What is private AI?
Private AI usually means an AI model that runs where only your business uses it: in a cloud setup reserved for you, or on hardware in your own building. The aim is to keep control of where your data goes. The term describes how a system is set up. On its own, it says nothing about how well it's secured.
Guidance from the National Cyber Security Centre (NCSC) treats security as something to get right at every stage, from design to day-to-day running, wherever a system is hosted.
Does private AI mean the model runs in your building?
Not always. Private AI can mean a dedicated cloud setup run by a provider for you alone, or hardware you own and keep on site. Only the second keeps everything in your building, and it brings the most work: someone has to update it, watch it and fix it when it breaks.
When is private AI worth it for a business?
When a rule you can point to says the work can't go to a shared outside service, or it has to keep working with no internet connection. If the reason is a general worry about safety, check first whether the business version of software you already have meets your rules.
For example, Microsoft says Microsoft 365 Copilot doesn't use your prompts or files to train the models behind it, and only shows people what they can already open. Whether that meets your rules is for you to decide, but it's worth checking before you pay for a build.
What does private AI cost?
The cost comes in three parts. Usage is what you pay to run the model, per request or for the hardware. Setup is connecting it to your documents and testing what comes back. Running it never stops: updates, monitoring and support. Ask any supplier to price all three.
There's no published figure we'd trust for what private AI costs a business of a given size, so price your own case from those three parts, including who does the running and how many hours a month it takes.
Private AI, on-premise or the software you already have?
The check compares four routes in order: software you already have, an app built on an outside AI service, a dedicated cloud setup, and hardware in your building. Each later route has to show why the earlier ones won't do, so you only pay for private AI when you need it.
| Route | When it fits | What it has to prove |
|---|---|---|
| Software you already have | Microsoft 365, Google Workspace or another system you pay for has an AI feature. | That it does the job within your rules. |
| An app built on an outside AI service | Your software leaves a gap, and an approved outside service is allowed. | That the provider's terms, the access rules and the running costs hold up. |
| A dedicated cloud setup | Your rules allow hosting with a provider, but not a service shared with other customers. | Why a separately hosted setup is needed, and who will run it. |
| Hardware in your building | It has to keep working with no internet connection. | That the model is good enough, and someone can look after the machines. |
What should you test first?
Test the business version of software you already pay for, against the same rules you'd set a private build. If it meets them, you may not need anything new. If it doesn't, write down exactly where it falls short, because that gap is what a private setup would have to fix.