Moving an AI workload between cloud providers is an application and operating-model migration, not a simple change of virtual machines. Before selecting a destination, inventory every dependency, establish a measured source baseline, map services and security controls, price the data move and dual-running period, and prove that the destination preserves model behavior, quality, latency, reliability and governance.
Provider equivalents, accelerator capacity, model catalogs, quotas, regional support and prices vary by provider pair and change frequently. Use this checklist to build an assessment and a production runbook, then verify every target detail with current provider documentation and a representative proof of concept.
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1. Define the migration boundary and success criteria
Start by writing down what is moving, what will remain in the source cloud, and what will be replaced or retired. A diagram should show applications, model endpoints, training jobs, data stores, pipelines, registries, secrets, monitoring, external APIs and event triggers. Include traffic between components, not just boxes representing services.
Capture a source baseline before changing anything. At minimum, record the model and runtime versions, accelerator type, workload shape, steady-state and peak request rates, latency percentiles, throughput, error rates, availability, model-evaluation results, security events and monthly operating cost. Include training, fine-tuning, batch inference and evaluation if they are in scope; an inference-only migration can have very different storage, compute and scheduling requirements.
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Set measurable acceptance thresholds
Define functional, AI-quality, performance, security, operational and financial criteria with the service owners. Examples include an allowed change in evaluation score, a p95 latency ceiling, maximum error rate, minimum throughput, recovery objectives, audit-log coverage and a maximum cost per request. There is no universal threshold that is safe for every model or business process, so record the workload-specific values before the proof of concept.
Classify each component’s disposition
For every component, choose one disposition:
- Rehost: move with minimal code and configuration changes.
- Replatform: adopt a managed database, container platform, endpoint or storage service while keeping the application contract largely intact.
- Refactor: change interfaces or code to fit the destination’s capabilities.
- Redesign: change the architecture, such as using a new serving pattern or data pipeline.
- Retain: keep a dependency in the source environment temporarily or permanently.
- Replace or retire: substitute a capability or remove it when it is no longer required.
Do not infer equivalence from similar service names. A replacement may differ in consistency, quotas, request formats, lifecycle operations, safety controls, regional placement or support responsibilities.
2. Build a complete dependency inventory
An inventory is the foundation for choosing a migration method. Record owners, versions, regions, data classifications, interfaces, peak usage and failure dependencies for each item. Include provider-specific SDK calls and resource identifiers in application code, infrastructure definitions and deployment scripts; these are common sources of hidden lock-in.
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| Area | What to record | Migration questions |
|---|---|---|
| Compute and runtime | VMs, containers, Kubernetes versions, operating-system images, drivers, GPU types, device plugins and autoscaling rules | Does the destination support the required accelerator, driver, orchestration version and scaling behavior in the required region? |
| AI services | Managed model endpoints, training and tuning jobs, batch services, safety filters, token limits, model versions and endpoint lifecycle settings | Are the same models, APIs, context limits, tuning methods and safety features available, and do requests and responses behave identically? |
| Data and artifacts | Object stores, databases, datasets, checkpoints, weights, embeddings, vector indexes, feature artifacts, cached prompts and evaluation sets | Can data be exported without losing metadata, precision, indexes, lineage or access controls? |
| Platform services | Queues, event buses, schedulers, registries, catalogs, secrets, key services, CI/CD, monitoring and alerting | What semantics, retention periods, quotas and operational tasks change? |
| Integration and access | External APIs, partner links, DNS, certificates, user identities, service accounts, roles and policy bindings | Which connections and permissions must work while components are split across clouds? |
Export configuration and infrastructure definitions into a reviewable inventory. Flag hardcoded project, subscription, account, region, endpoint and bucket identifiers. Also identify assumptions hidden in scripts, such as a particular metadata format, object-listing order, time zone or retry behavior.
3. Compare destination architectures, not just products
Map each source capability to one or more target options and document gaps, behavior differences, quotas, regional availability, support model and operational ownership. A container image can move more easily than a managed AI endpoint, but container portability does not move data, permissions, managed-service semantics or day-two operations.
Managed AI services
Check model availability, runtime and API compatibility, request and response schemas, token and context limits, content-safety controls, fine-tuning support, batch jobs, endpoint lifecycle, quotas and deployment geography. Verify whether the target exposes the same sampling controls, tool-calling behavior, streaming semantics, seed handling and logging options. Treat this as a provider-pair-specific investigation; no universal feature matrix establishes equivalence.
