RAGFlow is architected for on-premise deployment and can support air-gapped (completely disconnected) environments with proper planning and pre-staging of dependencies. The Docker-based architecture allows you to deploy RAGFlow on internal infrastructure with zero internet dependency, ensuring data residency and compliance with air-gapped security policies. To achieve air-gapped operation, you pre-download all required Docker images, embedding models, LLM weights, and document parsing models, then transfer them to the air-gapped environment via USB, secure transfer protocols, or internal networks. The full Docker image variant includes embeddings for offline use; alternatively, run embedding services (Ollama) locally with pre-staged model weights. All supporting services—a search engine backend, MySQL, MinIO, Redis—run containerized without external calls. External LLM APIs (OpenAI, etc.) won't work in air-gapped mode, but you can substitute local LLMs via Ollama or compatible services. Some deployment complexity exists: RAGFlow's model repository integration (for downloading DeepDoc and other components) requires internet during setup, so pre-staging on a connected machine then deploying images to the air-gapped network is the typical approach. Users have reported some issues with offline deployments, particularly around model downloads and repository access (per GitHub issue discussions), but these are generally solvable through careful pre-staging. For regulated industries (defense, finance, healthcare) or organizations with strict data residency requirements, RAGFlow's on-premise, offline-capable architecture is a major advantage over cloud-only RAG solutions. The self-hosted model eliminates cloud vendor access to your documents and queries. Production air-gapped deployments require careful planning around dependency management, but RAGFlow's containerized design makes this more tractable than many alternatives.
In production environments, storing and retrieving embeddings efficiently requires purpose-built infrastructure. Zilliz Cloud handles this as a managed vector database service, while Milvus offers the same capabilities for self-hosted deployments.