Deployment decisions

Spot vs on-demand GPUs: price the restart

Use interruptible capacity for jobs that can resume cheaply and tolerate a delayed finish. Use on-demand capacity when interruption risk conflicts with the deadline or service objective. Compare cost per completed job after lost work and restart overhead, rather than the discounted hourly rate alone.

Know what interruption means on the product

AWS documents interruption notices for EC2 Spot; Vast.ai documents interruptible instances that can be paused based on rental priority. The exact signal and resume behavior differ. Build recovery around the product you choose.

On-demand does not mean hardware cannot fail. It changes the purchasing and interruption model, while backups and application recovery remain necessary.

Know what interruption means on the product
ConditionInterruptibleOn-demand
Flexible overnight batchGood trial candidate with checkpointsUseful if recovery is costly
Interactive serviceNeeds a suitable baseline and failoverSimpler starting capacity model
Hard deadlinePrice delays and retries explicitlyConfirm capacity for the full window
No tested restoreFix recovery before committingStill needs backups

References: EC2 Spot interruption notices · Vast.ai instance types

A cheaper rate can lose on repeated work

Consider an illustrative eight-hour job. At $2 per GPU-hour it costs $16 in compute. At $1 per GPU-hour, a run that consumes ten paid hours after retries costs $10. If repeated failures expand it to eighteen paid hours, it costs $18.

The example ignores storage and engineering time to keep the arithmetic visible. Include them in a purchasing estimate, and keep waiting time separate from billable time. A job can be cheap but finish too late.

Reduce the amount of work at risk

Split independent batches into smaller tasks and save progress outside the worker. For training, choose a checkpoint interval from the cost of saving and the cost of repeating work. Test a restore before a long run.

Use a stable job identifier when publishing results so a retry does not create duplicate outputs. A queue should distinguish pending, running and durably completed work.

Use a deadline-aware policy

For a flexible job, start on interruptible capacity and define when to switch to another allocation if progress falls behind. Verify that fallback capacity and data access actually exist before depending on that plan.

For a customer-facing service, assess the entire failover path under load. A lower rate should not be purchased at the expense of a latency promise the application cannot meet.

Questions readers ask

Does an interruption warning guarantee time to save?

No. The checkpoint may take longer than the warning, and failure paths differ. Save during ordinary operation too.

Is on-demand failure-proof?

No. It still requires durable state, monitoring and a recovery plan.

Sources and editorial notes

Primary documentation reviewed October 9, 2026. Provider descriptions reflect published documentation. Recommendations are our analysis; worked figures are illustrative unless explicitly stated otherwise. No independent provider benchmark or uptime ranking is claimed.

Read the editorial policy. NeuraMesh is a prototype, not live verified GPU inventory.