The safest CoreWeave migration treats Kubernetes applications and container images as the portable core, while rebuilding GPU drivers, device plugins, storage, identity, networking, and topology for the destination. Create the target cluster before touching production, copy data and checkpoints while CoreWeave remains live, benchmark single-node and multi-node jobs, then perform a final delta sync and controlled cutover with rollback access retained.
Avoid choosing AWS, Azure, or Google Cloud from hourly GPU price alone. Equivalent GPU capacity, interconnect behavior, storage and checkpoint performance, regional quota, transfer charges, reliability, security controls, and operational ownership determine whether the moved workload is faster, slower, or more expensive.
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What moves cleanly and what must be rebuilt
A Kubernetes manifest can describe a Deployment, Job, namespace, service account, secret reference, resource request, and scheduling rule, but it cannot make a destination expose the same GPU devices, network fabric, storage class, IAM model, or topology. Keep the application specification portable and isolate provider-specific settings in overlays or separate values files.
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|---|---|---|
| Application | Container image digest, entrypoint, arguments, environment names, Kubernetes workload objects | Provider-specific endpoints, credentials, feature flags, and admission policies |
| GPU runtime | CUDA application code and image, when compatible with the target driver | GPU model, node shape, host driver, CUDA/driver matrix, NVIDIA device plugin, resource names, MIG or other partitioning |
| Distributed communication | NCCL configuration intent and collective operations | EFA, RoCE, InfiniBand, TCPXO/FastRak, network attachments, topology labels, NCCL plugins and tuning |
| Storage | Dataset and checkpoint formats, application paths | CSI drivers, StorageClass, POSIX semantics, object APIs, throughput, latency, snapshots, encryption and access policy |
| Operations | Health checks, metrics names and runbooks | IAM roles, logging agents, alert integrations, quotas, autoscaling and support procedures |
CoreWeave describes its platform as Kubernetes-native GPU compute with AI object storage, distributed file storage, and NVIDIA InfiniBand or Spectrum-X networking. Its current storage material (accessed in 2026) states maximum throughput of 7 GB/s per GPU for AI object storage and 1 GB/s per GPU for distributed file storage. Those figures describe CoreWeave services, not a promise that an equivalent service elsewhere will perform identically.
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Inventory the workload before provisioning anything
Make an export that can be reviewed by the platform, security, data, and finance teams. Record the immutable image digest rather than only a mutable tag such as latest. Include objects that are not visible in a single Deployment export, because a missing service account, secret reference, or topology rule can make a technically valid image fail at runtime.
Cluster and Kubernetes inventory
- Namespaces, Deployments, StatefulSets, Jobs, CronJobs, DaemonSets, Services, Ingress objects, ConfigMaps, Secrets references, PodDisruptionBudgets, NetworkPolicies, ResourceQuotas, LimitRanges, and admission policies.
- Every persistent-volume claim, access mode, requested capacity, filesystem, mount path, reclaim policy, snapshot relationship, and backup location.
- Service accounts, workload identity annotations, cloud credentials, registry pull secrets, external secret providers, and the minimum permissions each workload needs.
- Node selectors, affinity and anti-affinity, taints and tolerations, topology spread constraints, host-device mounts, privileged containers, and any dependency on CoreWeave labels or annotations.
- Ingress and egress destinations, DNS names, firewall rules, private endpoints, allowlists, proxies, license servers, artifact repositories, and monitoring or incident systems.
GPU, software, and performance inventory
- GPU model, count per node, VRAM, MIG or other partition profile, CPU and RAM ratio, local NVMe capacity, and the requested Kubernetes GPU resource.
- Host NVIDIA driver, CUDA toolkit and runtime versions, NCCL version, communication plugins, environment variables, and custom kernel or compiler requirements.
- For distributed jobs, process count, ranks per node, node count, all-reduce implementation, expected topology, and whether the code assumes InfiniBand, RoCE, EFA, or another transport.
