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Before moving an AI workload, prove that the destination can run its accelerators, models, managed services and dependent systems; reserve sufficient quota and capacity; confirm where every data flow will be processed; rebuild region-bound networking and security; and rehearse validation, cutover and rollback. A region switch is an architecture and compliance project, not a copy-and-paste operation.
Why a regional move is more than copying resources
An AI workload usually spans training or fine-tuning jobs, model artifacts, feature and operational databases, object storage, queues, inference endpoints, identity services, secrets, monitoring, CI/CD pipelines and client applications. Those components do not all share the same regional behavior. Some can be recreated from infrastructure definitions, some can be replicated, and some must remain connected to the original environment for a period of time.
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Cloud consoles may show a resource as available in a destination while a dependent accelerator, model version, private endpoint or quota is unavailable there. A successful migration therefore starts with an inventory and a set of measurable acceptance criteria, then proceeds through feasibility checks, a controlled transfer, testing and an explicit traffic cutover.
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Map the complete workload
Record every component that can affect correctness, performance or recovery:
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- Application code, containers, machine images and infrastructure definitions.
- Training data, feature stores, model checkpoints, registries and artifact buckets.
- Databases, caches, queues, schedulers and event or batch pipelines.
- Identity providers, service accounts, roles, policies, secrets and encryption keys.
- Inference endpoints, API gateways, load balancers, clients and downstream consumers.
- Logs, metrics, traces, audit records, backups and disaster-recovery copies.
- Third-party integrations, licensing hooks, webhooks and operational runbooks.
For each item, mark whether it will move, be recreated, be replicated temporarily, or stay in the source region. Do not assume that a provider’s generic move command applies to every resource type; relocation guidance from Microsoft separates evaluation, selection, migration and cutover because different services require different treatment.
Define measurable acceptance criteria
Capture a baseline in the source region before changing anything. Use the workload’s own representative prompts, datasets and traffic patterns to define pass/fail limits for:
- Model and application correctness, including deterministic regression cases.
- Throughput, queue depth and batch completion time.
- Interactive latency at normal and peak concurrency.
- Availability, autoscaling behavior and failure recovery.
- Access-control decisions, audit logging and alert delivery.
- Data-integrity checksums and replication lag.
- Rollback time and the maximum amount of traffic or data that may be lost.
These are planning thresholds for your workload, not universal cloud benchmarks. Obtain sign-off from application, platform, security, privacy and business owners before the migration window.
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Check accelerators, machine families and zones
Verify the exact GPU, TPU or other accelerator model, the machine family that exposes it, supported images and drivers, and the zone in which it can be provisioned. A region listed as supporting a cloud service may still lack a particular accelerator. Google Cloud documents that some regions do not offer Cloud GPU resources and maintains separate AI-oriented zones; treat those as capacity locations to verify, not as a guarantee of supply.
Confirm all of the following for the intended deployment shape:
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- Accelerator type, memory size, interconnect and maximum count per instance or job.
- Supported host CPU, local storage, operating system, container runtime and driver/CUDA or TPU software versions.
- Availability of the required managed training, pipeline, registry, vector-search and serving services.
- Whether the service is regional, zonal or global, and which control-plane location stores metadata.
- Placement rules for multi-zone or multi-host jobs, including whether the required topology exists in one zone.
Verify the exact model and endpoint behavior
Check that the model name, version, fine-tuning method, safety configuration and endpoint type are offered in the target. For a self-hosted model, confirm that the image and weights can be pulled into the destination without violating licensing or data-transfer rules. For a managed model, read its regional availability and routing documentation.
Do not infer processing geography from an endpoint’s name alone. AWS Bedrock geographic inference profiles, for example, route requests within a specified geography whose member regions and quotas are defined by the profile. Record the profile’s allowed destination regions and confirm that your data policy permits that routing. A cross-region profile can have different behavior from a single-region endpoint even when callers use the same API.
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Secure quota and capacity before the migration window
Separate service availability from usable capacity
A supported region does not prove that your account can start the workload. Check accelerator, vCPU, instance, storage, API-rate and managed-AI quotas in the destination account and project. Some limits are regional; others are project-wide or shared across regions. Google documents both regional and project-level quota patterns, while AWS migration guidance calls out target-region capacity and quotas as separate checks.
