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Preventing GPU cloud lock-in requires designing portability into every boundary of the stack, not merely keeping a second cloud account. Run workloads on upstream Kubernetes, package them as OCI images with pinned CUDA or ROCm and framework versions, expose accelerators through standard device-plugin resources, and isolate provider services behind replaceable adapters. Keep model serving behind portable formats and APIs, use CSI-compatible storage, express infrastructure in Terraform, and maintain tested manifests for at least two targets. Portability is only real when you can redeploy, restore data, meet correctness and performance targets, and understand transfer and egress costs on the alternate provider.
What GPU cloud lock-in actually includes
GPU lock-in is the effort, risk, and cost of moving an AI system when its assumptions are tied to one provider. The dependency may sit below the application—in a GPU instance type, driver image, kernel, or scheduler—or above it, in managed inference APIs, proprietary model stores, networking, monitoring, and identity services. A container that runs on two clouds but depends on one cloud’s object-store API is not fully portable.
AI workloads make this harder than ordinary web applications. Accelerators are exposed through vendor drivers and device plugins; CUDA and ROCm versions affect framework builds; GPU memory, interconnect topology, and availability differ by region; and a model checkpoint can be much larger than a typical application artifact. A migration can therefore fail even when the Kubernetes YAML applies cleanly.
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- Control-plane dependency: proprietary Kubernetes APIs, custom workload objects, or a managed service that cannot be recreated on another conformant cluster.
- GPU software dependency: a driver, operator, runtime, or framework build that is unavailable for the target accelerator.
- Data-plane dependency: provider-specific disks, snapshots, object-store semantics, network topology, or egress routes.
- Operational dependency: monitoring, incident data, quotas, autoscaling signals, identity, and deployment automation that cannot be exported or rebuilt.
Map these dependencies before choosing a second provider. The goal is not to make every implementation identical. The goal is to keep the contract between layers stable while allowing a provider-specific implementation underneath.
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The portable reference architecture
Use explicit boundaries so a provider change alters an adapter or node definition rather than the model-serving application. The following pattern keeps the interfaces common and confines unavoidable differences to small, documented areas.
| Boundary | Portable contract | Provider-specific material to isolate |
|---|---|---|
| Container | OCI image, pinned framework and accelerator runtime versions, documented environment variables | Base image registry, image-signing integration, and accelerator libraries selected for the target GPU |
| Cluster | Upstream Kubernetes Deployments, Jobs, Secrets, Services, Ingress, Helm, and Kustomize | Cluster creation, identity integration, load-balancer class, and storage classes |
| GPU | Device-plugin-exposed accelerator resources and scheduling constraints | Vendor driver, device plugin, operator, GPU model, topology, and resource name |
| Runtime | Portable model format or serving API, with a rebuildable framework backend | CUDA, ROCm, compiler, kernel, and hardware-specific optimization |
| Data | CSI-compatible block or file volumes, replicated object storage, portable model registry | Volume class, snapshot implementation, object-store endpoint, encryption key integration, and transfer path |
| Provisioning | Terraform or another portable infrastructure-as-code layer | Provider modules for GPU pools, quotas, networks, IAM, and regional capacity |
Keep the application-facing interfaces stable. For example, a serving Deployment should consume a model URI and configuration supplied through a Secret or ConfigMap, while a thin provider adapter translates that URI to the registry or object-store endpoint used in the target environment. Avoid embedding cloud SDK calls in the serving process merely to download a model or emit metrics.
Build an OCI container boundary that can move
Pin the accelerator software deliberately
Build images with explicit versions of the operating-system base, CUDA or ROCm, the machine-learning framework, inference libraries, and your application. Record the digest of every released image. A floating tag can silently select a different runtime after a provider changes its host driver or after a registry publishes a new build.
The host driver and the user-space libraries in the image must be a tested combination. Treat that combination as a release artifact: record the GPU model, host driver, container runtime, framework, and model-serving version that passed validation. When moving to AMD, rebuild the image for the ROCm-based stack and run the same correctness suite; do not assume a CUDA image can be retagged.
Keep cloud SDKs out of the core image
Do not make the serving binary depend on one provider’s SDK for essential behavior. Put registry authentication, object-store credentials, and cloud-specific telemetry in a sidecar, CSI integration, init process, or small adapter that can be replaced. The core image should start with a model path, endpoint, and ordinary environment variables that are supplied by Kubernetes.
