Cheap GPU rentals are not actually cheap if you ignore how the platform bills around the GPU.
Most teams overpay because they compare one number: hourly price. That misses the billing model, storage charges, provisioning delays, and hidden platform fees.
1. Check Billing Granularity
Hourly billing is expensive for short jobs. If your run takes 23 minutes, paying for a full hour is just waste.
Better: per-second or per-minute billing.
2. Check What Happens When You Stop the Pod
A stopped pod does not always mean zero cost. Persistent storage often keeps billing.
Ask directly: does storage keep billing while stopped?
3. Check Provisioning Time
A GPU that looks cheaper but takes 10 minutes to become usable is often worse than one that is ready in under a minute.
What matters: time from payment to SSH or Jupyter access.
4. Check Hidden Platform Fees
The compute price is only part of the bill.
Watch for:
- storage charges
- data transfer fees
- reserve requirements
- support or minimum spend rules
5. Check Whether the GPU Actually Fits Your Workload
A premium GPU is only worth it if your workload can use it.
If a 4090 can handle the job, renting an H100 just burns budget.
The Shortcut
Before renting any GPU, compare:
- total job time
- billing granularity
- stop-state storage rules
- real throughput per dollar
That is how you avoid surprise costs.
If you want to compare live options instead of guessing, Lumino lets you browse current GPU availability and pricing here:
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