On-Demand vs Spot GPU Instances: How to Choose Based on Your Workload

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Cloud providers offer spot GPU instances at discounts of up to 90% compared to on-demand pricing, but the capacity can be reclaimed at any time, making them unsuitable for all workloads. On-demand instances provide stable, uninterrupted access at a fixed rate, making them better suited for production inference, live demos, interactive notebooks, and deadline-sensitive tasks. Spot instances are most cost-effective for workloads that support checkpointing, automated restarts, or retries, such as model training sweeps and stateless batch jobs. The true cost of spot instances depends not just on the hourly rate but also on factors like restart overhead, storage, queue delays, and potential lost progress from interruptions. Experts recommend a hybrid approach — maintaining a stable on-demand baseline while using spot capacity for flexible, interruption-tolerant tasks — as the most practical way to manage GPU compute costs.

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