When a client asks whether their AI workload can stay inside their own environment, the honest answer starts as a question about what the workload actually is. A small dedicated machine on-site is a genuine option now in a way it was not a few years ago.
The comparison usually gets made badly. Someone puts hardware cost next to monthly instance cost, hardware wins on a spreadsheet, and the operational reality — who patches it, what happens when it fails, where it physically lives — is discovered afterwards.
I compare on the axes that actually decide it: what the workload is, how sensitive the data is, who will own the machine, what happens when it fails, and what the client's legal position requires. Then I recommend — including recommending cloud when that is the honest answer.
A framework for making the decision honestly rather than a default answer, with hybrid architectures treated as a first-class outcome instead of a compromise.
I am not stating a legal conclusion about PDPL or any other regulation, and I am not quoting current cloud pricing. Pricing changes and regulation is a matter for counsel; you would need to check both against current sources at the time of any actual decision.
Not cost. "Who is going to own this machine?" If there is no clear answer, I recommend cloud, regardless of what the spreadsheet says.
What it runs on