Request increases before copying data or scheduling a cutover. Ask the provider or account team to confirm:
- Steady-state quota for production inference, training and autoscaling.
- Temporary headroom for parallel source and destination environments.
- Reservation or committed-capacity requirements for scarce accelerators.
- Quota for the test window, including burst traffic and rollback capacity.
- Storage, snapshot, image and network-transfer limits during replication.
Write down the confirmation date, scope and expiry of any reservation. Capacity can change between planning and execution, so perform a final provisioning test immediately before the migration.
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Trace data, processing geography and governance
Draw every data path
Create a diagram that follows data from collection to deletion. Include source records, prompts, model inputs and outputs, embeddings, feature tables, checkpoints, temporary files, logs, traces, backups, replicas and metadata. Mark where inference requests are routed and where control-plane records are stored. Include disaster-recovery copies and support or diagnostic exports when the service can generate them.
For each path, record the source and destination country or jurisdiction, encryption state, retention period, access principals and deletion process. A model may be deployed in one region while a logging sink, backup bucket or geographic inference profile processes information elsewhere.
Obtain legal and security approval for the target geography
Have privacy, security and legal owners review the target against applicable data-protection laws, sector regulations, certifications, customer contracts and internal policies. Google warns that moving workloads between countries can introduce different legal and standards requirements; the answer depends on your data classification and service configuration, not on a provider’s region label alone.
Rebuild or validate the destination landing zone:
- Create encryption keys in the approved jurisdiction and test key-policy access.
- Recreate identity boundaries, least-privilege roles, service identities and break-glass access.
- Enable audit logs, retention controls, sensitive-data redaction and security monitoring.
- Replicate secrets through an approved mechanism rather than copying plaintext values.
- Document who can administer the destination and where administrative logs are retained.
Rebuild networking, naming and connectivity
Inspect region-bound network objects
Inventory VPC or VNet topology, subnet ranges, routes, peering, transit attachments, firewalls, private endpoints, load balancers, NAT or egress paths, VPNs and dedicated interconnects. Identify which objects are global and which are regional. In Google Cloud, a VPC is global but individual subnets are regional, so a destination deployment normally needs a new subnet plan even when the same VPC is used.
Check for overlapping CIDR ranges before creating the destination network. Validate private service access, DNS resolution, certificate names, firewall source ranges and egress allowlists from both regions. Measure the path to databases, object storage, identity services and customers; a functioning route can still add enough latency to break an interactive inference SLO.
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Plan endpoint discovery and DNS cutover
List every caller that discovers the service through a DNS name, static IP, service registry, configuration file or secret. Lower DNS time-to-live ahead of the change only after confirming that clients honor it. Test health checks, weighted or regional routing, connection draining and cached resolver behavior. Azure’s cross-cloud relocation guidance treats network discovery and DNS cutover as explicit planning activities because an endpoint change can affect systems outside the AI platform.
Decide how source and destination communicate during replication and rollback. Restrict that link to required ports and identities, monitor its bandwidth and cost, and define what happens if the link fails during a write or checkpoint transfer.
Choose a migration method for each component
There is no single relocation operation for a heterogeneous AI system. Select a method per resource and document its consistency guarantees.
| Component | Typical treatment to evaluate | Decision questions |
|---|---|---|
| Containerized services and infrastructure | Redeploy from versioned images and infrastructure-as-code | Are image registries, secrets, policies and provider-specific settings available in the target? |
| Object data and checkpoints | Replicate or copy, then verify checksums and metadata | Are retention, encryption keys, versioning and transfer limits preserved? |
| Databases and feature stores | Native cross-region replication, export/import or dual-write | What consistency, lag, cutover lock and rollback behavior is acceptable? |
| Queues and schedulers | Recreate and drain, replicate, or pause producers | How will duplicate, delayed or in-flight messages be handled? |
| Managed model endpoints | Provision an equivalent endpoint or change the routing profile | Is the exact model, quota, safety configuration and geography supported? |
| Identity, keys and observability | Recreate in the destination landing zone | Do policies, key access, audit sinks, alerts and dashboards work before traffic arrives? |
Keep the source environment intact until acceptance criteria pass and the rollback window closes. Decommission only after owners approve retention, billing and recovery consequences.
