Spot GPU Droplets are in public preview. Spot GPU Droplet pricing is separate from on-demand pricing and may change daily based on available idle GPU capacity.
Spot GPU Droplets vs On-Demand GPU Dropletspublic
Last verified 11 Aug 2026
DigitalOcean Droplets are Linux-based virtual machines (VMs) that run on top of virtualized hardware. Each Droplet you create is a new server you can use, either standalone or as part of a larger, cloud-based infrastructure.
GPU Droplets are available with on-demand or spot capacity. Both tiers provide the same GPU configurations, but they differ in availability, pricing, and the possibility of reclamation.
| On-Demand GPU Droplets | Spot GPU Droplets |
|---|---|
| Guaranteed capacity. After you create an on-demand GPU Droplet, its capacity remains available until you destroy it. | As-available capacity. Spot GPU Droplets use available idle GPU capacity, so DigitalOcean does not guarantee availability. |
| Fixed pricing. On-demand GPU Droplets have a fixed hourly rate. | Variable pricing. Spot GPU Droplets always cost the same as or less than the equivalent on-demand GPU Droplets, but pricing may change daily based on available capacity. Check the current rate in the DigitalOcean Control Panel. |
| No capacity-based reclamation. DigitalOcean does not reclaim on-demand GPU Droplets to reallocate capacity. | Capacity-based reclamation. DigitalOcean may reclaim a Spot GPU Droplet with at least two hours’ notice. We send the notice by email to both the team account and the account that created the Droplet. |
| Stable workloads. On-demand GPU Droplets are best for production, latency-sensitive, or stateful workloads that require continuous capacity. | Fault-tolerant workloads. Spot GPU Droplets are best for fault-tolerant workloads that can save progress, restart, and move to another instance. |
On-demand GPU Droplets are best for workloads including:
- Production inference
- Latency-sensitive applications
- Stateful applications
- Workloads that require stable, continuous capacity
Spot GPU Droplets are best for fault-tolerant workloads including:
- Batch model training
- Batch or asynchronous inference
- Hyperparameter tuning
- Rendering