Keep the workload portable at every layer. Package the model in an OCI-compliant container, schedule GPUs through Kubernetes device-plugin APIs, define infrastructure with Helm, GitOps, or Terraform, store models and data in exportable formats, and rehearse a move to a second provider before you need one. Put cloud-specific storage, identity, networking, and inference services behind replaceable adapters rather than calling them directly from application code.
This approach does not make GPU clouds identical. Driver and CUDA versions, GPU generations, interconnects, quotas, regional capacity, and support policies still differ. Portability means you can recreate the service with bounded, documented changes instead of rewriting the platform.
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What vendor lock-in looks like in an AI GPU deployment
Lock-in is not limited to a proprietary model API. It appears wherever a provider becomes the only place you can build, run, observe, recover, or pay for the workload. A model may be portable while its surrounding platform is not.
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| Layer | Portable choice | Lock-in warning | Exit test |
|---|---|---|---|
| Packaging | OCI image with pinned operating system, Python, CUDA, libraries, and model server | Image can be built or pulled only through a provider-specific registry | Pull the image from an independently accessible registry and run it on another cluster |
| Scheduling | Kubernetes APIs, labels, taints, tolerations, and device plugins | Application code calls a proprietary GPU allocation API | Deploy the same manifests after changing only provider-specific node labels |
| Infrastructure | Versioned Helm, GitOps, and Terraform definitions | Clusters or networks exist only through console clicks or custom extensions | Destroy and recreate a test environment from a clean repository |
| Data and models | Exportable weights, tokenizers, datasets, metadata, and evaluations | Artifacts live in a managed registry or feature store with no bulk export | Restore a complete model release from files outside the provider |
| Operations | OpenTelemetry, Prometheus-compatible metrics, portable logs, and standard alerts | Only a hosted dashboard or proprietary query language contains history | Ingest telemetry into a second monitoring stack and preserve incident records |
| Commercial terms | Usage-based capacity, documented quotas, and predictable egress | Minimum commitments, expensive data export, or unavailable capacity in your region | Model the cost and time of a 30-day migration, including data transfer |
Make this inventory before selecting a provider. Include the model registry, object store, feature store, secrets, identity, network, scheduler extensions, observability, hosted inference API, backup system, and billing commitments. For each item, name the export format, API, owner, retention period, and replacement you would use during an exit.
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Use Kubernetes as the portability layer
Kubernetes gives you a common control surface for workloads, services, policies, and scheduling. Google Cloud’s machine-learning documentation explicitly identifies Kubernetes as appropriate when a team wants “to avoid vendor lock-in and run your workloads anywhere with Kubernetes” or already has Kubernetes expertise or a multi-cloud strategy; see Google Cloud’s Kubernetes machine-learning guidance. CNCF Ambassador Max Körbächer describes the same role this way: “Kubernetes and the cloud native ecosystem provide the abstraction layer that prevents lock-in to any single provider’s AI stack,” in CNCF’s platform analysis.
The CNCF’s 2025 Annual Survey, reported in that 2026 article, found that 82% of container users ran Kubernetes in production. That adoption does not guarantee that two GPU clouds behave identically; it does mean Kubernetes APIs, packaging conventions, and operational skills are widely reusable.
Set a conformance baseline
Require a conformant Kubernetes distribution or a documented path to export manifests and state. Record the Kubernetes minor version, container runtime, CNI, CSI drivers, admission policies, ingress implementation, DNS, and autoscaler. Keep application manifests free of provider-specific annotations unless an adapter or an explicitly documented exception is required.
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Package every model server as an OCI container
Build an immutable image that contains the model server and its runtime dependencies. NVIDIA explains that AI models are deployed using Open Container Initiative-compliant standards and that conflicting dependencies are isolated in containers, improving deployment flexibility; see NVIDIA’s container-as-a-service documentation.
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Pin what can change
- Pin the base operating-system image by digest, not only by a moving tag.
