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Reduce GPU cloud lock-in by treating portability as a purchase requirement and an engineering property, not as a promise in a sales deck. Select a service that exposes upstream Kubernetes APIs and standard GPU device-plugin resources; keep provider-specific drivers, operators, networking, storage, identity and observability at replaceable boundaries; pin your container and accelerator software; and store every artifact in an independently accessible location. Before committing, rebuild the workload on a second provider from version-controlled definitions and record transfer, egress, missing features, performance and recovery effort. You cannot eliminate technical lock-in completely, but you can make switching an estimated, rehearsed operation instead of an emergency rewrite.
Why does GPU cloud lock-in matter?
AI infrastructure lock-in is the cost and risk created when a workload depends on one provider’s control plane, hardware lifecycle, data services or commercial terms. A lower GPU hourly rate can be outweighed by proprietary scheduling, difficult data export, unavailable replacement capacity or a contract that makes leaving expensive.
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Lock-in appears at several layers:
- Control plane: proprietary cluster APIs, schedulers, networking, ingress or storage interfaces that cannot be recreated elsewhere.
- Accelerator stack: a specific GPU model, driver, firmware, CUDA or ROCm release, operator, partitioning method or topology assumption.
- State: model weights, tokenizers, datasets, checkpoints, metadata, evaluations, logs, traces and caches stored in provider-specific services or disks.
- Operations: console-only workflows, undocumented limits, bespoke identity integrations and monitoring that cannot be exported.
- Commercial terms: egress, image-pull, replication, snapshot, reservation, support and termination charges.
The UK Government Digital Service and Government Commercial Agency state in Managing technical lock-in in the cloud (2019, updated 2026), “It’s impossible to avoid technical lock-in completely.” Their practical direction is to “maximise both the value and portability of the services where possible.” That is the right target: a stable application contract with replaceable provider implementations, not identical infrastructure in every location.
What does a portable GPU platform look like?
Portability means you can recreate the application and its operating conditions on another provider without changing business logic or losing essential state. It does not mean every provider offers the same GPU, quota, latency or managed feature.
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Application and container
Build an OCI image with pinned operating-system, framework and accelerator-runtime versions, then deploy by immutable digest. Keep the image independent of a provider’s registry by supporting pull and mirror operations from a registry you control or can move. Record the exact framework, CUDA or ROCm, compiler, kernel assumptions and model-serving dependencies used to produce each release.
Cluster contract
Use upstream Kubernetes objects—Deployments, Jobs, Secrets, Services and Ingress—along with Helm or Kustomize. Keep cluster creation, identity integration and provider networking in small modules rather than embedding them in application manifests. Standard kubectl, documented APIs and versioned manifests should be sufficient to install, update and delete the workload.
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Kubernetes device plugins are the stable mechanism for vendors to advertise GPUs and other specialized resources to the kubelet; the feature has been stable since Kubernetes v1.26. Your workloads can request a common resource contract while driver installation and hardware-specific implementation remain at the node layer. Express node selectors, taints, tolerations, topology constraints and partitioning requirements explicitly so a replacement provider can test them.
Runtime and serving
Use a portable model format or serving API with a rebuildable framework backend. Treat CUDA, ROCm, compiler flags, kernels and hardware optimizations as provider-specific adapters. Keep a correctness suite and a performance suite so an optimization can be removed or reimplemented without changing the model’s external API.
Data and artifacts
Place weights, tokenizers, datasets, checkpoints, metadata, evaluations, logs, traces and cache-rebuild instructions in exportable stores. Prefer CSI-compatible volumes and replicated object storage, but document the endpoint, encryption-key, snapshot and transfer substitutions required on another provider. A container image or persistent disk that exists only inside one account is not an exit plan.
Provisioning
Represent infrastructure with version-controlled Terraform or an equivalent declarative system. Keep provider-specific GPU pools, quotas, networks, IAM rules and regional-capacity settings in adapters. The shared module should define intent—such as a GPU worker pool with a minimum memory and a network policy—while each provider module maps that intent to its own API.
