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To avoid painful vendor lock-in when moving AI workloads to a neocloud, map every dependency that must move—not just the application code—then test whether critical data, AI assets, configurations and operations can be exported, restored and run in the destination environment. Compare providers against your actual workload and accelerator requirements, make provider-specific choices deliberately, and put usable data-return and transition rights into the contract.
Portability is not an all-or-nothing rule. A managed service may be worth relying on if it adds enough value, but the decision should include a realistic exit path, named owners and a tested understanding of the work and cost involved.
What vendor lock-in means for an AI workload
Vendor lock-in is the practical difficulty, cost or loss of capability involved in changing providers. For AI workloads, that difficulty can sit outside the code that serves or trains a model. A workload may also depend on a provider’s hardware setup, drivers, libraries, managed AI services, data formats, orchestration, evaluation, identity controls, monitoring and contractual terms.
A container can package an application and some of its dependencies, but it does not make all those layers interchangeable. For example, a container image does not guarantee that another provider has the same accelerator, compatible drivers and runtime, identical libraries, equivalent scheduling behavior, or a usable export of the model’s associated data and configuration.
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The Australian Government Architecture checklist makes the AI-specific diagnostic concrete: ask whether workloads depend on proprietary models, prompts, embeddings, training data, orchestration tools or evaluation methods, and whether critical data, workloads or configurations have been tested for movement or restoration. Those questions are useful before signing a contract and again when architecture or provider terms change.
Start by mapping what must move
Before choosing a neocloud—or planning a move to one—create a dependency inventory for each critical workload. Record not only what the workload uses, but also how you would replace, export or restore it. Give each critical dependency an owner who can explain its current role and the evidence that it can be moved.
Inventory the workload end to end
- Application and deployment: source code, container images, infrastructure definitions, build and release workflows, configuration, secrets handling and deployment permissions.
- Compute and runtime: accelerator type and capabilities, drivers, device plugins, runtime support, libraries, resource names, scheduling rules and any hardware-specific initialization.
- AI assets: model weights and other artifacts, model versions, prompts, embeddings, tokenization or preprocessing choices, training data, fine-tuning data and evaluation datasets or methods.
- Data and interfaces: databases, object storage, metadata, logs, input and output formats, APIs, event flows and integrations with other systems.
- Orchestration and operations: training and inference workflows, job scheduling, monitoring, alerting, logging, backup, recovery, incident response and service-continuity arrangements.
- Security and governance: identity, access policies, encryption, vulnerability management, audit records, data residency constraints, reuse restrictions and ownership.
- People and contracts: staff knowledge, supplier responsibilities, transition rights, data-return obligations, termination steps, offboarding charges and access to systems needed during a transition.
Classify each dependency as portable, replaceable with work, contractually constrained or provider-specific. The labels are a decision aid, not a claim that a component can move without testing. Note the effort, owner, proof and unresolved question for each high-impact item. If an export exists but nobody has restored it, record that as unverified rather than portable.
Decide where portability is worth its cost
Making every layer provider-neutral can require engineering effort, add operational complexity or limit a service’s functionality. The GOV.UK guidance Managing technical lock-in in the cloud, first published on December 17, 2019 and last updated September 3, 2026, advises weighing service value against portability. It notes that a managed service may reduce operational burden or offer enough value to justify lower portability; avoiding provider-specific services can also create engineering challenges or reduce functionality.
Australian Government Architecture similarly says lock-in is not inherently a problem when choices are deliberate, risk-informed and reviewed. The practical question is not “Can we eliminate every dependency?” but “Do we understand what we are accepting, why it is worth accepting, and what we would do if it stopped being acceptable?”
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Use a dependency decision record
For every provider-specific service or difficult-to-move asset that matters, record:
- What capability the choice provides and which workload uses it.
- Why a more portable option is not suitable, or why the additional portability work is not currently justified.
- Which assets or operations would be affected if the service changed.
- The conditions that would trigger a review, such as renewal, a material change in provider terms, a new workload or a major architecture change.
- The fallback, replacement approach or exit test, along with its owner and next review date.
This makes a managed capability an explicit trade-off rather than an accidental dependency. Reassess the record when the workload’s value, risk, requirements or provider relationship changes; an old decision may no longer fit.
Make data and AI artifacts usable outside the provider
“Downloadable” is not the same as “usable after export.” A data dump may omit metadata, permissions, lineage or configuration needed to interpret it. A model artifact may be present but depend on a particular runtime, library or preprocessing pipeline. An embedding export may not, by itself, preserve the method and source data needed to reproduce or use it safely.
