Running a private model on your own infrastructure

When on-premise makes sense

When the data is not allowed to leave, or when request volume pushes cloud spend above the cost of hardware.

Size the hardware to the load

Over-provisioning GPUs is the most common mistake in these projects. Measuring real load before buying removes months of spend.

Monitoring from day one

Without utilisation and latency metrics there is no way to tell whether you are short on capacity or slow in code.