What is GPU Spot Pricing?
Spot (or preemptible) GPU instances are spare compute capacity offered at discounted rates — typically 40–80% below on-demand prices. The trade-off: the provider can reclaim the instance with short notice (usually 30 seconds to 2 minutes) when demand increases. On-demand instances run until you terminate them, with guaranteed availability at a fixed price.
In 2026, GPU spot pricing has become more sophisticated. RunPod and Vast.ai operate marketplace models where spot prices fluctuate with real-time supply and demand. AWS and Google Cloud use fixed spot discounts with probabilistic interruption. CoreWeave offers "interruptible" instances with 30-second notice and 60–70% discounts. See current spot vs on-demand rates across all providers.
Spot Pricing by Provider
AWS EC2 Spot: H100 (p5.48xlarge) spot saves 60–70% vs on-demand. Interruption rates vary by region and instance type — us-east-1 p4d instances have historically had <5% hourly interruption rates. Google Cloud Preemptible/Spot: 60–91% discount, 24-hour maximum runtime, 30-second preemption notice. Azure Spot: 60–90% discount, variable interruption rates.
Specialist providers: RunPod community cloud spot from $1.89/hr for H100 (vs $2.89/hr on-demand). Vast.ai marketplace spot from $1.60/hr for H100. Lambda Labs does not offer spot — on-demand only. CoreWeave interruptible instances: 60% discount with 30-second notice. Compare RunPod vs CoreWeave pricing side-by-side.
Workloads Suited for Spot
Spot instances are ideal for: (1) LLM training with checkpointing — save model state every 15–30 minutes so interruptions lose minimal progress; (2) batch inference — stateless requests that can be retried on interruption; (3) data preprocessing pipelines — embarrassingly parallel tasks that can be restarted; (4) hyperparameter search — many short independent runs where interruption of one doesn't affect others.
Spot is not suitable for: (1) real-time inference APIs where interruption causes user-facing downtime; (2) long training runs without robust checkpointing; (3) stateful applications that can't tolerate data loss; (4) workloads with strict SLA requirements. For managed inference without interruption risk, compare LLM API providers as an alternative to self-hosted GPU inference.
Checkpointing Strategy for Spot Training
To use spot instances for LLM training safely: (1) checkpoint every 15–30 minutes to persistent storage (S3, GCS, or NFS); (2) use a spot interruption handler to trigger a final checkpoint on the 2-minute warning; (3) use a training framework with automatic checkpoint resumption (PyTorch Lightning, Hugging Face Accelerate, or DeepSpeed); (4) store checkpoints in a different availability zone or region from your compute.
With proper checkpointing, spot training typically costs 40–70% less than on-demand for equivalent training runs, even accounting for occasional restarts. See our guide on cheapest GPU cloud for AI inference for a full provider tier ranking.
Cost Comparison: Spot vs On-Demand
Example: Training a 7B parameter LLM for 100 GPU-hours on H100. On-demand (Lambda Labs): $2.49/hr × 100 = $249. Spot (RunPod): $1.89/hr × 100 = $189, but with ~10% expected overhead from interruptions and restarts = ~$208 effective cost. Savings: ~$41 (16%).
For longer runs (1,000+ GPU-hours), spot savings compound: $2,490 on-demand vs ~$2,080 effective spot cost = $410 saved. The longer the training run, the more valuable robust checkpointing becomes. Track H100 price history to see how spot and on-demand rates have moved over the past 90 days.