On-prem vs cloud AI: a decision framework for Australian boards
When sovereignty, cost and capability pull in different directions, here's a calm way to choose deliberately.
Most arguments about running AI in the cloud or on your own hardware start with the technology and end in a stalemate. That’s the wrong place to start. Begin with your data instead: what it is, who’s allowed to see it, and what happens if it leaves the building.
Start with the data, not the model
Sort your information into three piles. There’s data you’d happily hand to a public system. There’s data that’s commercially sensitive. And there’s data you’re legally or contractually bound to keep under your own control. When boards actually do this, the third pile usually turns out smaller than they feared, which means a hybrid setup is on the table rather than an all-or-nothing call.
Weigh three forces honestly
Sovereignty is about where the data has to physically and legally sit. Cost cuts the other way depending on the option: cloud is cheap to start and predictable as you scale, while on-prem trades a bigger upfront spend for a known long-run cost you actually own. Then there’s capability. The strongest models are easiest to reach in the cloud, but plenty of capable ones now run fine on modest local hardware.
This decision is rarely permanent. A lot of our work starts in the cloud to prove the use case quickly, then moves the sensitive workloads on-prem once it’s clear the thing is worth keeping. You’re not picking a side for life. You’re deciding deliberately, with the data sitting in front of you.
Turn the thinking into a plan.
A discovery call is a conversation, not a pitch. Bring the problem and we'll map the opportunity honestly.