✓ Zero-downtime deploys (down from ~25 min of downtime each release)
✓ GPU utilization up from 41% to 82%
✓ MTTR cut from ~3 hours to ~20 minutes
Before → after
| Metric | Before | After |
|---|---|---|
| Deploy downtime per release | ~25 min | 0 min |
| GPU utilization | 41% | 82% |
| Mean time to recover (MTTR) | ~3h | ~20 min |
An AI product company ran inference on a hand-rolled set of GPU boxes. Deploys took the service down, GPUs sat mostly idle, and incidents meant long nights of manual recovery.
The challenge
- Releases required taking the service offline, unacceptable for paying customers.
- GPU nodes were underutilized because workloads were placed by hand.
- No autoscaling, so capacity didn’t match demand.
- Ops knowledge lived in one engineer’s head.
What we built
- GKE cluster provisioned a managed Kubernetes cluster with GPU node pools via Terraform.
- Rolling deploys introduced canary + blue/green releases so new versions roll out with zero downtime.
- Bin packing tuned node pools and resource requests, raising GPU utilization dramatically.
- Autoscaling configured cluster and node autoscaling to follow inference demand.
- Observability wired Cloud Monitoring, alerts, and runbooks so recovery is minutes, not hours.
The results
Deploys went from ~25 minutes of downtime each to zero. GPU utilization jumped from 41% to 82%, and mean time to recover fell from about three hours to twenty minutes. The company now runs releases daily without customers noticing.
The takeaway
Kubernetes is worth it when you have real workloads to place and scale. Managed GKE plus disciplined rollout patterns turns fragile, manual deploys into a non-event.
Stack GKEGPUsTerraformCloud MonitoringIstio