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The practical way to reduce GPU cloud lock-in is to make portability a procurement requirement and then prove it with an independent rebuild. Keep your application on upstream Kubernetes APIs, isolate provider-specific GPU drivers and control-plane services at the infrastructure boundary, pin every software artifact, and store data and telemetry where another provider can access them. Before signing, require documented exports, API-driven operations, transparent egress terms, and a clean-room migration test. You cannot eliminate lock-in completely—the UK Government Digital Service and Government Commercial Agency say that is impossible—but you can make switching predictable enough that one provider’s feature, outage, quota, or price change does not stop your AI program.
What does GPU cloud lock-in include?
GPU lock-in is broader than being unable to move a virtual machine. It occurs when changing providers requires rewriting workloads, rebuilding data pipelines, replacing identity and networking integrations, or accepting an unmeasurable cost and outage risk. Treat it as a stack of dependencies rather than a single product choice.
Application and runtime dependence
A model can be tied to a particular CUDA or ROCm release, compiler, kernel, framework build, serving API, quantization library, or proprietary acceleration extension. An OCI image may move successfully while failing on the destination because its required driver, firmware, GPU memory, topology, or instruction set is unavailable.
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Managed Kubernetes services differ in their node lifecycle, GPU sharing controls, scheduler extensions, networking, ingress, storage, and identity behavior. A workflow that exists only in a web console or in a proprietary custom resource is difficult to reproduce with standard tools.
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State and commercial dependence
Weights, tokenizers, datasets, checkpoints, feature data, experiment metadata, logs, traces, caches, snapshots, and encryption keys can all become stranded. Contracts add another layer through egress charges, minimum commitments, reserved capacity, cross-region replication fees, image-pull charges, notice periods, and deletion obligations.
The UK guidance Managing technical lock-in in the cloud (2019, updated 2026) states that avoiding technical lock-in completely is impossible and that buyers should maximize both service value and portability where possible. Use that as a realistic objective: preserve a stable application contract while allowing infrastructure implementations to change.
How do I turn portability into a procurement requirement?
Write portability requirements into the request for proposal, proof-of-concept acceptance criteria, contract, and operating runbook. A provider should not receive full approval because it offers a low GPU hourly rate if the exit path is undocumented.
Ask for documentary evidence
- List the Kubernetes version, conformance status, supported APIs, admission controls, CNI, ingress, CSI drivers, scheduler extensions, GPU operator or device-plugin implementation, and upgrade policy.
- Provide supported GPU models, memory sizes, interconnect topology, driver versions, CUDA or ROCm releases, container runtime, firmware policy, and support windows.
- Document API and CLI operations for creating and deleting clusters, node pools, GPU instances, storage, networks, quotas, identities, snapshots, and routing.
- Specify export formats, transfer methods, retention periods, deletion guarantees, rate limits, and egress prices for every data and artifact class.
- State which features are available only in a console, require proprietary agents, or cannot be recreated outside the provider.
Make reproducibility an acceptance test
Require the provider to deploy a version-controlled sample using standard kubectl, Helm, manifests, or your chosen infrastructure-as-code tool. The test should create a GPU workload, persist a checkpoint, emit logs and metrics to an external destination, delete the environment, and restore it from exported artifacts. Record the commands and permissions needed; undocumented operator intervention is a portability defect.
Which control-plane standards should I compare?
Prefer upstream or conformant Kubernetes with replaceable networking, ingress, storage, and scheduling components. Standard APIs do not make every workload portable, but they keep the application contract separate from provider implementation.
| Area | Portability test | Evidence to request | Lock-in warning |
|---|---|---|---|
| Kubernetes API | Can the team apply and inspect resources with standard kubectl and documented APIs? |
Supported versions, conformance information, API deprecation policy, and export procedure | Required proprietary CRDs for ordinary Deployments, Jobs, Services, or Secrets |
| Networking | Can CNI, ingress, load balancing, and network policies be replaced without changing application code? | Component names, configuration schemas, and migration documentation | Provider-only annotations or an opaque service mesh required for basic traffic |
| Storage | Can CSI-compatible volumes and object data be copied to another environment? | CSI support, snapshot format, object API compatibility, and transfer procedure | Volumes or snapshots readable only through a proprietary appliance |
| Scheduling | Can workloads request GPU resources and constraints through standard resource requests, labels, taints, and affinities? | Scheduler behavior, topology labels, and pre-emption rules | Mandatory proprietary scheduler extensions with no equivalent manifest |
| Identity and policy | Can service accounts, roles, secrets, and policy definitions be recreated with a different identity provider? | OIDC or equivalent interfaces, RBAC export, key rotation, and break-glass procedure | Application credentials hard-coded to provider APIs or instance metadata |
NVIDIA’s platform requirements identify the absence of proprietary lock-in in CNI, ingress, CSI, and scheduler components as a “MUST” condition for the specified platform. Use the same principle even when evaluating a different hardware or software supplier.