Run an iterative validation before production traffic
- Build the landing zone. Create destination accounts or projects, networks, subnets, identities, keys, logging and policy controls.
- Provision a representative slice. Deploy the same model version, runtime, accelerator class and autoscaling settings used in production, or record every intentional difference.
- Transfer a controlled dataset. Verify object counts, checksums, schema, permissions, checkpoint integrity and replication lag.
- Exercise integrations. Test databases, queues, feature retrieval, secrets, private endpoints, DNS, monitoring, alerting and downstream APIs.
- Run correctness and load tests. Compare outputs against the baseline, then apply representative concurrency, batch size and failure conditions. Record latency distributions and throughput rather than relying on a single request.
- Test failure and recovery. Remove an instance, exhaust a quota in a controlled test, interrupt replication and confirm that alerts, retries, checkpoint recovery and operator access behave as designed.
- Conduct a rehearsal. Perform the exact data freeze, final replication, DNS or routing change and rollback procedure in a non-production window.
Iterative testing is safer than a one-time validation because each round exposes a different class of dependency. Fix failed controls and repeat the affected test before scheduling production cutover.
Execute cutover with an explicit rollback
Prepare the change window
Name an incident commander and owners for data, networking, application, security and communications. Freeze infrastructure and model changes, confirm quota and capacity one final time, verify backups, and publish the start and stop conditions. Define the source-retention period and the exact rollback triggers, such as error rate, latency, output divergence, replication lag or policy failure.
Shift traffic in controlled stages
Complete final replication, stop or fence writes that could diverge, and verify destination data. Shift a small, observable portion of traffic first when the routing layer supports it. Watch correctness, latency, accelerator utilization, throttling, queue depth, cost signals and security alerts. Increase the share only after each observation interval meets the predefined criteria.
Rollback and close out
If a trigger fires, stop the shift, restore routing to the source, preserve logs and identify writes that occurred in the destination. Reconcile or discard those writes according to the database and queue plan; do not assume an automatic reverse copy is safe. After the destination remains stable for the agreed period, remove temporary replication, update runbooks and dashboards, and decommission source resources only with written approval.
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When two or more destinations are feasible, score them with workload measurements rather than a generic “nearest region” rule.
| Axis | Questions to answer |
|---|---|
| Accelerators and services | Does the exact accelerator, machine family, model, endpoint and deployment zone exist? |
| Quota and capacity | Can the account obtain production, burst, migration and rollback capacity? |
| Jurisdiction | Where are inputs, outputs, logs, backups and routed inference processed? |
| Latency | What is the measured path to users, data stores, identity and dependent services? |
| Network and transfer | Are new subnets, links, egress paths, DNS changes or high-volume transfers required? |
| Resilience | Can the design tolerate a zone or regional failure, and can it be recovered? |
| Operating cost | What recurring compute, storage, transfer, logging and duplicated-environment costs result? |
Provider documentation does not supply a universal score or capacity figure for every workload. Recheck live regional availability, quota limits, routing profiles and service behavior immediately before committing.
Common reasons a move fails
- “Supported region” interpreted as guaranteed capacity: the accelerator quota or reservation is unavailable when provisioning starts.
- Hidden geographic processing: logs, backups or a managed inference profile send data outside the approved boundary.
- Assuming global networking: a regional subnet, private endpoint or interconnect is missing in the destination.
- Copying state without a consistency plan: databases, queues and checkpoints diverge while both environments run.
- Testing only a health check: the endpoint responds, but model outputs, throughput, autoscaling or downstream integrations fail under representative load.
- Changing DNS without a rollback: cached records and long-lived connections keep sending users to the wrong environment.
- Deleting the source too soon: operators lose recovery data before the destination has passed its stability period.
A destination is ready only when technical feasibility, capacity, geography, networking, validation and rollback evidence are all documented and approved by the owners responsible for those risks.
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