Prevent GPU cloud lock-in by making every provider-specific layer replaceable. Run workloads on upstream Kubernetes, package them as OCI containers with pinned CUDA or ROCm and framework versions, expose accelerators through Kubernetes device plugins, and keep storage, networking, model serving and provisioning behind standard interfaces. Maintain tested manifests for at least two targets—such as two clouds, or a cloud and bare metal—and rehearse a complete redeployment before you need to switch.
Portability is not achieved by Kubernetes alone. The target still needs a compatible driver, GPU capacity, runtime, storage, network path and observability stack. You must test numerical correctness, performance, recovery and the cost and duration of moving images, models and data. Treat those tests as an operational capability, not a one-time architecture diagram.
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What GPU cloud lock-in actually includes
Lock-in occurs when moving an AI service requires rewriting its application, rebuilding its model runtime, exporting data through a proprietary format, or accepting an outage because only one provider can supply a compatible GPU stack. It can exist at several layers simultaneously:
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| Boundary | Portable design | What remains provider-specific |
|---|---|---|
| Application and serving | OCI image, standard Kubernetes Deployment or Job, HTTP or gRPC API, and configuration supplied through Secrets and ConfigMaps | Ingress implementation, identity integration and managed-service adapters |
| Cluster | Upstream Kubernetes APIs, Helm or Kustomize, standard Services, Ingress, Jobs, Deployments, quotas, taints and labels | Control-plane lifecycle, node images and managed add-ons |
| GPU access | Kubernetes device-plugin resources and scheduling constraints | GPU model, driver package, firmware, operator and capacity reservation |
| Runtime | Rebuildable framework backends and portable model formats where they meet your accuracy and latency goals | CUDA, ROCm, kernel, compiler and vendor-specific performance libraries |
| Data | CSI block or file volumes, object storage replication, snapshots and documented export procedures | Storage classes, snapshot controllers, topology and egress billing |
| Provisioning | Terraform or another declarative IaC layer with thin provider modules | GPU instance names, quotas, regions, network primitives and billing APIs |
A design is portable only when the provider-specific column can be replaced without changing the serving contract or the model artifact itself.
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Use Kubernetes as the stable control-plane contract
Keep workload objects upstream-compatible
Use standard Kubernetes objects for the application: Deployments for long-running inference, Jobs for batch work, Services for internal discovery, Ingress for HTTP entry points, Secrets for credentials, and PersistentVolumeClaims for state. Helm or Kustomize can generate environment differences, but the resulting objects should remain ordinary Kubernetes resources. Avoid making a proprietary controller a prerequisite for starting the service.
NVIDIA’s requirements for AI clouds explicitly call for certified upstream Kubernetes, standard APIs and no mandatory proprietary extensions. The requirement is stated as: “No (Mandatory) Proprietary Extensions: Core functionality must remain portable to other standard environments.” Treat that as a useful acceptance test: if removing a provider add-on prevents the core workload from scheduling, the boundary is too tight.
Separate scheduling intent from provider names
Put abstract intent in your base manifests—GPU count, memory, architecture, topology and workload class—and map it to provider-specific labels in an overlay. Keep node selectors, taints and tolerations in that overlay. A deployment should not contain an instance type that exists in only one region unless the overlay is deliberately targeting that region.
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Use quotas and priority classes to protect critical inference services, but express them with standard Kubernetes resources. Record the expected number of GPUs, CPU, memory, ephemeral storage and network bandwidth for each service so the same requirement can be evaluated against every target’s capacity catalog.
Expose accelerators through device plugins, not cloud APIs
Kubernetes has stable GPU-management support for AMD and NVIDIA through vendor device plugins. The Kubernetes administrator must install the matching vendor driver and device plugin; Kubernetes does not install or maintain those drivers for you. A pod requests the resource name advertised by the installed plugin, and the scheduler places it on a node that can satisfy the request.
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Build a driver and plugin matrix
For every target, record the GPU model, host kernel, container runtime, driver version, device-plugin version, framework version and model-runtime version. Pin the combinations that passed validation. Do not assume that a newer driver is interchangeable with an older CUDA or ROCm user-space image; test upgrades as a complete tuple.
Use node labels and taints to prevent a workload built for one accelerator family from landing on another. If a service supports both families, maintain separate overlays or node pools and run the same correctness and performance suite against each. Resource names are plugin-defined, so your deployment template should receive them as configuration rather than hard-code a cloud provider’s SKU.
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The NVIDIA GPU Operator is validated across cloud providers, bare-metal installations, virtual machines with passthrough, vGPU environments, Kubernetes distributions and container runtimes. It can simplify driver, toolkit, device-plugin and monitoring installation, but the application should still depend on the standard resources and APIs those components expose. Keep operator manifests in a provider or platform layer, separate from application manifests, so an alternative installation method can supply the same contract.
Make the container boundary reproducible
Pin the user-space stack
Build OCI images with explicit versions of the operating-system base, CUDA or ROCm libraries, the machine-learning framework, inference server and application dependencies. Generate a software bill of materials and retain the exact image digest used in production. A mutable tag such as latest turns a provider move into an uncontrolled software change.
