Industry scenarios, not marketing case studies. No invented customer names, no logos — just the workloads and the constraints.
A payments team runs fraud-detection models that must score a transaction in milliseconds. They keep warm replicas in three regions, peer a VPC so scoring data never crosses the public internet, and pay only for the GPUs serving during settlement windows — autoscaling drops the fleet to zero overnight.
A robotics team ships defect-classification models that run against conveyor cameras. Autoscaling tracks the shift schedule, spinning GPUs up for first shift and down when the line stops. Observability attributes token cost down to each camera line, so the team knows what each line costs to run.
A legal-tech product reviews contracts in overnight batches on spot capacity, resuming from checkpoint on interruption. The entire workload stays inside a VPC for client confidentiality, and token accounting maps usage back to client billing.
We moved a serving stack that took two engineers a week to babysit. Now it autoscales and we read the dashboard instead of the pager.
Cold starts used to dominate our tail latency. Warm pools got us where our users expect us to be.
The VPC and compliance posture is what let us sign enterprise clients. That part matters as much as the GPUs.
Start on the free tier, and move to reserved or dedicated capacity as your workload demands.
Get started