Every GPU buyer eventually faces the same choice: keep paying by the hour, or commit to a block of capacity for months in exchange for a lower rate. The answer depends on how much of the reserved time you would actually use.
The break-even rule
If a reservation costs 20% less per hour than on demand, it pays off once you would use the GPUs more than 80% of the time. Below that, you are paying for idle hours that on demand would have let you skip. In general:
Reserve when expected utilization > 1 − discount
With a 12% discount the threshold is 88%; with 28% it drops to 72%. Long, steady workloads such as production inference or a training roadmap clear it easily. Bursty research rarely does.
What on demand is good for
- Experiments and proofs of concept where you don't know the final size yet.
- Bursts on top of a reserved base, for example a launch week.
- Trying a new GPU generation before committing to it.
What reservations are good for
- Production inference with predictable traffic.
- Training plans that run for months.
- Guaranteed availability when the market is tight. In March 2026 about half of GPU providers had no on-demand H100 capacity at all; reserved customers were not affected.
The hybrid that works for most teams
Reserve the baseline you are confident you will use every day, and cover peaks with on-demand capacity. Review the split every quarter: if your on-demand spend is consistently high, part of it should move into a reservation.
Questions to ask before you sign
- Is the capacity dedicated to you, or shared?
- What happens if a node fails: replacement time and credits?
- Can you move to a newer GPU generation mid-term?
- How is billing structured: monthly, quarterly, deposit?
