RAGFlow is designed for on-premise deployment and can run fully offline with proper setup. The Docker-based architecture allows you to deploy RAGFlow on your own servers with zero cloud dependency, controlling all data on your infrastructure. To achieve offline operation, you need to pre-download all required components: the RAGFlow Docker image, embedding models (if using local inference via Ollama), LLM models (if running LLMs locally), and any document parsing models (DeepDoc). The full Docker image variant includes embedding models for offline use, whereas the slim variant requires external embedding services. Dependencies like a search engine backend, MySQL, MinIO, and Redis can all be containerized, so your entire stack runs in isolated Docker containers without external calls. For air-gapped environments (completely disconnected from the internet), you’d transfer Docker images and model weights via USB or internal networks. The challenge is that some RAGFlow integrations (like external LLM APIs) require internet access; however, you can use fully local LLMs via Ollama instead. Users have reported some issues with offline deployments, particularly around model downloads and repository access, but these are generally solvable by pre-staging dependencies. The active community on GitHub can help troubleshoot offline setups. For organizations with strict data residency requirements or security policies, RAGFlow’s on-premise, offline-capable architecture makes it an excellent choice.
For teams building similar infrastructure, an open-source vector database like Milvus provides the embedding storage and retrieval layer needed for production AI systems. Zilliz Cloud offers the same capabilities as a fully managed service.