Running AI workloads efficiently means matching expensive accelerators to demand in real time. Our new orchestration service schedules GPU and inference jobs across your clusters with policy-driven guardrails that keep costs in check.
Schedule where it makes sense
The orchestrator continuously evaluates capacity, price, and locality across your environments, placing each workload where it runs best without manual intervention.
- Autoscale GPU pools based on live queue depth.
- Route inference to the lowest-cost region that meets your latency SLA.
- Pause and resume training jobs around committed-capacity windows.
Cost controls built in
Every scheduling decision respects the budgets and quotas you define. Set ceilings per team or project, and the platform enforces them before a job ever starts, so runaway spend is stopped at the source.
Observability from day one
Unified dashboards show utilization, throughput, and spend for every workload, giving platform teams the visibility to tune policies and prove ROI to the business.
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