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How should I package GPU software so images remain portable?
Pin the complete software contract in an OCI image, then keep the host-dependent pieces outside it. Portability fails when teams pin only the Python package list and assume the destination has a compatible accelerator stack.
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- Base operating-system image by immutable digest.
- Framework, compiler, Python or other language runtime, and serving components by exact versions and hashes.
- CUDA or ROCm runtime, communication libraries, tokenizer and model-library versions, and any custom kernels.
- Model and tokenizer checksums, configuration files, quantization settings, and benchmark scripts.
- OCI image digest, build recipe, dependency lockfile, and software bill of materials.
Keep node-specific implementation at the boundary
Drivers, firmware, device discovery, GPU operators, partitioning, topology settings, and kernel modules belong in a node image or infrastructure module rather than in application logic. The Kubernetes device-plugin mechanism is the stable contract for advertising GPUs and other specialized resources to the kubelet; its feature state has been stable since Kubernetes v1.26. Applications can request a resource such as a vendor GPU class while the node layer supplies the implementation.
That contract does not guarantee identical behavior. A destination still needs a compatible driver, accelerator generation, memory capacity, interconnect, and runtime. Test the image against every GPU class you intend to use and document which optimizations are optional, required, or disabled on fallback hardware.
How do I make data and artifacts easy to move?
Design an exit path before production data accumulates. Keep authoritative copies of model weights, tokenizers, datasets, checkpoints, metadata, evaluations, logs, traces, and cache rebuild instructions outside a provider-specific disk or image.
Define an export inventory
- Model artifacts: weights, tokenizer files, architecture configuration, preprocessing code, licenses, and checksums.
- Training state: optimizer state, random seeds, sharding layout, scheduler state, and checkpoint compatibility notes.
- Data: source datasets, transformed partitions, schemas, labels, feature definitions, and lineage metadata.
- Operations: manifests, Helm values, policies, dashboards, alert rules, logs, traces, and runbooks.
- Security: key ownership, encrypted backups, certificate authorities, secret references, and rotation records.
Specify how an export works
For each class, document the format, encryption, destination account, transfer tool, integrity check, retention period, deletion confirmation, and maximum retrieval time. Clarify whether transfer is online, requires a provider appliance, or incurs egress and cross-region charges. Keep at least one copy in an independently accessible account or location so a provider outage does not block the export itself.
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What automation and APIs prove that a provider is portable?
A portable service can be operated from version-controlled code, not just from a console. Require API or CLI coverage for the full lifecycle and keep provider-specific calls in small, replaceable modules.
Minimum lifecycle coverage
- Create, resize, label, cordon, drain, and delete GPU node pools.
- Set and inspect quotas, reservations, pre-emption behavior, and regional capacity.
- Create, snapshot, restore, replicate, and delete volumes and object stores.
- Configure networks, routes, firewalls, load balancers, DNS, and private endpoints.
- Create identities, roles, service accounts, keys, and rotation workflows.
- Export audit logs, metrics, traces, billing data, and usage records.
Store Terraform or equivalent modules, Helm charts, Kustomize overlays, policy definitions, and GitOps configuration in version control. Keep the common module focused on upstream resources; put provider GPU pools, quotas, IAM bindings, network shapes, and regional capacity in adapters. A clean separation lets you replace an adapter without rewriting application manifests.
How can I compare GPU performance and capacity fairly?
Compare measured behavior on your named workloads, not a generic GPU hourly price. Capacity, software lifecycle, networking, and autoscaling often dominate the cost of an AI service.
| Dimension | What to measure | What to record |
|---|---|---|
| Throughput | Training samples per second or inference tokens per second at the target batch, sequence length, precision, and model size | Workload commit, GPU model, driver/runtime versions, and utilization |
| Latency | Cold-start, queue, first-token, per-token, and tail latency under realistic concurrency | Percentile method, concurrency, autoscaling state, and network path |
| Utilization | Accelerator, memory, CPU, storage, and network utilization during steady state | Telemetry source and sampling interval |
| Scaling | Time to obtain a GPU, attach storage, start a pod, and serve traffic after a scale event | Region, quota status, node image, and pre-emption outcome |
| Multi-node networking | Collective communication performance, topology awareness, and failure recovery | Interconnect type, node count, framework version, and test script |
| Availability | Quota approval time, regional capacity, maintenance process, and replacement behavior | Named regions, support tier, and escalation path |
Managed and standard GPU modes can differ even within one provider. Google Cloud’s GKE documentation, for example, distinguishes driver installation, node lifecycle, GPU selection, and sharing controls between those modes. Treat each mode as a separate platform until your tests show otherwise.
