Choosing the Right LLM Platform: A Practical Guide to Dify, Coze, n8n, FastGPT, and RAGFlow
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“The following article is from 袋鼠帝AI客栈 Author 袋鼠帝”
Over time, I’ve shared a number of in-depth articles on workflow automation tools and LLM application platforms—especially around tools like Dify, Coze, n8n, FastGPT, and RAGFlow.
And without fail, the comments section is always filled with questions like:
“How does Platform A compare to Platform B?”“Which one should I choose?”
So here it is—your long-awaited comparative guide. Bookmark it. It’s packed with insights, and yes, it’s a long one (around 5,000 words), but worth the read.
In the rapidly evolving world of AI and LLM platforms, it’s easy to get stuck in “decision paralysis.”
But the key takeaway is this:
Each platform has its own strengths. The best one is the one that fits your needs.
This article breaks things down from a practical perspective—covering feature comparisons, real-world usage insights, and ideal application scenarios—to help you find the platform that works best for you.
Whether you’re an AI developer, a business user, or just getting started, this guide offers a clear path to making the right choice.
Quick Definitions
- LLM Application Platforms: Dify, Coze, FastGPT, and RAGFlow
- Workflow Platform with LLM support: n8n (distinct from the others in its focus on automation)
LLM platforms aim to simplify AI development, offering tools to help bring ideas to life faster, while enabling integration, management, and optimization of AI capabilities (via plugins, model management, etc.).
These platforms let you focus on business logic and user experience, not low-level technical infrastructure.
Overview of Each Platform
Dify – The Swiss Army Knife of LLM Platforms
Keywords: #OpenSource #LLMOps #ProductionReady
One-liner: An open-source LLM app development platform launched in April 2023—perfect for building production-ready AI apps with backend, model ops, and monitoring all baked in.
🌐 dify.ai
Dify combines Backend-as-a-Service and LLMOps concepts, making it accessible for both developers and non-tech users. It supports workflows, RAG pipelines, plugin systems, monitoring tools, and model management—all within a single platform.
Like a true Swiss Army knife, Dify covers a lot of ground. The idea is:
“You focus on innovation, Dify handles the rest.”
It supports Docker-based self-hosting (recommended: 2 cores, 4GB RAM), and the open-source community is thriving—over 98.3K stars on GitHub.
However, the flip side is that while it does everything decently, it doesn’t truly excel in any one area.
Also, for bots built on Dify, external integration is tricky since its API isn't OpenAI-compatible—making it harder to connect with third-party tools.
For users who just want to build something lightweight and fast, Dify might feel a bit heavy. Larger organizations will likely need custom development on top.
Best for: Technical teams, developers, or enterprises looking for scalable, professional-grade AI solutions.
Coze – The LEGO of LLM Platforms
Keywords: #NoCode #AIAgents #MultiPlatformDeployment
One-liner: Built by ByteDance, Coze empowers anyone to build and deploy AI agents using drag-and-drop tools and thousands of plugins—like building with LEGO blocks.
🌐 coze.com
Whether you can code or not, Coze lets you bring your AI ideas to life fast.
With rich plugins, knowledge base support, workflows, and visual UI tools, you can deploy AI agents across platforms like TikTok, Feishu, WeChat, Mini Programs, Discord, Telegram, and more.
There’s a domestic version (扣子) and an international version (Coze).
It’s not open-source, but it offers more out-of-the-box features than Dify.
Some of my favorite features include no-code app builders, web UI deployment, scheduled tasks, and a highly customizable agent setup.
Best for: Beginners, product managers, marketers, creators, and small teams with limited budgets or technical resources.
FastGPT – Lightweight RAG & Knowledge Base Pro
Keywords: #OpenSource #RAG #KnowledgeBase
One-liner: A lightweight, open-source RAG platform that lets you create smart knowledge-based Q&A systems powered by your private data.
FastGPT offers data ingestion, model invocation, RAG pipelines, and visual workflows all in one. You can import Word, PDF, and URLs to quickly build a domain-specific AI assistant.
Its RAG performance is impressive, and it's my go-to tool when building internal AI assistants or customer service bots for WeChat.
It also supports OpenAI-compatible APIs, making it easy to integrate into existing systems.
Docker deployment is supported, and it runs smoothly on 2 cores / 4GB RAM.
While less feature-rich and less community-active than Dify (24.2K GitHub stars), it’s lighter, easier, and better optimized for knowledge bases.
Best for: Developers or teams building AI-powered knowledge systems, internal documentation assistants, or AI customer support.
RAGFlow – The Knowledge Extraction Specialist
Keywords: #OpenSource #RAGEngine #DeepDocUnderstanding
One-liner: A high-end RAG engine focused on deep document understanding, capable of extracting structured knowledge from complex files.
Where FastGPT is a quick helper, RAGFlow is the expert.
Its core strength lies in fine-grained document analysis—e.g., extracting clauses from contracts or summarizing lengthy reports. It supports 10+ preprocessing types, advanced Q&A parameters, and even knowledge graph generation.
With Docker support (requires at least 4 cores / 16GB RAM), it’s heavier than FastGPT but offers higher ceiling in terms of capability.
Currently 53.1K GitHub stars.
Best for: Legal, healthcare, finance, or research fields where answer accuracy, explainability, and document complexity matter.
n8n – The Ultimate Open-Source Workflow Engine
Keywords: #OpenSource #WorkflowAutomation #LowCode
One-liner: n8n is an open-source, low-code automation platform designed to connect various services and streamline business processes.
🌐 n8n.ioWith a drag-and-drop interface and over 400 built-in integrations, n8n lets you build complex workflows visually—or dive into custom JS/Python if needed.
