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The most reliable way to avoid AI-cloud lock-in is to make portability an engineered, tested requirement rather than a promise in a sales meeting. Run on upstream-conformant Kubernetes, package workloads as OCI containers, keep infrastructure and policy declarative, isolate GPU-specific integrations, export data and telemetry, and prove that a representative workload can be redeployed on a second environment before production scale.
Specialized GPU clouds can provide scarce accelerators and useful managed services, but the same integrations that improve performance can make an exit difficult. Treat the Kubernetes API, data formats, identity, observability, security controls, and migration procedure as your portability contract.
What vendor lock-in looks like on an AI cloud
Lock-in is not limited to being unable to copy a virtual machine. An AI workload can become dependent on a provider through several layers at once: a proprietary Kubernetes control plane, an accelerator image, a storage API, a network policy engine, an identity service, an observability format, or a contract that makes exporting data impractical. A workload may appear to be a standard container while its deployment scripts, model pipeline, and incident procedures rely on those hidden dependencies.
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|---|---|---|
| Orchestration | Provider-only resource kinds, modified core APIs, or a mandatory proprietary scheduler | Upstream Kubernetes, standard Deployments, Services, Secrets, ServiceAccounts, admission webhooks, and projected tokens |
| GPU platform | Unpinned driver and CUDA combinations, undocumented fabric topology, or a provider-only device plugin | Versioned driver, CUDA, NCCL, runtime, topology, scheduler, image, and model-distribution choices tested on another accelerator or provider |
| Data | Proprietary snapshots, database exports that require a special service, or unknown egress charges | Open object and database formats, independent keys where possible, tested restores, and a written egress-time and egress-cost model |
| Networking and storage | Mandatory CNI, ingress, CSI, service-discovery, annotations, or custom resource definitions | Portable interfaces with every provider-specific annotation and CRD recorded in an inventory |
| Identity and policy | Application credentials tied to the provider IAM API or policies that cannot be represented elsewhere | Interoperable OIDC or SPIFFE-style identity, declarative policy, and an exportable mapping of roles and claims |
| Operations | Logs, metrics, traces, alerts, and runbooks available only in the vendor console | Telemetry exported to systems you control, with alert definitions and dashboards stored as code |
| Security | Keys, attestation, or confidential-computing controls available only on one hardware fleet | Documented trust requirements and a verification test on every target environment |
| Contract | Unclear deletion, export, notice, capacity, support, or migration-assistance terms | Explicit exit rights, service-level commitments, and assistance obligations |
Look for coupling before signing a capacity commitment. If a feature cannot be replaced, exported, or reproduced, classify it as a dependency and assign an owner, a version, and an exit test.
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Make upstream Kubernetes your portability contract
Kubernetes is useful as a portability boundary only when the provider preserves the upstream operating model. NVIDIA’s AI-cloud requirements call for official CNCF-certified upstream versions, unrestricted access to core APIs, and no mandatory proprietary extensions. The requirement is deliberately stronger than a provider saying that it is “Kubernetes compatible.” Your contract should identify the supported upstream version, the certification status, the upgrade policy, and the APIs that remain available to you.
NVIDIA’s requirements state: "No (Mandatory) Proprietary Extensions: Core functionality must remain portable to other standard environments."
The CNCF’s 2025 Annual Cloud Native Survey reported that 82% of container users run Kubernetes in production, and that 66% of organizations hosting generative AI use Kubernetes for some or all inference workloads. Those figures describe adoption, not a guarantee that any particular service is portable. CNCF also reported in March 2026 that the number of certified Kubernetes AI platforms was nearly twice the program’s November 2025 starting point. A growing ecosystem gives you more potential destinations, but only a conformance check and a redeployment test show whether your application can use them.
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- Use the upstream Kubernetes APIs for Deployments, StatefulSets, Jobs, Services, ConfigMaps, Secrets, ServiceAccounts, admission webhooks, and projected service-account tokens.
- Require a documented Kubernetes version and a support window that gives you time to test upgrades.
- Confirm that you can create, read, update, and delete the core resources through standard clients rather than a provider console alone.
- Obtain a complete list of provider CRDs, annotations, admission controllers, and scheduler extensions. Mark each one as optional, replaceable, or unavoidable.
- Ask whether a workload can run with the default Kubernetes scheduler and standard controllers. If a proprietary scheduler is required for basic operation, treat that as a material exit risk.
- Require access to cluster audit logs and admission decisions so that a second environment can reproduce security and compliance behavior.
