, simple customer service, etc. Users do not need to register a complex account, and can quickly verify their creativity by logging in with one click through WeChat/TikTok. However, Coze also has some disadvantages, such as relatively shallow functions and the inability to implement complex logic, such as multi-system data synchronization, scheduled tasks, custom code, etc. It only supports simple conversations and plug-in calls. Data is stored in the cloud, and enterprise-level scenarios require the purchase of the advanced version (the fee is charged according to the number of calls), and the deep integration capability is far weaker than n8n and Dify.

3. In-depth analysis of applicable scenarios

n8n is suitable for scenarios with complex automation and deep AI integration. In terms of enterprise-level process automation, such as supply chain management, it can connect to ERP (such as SAP), WMS (warehouse management system), and TMS (logistics system) to automatically process the entire process of "purchase order → inventory allocation → logistics scheduling → financial settlement". In terms of data platform construction, it can extract data from MySQL, MongoDB, Excel, etc. at regular intervals, synchronize to the data warehouse (such as Snowflake) after cleaning, generate reports, and notify management through WeChat for business, which improves data synchronization efficiency by 90%. In terms of deep integration of AI + business, such as intelligent customer service system, after user consultation, n8n calls the big model to generate answers, synchronizes the work order system to create tasks, and triggers WeChat for business to notify customer service to follow up, supports multiple rounds of dialogue and context memory, and improves customer service response speed by 50%. In terms of marketing automation, after user registration, a welcome email is sent, the model is called based on browsing behavior to generate personalized recommendations, synchronizes the CRM system to update customer tags, and triggers SMS marketing. An e-commerce platform has increased its repurchase rate by 25% through this process. In addition, n8n is also a must-have tool for technical teams with development capabilities. It can be used to customize nodes, connect to own systems, or achieve deep coupling of "AI models + traditional business systems", such as industrial robot control, financial risk control model integration, etc.

Dify is applicable to the development of AI applications driven by large models. In terms of knowledge base and intelligent question and answer, such as enterprise document customer service, after uploading product manuals and policy documents, Dify automatically builds a knowledge base and supports "document content question and answer + business process triggering". For example, if a user asks about the "return policy", the document terms can be returned and a return work order can be generated. A bank has reduced repeated consultations by 80% with this solution. In terms of vertical field tool development, such as code assistants (accessing the GitHub code library, generating code solutions), legal contract review (parsing PDF contracts, marking risk terms, and generating review reports), medical consultations (connecting to electronic medical records, generating diagnostic recommendations), etc., can all be quickly implemented through Dify. In terms of enterprise-level AI middle platform construction, a "model as a service" (MaaS) platform can be built to uniformly manage multiple large models (such as GPT-4, DeepSeek-V3), provide standardized API interfaces (such as customer service API, content generation API), support load balancing and dynamic expansion and contraction, and is suitable for large enterprises to build AI infrastructure. In terms of cross-border business support, it supports automatic switching of multi-language models to adapt to overseas market needs. For example, a cross-border e-commerce company used Dify to develop a multi-language customer service robot that handles consultations in Chinese, English, and Japanese at the same time, reducing labor costs by 60%.

Coze is applicable to scenarios : mainly for quick verification and lightweight applications. In terms of personal efficiency tools, such as WeChat/Feishu robots, you can automatically reply to messages, check the weather, send daily to-do items, organize meeting minutes, etc., which can be built in 5 minutes through Coze templates without server deployment. In terms of social media assistants, you can monitor Weibo/TikTok keywords, automatically reply to negative comments and mark them for manual review after finding them. A brand uses this tool to shorten the public opinion response time to within 5 minutes. In terms of MVP verification for small and medium-sized teams, such as e-commerce customer service robots, they can be quickly deployed in TikTok stores to handle high-frequency issues such as "logistics query" and "return and exchange", freeing up manpower to invest in complex consultations. A startup team used Coze to reduce customer service costs by 40%. In terms of educational lightweight applications, such as English word check-in robots (daily word push, user check-in, generation of learning reports), homework submission assistants (automatic reminder of homework deadlines, receiving files, and classified archiving), etc., you can serve thousands of users with zero technical investment. In addition, Coze is also suitable for lightweight data processing, such as simple data tasks such as "Excel data cleaning → generate charts → send emails", but it is not suitable for complex data synchronization or cross-system processes.

4. Horizontal comparison table

5. Practical cases: best practices in different scenarios

Case 1: Cross-border e-commerce automation

• n8n solution : realize full process automation through node combination, such as connecting Shopify order monitoring, MySQL inventory deduction (supporting multi-warehouse allocation logic), logistics API to obtain order numbers, enterprise WeChat notification operations, and calling OpenAI to generate customer service replies (including logistics links). Its advantage lies in deep integration across systems, inventory deduction supports complex business logic, such as pre-sale order priority processing, and local data storage complies with GDPR.

