| name | coreweave-migration-deep-dive |
| description | Migrate ML workloads from AWS/GCP/Azure to CoreWeave GPU cloud.
Use when moving inference services from hyperscaler GPU instances,
migrating training pipelines, or evaluating CoreWeave vs cloud GPU costs.
Trigger with phrases like "migrate to coreweave", "coreweave migration",
"move from aws to coreweave", "coreweave vs aws gpu".
|
| allowed-tools | Read, Write, Edit, Bash(kubectl:*), Grep |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","gpu-cloud","kubernetes","inference","coreweave"] |
| compatible-with | claude-code |
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
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:
- Node affinity: Use
gpu.nvidia.com/class instead of nvidia.com/gpu.product
- Storage: Use CoreWeave storage classes (
shared-ssd-ord1)
- 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
Next Steps
This completes the CoreWeave skill pack. Start with coreweave-install-auth for new deployments.
