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Guaranteed <30s spot recovery SLA

Spot GPUs that never lose your training run.

Infrastructure-agnostic orchestration that arbitrages spot capacity across AWS, GCP, and RunPod — with instant FUSE data hydration and zero-loss checkpoint recovery.

<30s
Recovery SLA — benchmarked in CI
85%
Max savings vs on-demand
3
Clouds arbitraged — AWS · GCP · RunPod
100%
Checkpoint progress retained
Illustrative spot rates — live prices vary by region and time.
Platform

Everything a training run needs to survive spot.

Recovery, data, placement, and governance — built in, not bolted on.

Sub-30s Preemption Recovery

Continuous checkpoint streaming and autonomous failover migration — zero lost gradient steps when a spot node dies.

T+0.0s
Signal detected
T+1.2s
Checkpoint flush
T+4.8s
Spot arbitrage
T+14.2s
FUSE mount
T+18.4s
Job resumed
Simulated on AWS EC2 Spot g5.12xlarge → GCP failover

Instant FUSE Hydration

S3/GCS mounts as POSIX in milliseconds — 8 MB block cache with a WAL SQLite index. No two-hour dataset stage-in.

Multi-Cloud Arbitrage

Hourly scans of AWS Spot, GCP preemptible, and RunPod place every job on the cheapest $/GPU-hr.

GPU Bin-Packing

Per-device pinning and Kubernetes time-slicing pack more jobs onto every physical GPU.

Quotas & RBAC

Fair-share GPU-hour budgets, scoped API keys, and Fernet-encrypted credentials at rest.

Docker & Kubernetes Native

Run via the Docker SDK or submit straight to your cluster.

Kueue Volcano Per-device GPU pinning
Cost Arbitrage

Prove the 85%. Drag the sliders.

Estimated spot arbitrage rates vs hyperscaler on-demand pricing.

GPU type $0.95/hr
H100
A100
L40S
RTX 4090
B200
Concurrent GPUs 16
Hours per month 480
AWS / GCP on-demand $31,488 / mo
AetherCompute spot $7,296 / mo
Monthly savings
$24,192
76.8% cost reduction
Developers

Ship a job in 10 lines.

YAML spec, Python SDK, CLI, or REST API — your call.

Cut your GPU bill by up to 85%.

Join AI teams running zero-loss spot training on AetherCompute.

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