Hello everyone.
I've shared many practical articles about workflow platforms and LLM application platforms.
Mainly including: Dify, Coze, n8n, Fastgpt, Ragflow
But almost every article's comment section has people asking, "How does platform XXX compare to platform YYY, and which one should I choose?"
Well, here it is! Remember to like, share, and save.
Indeed, facing rapidly evolving AI technology and the swift development of various LLM platforms, it's easy to get choice paralysis.
But I want to say that each platform has its own advantages, and you should choose the one that suits your needs.
This article will approach from a practical perspective, providing a detailed functional comparison, real-world usage experience, and specific application scenarios to help you find the most suitable platform among Dify, Coze, n8n, FastGPT, and RAGFlow.
Whether you are an AI developer, enterprise user, or an AI novice, this comparative analysis will provide you with a clear selection guide.
This article is 5000 words long and packed with valuable information. I recommend saving it!
First, let's clarify:
LLM application platforms include: Dify, Coze, Fastgpt, Ragflow
n8n is a bit special; it's primarily a workflow-oriented LLM platform.
The core value of LLM application platforms lies in significantly lowering the development threshold for AI applications, accelerating the process from concept to product launch, and providing developers with a set of tools (plugins, MCP tools, etc.) for integrating, managing, and optimizing AI capabilities.
Through these platforms, we can focus more on business logic and user experience innovation, rather than repetitive underlying technology construction.
Let's briefly understand the characteristics of these platforms:
n8n: Known for its powerful general workflow automation capabilities, it has actively embraced AI in recent years, allowing users to embed LLM nodes into complex automated processes.
Coze (扣子): Launched by ByteDance, it focuses on low-code/no-code AI Agent development, emphasizing rapid building and deployment of conversational AI applications.
FastGPT: An open-source AI Agent building platform, focusing on building knowledge base Q&A systems, providing data processing, model calling, and visual workflow orchestration capabilities.
Dify: An open-source LLM application development platform that combines BaaS and LLMOps concepts, aiming to provide a one-stop solution for rapid AI application development and operations, including Agent workflows, RAG Pipelines, etc.
RAGFlow: An open-source RAG engine based on deep document understanding, focused on extracting knowledge and providing high-quality answers from complexly formatted documents.
Platform Details
Dify: The Swiss Army Knife of LLM Platforms
Let's give Dify 3 keywords:
#OpenSource #LLMOps #ProductionReady
In short: Dify is an open-source LLM application development platform released in April 2023. If you want to build professional, production-ready AI applications and handle backend and model operations, Dify is the way to go.
Address: dify.ai
Dify focuses on "Backend-as-a-Service" and "LLMOps", aiming to enable developers and non-technical innovators to easily get started and quickly develop practical AI solutions.
It integrates RAG (Retrieval Augmented Generation) pipelines, AI workflows, monitoring tools, model management, and MCP functions into one platform.
It really is like a Swiss Army knife; it has almost every feature you could want.
The main idea is: "You just innovate, Dify handles the rest."
On a side note, Dify recently underwent a brand refresh.
Supports private Docker deployment, with a minimum server configuration of 2 cores and 4GB RAM.
The community activity is also good; it currently has 98.3K Stars on Github.
However, it always gives me a feeling of "jack of all trades, master of none", as if there's no particularly outstanding feature.
Another drawback is that Bots created in Dify, if intended for external services, do not have OpenAI API compatibility, which makes integration with external applications relatively difficult.
Additionally, it might be a bit too heavy for users who just want to quickly implement small features.
For large enterprise integration, secondary development on top of Dify would likely still be required.
Suitable for: Developers with some technical background, teams pursuing professionalism and efficiency, and enterprises requiring customized AI solutions.
Friends interested in Dify practical operations can refer to my previous articles on Dify.
Coze: The "LEGO" of LLM Platforms
#NoCode #AgentBuilding #MultiPlatformPublishing
In a nutshell: Coze (扣子), developed by ByteDance, aims to make "everyone an AI developer" with thousands of built-in tool plugins, allowing you to easily create and publish AI Agents like building with LEGO bricks.
Address: coze.cn
Regardless of whether you know how to code, Coze enables you to quickly bring your AI Agent ideas to life.