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Containers and Kubernetes
Confirm supported Kubernetes and container-runtime versions, GPU device plugins, driver compatibility, storage classes, ingress and load-balancing behavior, workload identity, policy enforcement, image-registry access and observability agents. Test node startup time and scale-out behavior under realistic accelerator demand. Recreate admission policies, network policies and pod security controls rather than assuming they follow the image.
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A stable inference contract can reduce future migration effort. One reference architecture from Google Cloud uses a unified frontend that routes by model name to replicas hosted on Google Cloud, GKE, Cloud Run, on-premises or another provider. This is an example pattern, not a requirement. A gateway adds routing, authentication, logging and failure-handling work, so use it when the portability and traffic-management benefits outweigh that complexity.
4. Discover networking before building the target
Draw the source topology and the planned split-cloud topology before allocating destination address ranges. Inventory VPC or VNet CIDRs, overlaps, subnets, route tables, NAT, firewalls, private endpoints, load balancers, DNS zones and resolvers, certificates, proxies and inbound and outbound flows. Include control-plane calls made by agents and deployment systems, not just user traffic.
List every service that remains in the source cloud during migration and its required direction, ports, protocols, latency and bandwidth. Decide whether internet paths, VPN or dedicated connectivity fit the workload’s scale, security requirements, schedule and budget; then validate the actual options and lead times with both providers.
Plan name resolution and rollback
Document DNS records, TTLs, split-horizon zones, health checks and certificate names. Test forward and reverse resolution while components are divided between clouds. Prepare a reversible traffic-shift method, such as weighted routing, a load-balancer change or a controlled client configuration update. Lower TTLs ahead of a cutover only after confirming that resolvers honor them; do not assume an immediate global change.
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5. Translate identity, secrets and security controls
Map human users, workload identities, federated identity, roles, policy bindings, service accounts, secrets, certificates and key access. Replace hardcoded provider identifiers and test the complete service path with least-privilege credentials. Console access is not proof that an application can authenticate, read a model artifact, write a log and call a dependent API.
Recreate separation of duties for dataset curation, model training, platform operations and security review. Verify deny rules as well as allow rules, including cross-project or cross-account access. Confirm that audit events identify the correct principal, resource, action and timestamp in the destination’s logging system.
Apply production security standards to temporary migration agents, staging buckets, transfer credentials, VPNs and parallel environments. Use short-lived credentials where possible, restrict transfer endpoints, encrypt traffic and storage, and monitor unusual reads or egress during synchronization.
6. Treat model and data lineage as first-class migration scope
Trace information from ingestion and labeling through preprocessing, training or fine-tuning, evaluation, deployment, inference, monitoring and retirement. Inventory source and fine-tuned model versions, weights, checkpoints, datasets, embeddings, vector indexes, feature artifacts, prompt templates, cached responses, red-team sets and registry metadata.
Check sovereignty and retention requirements
Classify source data and derived artifacts, then verify regional placement, legal or contractual restrictions and organizational policy with the responsible compliance team. Determine whether customer-managed or external keys, confidential processing or attestation are required by the threat model. Recreate encryption-at-rest and in-transit controls and document who operates each key.
Preserve supply-chain evidence
Carry forward the source-model identifier, artifact hashes, dataset references and classifications, evaluation results, responsible-AI review records and lineage links. Decide how prompt and input data will be filtered, whether data-loss controls or egress restrictions are needed, how much inference content belongs in logs, and how long logs and evaluation data must be retained. Before retiring the source environment, plan key revocation, secure deletion or policy-based retention and the evidence needed to demonstrate each action.
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7. Price the data move and the dual-running period
Measure actual sizes for datasets, images, checkpoints, weights, vector indexes and databases. Estimate transfer time from achievable throughput, concurrency and retry behavior rather than nominal link speed. Include time to prepare exports, validate checksums, build destination indexes and replay changes.
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Include less-visible transfer costs
- Source egress and inter-region transfer.
- Target ingestion, processing, API requests and storage.
- Archive restore or retrieval fees and temporary restored capacity.
- Transfer appliances or agents, including compute and placement near source filesystems.
- Compression, encryption, checksumming and staging storage.
- Parallel source and destination environments, duplicated observability and support.
- Engineering, security, compliance and incident-response effort.
AWS DataSync guidance warns that some source archive classes require restoration or incur retrieval charges, and that agent placement near source filesystems can affect the transfer design. Google Cloud notes that many services charge for egress. Exact rates depend on service, location, storage class and current pricing, so recalculate with the providers’ regional calculators and the measured workload.
