Total cost of ownership: GPU rental vs a cloud AI API
We rent and sell DGX Sparks, so weigh that against everything below. There are three ways to get an LLM running: call a per-token API, rent a dedicated GPU by the hour, or buy hardware outright. Each shows up as a different shape of cost over time, and which one wins depends entirely on your volume and how continuously you use the compute. This page uses only figures already published elsewhere on this site, no invented competitor or hardware prices.
The three options, side by side
| Cloud AI API | Rented dedicated GPU (GPUwerk) | Buy hardware outright | |
|---|---|---|---|
| Pricing model | Per token, billed separately for input and output, varying by model and provider | Per hour, billed per minute: $0.79/hour on-demand, one rate regardless of model, per pricing | One up-front payment, then the machine is yours |
| Up-front cost | None | None | EU street price roughly €3,800–€4,800 for a DGX Spark-class machine, per our before-you-buy guide |
| Cost when idle | Zero, you only pay for tokens generated | Full hourly rate while reserved and running; 75% of that rate for a customer-requested stop that holds the reservation, per pricing | Zero marginal cost once bought, but the capital is already spent regardless of use |
| Model choice | Whatever the provider offers, including closed frontier models not available to run yourself | Open-weight models only, whatever fits the hardware's memory | Open-weight models only, same constraint as rental since it's the same class of hardware |
| Data location and access | Depends entirely on the provider's terms and jurisdiction | EU-Central, dedicated single-tenant machine; see private LLM hosting | Your own building, full physical control |
| Ops burden | None, the provider manages capacity and serving | You manage the model server and application layer; GPUwerk manages the hardware | You manage everything: hardware, drivers, model server, and application layer |
| Best fit | Low or spiky volume, or a specific closed model is required | Steady, moderate volume, or testing a workload before buying | Very high, sustained utilization over a long period, confirmed in advance |
How the three costs actually move
A per-token API's cost is a straight line through the origin: zero traffic, zero cost, and the line's slope is the per-token price. It never has an idle-time problem because there's no dedicated machine sitting there between requests.
Rental cost is a flat rate per hour reserved, regardless of how busy the machine is during that hour. Cost per token generated on a rented machine falls as utilization rises and rises without bound as utilization falls, which is the same shape covered in more depth in our on-premise vs cloud API cost comparison.
Buying is a fixed cost paid once, after which additional usage is free at the margin (aside from power and any repairs). The purchase only pays for itself once accumulated rental-equivalent hours would have exceeded the purchase price. Our rent-vs-buy break-even page works this out in detail: at GPUwerk's $0.79/hour rate against the €4,300 midpoint of the published €3,800–€4,800 EU purchase range, continuous 24x7 use breaks even in roughly seven and a half months; below about 40 hours a week, renting stays cheaper than owning for years, because the machine is idle most of the time either way.
Where each option wins
- Cloud API wins at low or spiky volume, where an idle dedicated machine (rented or bought) would cost more than the tokens actually generated, or where the workload requires a specific closed frontier model that only runs on a provider's own infrastructure.
- Rented dedicated GPU wins once volume is steady enough to keep the machine reasonably busy, without wanting to commit capital or wait to confirm the workload fits before buying. It's also the right starting point regardless of where you expect to land, since it's the only one of the three that lets you measure your actual throughput before deciding anything else.
- Buying wins only past sustained, high utilization confirmed over enough hours to clear the break-even point in our rent-vs-buy analysis, and even then carries the ops burden and no-warranty-stopgap tradeoffs described on that page.
The practical path
Start on a rented dedicated GPU regardless of which option you expect to land on. It's the only one of the three that produces a real, measured throughput number for your actual model and workload, rather than a borrowed benchmark. From there: if your real volume turns out lower or spikier than expected, a cloud API may end up cheaper, in which case our Azure OpenAI comparison covers that side of the tradeoff in the same detail as this page covers rental vs buying. If usage is steady and heavy enough to clear the break-even point, GPUwerk sells and installs the same class of machine on-premise, so the rented node you already tested on can become the one you own.