- Checkpoint interval, checkpoint size, write pattern, restart command, retention policy, and the maximum recovery point and recovery time the business accepts.
Capture a baseline that can expose regressions
Run a representative training or inference window on CoreWeave and retain the measurements with the software and dataset versions. Capture samples per second or tokens per second, GPU utilization, input-pipeline throughput, step time, all-reduce time, checkpoint latency, failed-job rate, and time to recover from a killed worker. Measure a one-node run and the normal multi-node run separately. A job can be correct on one GPU and still lose most of its throughput when inter-node communication changes.
Choose the destination by workload behavior
Compare equivalent GPU generations and memory sizes, not merely a product name. Ask each provider for the exact region, quota, lead time, host topology, network mode, and storage service that will be available to your account.
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| Decision axis | AWS EKS | Azure AKS | Google Cloud GKE |
|---|---|---|---|
| GPU exposure | Validate the selected EC2 family, EFA device exposure, driver and device-plugin path, and scheduling constraints. EKS Auto Mode documents NVIDIA GPU workloads and automatic accelerator component management. | Choose a GPU-enabled Linux node pool. AKS-managed GPU node pools generally manage the NVIDIA driver, device plugin, metrics exporter, and GPU health signals. | Create a GPU node pool and select the documented NVIDIA driver path. Confirm that the machine family and accelerator quota are available in the required zone. |
| Distributed networking | EFA or RoCE resources may be required for relevant generations; verify NCCL and EFA compatibility on the actual instance type. | GPU families may expose InfiniBand-related resources; verify the network attachment, MPI/NCCL path, and topology placement. | Google documents NCCL Fast Socket and recommends GPUDirect RDMA for newer A3, A4, and later families; confirm TCPXO/FastRak and RDMA exposure where applicable. |
| Storage and checkpoints | Benchmark the chosen file and object services with your checkpoint pattern and parallelism. | Compare managed file, blob, and cache options; Storage Mover can stage data from AWS S3. | Benchmark the selected parallel file and object services, including metadata-heavy workloads. |
| Driver ownership | EKS Auto Mode can manage accelerator drivers and the device plugin; standard EKS designs may leave more choices to you. | AKS-managed GPU pools are the recommended path for most teams; self-managed components suit teams needing tighter control. | Follow the GKE driver lifecycle for the selected GPU and cluster version; pin versions where reproducibility requires it. |
| Capacity and quota | GPU and EFA capacity is regional and account-specific; request quota before the migration window. | GPU SKU capacity and quota vary by region and subscription; obtain a confirmed allocation. | Accelerator quota, zonal capacity, and machine-family availability must be checked together. |
| Best fit signal | Existing AWS identity, networking, and EFA expertise can reduce operational work. | Microsoft identity, enterprise networking, or a staged S3-to-Azure move may favor AKS. | GKE is attractive when the required accelerator family, GPUDirect RDMA path, and Google-native data tooling are available. |
Do not assume a provider’s managed Kubernetes mode supports every topology feature. Confirm the exact cluster mode, GPU family, Kubernetes version, driver version, and network plugin in a small target cluster before committing production data.
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Build the destination foundation first
- Select a geography and failure boundary. Choose the region and zones that meet data-residency, latency, disaster-recovery, and capacity requirements. Decide whether a second region is needed for checkpoints or recovery.
- Create the network. Build private subnets or equivalent segments for control-plane and worker traffic, route GPU nodes to required registries and storage endpoints, and restrict inbound access. Keep data paths private when policy or performance requires it.
- Create separate system and GPU node pools. Reserve non-GPU nodes for DNS, ingress, logging, metrics, and controllers. Give GPU pools explicit taints and labels so ordinary pods cannot consume expensive accelerators.
- Install the provider-supported GPU path. Use the managed driver and device-plugin approach unless a documented requirement justifies self-management. Verify that the node advertises the expected resource name and that a test pod can enumerate the GPU.
- Configure identity with least privilege. Create workload identities or roles for registry pulls, object access, checkpoint writes, secrets, logs, and metrics. Do not copy a CoreWeave credential into the new cluster as a shortcut.