For each critical asset, document the export format, export method, access required, associated metadata and intended restore path. Include data, metadata, logs, configuration, prompts, model artifacts, embeddings, training and evaluation material, and relevant records. Prefer open or reusable formats where practical, while checking that the destination can actually consume them.
Check ownership, restrictions and return rights
- Identify who owns each dataset, model artifact, prompt collection, evaluation asset and operational record.
- Check whether the provider or another party imposes access, reuse, retention or transfer restrictions.
- Record data residency or sovereignty requirements and whether an export or restoration process would comply with them.
- Confirm in the contract what data and configurations will be returned, in what usable form, when access ends, and what transition assistance or charges apply.
- Determine whether you can retrieve data and records needed for continuity, assurance, security and audit—not only the primary model inputs.
Ask the provider to explain the export and return process, but treat a successful restore test as stronger evidence than a description of a feature. Define who will verify the restored data and what result counts as usable for the workload.
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Validate accelerator and runtime compatibility
GPU portability needs direct validation. Kubernetes provides a device-plugin framework for advertising hardware resources such as GPUs to a cluster, and workloads can request those extended resources. That is a useful scheduling interface, not a guarantee of identical hardware or behavior across providers.
Kubernetes documentation explains that device plugins perform vendor-specific initialization before advertising devices. A shared Kubernetes API therefore does not ensure equivalent GPU models, drivers, performance, libraries, topology or runtime behavior. The Open Compute Project’s Scaling AI Clusters at Neoclouds workstream focuses on standardized hardware and software interfaces and management as a way to mix components from multiple vendors and reduce proprietary constraints. That describes a direction and scope of work; it does not establish that every provider already offers interchangeable systems.
Build a workload compatibility test
- Write down the requirements. Record the accelerator capabilities, driver and runtime support, libraries, memory and resource needs, device-plugin behavior, scheduling constraints and any topology assumptions required by the workload.
- Ask the destination provider for its actual environment details. Confirm the accelerator and supporting stack available for the target environment rather than relying only on a generic statement that GPUs or Kubernetes are supported.
- Deploy a representative workload. Use the workload, container and configuration you expect to run, with an appropriate test dataset and the same relevant orchestration path. A small compatibility exercise can expose missing dependencies before a full transition.
- Check functional results and operations. Verify that the job can be scheduled, access the required devices and libraries, read its inputs, produce expected outputs and be observed and managed with the required controls.
- Record differences and remediation. Document incompatibilities, code or configuration changes, operational workarounds, unresolved issues, owners and the date for another test.
This is a compatibility check, not a claim of equal performance. If performance or capacity is a business requirement, establish a separate acceptance test with workload-specific conditions; no general provider comparison or live migration result is established here.
Keep replaceable components loosely coupled
Where a component is important to replace, reduce unnecessary dependencies between it and the rest of the workload. Separate interfaces, configuration and data formats from provider-specific implementation details when the portability benefit justifies the work. Keep infrastructure definitions and deployment workflows in formats and tools usable across intended target environments where that is practical.
Do not build an abstraction for every service by default. An abstraction has its own design, maintenance and failure costs, and may hide capabilities the workload needs. Prioritize the components with the greatest combination of criticality, provider specificity and plausible reason to move. For a low-impact dependency, documenting the trade-off may be more proportionate than engineering a replacement interface.
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IEEE P2301, the Guide for Cloud Portability and Interoperability Profiles, covers standards-based choices across cloud application, portability, management and interoperability interfaces, file formats, software bills of materials and operating conventions. Its profile approach can inform procurement and architecture requirements. The IEEE page identifies an active project approved February 15, 2023, and refers to the 2301-2020 guide. It is guidance for specifying standards-based choices, not certification that a neocloud workload will migrate without adaptation.
Compare providers against the workload, not a slogan
Use the same questions for each candidate and test the answers against a representative workload or documented compatibility matrix. A shared container format or Kubernetes API is not enough to establish equivalent GPU behavior, export usability or operational controls.