Run a small “conformance slice” before committing: deploy a namespace with a standard Deployment, Secret, ServiceAccount, projected token, admission webhook, persistent volume claim, ingress, and network policy. Export the manifests, apply them to a second cluster, and record every change required. A portability contract is credible when this exercise succeeds without rewriting application logic.
Package the application so it can travel
Use OCI images with reproducible inputs
Build and distribute applications as OCI images. Pin base-image digests, operating-system packages, language dependencies, GPU libraries, and model-serving binaries. Generate a software bill of materials for each image and keep the build recipe in version control. Reproducible builds make it possible to determine whether a performance or security difference comes from the cloud or from an unintentionally changed image.
Keep model weights separate from the image unless there is a strong reason to combine them. A small serving image can be copied quickly, while large weights can be transferred through an object-store export process with checksums and encryption. Record the exact model revision, tokenizer, configuration, and quantization settings used by every deployment.
Keep infrastructure and policy declarative
Store Kubernetes manifests, Helm values or another templating input, image digests, autoscaling rules, network policies, admission policies, and secret references in version control. Prefer infrastructure-as-code resources that have equivalents outside the provider. A provider-only load-balancer object or firewall resource may be acceptable, but isolate it in a small module with a documented replacement.
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Separate environment values from the application definition. Region, node-pool labels, storage classes, ingress addresses, identity issuer, and GPU counts should be parameters. The deployment should not contain a provider name in a container command, model path, or business rule. Keep a generated, fully rendered manifest for every release so that an operator can inspect what will be applied elsewhere.
Isolate provider-specific integrations
Put cloud-specific code behind adapters. Examples include a storage client, secret-provider integration, queue connector, GPU telemetry collector, or autoscaling signal. Define a small internal interface and provide an implementation for the primary and alternate environments. Do not scatter provider SDK calls through the inference service or training code.
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Maintain a dependency register with the API or CRD name, version, owning team, replacement, export method, and last test date. This list is more useful than a generic claim that the application is cloud-agnostic: it shows exactly what must change during a move.
Treat the GPU software stack as a replaceable boundary
GPU portability is often harder than Kubernetes portability. A container can start successfully while delivering different throughput, numerical behavior, or failure characteristics because the host driver, CUDA compatibility, NCCL topology, fabric, runtime, scheduler, or GPU model changed.
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|---|---|---|
| GPU model and memory | Model, memory size, partitioning mode, interconnect, and minimum capacity per pod | Run a representative batch and inference workload; record throughput, latency, memory headroom, and accuracy |
| Host driver | Exact driver version, installation method, kernel constraints, and upgrade owner | Build the target node image from a pinned specification and run startup and recovery tests |
| CUDA and runtime | CUDA toolkit and runtime versions, container runtime, device-plugin version, and compatibility matrix | Start the same OCI image on the target and execute a smoke test that exercises all required kernels |
| NCCL and fabric | NCCL version, topology, transport, network interface, bandwidth assumptions, and environment variables | Run distributed communication tests at the intended scale and compare collective-operation time |
| Scheduler and placement | Node labels, taints, topology rules, gang scheduling, quotas, and preemption behavior | Submit the same training or inference workload under contention and verify placement and recovery |
| Model distribution | Registry or object-store location, checksum method, cache policy, and access identity | Restore weights from an exported location without the original provider credentials |
Keep accelerators in replaceable node pools. The application should request capabilities through labels, taints, resource limits, and node affinity rather than embedding a cloud-specific instance name. If a model requires a particular GPU generation, document that as a requirement and identify at least one alternate supply path or an agreed business exception.
Pin versions together. “Latest CUDA” is not a reproducible dependency, and a driver upgrade can change kernel availability or collective performance. Maintain a compatibility matrix and promote a new combination only after the alternate-environment test passes.
Make data export and egress measurable
Data is frequently the most expensive part of an AI-cloud exit. Training corpora, feature stores, checkpoints, logs, and model artifacts may be much larger than the serving image. Before production, define an export format, destination, encryption method, checksum process, and retention period for each data class.
- Use open object formats and documented compression. Include a manifest with object names, sizes, hashes, versions, and timestamps.
- For databases, test a logical export and restore into an independent engine or a neutral interchange format. A provider snapshot that can be restored only inside the original service is not an exit copy.
- Hold encryption keys under an identity and key-management boundary that can survive a provider change where policy allows. If provider-managed keys are unavoidable, document the re-encryption and rotation procedure.