Visual building, rich plugins, knowledge bases, and workflows are all available, and it supports one-click publishing to platforms like Douyin, Feishu, WeChat Official Accounts, Mini Programs, Discord, and Telegram.
There is an international version (Coze) and a Chinese version (扣子).
Coze is closed-source, but its features are richer than Dify.
I particularly like its code plugins, no-code mini-programs, web pages, and scheduled tasks.
Suitable for: AI beginners, product managers, operations personnel, creators who want to quickly build personalized AI Agents, and individuals and small teams with limited budgets and technical resources.
If you're unsure how to build an Agent in Coze, you can check out my previous article:
Building AI Agents with Coze
Kangaroo Emperor, Public Account: Kangaroo Emperor AI Inn - DeepSeek Integration with AI Agents, Super Fast Development, Even Beginners Can Easily Do It! [Hand-holding Tutorial]
FastGPT: The Knowledge Base Ace
#OpenSource #RAGKnowledgeBase
In a nutshell: FastGPT is a free and open-source AI knowledge base platform that allows AI to accurately answer questions based on your private data; it's your second "brain."
Address: tryfastgpt.ai
FastGPT offers data processing, model calling, RAG retrieval, and visual AI workflow, providing a one-stop service.
You can import documents in various formats (Word, PDF, web links, etc.) to quickly build an AI Q&A assistant for specific domains.
FastGPT's RAG effect is quite good. It can simply and quickly build a high-quality knowledge base. I used it before for customer service for my WeChat AI assistant product, and it worked very well.
I also helped some enterprise clients build knowledge bases with FastGPT; it's lightweight, simple, and easy to use.
It also provides an OpenAI-compatible API, making it very convenient to integrate into existing applications.
Supports Docker private deployment; it's best to run on a server with 2 cores and 4GB RAM.
Compared to Dify, its advantages include being more lightweight, having better knowledge base effects, and an OpenAI API compatible API, making it easier to integrate into other applications.
However, in terms of feature richness and some user experience aspects, it's not as good as Dify, and its community is less active. It currently has 24.2K Stars on Github.
But if you want to quickly build an AI application primarily focused on a knowledge base, I recommend trying FastGPT first.
Suitable for: Developers or enterprises needing to build internal knowledge bases or AI customer service, as well as AI enthusiasts interested in RAG technology.
FastGPT Practical Guide
RAGFlow: The Knowledge Base Expert
Tags: #OpenSource #RAGEngine #DeepDocumentUnderstanding
In a nutshell: RAGFlow is an open-source RAG engine.
Address: ragflow.io
RAGFlow's core competitiveness lies in "deep document understanding", for example, extracting clauses from contracts or summarizing lengthy reports. It also supports over 10 types of data preprocessing, offering a rich set of parameters for adjustment in both RAG knowledge base construction and Q&A stages. It also supports knowledge graph functionality.
RAG has fine granularity, and the knowledge base effect can be very high.
If FastGPT is a knowledge base novice, then RAGFlow is a knowledge base expert (as its name suggests).
Supports Docker deployment, but it is relatively heavy, requiring at least a 4-core 16GB server for smooth operation. It currently has 53.1K Stars on Github.
Suitable for: Industries with high demands for answer accuracy and traceability (e.g., legal, medical, finance), enterprises needing to process large amounts of complex documents, and RAG technology researchers and developers.
n8n: The Strongest Open-Source Workflow Platform
#OpenSource #WorkflowAutomation #LowCode
In a nutshell: n8n is an open-source low-code workflow automation tool focused on connecting various applications and services to form automated business processes.
Address: n8n.io
n8n's core is to build automated processes using visual nodes, and each node offers rich configuration parameters, allowing for high customization.
It provides over 400 pre-built integrations, covering various SaaS services and databases. Workflows can be built with simple drag-and-drop operations, or more complex customizations can be done with JS or Python code.
It includes Agent nodes, enabling quick integration with various large models, and also supports MCP.
In practical business scenarios, n8n can greatly improve work efficiency.
For example, Delivery Hero saved over 200 hours of work per month using n8n.
https://n8n.io/case-studies/delivery-hero/
StepStone also runs over 200 critical task processes with it.
https://n8n.io/case-studies/stepstone/
While n8n has many advantages, it is primarily a workflow platform. Its smoothness in the LLM aspect is not as good as other specialized LLM application platforms. Although it has the necessary LLM features, using them feels more cumbersome.