Publish to registries reachable from every target
Maintain a registry strategy that works during a migration or outage. Replicate signed images to locations reachable from each provider, or use a neutral registry that both can pull from. Test a clean-cluster pull rather than relying on a warm node cache. Measure pull time because a large image can lengthen autoscaling and recovery enough to affect service-level objectives.
Use upstream Kubernetes as the cluster contract
Keep the workload description in standard Kubernetes APIs. Deployments and Services should describe the application; Jobs should describe batch work; Secrets and ConfigMaps should carry configuration; Ingress should expose HTTP services; and Helm or Kustomize should apply environment differences without forking the application definition. Kubernetes distributions can differ in packaging, but an upstream API contract reduces the amount that must change.
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Expose accelerators through device plugins
Kubernetes documents stable GPU management support for both AMD and NVIDIA through vendor device plugins. The cluster administrator remains responsible for installing the matching vendor driver and device plugin. A Pod requests the accelerator resource exposed by that plugin rather than calling a provider’s proprietary GPU API.
Keep the resource key, node labels, taints, tolerations, and selectors in a small provider overlay. The main Deployment should not contain a cloud-specific instance identifier. Instead, an overlay selects a node pool with the required GPU class and supplies the target’s device-plugin resource name.
Decide whether to use an operator
NVIDIA GPU Operator is validated across cloud providers, bare metal, virtual-machine passthrough, vGPU, Kubernetes distributions, and container runtimes. It can standardize installation and lifecycle management for NVIDIA nodes, but it is still a component to version, monitor, and replace. Define who owns upgrades, how a failed driver rollout is rolled back, and whether the target provider permits the same operator mode.
For a multi-vendor fleet, keep the operator choice behind the GPU boundary. An NVIDIA node pool may use GPU Operator while an AMD pool uses its documented driver and device-plugin path. The application should see only the resource contract and the scheduling policy.
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Honor NVIDIA’s portability requirement
NVIDIA’s Requirements for AI Clouds v2.1 states: "No (Mandatory) Proprietary Extensions: Core functionality must remain portable to other standard environments." Apply that principle to every managed service you adopt. If a feature is essential, verify that it can be implemented with standard Kubernetes APIs or replaced without rewriting the model service.
Make the runtime and model-serving layer replaceable
Choose a model interchange boundary
Where the model and operators support it, use a portable format and a serving API so that the client does not know which GPU vendor is underneath. ONNX Runtime, AMD’s MIGraphX integration, and OpenXLA are documented examples of ecosystem components that can support cross-platform software. They do not make every model or kernel identical: unsupported operators, quantization behavior, and performance still require target-specific builds and tests.
Keep the external contract simple—request schema, response schema, authentication, streaming behavior, timeout, and error semantics. A provider migration should swap the backend image or compilation step while preserving that contract for callers.
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Separate optimization from correctness
Vendor-specific optimizations can be valuable, especially for high-throughput inference, but keep them as optional build targets or plugins. Maintain a reference path that produces the accepted output, then add CUDA- or ROCm-specific kernels behind the same interface. Record numerical tolerances for logits, probabilities, generated tokens, or other outputs; a result that is fast but changes model behavior beyond the accepted tolerance is not a successful migration.
Account for topology and capacity
A target GPU with the same nominal memory may have different interconnect, partitioning, or scheduling behavior. Capture the GPU model and topology in deployment metadata, and test the model’s memory footprint, batching, concurrency, and startup time on the actual target. Do not infer capacity from a provider’s marketing name alone.
Keep storage, networking, and data movement portable
Use CSI interfaces for persistent data
Request block and file storage through the Container Storage Interface (CSI), with storage classes supplied by the environment overlay. Test snapshots, restore, expansion, access modes, and failure behavior on every target. A CSI claim that provisions successfully is not enough if restoring a production-sized checkpoint takes longer than your recovery objective.
Replicate model artifacts and checkpoints
Keep model files, tokenizer data, configuration, and training checkpoints in a portable registry or replicated object store. Preserve checksums and metadata so a restored artifact can be verified before serving. Replication should be continuous enough to meet the recovery point you have promised, not an emergency copy started after a provider outage.