- Calculate both transfer time and transfer cost. The model should include API-operation fees, temporary storage, parallelism limits, checksum validation, and the time engineers spend supervising the export.
- Perform a restore drill, not merely an export. Measure the time from an empty target account to a queryable dataset and record any lost metadata, permissions, or lineage.
Ask the provider to state egress prices, rate limits, geographic restrictions, deletion timing, and whether support can assist during a large export. Put the assumptions in the commercial model; a low hourly GPU price does not compensate for an unaffordable data exit.
Use portable networking, storage, identity, and observability
Networking and storage
NVIDIA’s GB300 requirements explicitly call for no proprietary lock-in of the CNI, ingress, CSI, or scheduler, with upstream and open-source implementations remaining usable. Use that principle for every cluster, not only GB300 deployments. Select standard CNI and CSI interfaces where practical, and test a workload with a portable ingress controller and service-discovery mechanism.
Inventory every provider-specific annotation. An annotation that selects a load-balancer mode, attaches a public address, requests a special disk, or enables a GPU network may be necessary, but it must have a documented equivalent or a deliberate exception. Keep storage classes and network policies in separate overlays so that the application manifests remain unchanged.
Identity and secrets
Prefer OIDC-compatible workload identity or SPIFFE-style identities where feasible. Map service accounts to roles through claims that can be recreated on the target. Avoid putting long-lived provider access keys in pods. Use an external secret mechanism with an exportable bootstrap procedure, and test rotation after a cluster rebuild.
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Record the identity issuer, audience, token lifetime, trust roots, role bindings, and break-glass procedure. A redeployment that starts containers but cannot obtain a secret or call a required API is not portable.
Observability and policy
Export logs, metrics, traces, and alerts to systems you control. Store dashboards, recording rules, alert thresholds, parsers, and retention settings as code. Include GPU metrics, fabric errors, throttling, power limits, and model-level indicators such as latency, queue depth, token rate, and accuracy drift.
Use policy engines and admission rules that have a documented target implementation. Export audit logs in a format your security team can query independently. During an exit test, deliberately trigger an alert and a policy denial to confirm that operators retain the same detection and control capabilities.
Preserve security and trust across clouds
Portability does not mean weakening isolation. If models or training data must remain encrypted while running, include confidential-computing requirements in the portability plan. NVIDIA’s confidential-container architecture uses Kata Containers to run standard Kubernetes pods inside lightweight confidential virtual machines. Remote attestation can verify the hardware, guest, runtime policy, and GPU state before a key service releases encrypted model weights.
This changes the trust boundary while preserving the Kubernetes operating model. It does not make every provider interchangeable. For each target, verify the supported CPU and GPU trusted-execution environments, Kata and runtime versions, attestation service, verifier policy, key-release workflow, failure behavior, and evidence retained for audit.
- Define the measurement policy: approved hardware, firmware, guest image, runtime, Kubernetes configuration, and GPU state.
- Build the confidential workload from pinned images and record the expected measurements.
- Configure the key service to release a decryption key only after a successful attestation from the approved environment.
- Attempt deployment on an unapproved target and confirm that encrypted weights remain unusable.
- Repeat the test after a node replacement, runtime upgrade, and Kubernetes upgrade so that maintenance does not silently bypass the trust policy.
Keep attestation evidence and key-release logs outside the provider’s console. If the alternate cloud cannot meet the same trust policy, classify confidential inference as a portability constraint rather than claiming full equivalence.
Prove portability with a timed redeployment test
A written architecture is not evidence of mobility. Before committing to production scale, deploy a representative inference or training slice on a second provider, region, or accelerator. Choose a slice that includes the real image, model weights, data path, identity, networking, observability, autoscaling, and security controls.
- Freeze the baseline. Record image digests, Kubernetes and node versions, GPU model, driver, CUDA and NCCL versions, model revision, dataset revision, configuration, and performance counters.
- Export artifacts. Produce OCI image digests, signed manifests, SBOMs, infrastructure code, policy bundles, model files, database exports, telemetry schemas, and a secret-bootstrap procedure.
- Prepare an independent target. Use an account or project that does not inherit the source provider’s network, identity, registry, or key service. Create only the prerequisites documented in the runbook.
- Measure data movement. Time the export, transfer, checksum validation, and restore. Record bytes moved, parallel workers, throttling, temporary storage, and actual charges.
- Deploy without source credentials. Apply the standard manifests and target overlay. Any manual edit must be captured, explained, and assigned an owner.
