Reserved vs on-demand GPUs: calculate the utilisation break-even
A lower committed hourly rate only helps when the workload uses enough of the capacity you pay for.
· Arvica Cloud · Analysis & buying guide
Start with comparable offers
Use offers for the same complete configuration, billing currency and cost boundary. Include storage, transfer, support and tax consistently. Confirm whether a reservation is a commitment to pay, a capacity guarantee, or both. A discount without the capacity you need may not solve the operational problem; a capacity guarantee without flexibility may create a different budget risk.
An explicit hypothetical calculation
Assume a 30-day period of 720 hours. A reserved rate of R per GPU-hour charged for all 720 hours costs 720R per GPU. An otherwise equivalent on-demand rate of D breaks even at 720R ÷ D billable hours — for example, if D is 5/3 of R, at 432 hours, or 60% of the period. Use the rates in your Arvica live price and proposal.
What the simple formula leaves out
Add setup and migration costs, unused capacity between jobs and any minimum commitment. Check whether on-demand resources are actually obtainable when needed, and whether stopped instances keep storage charges. If a reservation has a fixed monthly fee plus usage charges, use that actual tariff instead of the simplified hourly model. Run a conservative, expected and high-usage case; avoid making the commitment depend on an optimistic forecast alone.
Arvica analysis: reserve a dependable baseline
A useful starting point is to consider a reservation for a predictable baseline and separately quote peak demand, if the contract and application permit that split. Record who can approve expansion, the notice period and how cancellation or renewal works. Send Arvica your GPU specification, expected billable hours, peak requirement and term to compare concrete options. The lowest hourly number is only one part of the decision.
Original evaluation guidance; not a customer case study or measured benchmark.
Download an editable worksheet to agree scope, compare configurations and decide whether to stop, adjust or scale. An evaluation method, not a customer case study.
Compare scope, allocation, storage, networking and pilot acceptance before committing to a B300 node. Includes a worked cost formula, not a supplier price.
Build a representative inference test with model, precision, context, concurrency and quality targets. Connect the measured result to a transparent GPU budget.