Measure network behavior and egress
Record the path and volume of image pulls, model downloads, checkpoint writes, inter-service traffic, and user responses. Network performance and topology controls differ among providers, and outbound transfer can become the largest migration or disaster-recovery expense. Measure transfer time and egress charges before cutover, including the initial replication and the traffic pattern during a rollback.
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Express infrastructure with thin, portable modules
Use Terraform or another declarative infrastructure-as-code layer for GPU node pools, quotas, taints, labels, networking, storage classes, and cluster add-ons. Put provider-specific resources in thin modules with the same input and output contract. For example, every provider module can accept a GPU class, minimum capacity, zones, and labels, then return the node-pool identifiers and resource keys needed by the Kubernetes overlay.
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Keep state and secrets under your control. Document how to back up state, recreate a workspace, rotate credentials, and import an existing cluster. A state file that can only be read through one provider’s console is an operational lock-in even if the HCL is portable.
Separate desired capacity from available capacity. GPU supply can be constrained by region and model. Define a fallback GPU class only when your model’s quality and performance tests permit it; otherwise, fail clearly rather than silently scheduling on hardware that cannot meet the service objective.
A migration runbook you can rehearse
Run this procedure on a non-production target, then repeat it during a controlled production exercise. Record timings, versions, errors, and manual interventions so the next rehearsal is measurable.
- Build a provider-neutral image. Package the service as an OCI image with pinned CUDA or ROCm and framework versions. Remove mandatory cloud SDK calls from the serving path and publish the image to registries reachable from every target.
- Prepare the target GPU layer. Install the target’s driver and device plugin, or the approved operator. Record the GPU model, driver, container runtime, framework, kernel, and device-plugin versions. Confirm that Kubernetes advertises the expected accelerator resource.
- Apply the same manifests. Deploy the unchanged application manifests and change only node selectors, resource names, storage classes, ingress settings, and provider-adapter values in an environment overlay. Review the rendered YAML before applying it.
- Run correctness and resilience tests. Compare outputs within agreed numerical tolerances, then test throughput, latency, concurrency, failure recovery, autoscaling, rolling updates, and checkpoint restore. Include cold starts and an empty-node image pull.
- Measure movement and charges. Time image pulls, model and data transfer, storage restore, and replication. Capture provider egress and any cross-zone or interconnect charges for both the initial move and a rollback.
- Soak and retain rollback. Keep replicated model artifacts, the last known-good image, and rollback manifests until the new provider completes a sustained production soak. Define the rollback trigger, owner, and maximum acceptable data loss before switching traffic.
What to test before switching providers
Correctness and numerical behavior
- Run a fixed corpus through both environments and compare outputs against explicit tolerances.
- Test deterministic settings where supported, while recognizing that parallel execution can still produce small numerical differences.
- Include tokenization, preprocessing, postprocessing, quantization, and serialization; a matching model file does not guarantee matching surrounding behavior.
Performance and capacity
- Measure p50, p95, and p99 latency, throughput, batch-size limits, GPU memory use, and startup time at realistic concurrency.
- Test the GPU classes you would actually obtain during a capacity shortage, not just the preferred class.
- Exercise autoscaling from zero or a cold pool so image, driver, and model initialization time is visible.
Reliability and operations
- Drain and replace a node, restart a device plugin, roll a driver or operator, and recover from a failed volume attachment.
- Restore a checkpoint and a serving replica from replicated artifacts after deleting the target namespace.
- Verify that logs, metrics, traces, alerts, and incident records can be exported and consumed by the same operational tools.
Security and governance
- Confirm image signatures, secret rotation, least-privilege identities, network policies, and audit records on both providers.
- Check where model data is encrypted, which keys are used, and whether the same retention and deletion process applies after migration.
