Cloud ML Workload Migration Deep Dive

v20260423

coreweave-migration-deep-dive

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

ML Cloud Migration GPU Kubernetes AWS CoreWeave Inference

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Overview

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

# 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:

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.

Info

Category Artificial Intelligence

Name coreweave-migration-deep-dive

Version v20260423

Size 2.11KB

Source jeremylongshore/claude-code-plugins-plus-skills

Updated At 2026-04-28