Renting is cheaper… until it's not.
Renting a GPU in the cloud feels like the smart move. No hardware, no waiting, pay for what you use.
For experiments and spiky demand, it is the smart move.
But picture a taxi. Great for a trip to the airport. Ruinous if you ride it eight hours a day, every day, for three years.
AI that runs all day, like an assistant answering your team's questions, is the eight-hours-a-day kind of ride.
The number that decides it is utilization: how much of the day your GPUs are actually working. Low utilization favors renting. High, steady utilization favors owning. Several vendor analyses put the break-even at roughly a dozen GPUs running around the clock, but their math differs, so treat any single figure as a starting point, not a verdict.
Most mature teams end up hybrid: a private cluster for the steady daily work, and the cloud for bursts and experiments. Privacy is the second reason to keep the steady work close to home, since your data never has to leave.
Do this today: write down how many hours a day your AI workload really runs. That one number tells you which side of the line you are on.
Source: Civo, GPU cloud vs. traditional on-premise (vendor analysis)