When building knowledge-based AI applications, choosing the right RAG (Retrieval-Augmented Generation) platform can make or break your project. The market has several open-source options, each with different strengths. Let's compare Casibase with three popular alternatives: Dify, RAGFlow, and FastGPT.
The Enterprise Foundation
What sets Casibase apart from day one is its enterprise-grade authentication through Casdoor integration. While other platforms bolt on authentication as an afterthought, Casibase treats it as a fundamental building block. You get SSO, OAuth providers (GitHub, Google, WeChat, QQ), and fine-grained access control without writing a single line of custom authentication code.
Dify offers basic user management but lacks the sophisticated multi-tenant capabilities that Casdoor provides. RAGFlow and FastGPT take minimalist approaches to authentication, which works for quick prototypes but becomes problematic when you need to deploy to actual users in production environments.
Architecture Philosophy
Casibase follows a clean separation between frontend (React) and backend (Golang with Beego), giving you the flexibility to customize either layer independently. The Golang backend handles high concurrency gracefully, which matters when you're serving hundreds of simultaneous users querying your knowledge base.
Dify embraces Python with a focus on visual workflow building. If your team prefers drag-and-drop interfaces for orchestrating LLM chains, Dify's approach feels natural. However, the Python runtime can become a bottleneck under heavy load compared to Casibase's compiled Go backend.
RAGFlow positions itself as a deep RAG engine, emphasizing document parsing quality over broad feature coverage. It excels at extracting structured information from complex PDFs and legal documents. FastGPT takes a middle path, offering decent document processing while maintaining simplicity.
Model Flexibility
Casibase supports an extensive array of language models out of the box: OpenAI's GPT series, Claude, Gemini, local models through Hugging Face, and Chinese models like ChatGLM, Ernie, and iFlytek. The embedding model support is equally comprehensive, from OpenAI to Cohere to local sentence transformers.
Dify has strong model support too, particularly for OpenAI and Anthropic models, with a growing list of integrations. RAGFlow focuses primarily on OpenAI compatibility, which keeps things simple but limits your options. FastGPT sits somewhere in between, supporting major providers but not the breadth that Casibase offers.
The practical difference emerges when you want to switch models mid-project or run A/B tests across providers. Casibase's provider abstraction layer makes this straightforward. With RAGFlow or FastGPT, you might find yourself rewriting integration code.
Knowledge Management Depth
All four platforms implement vector similarity search, but they differ in sophistication. Casibase provides granular control over embedding methods, storage backends (AWS, Azure, local), and retrieval strategies. You can fine-tune how documents are chunked, embedded, and indexed.
RAGFlow shines here with advanced document parsing that understands document structure, tables, and complex layouts. If your knowledge base consists of technical manuals or legal contracts, RAGFlow's parsing capabilities deliver cleaner chunks for embedding.
Dify makes knowledge management accessible through visual tools, letting non-technical users upload and organize documents. FastGPT takes a pragmatic approach, offering solid fundamentals without overwhelming users with options.
Beyond Q&A
Casibase isn't just a RAG system. It includes IM (instant messaging) and forum capabilities, turning it into a complete knowledge collaboration platform. You can build internal wikis where employees both query AI and discuss topics with each other. The security audit logging tracks not just what users ask, but how they interact with the system.
None of the other platforms attempt this breadth. Dify focuses on workflow automation and agent building. RAGFlow concentrates on retrieval quality. FastGPT keeps scope tight around conversational AI. Whether Casibase's broader feature set is an advantage or unnecessary complexity depends on your requirements.
Database and Deployment
Casibase supports MySQL, PostgreSQL, and SQL Server with extensible plugins for others. The containerized deployment options (Docker, Kubernetes) and Cloud platform integration make it production-ready from the start.
Dify typically runs on PostgreSQL with good containerization support. RAGFlow and FastGPT offer Docker deployments but with less documentation around scaling and multi-instance setups. If you're deploying a hobby project, any of these work. For production systems serving thousands of users, Casibase's operational maturity becomes valuable.
The Development Experience
FastGPT wins points for getting started quickly. Clone, configure your API keys, and you're chatting with your documents in minutes. Casibase requires more setup due to the Casdoor integration, but that upfront investment pays dividends when you need proper user management.
Dify's visual workflow builder appeals to teams mixing developers and domain experts who want to collaborate on prompt engineering. RAGFlow demands more technical expertise but rewards you with superior document understanding.
Casibase occupies interesting middle ground: easier than RAGFlow for most use cases, more powerful than FastGPT for production needs, and more enterprise-focused than Dify.
Making the Choice
Choose Casibase when you need enterprise features, plan to support multiple models, or want a platform that can grow from prototype to production. The Golang backend and mature authentication system make it suitable for serious deployments.
Pick Dify if you want visual workflow building and your team includes non-developers who need to participate in system design. The Python ecosystem and drag-and-drop interface lower the barrier to entry.
Select RAGFlow when document parsing quality matters more than anything else. If you're building a system to query complex technical documentation or legal texts, its parsing sophistication justifies the narrower feature set.
Opt for FastGPT when you need something working today and don't require enterprise features. It's perfect for personal projects or small team experiments where simplicity trumps scalability.
The truth is, all four platforms solve real problems. Casibase's bet on enterprise readiness, broad model support, and extensibility makes it particularly well-suited for organizations building knowledge systems they'll rely on long-term. The initial complexity pays off as your requirements inevitably grow beyond basic Q&A.