What private AI actually means (and what it doesn't)

“Private AI” turns up on just about every vendor slide now, and it means something different on every one of them. Before you sign anything, it pays to work out which version you’re actually being sold, because the gap between them is enormous.

Four models, four trade-offs

The Public API model is the one most people meet first. You send your prompts off to a third-party model and get answers back. It’s easy and it’s genuinely capable, but your data has left your boundary to get there. That’s fine for non-sensitive work and a real problem for everything else.

Private cloud tenancy is a step up. You get a hosted model with contractual guarantees about how your data is handled. Better, sure, but notice that “private” here is a promise written into a contract, not a wall you can see.

Self-hosted in your own cloud is where it starts to feel private in a way you can verify. The model runs in infrastructure you control, your data stays inside your environment, and you own the configuration and the logs rather than taking someone’s word for them.

On-prem or air-gapped is the far end. The model runs on your own hardware, with no internet connection at all if that’s what the work demands. You get maximum control for a higher upfront cost, and for genuinely sensitive workloads it’s the right answer.

Ask the question that cuts through

Forget the label on the slide. There’s one question that sorts the real thing from the marketing: can our data leave our control, and if so, under exactly what conditions? Make the vendor answer that in plain terms. A vague answer is a vague guarantee. Private AI comes down to where your data lives and who can reach it, full stop.