How do I isolate security, identity, networking, and observability?
Replaceability depends on treating these services as interfaces. Applications should not contain provider-specific identity calls, object-store assumptions, or telemetry endpoints.
Identity and secrets
Use an external identity interface such as OIDC where supported, map application roles to portable Kubernetes RBAC, and keep secret references—not provider access keys—in manifests. Define how to revoke and reissue credentials during a migration. Keep encryption keys under a control plane you can access independently, or document a tested key export and re-encryption process.
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Wrap object storage, feature stores, queues, and databases behind adapters with explicit consistency and failure semantics. Keep endpoint, bucket, volume, snapshot, and encryption-key substitutions in configuration. A CSI-compatible volume still needs a tested copy path; compatibility of the API does not imply compatibility of snapshots or performance.
Networking
Express service discovery, ingress, and policies with Kubernetes resources where practical. Isolate load-balancer annotations, private-link settings, firewall rules, and DNS integration in provider modules. Document external dependencies such as licensing servers, package mirrors, and telemetry collectors that could block a rebuild.
Observability
Export logs, metrics, traces, dashboards, alert rules, and GPU utilization data to an independently accessible backend. Preserve timestamps, labels, sampling settings, and retention policy so an incident after migration can be compared with the old environment.
What does a portable GPU reference architecture look like?
The goal is a stable application contract with replaceable provider implementations, not identical infrastructure in every location.
| Layer | Portable contract | Provider-specific adapter |
|---|---|---|
| Container | OCI image pinned by digest with reproducible build and software bill of materials | Registry authentication, image cache, and regional mirror |
| Cluster | Deployments, Jobs, Secrets, Services, Ingress, Helm, and Kustomize | Cluster creation, identity integration, admission policy, and upgrade channel |
| GPU | Device-plugin resources, requests, limits, labels, taints, and affinity rules | Drivers, operator, firmware, topology, partitioning, and GPU pool shape |
| Runtime | Portable model format or serving API with a rebuildable framework backend | CUDA, ROCm, compiler, kernel, and hardware-specific optimizations |
| Data | CSI-compatible volumes and replicated object data with documented schemas | Endpoints, encryption keys, snapshots, replication jobs, and transfer paths |
| Provisioning | Version-controlled infrastructure modules and policy | GPU quotas, networks, IAM bindings, regions, reservations, and capacity requests |
How do I run a clean-room exit rehearsal?
Run the rehearsal on a second target without relying on undocumented console state. The exercise measures whether your portability claims work under operational pressure.
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- Inventory dependencies. List every model registry, object store, feature store, secrets manager, identity provider, network service, scheduler extension, observability backend, hosted inference API, package mirror, and licensing endpoint.
- Classify each dependency. Mark it as an open standard, an adapter-wrapped provider implementation, or a direct dependency that must be removed.
- Freeze inputs. Record image digests, manifests, lockfiles, model and dataset checksums, driver/runtime requirements, quotas, and test commands in version control.
- Provision the target. Use the alternate provider’s API or IaC modules to create the cluster, GPU pools, networking, identities, storage, and observability destinations.
- Restore artifacts. Transfer data and metadata, verify checksums, recreate secrets with new credentials, and rebuild caches from documented instructions.
- Deploy unchanged application definitions. Apply the common Kubernetes manifests and only the target adapter overlays. Record every manual edit or exception.
- Run correctness tests. Compare tokenization, numerical tolerances, checkpoint restoration, batch behavior, and output quality against the reference environment.
- Run performance tests. Measure throughput, latency, utilization, scaling response, multi-node communication, and failure recovery on the same named workloads.
- Capture operational results. Record rebuild time, transfer volume, egress bill, quota delays, missing features, support interactions, and runbook gaps.
- Remediate and repeat. Convert every exception into an adapter, documented prerequisite, or accepted risk, then rehearse again after major provider or accelerator changes.
How should I score providers before signing?
Use a documented scorecard in which every answer has evidence or a reproducible test owner. A low price cannot compensate for an exit path that no one can estimate.