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How should you compare providers before signing?
Ask for documentary evidence and a reproducible test owner for every answer. A comparison that contains only GPU hourly prices omits the conditions that determine whether you can operate or leave the service.
| Comparison axis | Questions to ask | Evidence to retain |
|---|---|---|
| Control-plane portability | Is the service based on upstream or conformant Kubernetes? Can you use standard kubectl, Helm, manifests and documented APIs? Are CNI, ingress, CSI and scheduler components replaceable? |
API documentation, version matrix, exported manifests and a working install on a test cluster. |
| GPU software lifecycle | Which GPU models, memory sizes, topologies, drivers, CUDA or ROCm releases, container runtimes, operators and firmware are supported? Who installs and upgrades them, and for how long? | Support window, upgrade policy, compatibility matrix and a record of the node image used by your workload. |
| Data and artifact exit | Can you export weights, datasets, checkpoints, metadata, logs and traces in documented formats? What are transfer limits, retention rules, deletion guarantees, transfer paths and egress prices? | Written export procedure, sample export, restore test, price schedule and deletion certificate process. |
| Automation and APIs | Can APIs or CLIs create and delete clusters, node pools, GPUs, storage, networks and quotas? Are there console-only steps? | API schemas, CLI commands, service-account permissions and version-controlled modules. |
| Performance and capacity | What throughput, latency, utilization, autoscaling response, multi-node networking and storage throughput does your named workload achieve? How are quotas, pre-emption and regional shortages handled? | Reproducible benchmark scripts, workload-specific results, quota procedure and capacity commitments. |
| Security and identity replacement | Can secrets, keys, roles, audit logs and network policies be recreated with another identity and key-management system? | Export formats, rotation runbook, policy definitions and a clean-room restore test. |
| Commercial exit | What do egress, image pulls, cross-region replication, snapshots, idle reservations, minimum commitments, support tiers, notice periods and deletion cost? | Contract clauses, rate cards, commitment schedules and a modeled exit invoice. |
A published accelerator-platform requirement set treats “no proprietary lock-in of CNI, ingress, CSI, or scheduler” as a MUST. Use that kind of explicit requirement rather than accepting a general portability statement.
How do managed and standard GPU modes affect portability?
Even within one provider, two Kubernetes operating modes can expose different portability risks. Managed GPU modes may install drivers automatically, control node replacement and restrict GPU selection or sharing; standard modes may leave those choices to your team.
Documentation for managed and standard GPU operation on a major Kubernetes service shows differences in driver installation, node lifecycle, GPU selection and sharing controls. Record which mode you are buying, which settings are provider-controlled and how those settings are represented outside the console. If a mode hides the node image or prevents you from selecting a required driver, treat that as a dependency to test—not as an interchangeable implementation.
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Technical portability fails when the contract does not give you time, data and access to exercise it. Attach operational details to the agreement or service order.
- Export rights: define formats and procedures for model, dataset, checkpoint, metadata, log and trace export, including API limits and assistance during termination.
- Access during notice: preserve read and transfer access long enough to complete the documented export, with no unilateral suspension that strands data.
- Transparent charges: list egress, replication, snapshot, image-pull, support and deletion fees, plus the treatment of unused commitments.
- Deletion evidence: specify retention periods, backup expiry and the evidence supplied after deletion.
- Version and support commitments: identify supported GPU, driver, runtime and Kubernetes versions and the notice period for removing them.
- API availability: require documented API or CLI operations for provisioning, scaling, policy, monitoring and deletion; prohibit workflows that require an unexportable console action.
- Incident and capacity terms: define quota escalation, regional-capacity communication, support response and the information needed to reproduce a failure elsewhere.
Do not accept “portable” as an undefined adjective. Convert it into acceptance tests, export obligations and a price schedule.
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How do you prove that a provider is portable?
