Private AI: Local LLMs and Private RAG on Your Own Infrastructure
Private AI for companies whose data cannot leave their infrastructure: local LLMs, private RAG and on-premise AI, designed and run by an engineer who operates this stack himself.
Private AI: Local LLMs and Private RAG on Your Own Infrastructure
Private AI for companies whose data cannot leave their infrastructure: local LLMs, private RAG and on-premise AI, designed and run by an engineer who operates this stack himself.
Private AI means the models, the retrieval and the data stay on infrastructure you control — your servers, your cloud account or your office. It matters when documents are confidential, regulation limits where data may go, or you simply do not want every query to pass through a third party.
I run this stack myself: self-hosted models, embedding and reranking servers, vector search in Postgres, and the memory layer my agents use every day. CaBrain, my agent memory system, runs on exactly this kind of setup — Postgres with pgvector and BM25, with embeddings and reranking served by TEI on our own hardware.
What I set up
- Local LLMs served on your hardware or private cloud, sized to the task.
- Private RAG — ingestion, embedding, hybrid search and reranking over your documents.
- Access control so each user only retrieves what they are allowed to see.
- Monitoring for quality, latency and hardware use.
Private AI vs hosted AI
Hosted models are usually stronger and need no hardware. Local models keep data in-house and have predictable costs at volume, but need capacity planning and are generally smaller. Many companies use both: local for sensitive data, hosted for the rest — a split I design case by case.
Local vs cloud: questions to answer first
- Which data is sensitive, and what rules apply to it?
- How many requests a day, and how fast must answers be?
- Which languages — Arabic support narrows the model choice.
- Who will run and update the system after launch?
For agents built on this stack, see AI agents; for adding it to an existing product, see AI integration. Engineering notes live under AI engineering.
FAQ
Are local models good enough?
For retrieval-grounded answers, classification and extraction, well-chosen open models often are. For open-ended reasoning, hosted frontier models are still ahead; test on your own tasks before deciding.
What hardware is needed?
It depends on the model size and traffic. Embedding and reranking need modest resources; generation models are the main cost. I size it from your expected volume.
Can private AI work in Arabic?
Yes, but model choice matters more. I test Arabic retrieval and generation separately before recommending a setup.
What is included5
- Local LLM serving sized to your workload
- Private RAG: ingestion, hybrid search, reranking
- Per-user access control
- Arabic and English evaluation
- Monitoring and operations handover