Compare providers on the interfaces that determine exit cost
Do not compare only hourly GPU prices. Evaluate the following questions in a scored worksheet, with evidence from a working cluster rather than a sales description.
| Comparison axis | Questions to answer before commitment |
|---|---|
| Kubernetes conformance and API access | Can you use upstream APIs, obtain cluster-admin capabilities needed for drivers and device plugins, and export manifests and events? |
| GPU supply and scheduling | Which GPU types are available in each region, what capacity guarantees exist, and can you control labels, taints, topology, and quotas? |
| Driver and operator lifecycle | Who installs and upgrades the driver or operator, what maintenance window applies, and how is a failed upgrade rolled back? |
| Runtime and kernel support | Which container runtimes, kernels, passthrough modes, and vGPU modes are supported by the target software stack? |
| Storage CSI behavior | Are block and file drivers available, and do snapshot, replication, restore, expansion, and cross-zone operations meet the recovery objective? |
| Network and egress | What throughput and topology controls are exposed, and what charges apply to model replication, rollback, and user traffic? |
| Observability and incident export | Can metrics, logs, traces, audit events, and incident timelines be retrieved through documented interfaces? |
| Terraform and state portability | Can modules remain thin, state be backed up independently, and resources be recreated without a proprietary console workflow? |
| Recovery options | Is there a second region or provider with tested GPU capacity, replicated artifacts, and a documented traffic-switch procedure? |
Common failure modes and fixes
The image starts but cannot see a GPU
Check the host driver, device plugin or operator health, container runtime integration, node taints, and the Pod’s requested resource key. Confirm that the target node advertises the resource before debugging application code. A Kubernetes manifest can be syntactically valid while the node is missing the vendor components that make an accelerator schedulable.
The same model produces different results
Compare framework and runtime versions, precision and quantization settings, compiler flags, unsupported operators, preprocessing, and random-seed handling. Establish an explicit tolerance and keep a slower reference path for diagnosis. Do not accept a migration based solely on a successful HTTP health check.
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Measure each cold-start segment: quota allocation, node boot, driver initialization, image pull, model download, and compilation. Reduce image and artifact size where safe, keep a warm capacity floor, or adjust the service objective. Document the trade-off rather than hiding it in a provider-specific autoscaler.
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Restore works on paper but misses the recovery objective
Time a full restore of production-sized volumes and checkpoints, including verification and Pod scheduling. Improve replication frequency, retain a pre-warmed target, or revise the recovery objective. A snapshot feature is not evidence of recoverability until the complete procedure has been timed.
A provider feature has become a hidden hard dependency
Search manifests, images, startup scripts, Terraform modules, and runbooks for provider SDKs, proprietary resource objects, endpoint formats, and IAM assumptions. Replace essential calls with a standard interface or isolate them in an adapter with a tested alternative. Mark optional optimizations clearly so they are not mistaken for requirements.
Operating portability as a continuing practice
Portability decays when only one environment is exercised. Keep a small secondary deployment alive or schedule regular redeployment drills. Rebuild the container when a driver, framework, or model version changes, and rerun correctness and performance gates before promoting it to both providers.
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Assign ownership for each boundary: platform engineering for drivers and clusters, the model team for numerical and quality tests, security for identity and artifacts, and finance or operations for transfer and capacity measurements. A migration plan without named owners is not an executable rollback.
How to judge whether you are portable
Use these exit criteria before declaring a workload multi-cloud:
- The same application image and Kubernetes release process deploy to two targets, with differences confined to documented overlays and adapters.
- Both targets have a tested driver and device-plugin or operator path for the required accelerator.
- Model outputs, latency, throughput, autoscaling, failure recovery, and checkpoint restore meet written thresholds on each target.
- Images, models, checkpoints, manifests, Terraform state, logs, and incident data can be retrieved without the original provider’s console.
- Transfer time, storage restore time, egress exposure, and rollback triggers are known from a rehearsal.
- The team can switch traffic and restore the previous provider within the stated recovery objectives.
There is no authoritative independent statistic that assigns one universal dollar figure to GPU cloud lock-in; the cost depends on model size, data gravity, GPU scarcity, performance targets, and operational maturity. Measure those variables in your own rehearsal instead of relying on a generic percentage.
The practical decision
Choose the provider that meets your GPU capacity, performance, security, and recovery requirements, but keep the application independent of its differentiators. Upstream Kubernetes, OCI images, device-plugin resources, pinned and tested runtimes, portable model APIs, CSI storage, replicated artifacts, and thin Terraform modules provide the boundaries. Regular cross-provider tests turn those boundaries from an architectural aspiration into a real ability to move.
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