Run an exit rehearsal before production and repeat it after major provider or accelerator changes. The exercise should use the same version-controlled definitions and artifacts that a real migration would use.
- Inventory dependencies. List model registries, object stores, feature stores, secrets managers, identity providers, network services, scheduler extensions, observability backends and hosted inference APIs.
- Classify each dependency. Mark it as an open standard, an adapter-wrapped provider implementation or a direct dependency to remove. Record the owner and the replacement interface.
- Freeze reproducible inputs. Capture OCI image digests, Helm charts, Kustomize overlays, infrastructure modules, policy files, model versions, dataset manifests, lockfiles and node compatibility requirements.
- Prepare a second target. Use another provider or an independently controlled environment. Provision it with the same declared intent, then map only the provider-specific adapters.
- Restore state. Transfer artifacts and metadata, recreate keys and identities, rotate credentials and rebuild caches from documented instructions. Verify checksums and model lineage.
- Run correctness tests. Compare outputs, tokenization, evaluation results, safety checks and failure handling against the source environment.
- Run performance tests. Measure throughput, latency, utilization, autoscaling response, storage throughput and multi-node communication on named workloads. Record hardware, driver and runtime versions with each result.
- Record the exit ledger. Capture rebuild time, transfer volume, egress bill, missing features, manual steps, quota delays and the operational runbooks that needed editing.
- Set a remediation deadline. Remove direct dependencies or document an accepted exception with an owner, measured value and review date.
A rehearsal is successful only when another operator can follow the runbook without access to the original provider’s console or undocumented support intervention.
How should you score providers?
Use a weighted scorecard that reflects the workload’s failure modes, and require documentary evidence or a reproducible test for every score. Do not let a favorable GPU rate compensate automatically for an unmeasured exit risk.
| Category | Pass condition | Fail signal |
|---|---|---|
| Standards and APIs | Upstream Kubernetes resources, documented APIs and replaceable CNI, ingress, CSI and scheduler components. | Proprietary control-plane objects or console-only lifecycle actions. |
| Accelerator reproducibility | Named GPU, driver, runtime and firmware combinations have a documented support window. | Hidden node images, unpinned drivers or unsupported upgrade paths. |
| Data exit | Artifacts export and restore in documented formats with known limits and charges. | Unexportable state, opaque backups or an unpriced transfer path. |
| Operational automation | Provisioning, scaling, policy, monitoring and deletion run from version-controlled code. | Critical steps require a web console or provider ticket. |
| Measured service quality | Named workloads have repeatable performance and capacity tests. | Only synthetic benchmarks or unverified capacity claims. |
| Exit economics | Commitments, transfer, support and deletion costs can be modeled. | Fees are absent, discretionary or disclosed only after termination. |
Accept a provider-specific feature only when its measured value clearly exceeds the engineering and financial cost of leaving. Record the exception, the interface that contains it and the test that would trigger reconsideration.
What are the most common lock-in traps?
Proprietary scheduling extensions
A custom scheduler or resource object may deliver useful placement or sharing controls, but it can make manifests unusable elsewhere. Keep the application on standard Kubernetes scheduling and isolate the extension behind a chart or adapter with a documented fallback.
Hidden driver and firmware coupling
An image that starts on one GPU may fail on another because of driver, CUDA or ROCm compatibility, firmware, topology or kernel assumptions. Maintain a compatibility matrix and test the exact image digest on each supported accelerator class.
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Provider-only data services
Feature stores, registries, secrets systems and hosted inference endpoints often hold the state that is hardest to move. Define export formats and replacement APIs before adopting them, and keep a periodically tested copy outside the provider’s exclusive control.
Console drift
Manual edits to node pools, firewall rules, quotas or sharing settings create an environment that code cannot rebuild. Treat the console as a break-glass tool; reconcile changes into version control or remove them.
Unpriced egress and commitments
Transfer and unused reservations can dominate a migration bill. Model a full export, a restore and the period of overlap using the provider’s written rates, then include the result in the investment decision.