| Evaluation area | Questions to answer | Evidence to request or produce |
|---|---|---|
| Hardware and runtime | Are the required accelerators, drivers, device plugins, libraries and runtimes supported in the target environment? Can the representative workload run there? | Environment details and a workload compatibility test with recorded results and blockers. |
| AI assets | Can model artifacts, prompts, embeddings, datasets and evaluation methods be exported in usable form and restored elsewhere? | Export formats, documented methods, ownership and restrictions, plus a restore test for critical assets. |
| Data and interfaces | Are formats and interfaces reusable? Can data, metadata, logs and configuration be exported without technical or contractual blockers? | Export and return procedures, format details, access requirements and confirmed rights. |
| Operations and security | Can identity, monitoring, logging, encryption, vulnerability management and continuity controls be maintained during a move? | Control mapping, operational responsibilities, transition arrangements and recovery or continuity plans. |
| Contracts and exit | What transition rights, data-return obligations, access rights, termination requirements and offboarding charges apply? | Contract language and an exit plan that names the steps, owners and expected obligations. |
| Cost and value | What capability does a provider-specific service add, and what engineering, migration, dual-running and exit work could be required? | A workload-specific estimate that includes transition, reconfiguration, testing and continuity needs. |
The Government of Canada’s Right Cloud Selection Guidance notes that lock-in risk differs across service models and recommends multi-vendor standards and exit planning. Infrastructure-as-a-service may be more commoditized than software-as-a-service, but that does not make AI workloads automatically portable: AI platform and managed-model services can introduce their own dependencies. Compare the specific services and workload rather than treating a service-model label as a portability verdict.
These evaluation axes are a decision framework, not a ranking of current neocloud providers. Provider availability, prices, GPU capacity, egress charges, contract terms and live migration performance vary and are not established here; obtain current, workload-specific details from the providers under consideration.
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Write an exit plan that can be executed
An exit plan is useful only if it includes the people, rights, systems and time needed to carry it out. Australian Government Architecture’s checklist covers data export, vendor transition rights, offboarding costs, continuity, security, exit testing and review governance. Turn those topics into a plan with named owners rather than leaving them as contract or architecture assumptions.
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Include these plan elements
- Scope and triggers: identify the workloads, assets and services in scope, plus the events that would start a review or transition.
- Dependency and ownership map: identify technical and contractual dependencies, responsible teams and suppliers, and any decision records that need review.
- Data return and access: specify export formats and procedures, the rights and credentials required, retention and deletion expectations, and how the receiving environment will validate restored assets.
- Transition sequence: state which services must be prepared first, what must be reconfigured, how changes will be tested and how the old and new environments will be coordinated.
- Continuity and security: document backup, recovery, failover, incident response, identity, encryption, logging and other controls that must remain effective during transition.
- People and cost: account for migration, offboarding, staff, assurance, supplier transition, reconfiguration, testing and dual-running work.
- Acceptance and governance: name the decision-maker and test owners, record unresolved blockers and remediation owners, and set a next review date.
Do not assume termination is the first moment when an export can be tested. Schedule a safe export, restore or migration exercise for critical workloads while the original environment is available and the people who understand it can help. The test should verify the relevant assets and operating controls, not merely that a transfer completed.
What to do when a portability test fails
A failed test is useful evidence if it produces a specific remediation decision. Avoid recording only “migration not supported”; identify the point of failure and whether it is technical, contractual, operational or a combination.
- Export completes, restore fails: check for missing metadata, incompatible formats, access restrictions, dependent configuration or an unsupported runtime. Assign an owner to repair the export path or document a replacement method.
- Workload deploys, accelerator request fails: compare the requested resource name, device-plugin setup, drivers and runtime support with the destination’s actual configuration. Do not infer compatibility from container startup alone.
- Workload runs, required controls are absent: map identity, logging, encryption, monitoring and incident processes to the destination and decide whether they can be recreated before the workload is considered transferable.
- Contract prevents or delays return: escalate the relevant transition, access and termination terms before relying on the provider for a critical workload. Record any unresolved restriction as a material exit blocker.
- Dual-running or transition effort exceeds expectations: revise the exit estimate and decide whether to reduce the dependency, accept it with explicit approval, or change the continuity plan.
For each blocker, record impact, workaround, accountable owner, due date and retest criteria. If the organization accepts the blocker, document who approved that risk and when the decision will be reviewed.
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Portability changes as workloads, services and contracts change. Review the dependency inventory and exit plan at contract renewal, after a major architecture change, when introducing a new AI workload, when provider terms materially change, and at other triggers defined by the organization. Also revisit a decision if an export or restore test fails, a critical dependency changes ownership, or the workload’s continuity needs increase.
GOV.UK’s cloud lock-in guidance emphasizes ongoing reassessment rather than treating portability as a one-time procurement check. The Australian guidance likewise treats lock-in decisions as choices to be managed and reviewed. Keep the latest test result, remediation status and next review date with the workload’s operational documentation.
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