Guide · Private AI
Private RAG without OpenAI: how to run AI on your own machines
If your data can't go to the cloud, the AI has to come to your data. Here's how a private, on-premise RAG system works — the kind we built for a pharma client with zero external API calls.
Why "private" matters more than "smart"
For most teams, the blocker to using AI isn't capability — it's confidentiality. If your documents contain patient records, financial data, legal matters, or proprietary IP, sending them to OpenAI or Anthropic may be off the table on day one. Compliance says no, and no amount of accuracy changes that.
A private RAG system solves this by bringing the AI to your data instead of sending your data to the AI. The language model, the embeddings, the vector database, and your documents all run on machines you control. There are zero calls to external AI providers. Nothing leaves your network.
What "runs on your own machines" actually means
A private, on-premise RAG system has four parts, and all four stay inside your infrastructure:
- A local language model. Open-weight models — Llama, Mistral, Qwen — run on your own GPUs. No API key, no per-token cost, no data sent out.
- Local embeddings. The system that converts your documents into searchable vectors runs locally too, so document contents never transit a third party.
- An on-prem vector database. The index of your documents lives on your servers.
- Your documents, where they already are. Nothing is copied to an external service to make this work.
We built exactly this — for a pharma client
Aru Pharma needed to query internal documents with AI, but regulatory and confidentiality constraints ruled out external AI APIs entirely. We delivered a retrieval-augmented generation system that runs completely on their local machines: local model, local embeddings, local vector store. Zero OpenAI or Anthropic calls. Their data never left their infrastructure — and their team can ask questions of their documents in plain language, with cited sources.
"But are local models good enough?"
This is the honest question, and the honest answer is: for question-answering over your own documents, yes — more than most people expect. Well-chosen open models (Llama, Mistral, Qwen) combined with good retrieval get close to API-model quality on document tasks. And when a private RAG system underperforms, the cause is almost always the retrieval, not the model — which is fixable. We benchmark local models against your actual documents before committing, so you see the real quality on real data, not a vendor's demo.
What it costs and how long it takes
A fixed-scope private RAG proof-of-concept runs $10,000–$25,000 and takes about three weeks: week one for document ingestion and a baseline pipeline, week two for retrieval tuning and evaluation on your real questions, week three for the interface and deployment on your infrastructure. You own the result outright — no ongoing per-token bills, because there are no external API calls to bill for. See our private RAG development page for the full breakdown.
Common questions
Can I really run AI without OpenAI or Anthropic?
Yes. Open-weight models like Llama, Mistral, and Qwen run entirely on your own hardware. For document question-answering, they reach quality close to API models when paired with good retrieval — and nothing leaves your network. We built exactly this for a pharma client with zero external API calls.
Is a private RAG system HIPAA/GDPR safe?
It removes the single biggest exposure — sending data to a third party — because there are no external API calls. The full compliance picture depends on your infrastructure and controls, which we design in (encryption, access control, audit logs). It is the right architecture for HIPAA, GDPR, and similar regimes.
How much does a private RAG system cost?
A fixed-scope PoC runs $10,000–$25,000 over about three weeks. Because the model runs locally, there are no per-token API bills — you own the system outright. Ongoing cost is just your own hardware and hosting.
Will local models be accurate enough for our documents?
For Q&A over your own content, usually yes. When results fall short, it's almost always retrieval quality, not the model — and that's fixable. We benchmark local models on your actual documents in week one, so you see real quality before committing further.
Thirty minutes. Tell us what your team needs to ask its documents and what your compliance constraints are. We'll tell you honestly whether a private RAG system fits, what it costs, and how fast it ships.
Scope a private RAG PoC →