Spot GPU Instances:
Up to 50% Off NVIDIA GPUs.
Run batch jobs, training experiments, and non-urgent workloads on discounted spot instances. Same NVIDIA GPUs, same performance, lower price. Instances can be terminated when demand spikes.
- Cost Savings
- Up to 50%
- Hardware
- Same
- Billing
- Per-Min
How Spot Instances Work
Idle Capacity
You Deploy
You Run Your Job
Termination
Built for These Workloads
Any workload that can tolerate interruption is a candidate for spot pricing. Checkpoint your work, and restarts are painless.
Hyperparameter Tuning
Data Preprocessing
Model Evaluation
Research and Prototyping
Spot vs Dedicated
| Feature | Dedicated | Spot |
|---|---|---|
| Pricing | Standard per-minute rates | Up to 50% off dedicated |
| Availability | 99.99% SLA, non-interruptible | Subject to capacity |
| Best for | Production, critical training | Batch jobs, experiments |
| Billing | Per-minute | Per-minute |
| GPU Models | All models | Select models (availability varies) |
| Interruption | Never | Can be terminated when capacity is reclaimed |
Mix Spot and Dedicated
Run your critical training loop on a dedicated instance (99.99% SLA) while offloading evaluation jobs to spot. Both are on-demand tiers, so you can mix them across your pipeline to optimize cost without sacrificing reliability where it matters.
Critical training on dedicated instances with 99.99% SLA
Evaluation and validation on discounted spot instances
Data preprocessing on spot for cost-efficient pipelines
Compare Instance Types
Dedicated
Spot
Reserved
Spot Instance FAQ
How spot capacity is priced and reclaimed, and when it is the right call.
Spot instances let you rent GPUs at a discount by using idle capacity in our data center network. They run on the same hardware as dedicated instances but cost up to 50% less. Both spot and dedicated are on-demand tiers with per-minute billing. The tradeoff: spot instances can be terminated when demand for that GPU model increases, whereas dedicated instances carry a 99.99% SLA.
Savings vary by GPU model and current demand. Typical discounts range from 20% to 50% off the dedicated rate. Check the pricing page for current spot rates on each GPU model.
View pricingSpot instances can be terminated when demand for that capacity is reclaimed. There is no grace period, so design your pipeline with regular checkpointing. Save progress frequently, and restarts pick up from the last checkpoint.
Any workload that handles interruption gracefully: hyperparameter searches, data preprocessing, model evaluation, batch inference, and research experiments. If you can checkpoint and restart, you can save money with spot.
Yes. Both are on-demand tiers, so you can mix them freely. Many teams run critical training jobs on dedicated instances (99.99% SLA) while offloading evaluation, preprocessing, and experimental runs to spot. This hybrid approach gives you reliability where it counts and savings everywhere else.
Halve Your GPU Bill
Same GPUs. Same data centers. Same performance. If your workload can tolerate interruption, there's no reason to pay full price.