Keep cloud SDKs, provider metadata clients and managed-queue libraries out of the core serving image. If the application needs object storage, put that access behind a small adapter with an S3-compatible or another documented interface, then supply credentials and endpoints through configuration. A provider-specific sidecar or external gateway can implement the adapter without contaminating the model image.
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Publish to registries reachable from every target
Replicate signed images to registries in each approved region or to a registry that all targets can reach over a controlled network path. Measure the time to pull a cold image on the largest node pool; a migration that looks complete on paper can fail when nodes spend minutes downloading multi-gigabyte layers during an autoscaling event. Keep an offline or independently hosted copy of the image manifest and signatures for disaster recovery.
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Portability does not mean every model runs identically on every accelerator. It means you can rebuild and validate the serving path without changing the external API or retraining solely because a provider changed. Use ONNX Runtime, MIGraphX, OpenXLA or framework backends that support your model operators and can be built for each target. AMD identifies ONNX Runtime and MIGraphX integrations, OpenXLA, and an open modular reference stack as ways to support cross-platform AI software.
Before standardizing on a format, check operator coverage, dynamic-shape behavior, quantization support, custom kernels and fallback paths. A model that silently falls back to the CPU may be functionally correct but operationally unusable. Store the original training checkpoint, converted artifact, conversion tool version and calibration data together so you can regenerate an artifact for another backend.
Keep the serving contract independent
Expose a stable request and response schema over HTTP or gRPC, and keep batching, token limits, timeout behavior and error codes documented. Put provider-specific optimizations—kernel fusions, graph compilation caches, MIG partitioning or vendor quantization—in a runtime configuration layer. The client should not know whether the server uses CUDA, ROCm or a CPU fallback.
Use standard storage and networking interfaces
Design the data boundary first
Use CSI-compatible block or file volumes for mutable state, and object storage for model artifacts, datasets and checkpoints. Replicate artifacts to a second provider or an independently controlled object store. Document the export format, encryption keys, metadata and restore command for every critical dataset.
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Test a full restore, not just object listing. Measure the time to copy a representative model and checkpoint set, rebuild indexes, attach volumes and bring replicas to a ready state. Include snapshot consistency and application quiescing in the procedure. A storage class that supports snapshots in one cluster may require a different controller or region topology in another.
Make network assumptions explicit
Record required throughput, latency, maximum transmission unit, east-west traffic, ingress path and topology constraints. Use standard Services and Ingress objects, while keeping load-balancer annotations in overlays. Measure cross-region transfer and egress charges before a cutover; the cost of moving a large model repository or daily feature stream can dominate the price difference between GPU instances.
Keep infrastructure-as-code replaceable
Express node pools, quotas, labels, taints, firewall rules, subnets, routes, storage classes and monitoring endpoints in Terraform or another declarative IaC layer. Put provider-specific resources in thin modules with the same input and output contract. The portable module should accept requirements such as GPU count, memory, architecture, zone constraints and minimum network performance; a provider module maps those requirements to actual instance types.
Store state separately from application code, encrypt it, and document how to migrate or recreate it. Do not make a provider’s proprietary state data the only record of cluster configuration. Keep rendered Kubernetes manifests and the exact module versions in source control so a second target can be rebuilt without access to the first provider’s console.
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Migration runbook for a second cloud or bare metal
- Build the provider-neutral image. Pin the base operating system, CUDA or ROCm, framework and serving runtime. Publish the digest and software bill of materials to registries reachable from every target.
- Prepare the target GPU layer. Install the target’s driver and device plugin, then record GPU model, driver, container runtime, kernel and plugin versions. Verify that the advertised resource appears on every intended node.
- Render the same Kubernetes release. Apply the base manifests and change only node selectors, resource names, storage classes, ingress settings and provider-adapter values in an environment overlay.
- Run correctness tests. Compare representative outputs with defined numerical tolerances. Include deterministic cases, edge inputs, tokenization, preprocessing, postprocessing and any safety filters. A response that is merely plausible is not a passing result.
- Run performance and resilience tests. Measure cold-start time, steady-state throughput, tail latency, GPU utilization, memory pressure, autoscaling behavior, pod eviction, node loss, rolling upgrades and checkpoint restore.
- Measure transfer and financial impact. Record image-pull time, model and dataset transfer duration, storage restore time, inter-region traffic and egress charges. Use the same workload volume and retention period when comparing targets.
- Soak and retain rollback. Operate the new deployment under representative production load for a sustained period. Keep the previous provider’s manifests, image digests, replicated artifacts and access credentials until the new target has passed the agreed availability and quality thresholds.