- Install storage and network integrations. Add CSI drivers, network attachments, topology labels, and any RDMA or EFA components required by the GPU family. Define destination StorageClasses with explicit reclaim and encryption policies.
- Enable observability before the application. Collect node health, GPU utilization and memory, ECC or hardware errors where exposed, pod events, network errors, storage latency, checkpoint duration, and cost allocation labels. Configure alerts and a dashboard before the first canary.
- Capture the whole foundation as infrastructure-as-code. Version the cluster, IAM, networks, node pools, policies, and monitoring. A reproducible target is also your rollback and disaster-recovery plan.
Map GPU resources and interconnects deliberately
Never copy CoreWeave node labels, device annotations, or resource names without checking what the destination actually exposes. NVIDIA readiness guidance describes materially different resource models: AWS may expose EFA or RoCE resources for relevant generations, Azure uses InfiniBand-related resources for supported configurations, and GCP H100 guidance uses TCPXO/FastRak.
AWS
For EKS, validate the exact EC2 instance family, EFA device visibility, security-group rules, placement constraints, and CUDA/NCCL compatibility. EKS Auto Mode documents NVIDIA GPU workloads, EFA, automatic accelerator driver and device-plugin management, and scale-to-zero behavior for idle batch inference. Treat those capabilities as configuration to verify, not as proof that every EKS cluster has the same behavior. Run an NCCL test across the intended node count and inspect whether traffic uses the expected high-speed path.
Azure
AKS supports GPU-enabled Linux node pools. Microsoft describes two approaches for NVIDIA workloads: AKS-managed GPU node pools, which manage the NVIDIA driver, device plugin, metrics exporter, and GPU health signals for most scenarios, and self-managed components for teams that need tighter control. Whichever path you select, pin the driver and CUDA compatibility you validated, and ensure the pod’s node selector and toleration match only the intended GPU pool.
Google Cloud
GKE requires an appropriate GPU node pool and NVIDIA driver path. Google documents the NCCL Fast Socket plugin for more efficient GKE workloads and recommends GPUDirect RDMA for newer A3, A4, and later machine families. GKE also supports multi-instance GPU partitioning for GB200, B200, H200, H100, A100, and RTX PRO 6000 GPUs, providing hardware isolation and predictable quality of service. Partitioning changes the resource shape presented to Kubernetes, so test scheduler behavior and memory limits before using it in production.
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Move datasets and checkpoints while CoreWeave stays live
Run data migration as a separate workstream from compute migration. First copy immutable datasets and historical checkpoints; then keep the source workload running while new or changed files are synchronized. Preserve ownership, permissions, timestamps, symlinks, object metadata, encryption settings, and directory semantics when the application depends on them.
Use a staged transfer
- Classify data. Separate immutable training data, mutable feature data, active checkpoints, logs, caches, and disposable scratch space. Only the first five categories normally need a deliberate migration plan.
- Seed in parallel. Use parallel object or file transfer sized to the source and destination services. Avoid a single serial copy for multi-terabyte datasets, but cap concurrency so the transfer does not starve the live training job.
- Verify every batch. Calculate checksums at the source and destination. For object stores, compare content hashes and size; for filesystems, use a manifest that includes path, size, modification time, and checksum. A successful copy command is not data validation.
- Run online delta synchronization. Repeat the transfer while CoreWeave continues writing. Record the synchronization watermark and list files changed during each pass.
- Freeze writes for the final pass. Quiesce training or stop checkpoint writers, perform the final delta, and verify that the source and destination manifests agree before switching jobs.
CoreWeave Zero Egress Migration
CoreWeave’s Zero Egress Migration page states that transfers can originate from AWS, Azure, Google Cloud, IBM, Alibaba, and other major clouds, with managed migration and end-to-end validation. It advertises $0/GB egress under stated eligibility and contract conditions, while citing legacy hyperscaler egress of up to $0.12/GB. The page also states that an account closure is not required. These are program-specific terms: obtain written confirmation of eligibility, covered regions and services, transfer limits, validation scope, and expiration before scheduling the move.
