Before moving an AI workload, document what it depends on, why it is moving, and how success will be measured. Then verify the target provider’s services, regions, capacity, networking, security controls, and operating requirements; test the migrated workload against a source-platform baseline; and set cutover and rollback criteria before changing production traffic. A cloud-to-cloud migration is more than copying virtual machines, and it does not automatically make a workload cheaper, faster, safer, or more reliable.
What should you check before moving an AI workload to a new cloud provider?
Start with the workload’s business purpose and current operating baseline. Use the checklist below to gather the information needed to decide what to move, design the target, and judge whether the result is acceptable.
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- Reason and outcome: Record the business driver, such as a required capability, changed service-level needs, a security practice, or reducing dependence on provider-specific features. Separate mandatory outcomes from hoped-for benefits.
- Owners and consumers: Identify the teams responsible for application and model-serving components, the systems and users that depend on them, and the workload’s criticality.
- AI components and data: Inventory models, model artifacts, serving or training components, datasets, feature and metadata stores, logs, and the rules governing their movement and retention.
- Platform dependencies: Record compute, storage, databases, networking, identity, managed services, and custom integrations, as well as external APIs, queues, secrets, scheduled jobs, and build and deployment pipelines.
- Operations: Document monitoring, backup, recovery, incident response, and deployment procedures, including who owns each process.
- Baseline: Capture existing key performance indicators (KPIs), service-level agreements (SLAs), service-level objectives (SLOs), workload behavior, and operating costs. These are the comparison points for acceptance testing.
Microsoft’s migration guidance calls out networking, identity, databases, compute, storage, and custom integrations as migration concerns, not just the application host. Its Azure strategy guidance also recommends assessing the workload and mapping source services to target counterparts.
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Choose a strategy for each workload based on its business driver, compatibility, constraints, team skills, and integration complexity. The options involve different levels of change and should not be treated as interchangeable.
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| Strategy | What changes | Useful when | Main consideration |
|---|---|---|---|
| Rehost | Move with minimal application changes. | Reducing changes during the move is a priority and the workload can run on the target with little modification. | Existing performance, reliability, or architectural problems can move with it. |
| Replatform | Change the hosting layer or adopt a different managed-service layer while retaining much of the application. | The target platform offers a suitable hosting approach without requiring a broader redesign. | Service and operational differences still need assessment and testing. |
| Refactor or rearchitect | Change code or redesign the workload more substantially. | The desired outcome or target constraints cannot be met by a minimally changed move. | More extensive changes bring additional implementation and validation work. |
| Retire, replace, rebuild, or retain | Stop moving the workload as-is: remove it, adopt a replacement, rebuild it, or leave it where it is. | Moving the existing workload is not the best fit for its business value, dependencies, or constraints. | Confirm the decision against actual dependencies and business requirements. |
These strategy categories follow Microsoft’s Azure migration guidance. They are planning choices, not guarantees of a particular outcome.
How do you verify the target provider can run the workload?
Build a source-to-target capability map before committing to a migration design. For every source service or component, record the intended target capability, any gap, required code or configuration change, and who will operate it afterward. A service with a similar name is not proof of equivalent behavior or responsibility.
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- Confirm that required models and serving or training capabilities are supported under the intended deployment conditions.
- Check whether the necessary compute and accelerator types, regions, quotas, storage, network paths, and managed AI services are available for the specific account and deployment.
- Identify compatibility changes in application code, model packaging, APIs, data formats, deployment pipelines, and custom integrations.
- Record changes in operational responsibility, including which team manages patching, scaling, monitoring, backups, and recovery for each target service.
Availability of a particular model, accelerator, quota, or managed service depends on the target provider, region, account, and workload. Verify it directly for the intended deployment; general migration guidance cannot establish availability for a specific reader.
What must be planned for data, identity, security, and compliance?
Set the data-handling and access requirements before copying datasets, artifacts, or logs. The applicable controls depend on the target provider, service, region, and contractual terms.
Rank #3
- Identify the compliance and residency requirements that apply, and document where data may be stored, transferred, and processed.
- Define data movement, retention, and deletion needs for datasets, model artifacts, feature stores, metadata, and logs.
- Map people, services, and migrated resources to target identity roles and policies. Verify that access is appropriate for each task rather than assuming source roles transfer cleanly.
- Have the security team review migration tools and services before use where organizational policy requires it.
- Include encryption and the handling of secrets in the migration design and validation plan.
AWS migration security guidance emphasizes identifying data-handling and compliance needs, validating target identity roles, and reviewing migration tools. Those checks must still be applied to the actual target environment and organizational requirements.
How should you design cross-cloud networking and DNS?
Discover the current topology and required traffic flows before designing target connectivity. Microsoft’s cross-cloud networking guidance warns that designing before this discovery can lead to address conflicts, connectivity gaps, and security blind spots. Its implementation guidance is Azure-specific, so check the target provider’s own implementation details separately.
- Map network address ranges and look for overlap between environments.
- List the traffic flows among users, services, data stores, external APIs, and dependent systems; record firewall rules and routing requirements.
- Assess bandwidth, throughput, and latency needs, especially for paths carrying data or serving time-sensitive requests.
- Specify encryption and any required private connectivity, along with the monitoring needed to detect connectivity problems.
- Plan redundancy for required network paths and plan DNS changes so dependent systems can resolve services as traffic moves.
What should you test before switching cloud providers?
Create a migration runbook that orders the work, names owners, defines the maintenance window, and states who can approve the cutover. Test iteratively, then compare results with the source baseline and the acceptance criteria written before the target was built. AWS’s rehosting checklist and Microsoft’s migration overview both distinguish planning, discovery, build, testing, and cutover as separate work; Microsoft also calls for iterative validation against requirements.
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- Build the target and migrate in stages. Use the planned sequence for infrastructure, data, and application components, with owners assigned to each step.
- Validate functional behavior. Check application flows, integrations, data access, and model behavior against the workload’s requirements. Include model outputs where applicable.
- Measure performance and reliability. Compare relevant latency, throughput, and reliability measures with the source baseline under representative test conditions.
- Verify controls and cost. Check security controls and compare total operating cost for the workload against the baseline. Include migration effort and ongoing data movement where relevant to the decision.
- Approve or roll back. Require named sign-off against the acceptance criteria. If a predefined rollback trigger is met, follow the runbook rather than continuing the cutover by default.
Do not assume that a successful deployment proves the workload is faster, cheaper, more secure, or more reliable. Those outcomes are established only by comparison with your own requirements and baseline.
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How do you operate the new environment and retire the old one safely?
Include the operating handoff in acceptance, not as an informal task after launch. Confirm that the responsible teams can observe, protect, and recover the workload in the target environment.
- Verify monitoring and alerting for the application, model-serving components, infrastructure, and dependencies.
- Confirm that backup and recovery procedures exist for the migrated data and services, and that owners know how to use them.
- Document incident response and escalation responsibilities for the new operating model.
- After the workload passes its agreed criteria, check for remaining consumers, integrations, scheduled jobs, and data dependencies before decommissioning the source.
- Measure any later optimization or modernization work separately rather than treating it as an automatic benefit of migration.
AWS’s Migration Lens evaluates migration decisions across operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. Microsoft Learn’s Azure-specific guidance puts it plainly: “Complete the migration first, then optimize and modernize.”
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