- Run functional and performance checks. Verify startup, health probes, inference or training output, latency, throughput, memory use, distributed communication, checkpointing, autoscaling, and recovery from a failed node.
- Exercise operations and security. Confirm logs, metrics, traces, alerts, policy denials, identity rotation, secret rotation, backup restore, and confidential-computing attestation if required.
- Score the result and remediate. Compare restore time, performance variance, data-transfer cost, observability gaps, accuracy differences, and engineering effort. Fix high-impact gaps and repeat the test after material platform changes.
| Measure | What to capture | Decision use |
|---|---|---|
| Redeployment time | Elapsed time from exported artifacts to a healthy, serving workload | Sets the realistic recovery and migration window |
| Performance variance | Throughput, p95 or p99 latency, utilization, memory headroom, and training step time against the baseline | Shows whether a cheaper or different GPU changes service capacity |
| Data exit | Bytes, duration, transfer rate, validation failures, and billed cost | Quantifies the financial and operational barrier to leaving |
| Operational effort | Engineer-hours, manual changes, tickets, and runbook exceptions | Exposes undocumented platform dependencies |
| Capability gaps | Missing APIs, telemetry, policy controls, hardware features, or residency options | Identifies risks that require a contract exception or redesign |
Set a pass threshold before the exercise. For example, require the target to meet the application’s declared latency and accuracy objectives, restore data within the business recovery window, and reproduce mandatory security and audit controls. The exact thresholds belong to your workload; the important point is that they are agreed before a failed test can be rationalized away.
Compare specialized AI clouds on evidence, not slogans
Use the same questionnaire and test workload for every candidate. A provider that offers more GPU types is not automatically more portable if its control plane or data export is proprietary.
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| Comparison axis | Questions to ask | Evidence to request |
|---|---|---|
| Upstream Kubernetes conformance | Which official versions are certified, and how long are they supported? | Certification reference, version policy, upgrade procedure, and conformance-slice results |
| Standard API and plugin access | Can core APIs, CNI, ingress, CSI, and scheduler components be used without mandatory proprietary replacements? | CRD and annotation inventory, architecture diagram, and exported manifests |
| GPU and driver lifecycle | Who controls drivers, CUDA compatibility, firmware, topology, and maintenance windows? | Compatibility matrix, node-image process, change notices, and benchmark results |
| Data export and egress | What formats, limits, prices, deletion guarantees, and support apply to a large export? | Tariff, API limits, sample export, restore evidence, and deletion certificate process |
| Identity, observability, and policy | Can roles, telemetry, alerts, and audit records be recreated elsewhere? | OIDC or SPIFFE details, export schemas, alert-as-code examples, and audit-log access |
| Geographic capacity and residency | Where are the required GPUs available, and can data and support remain in the required jurisdictions? | Region capacity commitments, residency terms, and outage alternatives |
| Tenant isolation | What hardware, network, storage, and confidential-computing isolation is provided? | Architecture, attestation support, incident process, and independent assurance reports |
| SLA and support | What is guaranteed for capacity, repair, upgrades, incidents, and migration assistance? | Service-level definitions, credits, escalation paths, and named responsibilities |
| Measured redeployment | How long and how much did the representative workload take to move? | Signed test record with time, cost, throughput, accuracy, gaps, and remediation owners |
Score each axis on a defined scale, such as zero for unavailable, three for documented but untested, and five for tested and contractually supported. Keep technical portability and commercial risk as separate scores so that a strong API story does not hide unfavorable egress or capacity terms.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Negotiate the exit before production
Procurement should turn the architecture into enforceable obligations. At minimum, request clauses covering:
- Data export: formats, APIs, maximum transfer rates, egress prices, assistance, encryption, integrity validation, and a deadline for completing an export.
- Deletion: customer-controlled deletion requests, backup-retention limits, verification evidence, and treatment of replicas and caches.
- Access during incidents: audit logs, diagnostic data, emergency console or API access, and cooperation with your incident team.
- Notice: advance notice for Kubernetes, driver, firmware, GPU, API, pricing, region, or service changes that could affect portability.
- Capacity: reservation terms, substitution rules, repair targets, and what happens when the contracted GPU type is unavailable.
- Upstream support: supported Kubernetes versions, conformance expectations, deprecation timelines, and the right to use standard plugins.
- Migration assistance: named technical contacts, reasonable professional-services rates or included hours, and delivery of configuration and telemetry needed to leave.
- Security: key ownership, attestation evidence, vulnerability disclosure, isolation commitments, and responsibilities for confidential-computing failures.