Optimizing for one GPU generation
A kernel or compiler optimization may produce excellent results on one accelerator while narrowing future choices. Keep an unoptimized correctness path and benchmark alternatives so the optimization remains an optional adapter.
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Build an exit-cost model before signing, using measured quantities rather than a percentage of compute spend. Include:
- Data volume for weights, datasets, checkpoints, metadata, logs and traces, multiplied by the quoted transfer and any cross-region rates.
- Temporary overlap: source and destination GPU, storage, networking and support charges while both environments run.
- Engineering time to adapt provider modules, identity, networking, storage, observability and accelerator settings.
- Validation capacity for correctness, performance, security and recovery tests.
- Commercial charges for snapshots, image pulls, replication, notice periods, idle reservations, minimum commitments and deletion.
- Operational risk from quota delays, unavailable GPU models or missing managed features.
Keep assumptions beside each figure: region, currency, one-time versus recurring charge, contract tier, data date and whether the number came from a quote or a reproducible test. Recalculate after a major model, data or accelerator change.
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What is a practical implementation sequence?
Teams can reduce lock-in incrementally without operating two production clouds immediately.
- Define the application contract. Specify model inputs and outputs, batch and online interfaces, data schemas, required GPU memory, latency targets and correctness tolerances.
- Separate adapters. Create interfaces for object storage, secrets, identity, networking, metrics, traces, registry access and inference APIs. Put provider SDK calls only behind those interfaces.
- Pin and attest builds. Lock image digests, framework and accelerator versions; record the build provenance and supported GPU matrix.
- Automate the primary environment. Provision clusters, pools, policies, storage and observability from code, with no undocumented manual step.
- Construct a low-cost secondary target. It may be a smaller region or limited-capacity environment, but it must exercise the same APIs, export path and restore process.
- Run scheduled restore tests. Rebuild from a clean account or project, rotate credentials and execute correctness checks. Track elapsed time and manual interventions.
- Review exceptions quarterly. Retire adapters whose value no longer exceeds their exit cost, and update the compatibility and commercial records after provider changes.
How often should the exit rehearsal be repeated?
Repeat it after a major provider change, accelerator or driver upgrade, Kubernetes version change, data-store migration, identity redesign or material model-serving rewrite. A scheduled cadence should also exist for stable systems, because an untested export path can decay as credentials, APIs and retention policies change.
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Keep the evidence: manifests, image digests, artifact checksums, test outputs, transfer records, invoices, runbook edits and unresolved gaps. That record turns portability from an aspiration into an auditable operating capability.
What is the final decision rule?
Choose the provider whose measured performance, capacity, security and operational value justify its switching cost while preserving a standard application contract. Reject opaque or UI-only workflows, unexportable state, mandatory proprietary control-plane components and contracts whose exit costs cannot be estimated. The objective is not identical infrastructure everywhere; it is the ability to replace a provider implementation without rewriting the AI application or losing control of its data.
Frequently Asked Questions
Can a portable OCI image run on any GPU?
No. The image still depends on a compatible driver, accelerator runtime, firmware, kernel, memory size and instruction set. Maintain a tested compatibility matrix and keep a correctness path for hardware that lacks a provider-specific optimization.
Do I need to run production on two providers to avoid lock-in?
Not necessarily. A version-controlled clean-room rebuild and regular restore rehearsal can expose most portability failures without paying for two full production footprints. Use dual production only when the business requires continuous provider-level failover or the rehearsal reveals unacceptable recovery time.
What evidence should a provider give me about data deletion?
Request the retention period for active data and backups, the deletion workflow, exceptions for legal holds, and the form of completion evidence. Test the documented export and deletion sequence in a non-production account before accepting the contract.
How can I compare GPU performance fairly across providers?
Run the same pinned image, model, precision, batch settings, dataset and driver-supported configuration on named workloads. Record throughput, latency, utilization, scaling response, storage throughput and multi-node behavior together with GPU and software versions.
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