coreweave-migration-deep-dive

# CoreWeave Migration Deep Dive ## Cost Comparison | Instance | AWS | CoreWeave | Savings | |----------|-----|-----------|---------| | 1x A100 80GB | ~$3.60/hr (p4d) | ~$2.21/hr | ~39% | | 8x A100 80GB | ~$32/hr (p4d.24xl) | ~$17.70/hr | ~45% | | 1x H100 80GB | ~$6.50/hr (p5) | ~$4.76/hr | ~27% | ## Migration Steps ### Phase 1: Containerize ```bash # If running on bare EC2/GCE, containerize first docker build -t inference-server:v1 . docker push ghcr.io/myorg/inference-server:v1 ``` ### Phase 2: Adapt YAML for CoreWeave Key changes from AWS EKS / GKE: 1. **Node affinity**: Use `gpu.nvidia.com/class` instead of `nvidia.com/gpu.product` 2. **Storage**: Use CoreWeave storage classes (`shared-ssd-ord1`) 3. **Networking**: CoreWeave provides flat networking within VPC ### Phase 3: Parallel Deploy Run both old and new infrastructure simultaneously, gradually shift traffic. ### Phase 4: Cut Over Decommission old GPU instances after validation period. ## Common Gotchas | Issue | Solution | |-------|----------| | Different CUDA drivers | Match container CUDA to CoreWeave node drivers | | Storage migration | Use rclone or rsync to move data to CoreWeave PVC | | DNS changes | Update ingress/load balancer DNS | | IAM differences | CoreWeave uses kubeconfig, not IAM roles | ## Resources - [CoreWeave Pricing](https://www.coreweave.com/pricing) - [CoreWeave Documentation](https://docs.coreweave.com) ## Next Steps This completes the CoreWeave skill pack. Start with `coreweave-in...