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| Decision axis | Pass condition | Fail signal |
|---|---|---|
| Standards and control plane | Upstream APIs and replaceable CNI, ingress, CSI, and scheduler components | Opaque mandatory control-plane services |
| GPU lifecycle | Documented models, drivers, runtimes, firmware, upgrades, and support windows | Unstated compatibility or forced upgrades without a test window |
| Data exit | Complete export formats, transfer method, retention, deletion, and price in writing | Unexportable state or unknown egress liability |
| Automation | API/CLI and IaC coverage for create, operate, scale, and delete | UI-only operations or undocumented support tickets |
| Performance and capacity | Measured named-workload results and a quota escalation process | Marketing benchmarks without workload conditions |
| Security replacement | Reproducible identity, key, network, and audit-log migration | Application credentials tied to provider metadata |
| Rebuild effort | Successful clean-room rehearsal with recorded time and exceptions | Manual steps that cannot be scripted or audited |
| Commercial terms | Exit fees, commitments, reservations, support, notice, and deletion terms are calculable | Discounts that penalize multi-provider use or undefined termination charges |
Score each axis with a simple internal scale, but keep the evidence beside the score. If a provider refuses a test or cannot state an exit cost, record that uncertainty as risk rather than awarding a neutral mark.
Which contract terms create avoidable lock-in?
Commercial barriers can make a technically portable design impractical. OECD competition analysis identifies discount structures, incentives that discourage multi-provider use, and egress fees as possible switching barriers.
- Egress and replication: obtain current rates, free allowances, destination restrictions, and billing examples for a full artifact restore.
- Commitments: clarify minimum GPU hours, reservation expiry, unused-capacity treatment, and whether discounts require exclusivity.
- Snapshots and images: ask whether exports, cross-region copies, and image pulls are charged separately.
- Support and notice: define response targets during migration, termination notice, transition assistance, and access after cancellation.
- Deletion: specify when provider copies, backups, logs, and encryption-key material are deleted and what evidence you receive.
When is a provider-specific feature worth accepting?
Accept it only when its measured value clearly exceeds the cost and risk of leaving. A proprietary inference engine may deliver a material latency or utilization benefit, but it should sit behind a serving API with a tested fallback, and its model artifacts must remain exportable.
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Reject opaque or UI-only workflows, mandatory proprietary control-plane components, unexportable state, and contracts whose exit costs cannot be estimated. If a feature is indispensable, record its owner, replacement candidates, migration trigger, and the test that will show whether the benefit still justifies the dependency.
What are the most common portability mistakes?
- Comparing only hourly GPU prices: include transfer, idle reservations, quota delays, storage, support, and rebuild labor in the decision.
- Assuming an OCI image is enough: verify driver, firmware, runtime, topology, and accelerator memory compatibility.
- Keeping the registry and checkpoints in one account: maintain independently accessible copies and test restoration.
- Relying on console configuration: export settings and recreate them from code before production approval.
- Ignoring identity and keys: rehearse credential rotation and re-encryption, not just data copying.
- Testing a toy workload: use production-shaped models, sequence lengths, concurrency, and multi-node behavior.
- Never repeating the rehearsal: rerun after provider, Kubernetes, driver, accelerator, or architecture changes because portability can regress silently.
Frequently Asked Questions
Can Kubernetes YAML alone guarantee that my AI workload is portable?
No. YAML covers many application resources, but portability also depends on GPU drivers and firmware, storage snapshots, identity, networking, quotas, model artifacts, and performance characteristics. Treat manifests as one layer of the exit package and validate the complete rebuild on another target.
Should a startup run production workloads on two GPU providers immediately?
Not necessarily. Operating two environments from day one adds testing, observability, security, and capacity-management work. A version-controlled second-target rehearsal can provide evidence of portability first; active dual running becomes worthwhile when outage tolerance, regional capacity, or bargaining power justifies the added operating cost.
How often should an AI team repeat its GPU-cloud exit test?
Repeat it after major provider, Kubernetes, driver, accelerator, storage, identity, or serving-stack changes. Teams with frequent releases can schedule a smaller restore-and-inference check regularly and perform a full training and multi-node rehearsal whenever the hardware or runtime contract changes.
What should I do if a provider will not disclose egress pricing or export limits?
Treat the missing information as an unpriced exit risk, not as a favorable assumption. Request a written rate card, limits, retention and deletion terms, and a sample bill for restoring your expected artifact volume; if the provider declines, escalate the requirement or select an option with calculable costs.
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