For serving, model actual traffic patterns, hardware, utilization, scale-out frequency, caching, redundancy and idle capacity. Include training, fine-tuning and evaluation costs if those workloads move. A lower unit price can disappear when utilization is lower, replicas are required for availability or a gateway adds another processing tier.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Prove behavior in a representative proof of concept
Use production-shaped data and traffic patterns, with sensitive data handled under the approved controls. Run the source and target paths in parallel and compare:
- Functional outputs, schema compatibility and deterministic test cases.
- Model quality, evaluation scores, safety behavior and red-team results.
- Latency percentiles, throughput, queue depth, timeout and error rates.
- Availability, failover, recovery time and recovery point behavior.
- Identity enforcement, encryption, audit records and data-loss controls.
- Metrics, traces, logs, alert routing and on-call procedures.
- Cost per request, batch or training run under realistic utilization.
Test peak and degraded conditions, including accelerator scarcity, dependency failure, expired credentials, lost network paths, malformed inputs, oversized contexts and partial data synchronization. Record thresholds and deviations; do not approve a migration based on a single successful demonstration.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems9. Write and rehearse the cutover runbook
- Assign owners and dependencies. Name technical, security, data, compliance, communications and approval owners for every step.
- Synchronize and validate data. Perform an initial copy, capture changes, verify checksums and rebuild or validate indexes and registries.
- Freeze or quiesce writes when required. Define the freeze window, user communication and a method to detect writes that bypass the freeze.
- Deploy and observe the target. Confirm configuration, identities, network paths, dashboards, alerts, quotas and autoscaling before accepting real traffic.
- Shift traffic using the rehearsed method. Use phased, blue-green or maintenance-window cutover according to statefulness, consistency and downtime tolerance.
- Evaluate against the baseline. Watch functional, AI-quality, performance, security and cost gates for the agreed observation period.
- Trigger rollback when a gate fails. Route traffic to the source, restore write authority according to the data plan, preserve evidence and investigate before another attempt.
- Decommission deliberately. Keep source capacity and required data available through the rollback window, then revoke credentials, remove temporary access, delete or retain data under policy and update asset and model lineage.
Rehearse the complete procedure before production, including the rollback. A cutover that works only when every dependency is healthy is not a tested cutover.
10. Compare multiple target options with one scorecard
When more than one destination is viable, score each against the same workload-specific evidence rather than headline service names.
| Decision axis | Evidence to collect |
|---|---|
| Capability | Model and runtime support, managed-service behavior, tuning, safety controls, quotas and lifecycle operations |
| Portability and lineage | Export formats, artifact integrity, metadata, registry compatibility, data movement and provenance retention |
| Performance | Accelerator availability, latency percentiles, throughput, scaling time, availability and regional coverage |
| Security and compliance | Identity federation, private networking, key custody, residency, audit detail, retention and separation of duties |
| Economics | Transfer, retrieval, storage, serving, training, support and dual-run total cost under measured usage |
| Operations | Staff skills, monitoring, incident response, provider support, reliability and rollback complexity |
Require a written explanation for every score and link it to a test, configuration record or current official documentation. Recheck GPU capacity, model catalogs, regional support, quotas, security features and prices immediately before committing, because these details can change after the initial assessment.
Common failure modes and recovery actions
“The container ran, but the workload did not behave the same”
Compare drivers, kernels, device plugins, precision settings, tokenizer or library versions, environment variables and model-loading paths. Rebuild the image and rerun quality and latency tests with the same inputs.
“The target endpoint is reachable, but requests fail”
Inspect identity bindings, private routes, DNS, certificates, firewall rules, quotas and request limits. Test with the workload’s service identity and exact production request shape rather than an administrator’s console session.
“The copy finished, but retrieval or inference is wrong”
Verify checksums, metadata, index versions, embedding dimensions, document filters, registry references and eventual-consistency assumptions. Rebuild derived indexes from an authoritative export when integrity cannot be proven.
“The destination is cheaper on paper but costs more in operation”
Recalculate with observed utilization, replicas, idle accelerator time, egress, logging, support and staff effort. Include the full period during which both environments remain available.
The Bottom Line
The safest cloud-to-cloud AI migration is an evidence-driven operating-model change: inventory first, map every dependency and control, price the real transfer and overlap, validate the target against a measured baseline, and cut over only with a rehearsed rollback and explicit acceptance gates.
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