Guide · Private AI

What a private RAG system costs (and what drives the price)

A private RAG system lets AI answer from your own documents, on your own infrastructure. Here's what it realistically costs to build and run.

What you're actually paying for

A private RAG (retrieval-augmented generation) system lets an AI answer questions from your own documents — running on your infrastructure, without sending anything to an outside service. You're paying for two things: the one-time build that connects the AI to your documents securely, and the ongoing cost of running it on your own hardware. Both are more predictable than most people expect.

The build: proof of concept first

We start with a proof of concept, not a big-bang project. A working private RAG POC — ingesting a real slice of your documents, answering real questions, running privately — typically lands in the $10,000–$25,000 range over about three weeks. That gets you something real to test against your own data before committing to a full rollout.

What drives the price

• Volume and variety of documents: a clean set of PDFs is simpler than a sprawl of formats, scans, and systems.
• Accuracy and citation requirements: the higher the bar for correct, traceable answers, the more retrieval tuning involved.
• Where it runs: your servers, a private cloud, or fully air-gapped — stricter isolation means more setup.
• Integrations: connecting it to the systems where your documents actually live.

The running cost

This is where private RAG differs sharply from cloud AI. There's no per-question bill to an outside provider — because the model runs on your own hardware, you're paying for that hardware and hosting, not per token. For high query volumes that's a major long-term saving: your cost doesn't climb every time your team uses it more.

Why this is often cheaper at scale

An API-based system is cheap to start and expensive to run at volume — every query is a charge, forever. A private system costs more to build and near-nothing extra per query. If your team asks it thousands of questions a month, or you're in an industry where data can't leave your building anyway, private wins on both cost and control. We built exactly this for a pharma client with zero external API calls. See private RAG development and on-prem AI for regulated industries.

Common questions

How much does a private RAG system cost?

A working proof of concept — ingesting a real slice of your documents and answering real questions privately — typically runs $10,000–$25,000 over about three weeks. A full rollout is scoped from there, once the POC proves value on your own data.

What are the running costs of a private RAG system?

Because the model runs on your own hardware, there's no per-question bill to an outside provider. You pay for the hardware and hosting, not per token — so cost doesn't climb as your team uses it more. At high volume that's a major long-term saving.

What makes a private RAG project cost more?

The volume and variety of documents, how high the accuracy and citation bar is, how strictly isolated it must run (private cloud vs fully air-gapped), and how many systems it connects to.

Is private RAG cheaper than using OpenAI or Claude APIs?

At scale, often yes. APIs are cheap to start but charge per query forever. A private system costs more to build and almost nothing extra per query. For high query volumes — or data that can't leave your building — private wins on cost and control.

Why start with a proof of concept?

Because it gets you something real to test against your own documents before committing to a full rollout. You see actual answer quality on your data, confirm the value, and scope the full build with confidence instead of guessing.