Accelerator ROI Console

The buy-versus-rent question for AI accelerators looks like a hardware decision but is really a finance one, and it comes down to a single variable: utilization. Owning is a fixed cost — you pay the capex, the power, and the operations whether the GPUs run flat out or sit idle in the rack. Cloud is pay-per-use — you pay only for the hours you actually consume, plus a markup. Those two cost structures cross at a break-even utilization, and which side of it you're on is the answer.

Above the break-even, owning wins decisively. A cluster running training jobs around the clock spreads its fixed capex over enormous useful work, beating the cloud's per-hour margin many times over — which is why hyperscalers and serious AI labs own. Below it, the calculus inverts: a fleet that's busy a quarter of the time wastes three-quarters of the capacity it paid for, and the cloud's pay-per-use is genuinely cheaper despite the markup, which is why startups and bursty workloads rent.

The trap is comparing sticker prices instead of total cost of ownership. The capex is visible, but the electricity — multiplied by datacenter PUE and accumulated over years — and the operations and maintenance are not, yet they're real money that the cloud rate quietly bundles in. An honest comparison adds all of it on the owning side, which is why this console includes energy, PUE, and an operations percentage, not just the purchase price.

Beyond the totals, the timing matters. Payback tells you how fast the capex is recovered against renting, and NPV weighs future savings against the cost of capital — a positive NPV means buying creates value. A payback comfortably inside the hardware's useful life and a positive NPV both point to owning; otherwise rent. Once you know you're buying, size the workload that fills the GPUs in the Training Cost console and the facility power in the Data Center Power console.