Route every training run to the cheapest GPU, automatically.
SkyPilot sends ML jobs across AWS, GCP, and Azure. When spot prices rise or capacity disappears, jobs move automatically, reducing overpayment and manual monitoring.
| Job ID | Cloud | Instance | $/hr | Status |
|---|---|---|---|---|
| job-001 | AWS | p4d.24xlarge | $31.77 | RUNNING |
| job-002 | GCP | a2-highgpu-8g | $27.84 | RUNNING |
| job-003 | AZR | NC96ads-A100 | $30.50 |
GPU budgets leak in three spots.
Reserved GPUs sit idle
You reserve for peak demand, but training ends early. Reserved instances bill you whether GPUs are active or idle.
Jobs stall in the wrong zone
Spot capacity vanishes, the job stalls, and on-call scrambles. GPU hours disappear while your team finds a new queue location.
Manual cloud price shopping
Engineers compare provider pricing instead of training models. Knowing which region is cheapest this week is tribal knowledge, not a strategy.
Submit once. SkyPilot does the rest.
From config to the cheapest node in three steps.
Write a training config
Describe the job in YAML, Python, or the CLI. Choose GPU type, dataset location, and run command. SkyPilot manages the rest.
SkyPilot scans live prices
SkyPilot checks live spot and on-demand prices across AWS, GCP, and Azure, then chooses the least expensive node meeting your needs.
Jobs launch and migrate
Jobs start on the cheapest node. If spot prices rise or capacity is interrupted, SkyPilot checkpoints and migrates them.
Results from early-access teams.
Internal benchmark data from 12 early-access teams during 3-month pilots.
Our pilot measurements saturated spot capacity that idle reserved instances would have left unused.
Checkpoint-and-resume testing covered 200+ spot interruption events in early-access pilots.
Made for the workloads you run.
LLM fine-tuning runs
Send multi-GPU fine-tuning jobs to the cheapest A100 or H100 spot capacity across providers, without cloud-specific scripts.
Batch inference pipelines
Send overnight inference queues to off-peak spot prices. Scale down when complete and avoid paying for idle nodes between batches.
Multi-node training
Coordinate multi-node training across varied cloud instances with automatic recovery. SkyPilot manages coordination, not your YAML.
From teams already lowering GPU costs.
Our monthly AWS compute bill fell 40% in the first month. When spot prices rose, automatic migration kept a 72-hour fine-tuning run moving by shifting it to GCP.
Multi-cloud GPU routing once took a week of DevOps work. With SkyPilot, it took a YAML file and a CLI command. The cost attribution dashboard alone pays for the subscription.
Stop paying for GPUs your jobs do not use.
Join ML teams running leaner compute budgets.