In our experience GPU quotes are more often too large than too small. Sizing is not guesswork — it comes out of three numbers you already have, and getting it right is usually the difference between a project that clears its business case and one that does not.
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| Criterion | Public API | Rented cloud GPU | Owned hardware |
|---|---|---|---|
| Upfront cost | None | None | Significant |
| Cost shape | Per token | Per hour, running or not | Depreciation + power |
| Cost at high sustained volume | Worst | Middle | Best |
| Cost at low or spiky volume | Best | Middle | Worst |
| Data stays on your hardware | No | No | Yes |
| You operate it | No | Partly | Yes |
| Sensible for | Prototypes, low volume | Bursty or uncertain load | Steady production volume |
The crossover calculation is not complicated and we do it in the open: take your projected monthly token volume, price it at the API rate you would actually pay, and compare it against the monthly cost of ownership — hardware amortised over three years, plus power, plus a realistic fraction of an engineer. The number that decides it is usually the last one, and it is the one most comparisons leave out.
Two honest observations from doing this repeatedly. First, the arithmetic favours ownership sooner than most people expect once volume is genuinely sustained, because API pricing is linear and hardware is not. Second, it favours the API far longer than vendors of hardware will tell you when volume is spiky, because an idle GPU costs the same as a busy one.
Very often, yes. A single well-specified accelerator runs a capable mid-size open model for a meaningful number of internal users, and it is the right way to prove the deployment before committing to a cluster. Scaling out later is a known quantity; scaling down after over-buying is a write-off.
Not usually. Memory capacity matters more than raw compute for most inference workloads, and previous-generation cards with generous memory are frequently better value than current-generation cards with less. That is a specification question we work through with your actual model, not a brand preference.
Sometimes nothing measurable, sometimes a lot — it depends on the model and the task, which is why it is a measurement rather than a rule. We run your evaluation set at full and reduced precision and show you both numbers before anyone signs a purchase order.
Real and worth including, and usually a smaller line than people fear relative to the hardware amortisation. It matters most for the office-based on-premise case, where it can also be a physical constraint on what the room can actually host.
Send us the workload. We will size it properly and tell you if the quote you are holding is too big.
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