Some data cannot leave your infrastructure: classified documents, healthcare records, financial data, legal privileged communications, or any data subject to residency requirements. Private RAG runs the entire pipeline — embedding, retrieval, and generation — on your own infrastructure. The trade-off is clear: you get complete data control, but you accept higher infrastructure costs and lower model quality (open-source models are generally less capable than cloud-hosted proprietary models).
The Private RAG Stack
A private RAG stack replaces cloud services with self-hosted alternatives: embeddings (local embedding model instead of OpenAI/Anthropic embeddings), vector database (self-hosted pgvector, Qdrant, or Milvus instead of cloud-hosted Pinecone/Weaviate), LLM inference (self-hosted model via vLLM, TGI, or Ollama instead of cloud APIs), and orchestration (LangChain, LlamaIndex, or custom pipeline instead of hosted services). Every component runs on your infrastructure — no data leaves your network.
Model Selection for Private RAG
Private RAG requires open-source models. The quality gap between open-source and proprietary models has narrowed significantly. For RAG specifically, the retrieval quality matters more than the generation quality — a good retrieval pipeline with a mediocre model often outperforms a poor retrieval pipeline with a great model. Recommended: use a 7B-13B parameter model for generation (Llama 3, Mistral, Gemma) and a dedicated embedding model (BGE, E5, GTE) for retrieval.
Pro Tip
For private RAG, invest in retrieval quality first. A 7B model with excellent retrieval outperforms a 70B model with poor retrieval.
Infrastructure Requirements
Private RAG infrastructure: GPU servers for model inference (1-4 GPUs for 7B-13B models), CPU servers for embedding and vector search, storage for the vector database (plan for 1GB per 100K document chunks), networking (low-latency internal network between components), and monitoring (track inference latency, retrieval quality, and resource utilisation). Total infrastructure cost: $2K-$10K/month for a moderate-scale deployment. This is more expensive than cloud APIs for small volumes but cheaper at scale (above ~100K requests/month).
Conclusion
Private RAG is a compliance-driven architecture choice. It gives you complete data control at the cost of higher infrastructure complexity. The quality gap is narrowing, and for RAG specifically, retrieval quality matters more than model quality. Invest in retrieval first, then upgrade the model as open-source options improve.
Key Takeaways
- Private RAG: self-hosted embeddings, vector DB, LLM, and orchestration — no data leaves your network
- For RAG, retrieval quality matters more than generation quality — invest there first
- Open-source models (7B-13B) are sufficient for most RAG use cases
- Infrastructure cost: $2K-$10K/month — cheaper than cloud APIs at scale (100K+ requests/month)
- Private RAG is a compliance choice, not a quality choice — architect for data control