Azure transfer options
Microsoft describes Azure Storage Mover as a fully managed service for migrating files and folders from on-premises systems or AWS S3 to Azure Storage while minimizing downtime. Microsoft also recommends Data Box when network transfer is constrained, followed by Storage Mover for online catch-up and delta synchronization. Select the path by measuring the available link, transfer window, encryption requirements, and the cost of shipping or handling a physical appliance.
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- GPU resource requests and limits, node selectors, taints, tolerations, and topology spread rules.
- StorageClass names, PVC definitions, CSI parameters, mount options, object-store endpoints, and snapshot policies.
- Network attachments, RDMA or EFA settings, security groups, private endpoints, DNS, and service exposure.
- IAM or workload-identity bindings, secret-provider references, registry credentials, and encryption keys.
- Logging, metrics, tracing, cost-allocation labels, and alert destinations.
Keep the CoreWeave and destination overlays side by side so a rollback is a configuration change rather than a manual reconstruction. Do not “fix” a failed migration by rebuilding the image unless the test proves a genuine driver, library, or architecture incompatibility. Changing the image and infrastructure at once makes the failure difficult to diagnose.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Canary tests and acceptance criteria
Run a small canary before moving the full dataset or fleet. The canary should fail loudly if a driver, mount, identity, network, or checkpoint assumption is wrong.
- Schedule a pod onto the intended GPU pool and confirm the expected GPU model, VRAM, driver, CUDA runtime, and Kubernetes resource are visible.
- Run representative CUDA kernels and verify numerical correctness against a known result.
- Run NCCL collectives on one node, then across the production node count. Record all-reduce time, bandwidth, retries, and failed ranks.
- Read a representative dataset and measure input-pipeline throughput, cache behavior, metadata latency, and CPU utilization.
- Write a real checkpoint to the destination storage, measure latency and size, restart from it, and verify model state and optimizer state.
- Kill a worker or node and verify that the controller reschedules it, the job recovers, and the recovery time meets the agreed target.
- Confirm logs, metrics, GPU health signals, alerts, audit records, and cost labels appear in the normal operations systems.
Set acceptance thresholds before the test. A practical decision record includes throughput, all-reduce time, checkpoint latency, GPU utilization, failure rate, recovery time, and daily cost compared with the CoreWeave baseline. Treat a distributed-training regression as a release blocker even when single-GPU correctness is perfect.
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- Announce a change window. Identify the owner for compute, data, networking, security, and communications. Freeze image and manifest changes unless they are part of the cutover.
- Quiesce writes. Stop new training jobs, pause checkpoint writers, or place the application in its documented maintenance mode. A running writer can invalidate an otherwise correct copy.
- Run the final synchronization. Transfer changed files and objects, compare checksums and model artifacts, and record the final source watermark.
- Switch submission or ingress. Update the job queue, scheduler endpoint, DNS, service routing, or API gateway. Keep the old path available but prevent accidental dual writes.
- Observe the acceptance window. Watch error rate, step time, all-reduce time, GPU utilization, input throughput, checkpoint latency, storage errors, and spend. Compare with the baseline rather than relying on a green pod status.
- Rollback if a threshold is breached. Stop destination writers, preserve logs and failed checkpoints, switch submission back to CoreWeave, and replay only verified source data. Do not delete the source cluster until the agreed rollback window has passed.
Retain CoreWeave images, manifests, checkpoints, access credentials, and network paths for the rollback period. Decommission in stages: first scale down workloads, then remove temporary transfer infrastructure, and finally delete storage and cluster resources after retention and audit obligations are satisfied.
Costs, quotas, and security checks that change the answer
- Capacity: A lower listed GPU rate is irrelevant if the required model is unavailable in the needed region or your quota request is unresolved. Obtain a capacity confirmation, not only a catalog listing.