- Record the Python version, package lockfile, compiler toolchain, CUDA toolkit, cuDNN and other accelerator libraries, and model-server version.
- Store the model revision, tokenizer files, preprocessing code, safety rules, and launch arguments as versioned artifacts.
- Generate a software bill of materials and scan it in the same pipeline for every rebuild.
- Sign images and verify signatures at admission so a migration does not weaken supply-chain controls.
Use a registry that can be reached independently of the GPU provider, or replicate signed images to at least one external registry on a schedule. Test a fresh pull with no cached layers. Otherwise a provider outage can prevent both deployment and recovery.
Keep runtime configuration outside the image
Pass endpoint names, batch limits, model paths, feature flags, and credentials through Kubernetes Secrets, ConfigMaps, or an external secret adapter. Never bake provider-specific bucket names, IAM roles, or private hostnames into the image. The same digest should run in development, on the primary cloud, and during a migration rehearsal.
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Kubernetes device plugins let a vendor advertise GPUs through the kubelet and let pods request those devices. This creates a common scheduling contract; the API is documented in the Kubernetes device-plugin documentation. Request GPUs through resource limits and scheduling constraints rather than embedding a provider’s allocation API in application code.
A portable manifest can request a resource such as nvidia.com/gpu, but that line alone does not make performance portable. The driver branch, CUDA compatibility, GPU memory, MIG or virtual-GPU mode, topology, PCIe or NVLink connectivity, kernel, container runtime, and multi-node fabric all affect correctness and throughput.
Create a compatibility matrix
| Check | Record for each provider | Why it matters |
|---|---|---|
| GPU model and memory | Exact accelerator, memory size, partitioning mode, and availability | Determines whether the model fits and whether batching can remain unchanged |
| Driver and CUDA | Driver branch, CUDA runtime, cuDNN, and supported container images | Prevents launch failures and silent performance changes |
| Topology | GPU-to-GPU links, NUMA placement, PCIe layout, and cross-node fabric | Changes collective communication and multi-GPU inference speed |
| Virtualization | Dedicated, MIG, vGPU, or shared scheduling mode | Changes isolation, memory limits, and latency variability |
| Scale behavior | Quota, autoscaling delay, spot interruption, and reservation rules | Determines whether SLOs survive bursts or capacity loss |
Before committing, compare the exact combination of Kubernetes distribution, operating system, runtime, GPU Operator version, and virtualization mode. NVIDIA’s GPU Operator support matrix spans multiple combinations, and a migration is safe only when both the source and target combinations are supported.
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Keep the control plane and data plane exportable
Design model serving so provider storage interfaces are adapters, not application assumptions. NVIDIA’s inference reference architecture separates model artifacts, endpoint configuration, logs, traces, metrics, and cache from provider storage interfaces; the pattern is described in NVIDIA’s inference reference architecture.
Export these artifacts
- Model weights, tokenizer and vocabulary files, configuration, quantization metadata, and checksums.
- Training and evaluation datasets, schemas, lineage, data-use permissions, and preprocessing code.
- Endpoint manifests, routing rules, autoscaling thresholds, rate limits, and safety policies.
- Feature definitions and materialized data, or a reproducible job that can rebuild them elsewhere.
- Logs, traces, metrics, dashboards-as-code, alert rules, incident records, and SLO history.
- Encryption-key ownership records, secret names, rotation procedures, and revocation evidence. Do not export secret values in source control.
Use ordinary files and open formats wherever possible: object storage compatible with standard APIs, JSON or Parquet for records, OpenTelemetry for traces, and Prometheus-compatible metrics. If a managed service adds value, define a scheduled export and a restore test before treating it as production-critical.
Make provisioning reproducible with APIs and code
Every cluster, node pool, GPU selection, network, firewall rule, storage class, identity binding, and observability component should be represented in version control. CNCF describes conformant tenant clusters operated with plain kubectl, Helm, and Argo CD, plus API-first provisioning through Terraform or GitOps, as an exit path that avoids proprietary extensions; see CNCF’s AI-factory guidance.