• Dify solution : The core lies in the combination of intelligent customer service and knowledge base. For example, after the order data is connected, the logistics query script is generated through the RAG model (combined with historical order data), and connected to the customer service work order system (such as Zendesk). Its advantage is that the customer service response is more intelligent, for example, it can inform "your package has arrived at Shanghai Customs and is expected to be cleared within 3 days", which is suitable for building a closed loop of "intelligent question and answer + work order processing".

• Coze solution : Focus on rapid deployment, select the "e-commerce customer service template", configure the "logistics inquiry" and "return and exchange" dialogue process, and publish it to the Shopify store chat window. Its advantage is that it can be launched within 30 minutes, which is suitable for handling high-frequency simple problems, but it cannot handle complex logic such as inventory synchronization.

Case 2: Building an enterprise knowledge base

• n8n solution : highly customizable, such as SFTP monitoring of new documents, PDF parsing (calling Python nodes), vector database storage (Milvus), calling OpenAI to generate answers, and creating knowledge cards in the OA system (automatically associated with department permissions). Its advantages are support for document version management, permission control (such as financial documents are only visible to the finance department), and custom parsing logic (such as extracting key terms of contracts).

• Dify solution : out of the box, after uploading the document with one click, the knowledge base is automatically generated (supporting rich text replies and link jumps), and the question-and-answer process is configured (such as "reimbursement process" triggering document section jumps). Its advantages are built-in document parsing and similar question recommendations, which are suitable for quickly building an "employee training knowledge base" without having to worry about vector database configuration.

• Coze solution : lightweight application, select "document question and answer robot", set keywords to trigger replies after uploading documents. Its advantage is that it is suitable for internal use by small teams, such as "company system query", but it does not support complex permissions and document parsing.

Case 3: Personal Productivity Tools

• n8n solution : suitable for technology enthusiasts, such as monitoring through RSS subscription, AI-generated news summaries, Notion automatic archiving, and scheduled email push (combined with JavaScript nodes to control the length of summaries). Its advantage is that it is highly customizable and suitable for creating personalized information aggregation tools.

• Coze solution : Suitable for users with no basic knowledge. Select "News Push Robot", configure subscription keywords, push summary to WeChat at a fixed time every day, and click the link to jump to the original text. Its advantage is that it does not require code and can be completed in 5 minutes, which is suitable for quickly meeting basic needs.

Selection Guide

1. Clarify the priority of requirements : If the requirements focus on complex processes and data security, n8n is preferred, which is suitable for enterprise-level automation and cross-system integration; if the requirements focus on large model applications and knowledge base construction, Dify is preferred, which is suitable for scenarios such as intelligent customer service, contract review, and multi-language support; if the requirements are for rapid online launch and lightweight scenarios, Coze is preferred, which is suitable for personal assistants, simple customer service, MVP verification, etc.

2. Assess team capabilities : For teams with no basic knowledge, Coze is a more suitable choice as it is easy to get started with zero-code and template-driven features. For small and medium-sized technical teams, Dify's low-code model takes into account both ease of use and AI capabilities. For senior development teams, n8n's fully customizable features provide unlimited expansion possibilities.

3. Consider cost and compliance : When budget is limited and data is sensitive, n8n's open source self-hosted model is free and data is self-controllable; in enterprise-level compliance and high-concurrency scenarios, Dify's production-level support and complete compliance functions are ideal choices; for personal monetization and traffic-oriented needs, Coze relies on the ByteDance ecosystem to quickly reach users.

Future Trends

1. n8n : Plans to launch the "AI Node Market" to gather large model processing and data science nodes shared by developers around the world, and is committed to becoming a super connector of "AI + traditional tools". At the same time, it will strengthen enterprise-level functions such as workflow version control, fault recovery, and performance monitoring, with the goal of becoming a standard for enterprise automation infrastructure.

2. Dify : Focus on developing the "model fine-tuning + production monitoring" capability, support private model deployment, such as the access of large models developed by enterprises, benchmark AWS Bedrock. Deepen industry solutions, launch exclusive templates such as finance and medical care, and lower the threshold for development in vertical fields.

3. Coze : Relying on traffic portals such as Douyin and Feishu, it has created an "AI application supermarket" and provided a large number of industry templates, such as e-commerce shopping guides and education training, so that ordinary people can quickly realize profits through zero code. It has strengthened multimodal support, accessed voice and video interactions, and expanded intelligent hardware control scenarios, such as real-time interactive robots for Douyin live broadcasts.

Conclusion

Choosing n8n, Dify or Coze is essentially to determine the most suitable platform based on your own needs, team capabilities and cost considerations. n8n is suitable for users who pursue "borderless automation" and want to control every line of code; Dify is suitable for users who focus on "large model landing" and need enterprise-level stability; Coze is suitable for users who just want to "quickly try and fail" and build lightweight applications with zero code. The value of technology lies in simplifying complexity. I hope this guide can help you find the most suitable AI automation platform and make it a powerful tool to improve productivity.

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