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Before moving an AI workload, document what it depends on, why it is moving, and how you will prove the target environment is ready. Map the source services to target capabilities, verify data, security, identity, networking, and regional requirements, then test the migrated workload against a source-platform baseline. Plan the cutover and rollback before production traffic moves; a provider change alone does not guarantee lower cost, better performance, or stronger reliability.

Why is the workload moving, and what will count as success?

Start with the business reason, not a target service catalog. A move might be driven by a required capability, changed service-level needs, security practices, or a desire to reduce dependence on provider-specific features. Separate requirements from hoped-for benefits: wanting lower cost, for example, is not evidence that the move will deliver it.

Write measurable acceptance criteria before building the target. Use the current workload as the baseline and specify what must remain functional and what must improve. Depending on the workload, criteria can cover model behavior, response time, throughput, availability, recovery, security controls, operating effort, and total cost. Google Cloud describes cloud-to-cloud migration as one route to provider features or reduced lock-in, but that general rationale does not establish that a particular workload will achieve either outcome.

What does the AI workload actually depend on?

Inventory the full production system, not just its serving or training compute. A cloud migration can affect networking, identity, databases, compute, storage, and custom integrations. Microsoft’s migration guidance treats these as connected migration concerns and recommends assessing the workload and mapping source services to target counterparts.

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Application, model, and data components

  • List application, training, inference, and model-serving components; identify owners, consumers, criticality, and dependencies between them.
  • Record model files, datasets, feature stores, metadata stores, databases, logs, and other persistent state. Note where each is stored, how it is accessed, how it moves, and any retention or recovery needs.
  • Document external APIs, queues, secrets, scheduled jobs, build and deployment pipelines, and custom integrations.

Operations and baseline

  • Capture the current monitoring, alerting, backup, incident-response, and recovery procedures, including who owns each one.
  • Record relevant key performance indicators, service-level agreements, and service-level objectives, along with actual workload costs and operating practices. These establish the comparison point for acceptance testing.

Which migration strategy fits this workload?

Choose a strategy per workload according to its business driver, compatibility, constraints, team skills, and integration complexity. The common choices differ in how much changes:

Approach What changes When to consider it
Rehost Move with minimal application changes. When speed or limited change is important and the existing design can run acceptably on the target.
Replatform Change the hosting layer or adopt a different managed service while retaining much of the application. When the target offers a suitable platform capability and the change is worth the compatibility and integration work.
Refactor or rearchitect Change code or system design more substantially. When the business goal, technical constraints, or target requirements justify a larger redesign.
Retain, replace, rebuild, or retire Keep the workload where it is, adopt a replacement, rebuild, or stop running it. When moving it unchanged is not the best fit or the workload no longer needs to run.

Rehosting minimizes change, but it can carry existing performance, reliability, or architectural problems forward. Microsoft’s strategy guidance frames the choice around business drivers and constraints; it does not imply that lift-and-shift fixes the workload.

Will the workload work the same way on another cloud?

Do not assume that a source service has a one-to-one equivalent or identical behavior in the target environment. For every source capability, record the proposed target service, any gap, required code or configuration changes, and which team will take on changed operational responsibilities.

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Check availability under the actual deployment conditions

  • Confirm that required compute and accelerator types, managed AI services, storage, and regions are available for the intended account and deployment.
  • Verify quotas and service limits with the target provider rather than assuming that an advertised capability can be provisioned for this workload.
  • Check model and framework compatibility, data formats, serving behavior, and integration requirements in the target setup.

Model availability, accelerator supply, quotas, and service support vary by provider, region, account, and workload. The reviewed provider guidance does not establish availability for a particular reader’s deployment; verify it directly with the named target provider.

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Are data, identity, security, and compliance requirements covered?

Before moving data, identify applicable compliance and residency requirements and document where data may be stored or processed. Check the actual target provider, service, region, and contractual terms; a service name or region label by itself does not establish that a workload meets a particular obligation.