- Data transfer: Include source egress, destination ingress, inter-region replication, API requests, physical-transfer handling, and recurring checkpoint traffic. CoreWeave’s $0/GB 0EM statement applies only under its stated program conditions; legacy hyperscaler egress can reach up to $0.12/GB according to that program page.
- Storage performance: Price the throughput and IOPS the checkpoint pattern needs. A cheaper volume that extends every checkpoint can cost more in idle GPU time.
- Idle capacity: Include system nodes, warm GPU pools, autoscaler delays, reserved capacity, and minimum storage charges. Scale-to-zero features can help batch inference but may add startup latency.
- Reliability: Compare zone failure behavior, managed control-plane guarantees, GPU replacement procedures, checkpoint recovery, and support escalation.
- Security and compliance: Recreate encryption, key ownership, private networking, identity boundaries, audit logs, vulnerability scanning, and retention policies. A copied secret is not a completed security design.
- Quota operations: Request GPU, EFA/RDMA, IP, storage, and API quotas early. Test the quota limits with the same parallelism used in production.
Common failure modes and fixes
Pods remain Pending
Inspect the scheduler event for an unavailable GPU resource, mismatched taint or toleration, wrong node selector, insufficient quota, or topology constraint. Compare the requested resource name with the device plugin’s advertised name and remove CoreWeave-only labels from the destination overlay.
CUDA initializes but the job crashes later
Check the host driver and container CUDA compatibility, NCCL version, architecture support, and any custom kernel. Re-run the canary with the exact production image digest and collect the driver, CUDA, and NCCL versions in the incident record.
Single-node performance is normal but multi-node training regresses
Measure all-reduce and point-to-point bandwidth separately. Confirm that EFA, InfiniBand, RoCE, or GPUDirect RDMA is actually selected, that placement keeps workers in the intended topology, and that the correct NCCL plugin and environment settings are loaded. Do not compensate for a missing high-speed path by merely adding more GPUs.
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Compare metadata latency, sustained write bandwidth, concurrency, mount options, and object multipart settings. Verify that the destination filesystem provides the POSIX behavior the application expects. Check that the identity can create, list, rename, and delete temporary files, not just read them.
Data appears present but the model is wrong
Compare manifests and checksums, not directory sizes alone. Check for omitted hidden files, symlinks, object-version selection, line-ending transformations, incomplete multipart uploads, and a final-delta race. Preserve the exact checkpoint and dataset version used by the accepted canary.
Costs rise after a successful cutover
Break the bill into GPU runtime, system nodes, storage, transfer, inter-zone traffic, logs, snapshots, and idle time. Look for a replica or autoscaler that keeps GPU nodes warm, a checkpoint path crossing regions, and duplicated data retained for rollback longer than planned.
Migration acceptance checklist
- Every image is pinned by digest and has a tested destination overlay.
- GPU model, VRAM, driver, CUDA, NCCL, device plugin, and partitioning behavior are recorded.
- One-node and multi-node benchmarks meet the predeclared thresholds.
- Datasets and checkpoints have verified source and destination checksums.
- Final delta synchronization and write freeze procedures have been rehearsed.
- IAM, secrets, private networking, encryption, logging, metrics, and alerts pass review.
- GPU, network, storage, IP, and API quotas are confirmed for the production scale.
- Rollback can be completed within the agreed recovery-time objective.
- Cost estimates include transfer, storage, idle capacity, and inter-zone traffic, not only GPU hours.
- CoreWeave access and artifacts remain available until the rollback window closes.
Bottom line
For most teams, the migration does not require rewriting GPU jobs: keep the container and Kubernetes application definition, then replace the assumptions that were supplied by CoreWeave. Build the target foundation, map its GPU and interconnect resources, transfer data with checksums and a final delta, and accept the destination only after distributed-training and checkpoint tests meet written thresholds. AWS, Azure, and Google Cloud can all host the workload, but the right choice is the one that can provide the required GPU capacity, communication fabric, storage behavior, quota, security, and total operating cost in the region you actually need.
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