The learning curve is also the steepest among these platforms, requiring some logical thinking and initial learning investment, but efficiency will greatly improve once mastered.
It also supports Docker private deployment and is not resource-intensive; a 1-core 1GB server should be able to run it.
Suitable for: Teams requiring highly customized automation processes, developers, and small to medium-sized enterprises aiming for maximum efficiency.
Friends interested in n8n practical use cases can check out my previous articles on n8n.
Cross-platform Feature Comparison of 5 Major Platforms
To help everyone understand the differences and advantages of these five platforms more clearly, I have compiled a detailed comparison table, objectively analyzing from multiple dimensions:
Please note: Coze is no longer free.
Practical Advice for Platform Selection:
From my actual experience, if you are just starting with AI application development and want to see quick results, Coze is the easiest choice to get started with.
If your work or business involves data flow between multiple systems and services, and you need automated processing, n8n's powerful automation workflows will save you a lot of time.
If you want to build an internal enterprise knowledge base or Q&A system, FastGPT and RAGFlow are priority considerations. They are both strong in RAG, with FastGPT being lighter and RAGFlow heavier (but with higher potential).
For teams with long-term plans and needing to build scalable enterprise-grade AI applications, Dify's complete ecosystem and enterprise features are a good choice.
To make it more intuitive, based on my actual usage experience and the characteristics of each platform, I have compiled the "User Suitability Rating Chart" below (full score 5 points), hoping to help you quickly identify which platform corresponds to your needs:
You can also refer to the image below:
Key Considerations for Selection
Before making a final decision, I recommend considering the following key factors, which will directly impact your user experience and long-term effectiveness:
Budget:
Open-source platforms can be self-hosted for free, but you need to consider server and maintenance costs; cloud services are paid per usage or subscription, with lower upfront costs but potentially higher long-term expenses. Choose the appropriate solution based on your resources and business scale.
Technical Capability:
Assess your or your team's technical background and willingness to learn. If technical strength is limited, a no-code platform like Coze would be more suitable; if you have a strong technical team, platforms like Dify or n8n that offer more customization capabilities might be considered.
Deployment:
Consider whether local data privatization is needed. Self-hosting solutions offer higher data security and privacy protection but require more technical support; cloud services provide quick deployment and low maintenance costs but may involve data security risks.
Core Functional Requirements:
List your most critical requirements in detail and see which platform best meets these key points. For example, if RAG capability is most important, then FastGPT or RAGFlow might be more suitable than Coze; if complex workflows are needed, n8n or Dify would be better choices.
Platform Sustainability:
Evaluate the platform's update frequency, community activity, and long-term support. For open-source projects, look at community activity and contributor numbers; for commercial products, look at company background and market performance. This directly relates to whether your chosen platform can develop long-term and keep up with technological changes.
Data Security and Compliance:
Especially for enterprise users, data privacy protection, access control, and compliance are crucial. Open-source self-hosted platforms have an advantage in data security as data can be fully kept within your own environment; commercial platforms require careful review of their privacy policies and data processing agreements.
By carefully evaluating the above factors and combining them with the preceding comparative analysis, I believe everyone should be able to find the LLM application platform that best suits their needs.
"Finally"
After this comprehensive comparative analysis,
I hope everyone has a clearer understanding of Dify, Coze, n8n, FastGPT, and RAGFlow.
There is no perfectly ideal tool, only the one most suitable for current needs and development stages.
My advice is:
If possible, start by trying platforms with a lower barrier to entry (like Coze) to familiarize yourself with the basic concepts and processes of LLM application development;
Later, as your needs become more complex and your technical skills improve, gradually transition to more professional platforms (like Dify or n8n).
AI Agent is a rapidly developing field, and each platform is evolving and improving quickly.
I hope this analysis provides a basic reference framework,
Helping everyone find the right tools and direction in this AI era full of opportunities and challenges.
If you have any other questions or experiences to share, feel free to discuss them in the comments section~
That's all. If you've read this far and found it helpful, please give it a like, share, and save. If you want to receive push notifications first, you can also star ⭐ me~ Thank you for reading my article, and see you next time.
/ Author: Kangaroo Emperor AI Inn
/ For submissions or disclosures, please contact email: wzglyay@virxact.com