It now supports LLM Agent nodes, MCP, and more—making it a strong player in the AI+Automation space.
Real-world case studies show its impact:
- Delivery Hero saves 200+ hours/month
- StepStone runs 200+ critical workflows with it
Although it supports LLMs, it’s still first and foremost a workflow platform. LLM capabilities are solid but not as fluid as purpose-built AI platforms like Dify or Coze.
Steeper learning curve than others, but once you get it, the efficiency gains are massive.
Docker deployment is supported, and it’s extremely light—1 core / 1GB RAM is enough to run.
Best for: Developers and teams needing complex workflow automation, especially when integrating with multiple systems.
Feature Comparison: Five Platforms at a Glance
To help you navigate the differences more easily, here’s a visual comparison chart (see image above in original post). Key areas include deployment model, API compatibility, RAG support, no-code options, and integration capabilities.
⚠️ Note: Coze is no longer free for some features.
Criteria | Dify | Coze | n8n | FastGPT | RAGFlow |
Core Focus | Comprehensive LLM application platform (BaaS + LLMOps) | Rapid development & multi-platform publishing of AI apps & chatbots | Workflow automation & app integration | AI knowledge-base construction & Q-A system | RAG engine focused on deep document understanding |
Open-Source | Yes | No (Bytedance product) | Yes | Yes | Yes |
Deployment Options | Cloud SaaS, self-hosted, enterprise edition | Cloud service | Cloud service; self-hosted (Docker, desktop app) | Cloud service; self-hosted (Docker) | Cloud demo; self-hosted (Docker) |
Target Users | Developers, non-technical innovators, teams of any size, enterprises | All users (regardless of coding experience), developers, creators | Technical teams, developers, data-driven companies, SMEs | Developers, enterprises, users needing domain-specific AI assistants | Developers, enterprises, users requiring RAG capability, niche industries |
Key Features | RAG pipelines, AI workflows, agents, model management, observability, plugin marketplace | Chatbot builder, visual workflows, knowledge-bases, multi-platform publishing, WebSDKs/APIs | Visual workflow builder, 400 + integrations, blended low-code/code, AI Transform node | Knowledge-base management, automated retrieval, RAG core, visual AI workflows (Flow) | RAG engine, deep doc understanding, citation-aware Q-A, low-code platform |
Pricing | Free to start; paid subscription (Pro $59, Team $159); Enterprise tier | Free (major functions currently free) | Self-hosted free; cloud pay-as-you-go by executions (~$20–$22 / month) | Free (open-source); cloud usage-based pricing | Free (open-source) |
Workflow / Orchestration | Strong (visual, agentic) | Medium (visual) | Very strong (visual nodes, core feature) | Medium (visual Flow) | Medium (RAG workflows) |
RAG Capability | Strong (core capability) | Medium (via knowledge-base) | Medium (integratable) | Strong (core capability) | Very strong (core, deep understanding) |
No-Code / Low-Code | Yes | Yes (primarily no-code) | Yes (low-code + code) | Yes | Yes (low-code) |
Ease of Use | Medium-High (many features to learn) | High (beginner-friendly) | Medium (quick to start, harder to master) | Medium-High (intuitive UI) | Medium (tech-oriented) |
Community & Ecosystem | Active, steadily growing | Backed by big tech, large user base | Very active, resource-rich | Growing, active on GitHub | Emerging, niche-focused |
Learning Curve | Medium; complete official docs | Low; almost no learning needed | Easy at basics, complex at advanced level | Medium-Low; clear concepts | Medium-High; requires grasp of RAG theory |
Choosing the Right Tool: My Practical Advice
Based on my hands-on experience:
- New to AI dev? Start with Coze—fastest to see results, very beginner-friendly.
- Need to integrate multiple tools and automate tasks? Go with n8n.
- Focused on building a smart knowledge base or Q&A bot? Try FastGPT (lightweight) or RAGFlow (advanced).
- Looking to build a scalable, production-grade AI app? Use Dify.
Non-technical Users / Creators
Developers / Technical Teams
Enterprise Users (General Needs)
Entrepreneurs / Startups
Figure: User-Suitability Radar Chart for Each Platform (higher scores indicate better suitability)
Key Considerations Before You Choose
- Budget:
Open-source = no license fee, but server and maintenance costs add up.
Cloud = faster start, but ongoing usage costs. Balance accordingly.
- Technical Skills:
No-code tools like Coze are great for beginners.
Platforms like Dify and n8n need more hands-on skills.
- Deployment Needs:
Do you need private hosting for security/compliance reasons?
If yes, go for platforms that support Docker-based deployment.
- Core Feature Requirements:
Make a checklist. Prioritize what you really need: RAG? Workflow automation? API compatibility?
- Sustainability:
Look at update frequency, community engagement, company backing.
For open-source: GitHub stars, contributors.
For commercial tools: reputation, roadmap, user base.
- Data Security & Compliance:
Crucial for enterprise users. Open-source/self-hosted offers better control.
Always review cloud providers’ privacy and data handling policies.
Final Thoughts
Hopefully, this deep-dive comparison gives you a clear picture of what each platform excels at.
There’s no such thing as a “perfect” tool—just the right one for your needs and stage of development.
If you’re just starting out, try a low-barrier tool like Coze to get comfortable.
As your needs grow and your technical skills improve, you can gradually move to more advanced tools like Dify or n8n.
LLM platforms are evolving fast. Stay flexible, experiment often, and find what works best for you.
Let this article serve as your practical starting point in the world of AI-powered tools.
Let me know if you'd like me to turn this into a blog-ready Markdown, LinkedIn article, or summarized version!
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