A practical repository layout
images/: Dockerfiles, lockfiles, SBOM and signing configuration.charts/: portable Helm charts and provider-specific values files.clusters/: Kubernetes version, node pools, policies, and add-ons.infra/: Terraform modules for network, storage, identity, and GPU capacity.models/: artifact manifests, checksums, evaluation reports, and release metadata.observability/: dashboards, alerts, recording rules, and OpenTelemetry configuration.
Use pull requests and automated plans for changes. A clean-room test should be able to run terraform plan, apply the approved modules, install the chart with helm upgrade --install, and deploy manifests with kubectl apply or a GitOps controller. The commands are less important than the property that no undocumented console action is required.
NVIDIA’s AI-cloud requirements call for certified upstream Kubernetes versions and show a preference for Terraform providers. Treat those as procurement checkpoints: ask which Kubernetes versions are certified, whether lifecycle operations are available through an API and Terraform provider, and how state can be exported if the account is closed.
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Choose a GPU cloud with an exit plan
Send every candidate the same technical questionnaire and score evidence, not marketing claims.
| Comparison axis | Questions to answer before purchase | Evidence to retain |
|---|---|---|
| Standards and conformance | Is the Kubernetes distribution conformant? Which versions and extensions are required? | Versioned support statement and export procedure |
| GPU and runtime compatibility | Which GPU models, driver branches, CUDA versions, partitioning modes, and runtimes are offered? | Support matrix and a test node allocation |
| Lifecycle APIs | Can clusters, node pools, GPUs, storage, networks, quotas, and deletion be managed by API, CLI, and Terraform? | API schemas, provider documentation, and sample state export |
| Storage and network portability | Can images, model artifacts, backups, DNS, certificates, and network policies be recreated elsewhere? | Bulk export timings, formats, and egress pricing |
| Observability | Can logs, traces, metrics, alerts, and dashboards leave the platform? | OpenTelemetry or Prometheus compatibility and retention terms |
| Security and isolation | How are tenants isolated? Which identity, key-management, admission, and audit controls are portable? | Architecture diagrams, audit export, and deletion attestations |
| Performance | What are measured latency, throughput, utilization, and scale-up behavior for your model and batch sizes? | Reproducible benchmark method and raw results |
| Capacity and support | What are regional quotas, lead times, interruption policies, and escalation paths? | Written quota and support commitments |
| Economics | What do egress, image pulls, replication, idle reservations, storage, and minimum commitments cost? | Scenario-based 30-day and migration-cost estimates |
| Recreation time | How long does a clean deployment take on another provider? | Timed rehearsal with a defined success threshold |
Run a migration rehearsal before production
A second provider is useful only if you can actually deploy there. Rehearse with a small, non-critical cluster while the original environment is healthy.
- Provision the target. Create a small target cluster using the same Terraform, Helm, and GitOps definitions. Record every provider-specific value you had to add.
- Pull immutable artifacts. Pull the OCI image from an independently accessible registry and retrieve model files, tokenizers, datasets, and metadata from an external or replicated object store. Verify checksums and signatures.
- Run the same tests. Execute correctness, latency, throughput, cost, and failure-recovery benchmarks without changing application code. Use identical prompts, batch sizes, sequence lengths, and warm-up rules.
- Compare platform behavior. Measure GPU utilization, interconnect behavior, autoscaling delay, telemetry completeness, security controls, and behavior during node or zone loss.
- Classify differences. Record every provider-specific change. Convert recurring differences into a versioned adapter or values file; keep one-off exceptions documented with an owner and removal condition.
- Test exit operations. Exercise deletion and export APIs, data-retention periods, backup restoration, certificate replacement, identity removal, and secret revocation. Confirm that the provider can delete retained copies when your contract requires it.
Set a pass/fail threshold before the rehearsal. For example, require the target to meet the model’s correctness criteria, an agreed latency and throughput range, an acceptable cost envelope, and a documented recovery time. If the test fails, fix the portability boundary rather than quietly accepting a permanent dependency.