Test portability as an operational property
| Test area | What to measure | Evidence to retain |
|---|---|---|
| Scheduling | GPU resource discovery, taints, tolerations, topology and quota behavior | Rendered manifests, node labels, scheduler events and time to ready |
| Numerical behavior | Output agreement within documented tolerances across representative inputs | Golden test set, tolerances, backend and compiler versions |
| Performance | Throughput, p50/p95/p99 latency, cold start, utilization and memory headroom | Load profile, pod count, GPU model and test results |
| Reliability | Node failure, pod eviction, rolling upgrade, autoscaling and recovery time | Failure timeline, alerts, recovery steps and data-loss result |
| Data movement | Image pull, artifact transfer, snapshot export, restore and re-index duration | Dataset size, network path, throughput and elapsed time |
| Economics | GPU rental, storage, transfer, egress, idle capacity and engineering effort | Region, billing period, workload volume and assumptions |
| Observability | Metrics, logs, traces, GPU telemetry and incident export | Dashboard definitions, retention settings and exported samples |
Set pass/fail thresholds before the test. For example, define an allowed p99-latency increase, maximum restore duration and numerical tolerance for each model. Without thresholds, a migration can be declared successful despite a hidden quality or availability regression.
Compare providers on portability, not advertised GPU names
| Comparison axis | Questions to answer before committing |
|---|---|
| Kubernetes conformance and API access | Is the cluster based on certified upstream Kubernetes? Can you use standard APIs, admission controls, CRDs and network policies without mandatory proprietary extensions? |
| GPU capacity and scheduling | Which AMD and NVIDIA models are available, in which regions, with what capacity guarantees, quotas, topology controls and reservation terms? |
| Driver and operator lifecycle | Who installs and upgrades drivers, how are maintenance windows announced, and can you pin a validated driver and device-plugin combination? |
| Runtime and kernel support | Which container runtimes, kernels, virtualization modes, passthrough and vGPU configurations are supported? |
| Storage portability | Which CSI drivers exist for block and file storage? Are snapshots, replication and cross-region restore available in a documented format? |
| Network behavior | What bandwidth, latency, topology controls, load-balancer options and egress prices apply to the actual regions you will use? |
| Observability and incidents | Can you export metrics, logs, traces, GPU telemetry and incident records through documented APIs? |
| IaC and state | Are Terraform providers maintained, and can modules be replaced without importing opaque provider state into application logic? |
| Recovery options | Is a second region, a second provider or bare-metal target available with compatible GPU and storage capacity? |
Common failure modes and how to correct them
The image works only on one vendor’s nodes
This usually indicates an unpinned or incompatible driver/runtime pair, a proprietary binary extension or an unsupported model operator. Rebuild with a documented base image, test the framework backend on the second accelerator family and fail deployment when a required operator would fall back to CPU.
The manifests deploy but pods never schedule
Inspect the device-plugin resource name, node labels, taints, tolerations, quotas and topology constraints. A generic request for “one GPU” is not enough if the target advertises a different resource or has no available capacity in the selected zone. Keep those values in the environment overlay rather than editing application YAML by hand.
Performance is far below the original cluster
Check batch size, precision, graph compilation, kernel selection, memory bandwidth, interconnect topology and CPU or network bottlenecks. Re-run with the same model artifact, traffic mix and concurrency. If the alternate backend needs a different optimization, store it as a target-specific runtime profile while preserving the same API and correctness tests.
Restoring data takes longer than the outage budget
Measure restore and re-index time during normal operations, not during an incident. Reduce the recovery set, replicate artifacts in advance, test incremental snapshots and document a warm-start procedure. If the required recovery time cannot be met, the storage design—not the GPU choice—is the portability blocker.
Unexpected transfer charges erase the price advantage
Map every data path, including image pulls, feature pipelines, checkpoint uploads, cross-zone replication and client egress. Keep frequently used artifacts close to the serving cluster, use regional replicas where appropriate and include transfer assumptions in the provider comparison.
Governance that keeps portability from decaying
- Maintain a support matrix covering GPU model, driver, runtime, framework, operator and kernel versions.
- Run the portability test suite on every driver, framework, operator or Kubernetes upgrade.
- Keep production manifests for at least two targets continuously deployable, even if one is used only for disaster recovery.
- Review new dependencies for proprietary APIs, export restrictions, opaque state and data-residency implications.
- Rotate replicated credentials and verify that model artifacts can be pulled without the primary provider’s control plane.
- Export observability and incident data so a provider outage does not remove the evidence needed to diagnose it.
- Document an owner, recovery objective, rollback trigger and decision authority for every migration.
No authoritative independent statistic establishes a universal percentage for GPU cloud lock-in cost. The measurable exposure is specific to your workload: provider-dependent code, data volume, transfer routes, recovery time, GPU scarcity and engineering capacity. A small, repeatable redeployment exercise provides more useful evidence than a generic industry estimate.
The practical portability standard
A portable AI deployment can be rebuilt from source-controlled IaC, OCI images, model artifacts and documented configuration on a second target; it can obtain GPUs through that target’s device plugin; it produces numerically acceptable results at an agreed service level; and its data, observability and rollback procedures work without the original provider’s proprietary control plane. Kubernetes, Terraform, CSI and portable model runtimes provide the interfaces, but regular cross-target tests are what make the interfaces real.
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