  • Map access for people, services, and migrated resources. Validate target roles and policies rather than copying source permissions without review.
  • Identify secrets, keys, encryption requirements, and the systems that need them. Confirm how the target environment will manage and monitor access.
  • Have the security team review migration tools and services before use where organizational policy requires it.
  • Confirm that data transfer, retention, backup, and recovery practices meet the workload’s requirements.

AWS migration guidance highlights security and compliance planning; Microsoft’s migration overview also includes identity among the areas to address. Apply those checks to the actual target services and organizational requirements.

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How should cross-cloud networking and DNS be designed?

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 detailed implementation guidance is Azure-specific, so treat the following as planning questions and verify the design in the target provider’s environment.

  • Record address ranges and identify possible overlap between source and target networks.
  • Map traffic between workload components, users, dependencies, and external services. Note bandwidth, throughput, and latency sensitivity.
  • Review routing, firewall policy, encryption, and any private connectivity requirements.
  • Plan redundancy and monitoring for the required network paths.
  • Inventory DNS names and dependencies, then define how name resolution will work during traffic migration and after cutover.

Network design is part of workload readiness, not a final connectivity task: a migrated service can be present in the target cloud yet remain unusable by its dependent systems.

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What should be tested before switching providers?

Build a migration runbook that assigns owners and defines the order of operations, maintenance window, traffic changes, sign-off criteria, and rollback triggers. Test in stages rather than treating a successful deployment as proof that the migration is complete. AWS rehosting guidance and Microsoft’s migration overview describe planning, discovery, build, testing, and cutover as distinct work; Microsoft also calls for iterative testing against requirements.

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Compare the target with the source baseline

  • Function: Check application flows, integrations, data access, and expected model behavior where applicable.
  • Performance: Compare latency and throughput using representative workload conditions and the acceptance criteria you set.
  • Reliability: Exercise monitoring, alerting, backup, recovery, and relevant failure procedures.
  • Security: Verify identity policies and security controls in the deployed target environment.
  • Cost: Compare expected and observed operating costs for the workload, accounting for its actual usage and migration-related data movement.

Define rollback triggers in advance—for example, a failure to meet an agreed acceptance criterion—and make clear who can call a rollback and how it will be executed. The trigger should be specific to the workload; there is no universal performance or cost threshold for every migration.

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What must be ready for cutover and ongoing operations?

Cut over only after the agreed validation and sign-offs are complete. The runbook should identify traffic changes, owners, the maintenance window, and the decision points for proceeding or reverting. Keep the source environment available according to the rollback plan until the target has passed the required acceptance checks.

Include monitoring, alerting, backup, incident response, and an operations handoff in acceptance. After the target is operating as intended, confirm that no remaining integrations, scheduled jobs, or other dependencies rely on the source before decommissioning it. Set source-retirement criteria and an owner rather than assuming it is safe to shut down immediately.

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Optimization can follow migration, but measure it against the baseline. Microsoft Learn’s Azure-specific guidance puts the sequence plainly: “Complete the migration first, then optimize and modernize.” That is a sequencing recommendation, not evidence that migration itself improves cost, performance, security, or reliability. AWS’s Migration Lens likewise evaluates migration decisions across operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability.

How to make the target-provider decision

When comparing target providers or architectures, score each against this workload’s requirements rather than choosing a universal winner. Use the same criteria for every option:

  • Required models, training or serving capabilities, compute and accelerator types, and regional availability.
  • Data location, compliance obligations, identity and access controls, and security review requirements.
  • Service gaps, integration changes, provider-specific dependencies, and the effort needed to maintain portability.
  • Network topology, latency, throughput, DNS, and data movement requirements.
  • Functional and performance test results against the source baseline.
  • Reliability and operating burden, including team skills, monitoring, backup, and recovery.
  • Total cost for the workload’s actual usage, including migration effort and ongoing data movement where relevant.

Provider migration frameworks help organize these questions, but they do not supply a head-to-head benchmark for this workload. Validate the result with target-specific availability, contractual, cost, and test information before committing to a cutover.

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