Control performance and cost differences
Portability can increase engineering effort and may prevent you from using a provider’s most specialized service. Quantify that trade-off instead of assuming the cheapest GPU-hour is the cheapest deployment.
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- Benchmark the whole service: include image pull time, model download, startup, warm-up, inference, batching, queueing, logging, and scale-down.
- Measure utilization: compare memory headroom, compute utilization, power or thermal throttling where exposed, and idle time.
- Include transfer costs: price initial model and dataset replication, ongoing egress, cross-region traffic, image pulls, and backup restores.
- Model capacity risk: a lower hourly rate is not useful if quotas or regional shortages prevent you from obtaining GPUs during a demand spike.
- Test failure economics: include interrupted instances, rehydration time, duplicate work, and the cost of keeping a warm secondary capacity pool.
Keep SLO definitions and benchmark suites provider-neutral so a result from one cloud can be compared with another. Store raw measurements with the model release and hardware details; otherwise later comparisons will mix software changes with hardware effects.
Common portability failures and fixes
The image runs, but the model crashes on startup
Usually the target driver, CUDA runtime, GPU architecture, or available memory differs. Check the support matrix, rebuild only when necessary, and keep separate image variants with identical application-level interfaces. Validate model loading and generation on every supported GPU class.
Pods remain pending
Inspect resource names, node labels, taints, tolerations, quotas, and device-plugin health. A provider may expose a different GPU resource or partitioning mode. Keep those values in a provider-specific values file, not in application code.
Multi-GPU inference is slower
Compare topology and collective-communication paths. A pair of identical GPUs connected through different fabrics can have very different all-reduce and tensor-parallel performance. Re-size the deployment or choose a topology-aware placement rule rather than assuming the same replica count will meet the SLO.
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Restore from exported model artifacts, manifests, and telemetry definitions in a clean account. If a registry, feature store, secret manager, or hosted endpoint has no tested export, it is a single-provider dependency; add an adapter, replication job, or replacement before production.
Deletion leaves data behind
Ask for retention and deletion semantics for snapshots, logs, backups, replicas, caches, and support systems. Test revocation and deletion through APIs, and retain the provider’s confirmation with the system’s compliance records.
Govern portability as an ongoing control
Assign an owner for the exit plan and review it whenever the model, GPU generation, runtime, region, or managed service changes. At least quarterly, verify that:
- Images can be rebuilt from pinned inputs and pulled without the primary provider.
- Model and dataset exports are complete, checksummed, and restorable.
- Terraform, Helm, and GitOps definitions still create a working cluster on the secondary target.
- Device-plugin, driver, CUDA, and GPU-operator combinations remain supported.
- Logs, metrics, traces, alerts, audit records, and secrets can be migrated or recreated.
- Quotas, egress prices, regional capacity, and contract commitments have not changed the exit calculation.
Cloud-specific features are acceptable when they are isolated behind a stable interface and accompanied by an export path. The goal is not to avoid every managed service; it is to ensure that adopting one does not make your model, data, or operating procedures impossible to reproduce elsewhere.
Quick Recap
A procurement-ready decision checklist
- Define the model’s correctness, latency, throughput, availability, security, and recovery requirements.
- Inventory every dependency across packaging, orchestration, hardware, data, networking, identity, observability, and billing.
- Require Kubernetes conformance, lifecycle APIs, Terraform or equivalent automation, and documented export procedures.
- Demand the exact GPU, driver, CUDA, topology, virtualization, quota, and regional-capacity details needed by the workload.
- Specify OCI images, signed artifacts, open telemetry, and version-controlled manifests in the platform design.
- Price egress, replication, idle reservations, image pulls, backups, and migration labor—not only GPU runtime.
- Make a second-provider rehearsal a release gate, with recorded performance, cost, security, and recovery results.
- Review and retest the exit plan after material changes to models, runtimes, hardware, regions, or contracts.
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