CoreWeave Production Checklist

Inference Services

• GPU type and count validated for model size
• Autoscaling configured (KServe or HPA)
• Health and readiness probes set
• Resource requests AND limits specified
• Node affinity targeting correct GPU class
• minReplicas >= 1 for production (no cold starts)

Storage

• Model weights in PVC (not downloaded at startup)
• Checkpoints saved to persistent storage
• Storage class appropriate (SSD for inference, HDD for archival)

Security

• Secrets for model tokens and registry access
• Network policies applied
• Container images from trusted registries

Monitoring

• GPU utilization metrics collected
• Inference latency and throughput tracked
• Alert on pod restarts and OOM events
• Log aggregation configured

Rollback

kubectl rollout undo deployment/my-inference
kubectl rollout status deployment/my-inference

Resources

• CoreWeave CKS

Next Steps

For upgrades, see coreweave-upgrade-migration.