Make the timed redeployment test an acceptance criterion and repeat it after major platform changes. A clause that merely permits export is weaker than one that defines a usable format, a service level, and an accountable contact.
A practical runbook for leaving an AI cloud
1. Freeze and inventory
Declare a migration window, stop untracked changes, and export the dependency register. Capture running manifests, image digests, model and dataset versions, feature-store schemas, secrets metadata, policies, dashboards, alerts, and network flows. Mark anything that exists only in the provider console.
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Create the target identity, network, cluster, node pools, registries, key services, and logging destinations from version-controlled definitions. Apply baseline policies before importing data. Keep the target isolated until validation is complete.
3. Move and validate data
Transfer model artifacts, checkpoints, datasets, and databases through the documented export path. Validate hashes, row counts, object counts, permissions, timestamps, and lineage. Re-encrypt under the target key policy where required.
4. Deploy the application slice
Apply standard manifests and the smallest necessary target overlay. Resolve provider-specific annotations through the adapter or overlay, not by editing application code. Confirm that service accounts, projected tokens, secrets, storage, ingress, and network policies work.
5. Tune and compare
Run the baseline workload, then tune only documented target parameters. Compare throughput, latency, GPU utilization, communication time, cost per request or training step, model outputs, and failure recovery. Keep tuning changes separate from correctness fixes.
6. Cut over and retain rollback
Use a controlled traffic shift or checkpoint boundary. Keep the source deployment available for the agreed rollback period, with a clear rule for data synchronization and a final consistency check. Monitor application and GPU-level indicators during the shift.
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After the rollback window, revoke source credentials, remove replication jobs, request deletion of source data and snapshots, and obtain the contractual evidence. Update the dependency register and record lessons for the next move.
Common failure modes and fixes
“It is Kubernetes, but the manifests do not apply elsewhere”
Usually the application depends on CRDs, annotations, admission behavior, or a provider-only storage or load-balancer object. Export the CRD and annotation inventory, replace optional features with standard controllers, and isolate unavoidable objects in a target overlay.
The container starts but GPU performance collapses
Check the driver, CUDA, NCCL, fabric, topology, power limits, and scheduler placement as a set. Compare kernel-level and collective-operation measurements, not just average request latency. Pin a tested compatibility matrix and choose a different node pool or accelerator if the target cannot meet the declared objective.
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Export succeeds, but restore is unusable
Snapshots may omit metadata, permissions, indexes, or model registries. Run a full restore into an empty target, validate checksums and application queries, and include the restore procedure in the timed test. Keep logical exports for data that must survive a provider change.
Costs exceed the estimate
Include egress, API operations, temporary storage, parallel workers, retries, validation, target ingestion, and engineering time. Request a written quote for the actual data volume and transfer path, then repeat the estimate with a slower rate to account for throttling.
Operators lose visibility during migration
Export telemetry before moving traffic. If dashboards depend on a provider query language or proprietary labels, translate them into portable metrics and traces, and test an alert from symptom to notification on the target.
Confidential workloads fail attestation
Compare the measured hardware, firmware, guest image, Kata or runtime version, policy, and GPU state with the verifier’s allow-list. Do not weaken the key-release policy to make a deployment pass; either qualify the target or document confidential inference as a non-portable capability.
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Portability has an engineering cost. Maintaining adapters, duplicate environments, export pipelines, and test capacity may be unnecessary for a short-lived experiment, but the cost is easier to control before a workload accumulates terabytes of data and years of operational history.
Use a simple decision model with four inputs:
- Migration impact: revenue, safety, compliance, or research delay if the provider becomes unavailable.
- Switching probability: capacity volatility, price exposure, regional requirements, roadmap uncertainty, and concentration of your GPU supply.
- Portability investment: adapter development, second-environment capacity, testing, data replication, and staff training.
- Measured exit result: redeployment time, transfer cost, performance variance, security gaps, and manual effort from the timed test.
For a high-impact production service, require a passing exit test and contractual export rights before purchasing large reserved capacity. For a lower-impact experiment, record the dependency register and export a clean copy of code, data, and model artifacts so that a later portability investment remains possible. The decision should be explicit; “we can move later” is not a plan unless a team, budget, target, and test date exist.
The durable strategy is straightforward: keep the application on standard Kubernetes interfaces, keep the GPU stack versioned and replaceable, keep data and telemetry under your control, and rehearse the move while the original environment is still healthy. Specialized AI infrastructure can then be a performance choice rather than a permanent dependency.
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