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The safest way to move an AI workload between cloud providers is a staged migration: inventory and baseline the current system, choose a migration strategy, build the destination landing zone, replicate data and artifacts securely, deploy in parallel, validate model and operational behavior, cut traffic over with rollback available, and decommission the source only after acceptance.
Treat the workload as more than application code. The migration set usually includes model files, training data, feature pipelines, vector indexes, prompts, evaluation datasets, secrets, container images, GPU and driver dependencies, APIs, queues, databases, infrastructure policies, and observability. A model can load successfully in the new cloud while quality, latency, cost, or recovery behavior still fails; each of those properties needs an explicit acceptance test.
Define what a successful migration means
Start by writing measurable acceptance criteria before selecting the destination design. Use the source environment as the comparison baseline, not a generic cloud benchmark. At minimum, define acceptable values for functional equivalence, model-quality change, p50 and p95 latency, throughput, peak concurrency, availability, recovery point objective (RPO), recovery time objective (RTO), security controls, monthly run rate, and data-transfer or replication cost.
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Capture measurements during normal operation and representative peaks. Microsoft assessment guidance calls for an inventory of the following items:
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- CPU, memory, disk I/O, network throughput, storage capacity, and peak concurrency.
- Response-time distributions, queue depth, batch or training-job throughput, startup time, and autoscaling behavior.
- GPU model, memory, utilization, driver and CUDA versions, accelerator count, and scheduling constraints.
- Operating-system, runtime, library, framework, container, model-serving, and infrastructure-as-code versions.
- Licenses, service accounts, API keys, encryption settings, firewall rules, external APIs, messaging, ETL, shared databases, and compliance obligations.
- Current backup, restore, failover, incident-response, logging, alerting, and audit procedures.
Record the exact model and dataset versions used in each baseline run. For generative systems, preserve prompts, sampling parameters, tokenizer versions, safety filters, and a fixed evaluation set. For predictive systems, preserve feature definitions, preprocessing code, label windows, and threshold settings. Without this record, a target result that looks different cannot be attributed to the infrastructure move.
Choose the migration strategy deliberately
There is no single best migration pattern. The right choice depends on schedule, portability, provider-specific dependencies, and how much redesign the business can absorb during the move.
| Strategy | What changes | When it fits an AI workload | Main trade-off |
|---|---|---|---|
| Retire | Remove an unused or redundant component. | Duplicate endpoints, abandoned experiments, or obsolete pipelines are discovered during assessment. | Requires evidence that no consumer, audit requirement, or rollback plan still depends on it. |
| Retain | Leave a component in the source cloud temporarily or permanently. | A regulated dataset, legacy database, or provider-specific service cannot move in the current wave. | Creates ongoing cross-cloud networking, identity, and operating overhead. |
| Rehost | Move the workload with no application-code changes. | Containerized runtimes and portable model servers already exist, and schedule matters more than optimization. | Provider-specific storage, IAM, GPUs, and monitoring still need target equivalents. |
| Relocate | Move a compatible environment or cluster with minimal reconfiguration. | The target offers a supported relocation path for the existing platform. | Compatibility and regional availability must be verified; relocation does not remove hidden dependencies. |
| Repurchase | Replace a self-managed component with a different managed product or service. | A managed feature store, vector database, registry, or serving service materially reduces operations. | Data conversion, API changes, licensing, and lock-in can increase the migration scope. |
| Replatform | Make limited changes to use target platform services. | A move between managed machine-learning services is possible while model and data interfaces remain compatible. | Managed-service semantics, quotas, identity, and observability differ between providers. |
| Refactor or rearchitect | Restructure code or redesign the system while preserving required behavior or outcomes. | Provider APIs, GPU scheduling, data stores, or serving architecture prevent the target from meeting requirements. | Offers the largest long-term benefit but has the highest delivery and validation risk. |
AWS describes these seven Rs as retire, retain, rehost, relocate, repurchase, replatform, and refactor. Its migration model groups work into assess, mobilize, and migrate and modernize. Microsoft distinguishes rehost, replatform, refactor, and rearchitect in similar terms. Choose one strategy per component rather than forcing an entire AI platform into one category.
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Create a service map that follows data and control flow from ingestion to prediction or generation. Include identity and access, object storage, databases, queues, secrets, networking, DNS, observability, image and model registries, GPU compute, training orchestration, model registry, feature store, vector database, inference endpoint, governance, and external services.
Separate portable interfaces from provider-specific implementations
For each dependency, document its interface, data format, consistency guarantees, retention policy, authentication method, network path, backup method, and replacement in the destination. Provider-specific IAM semantics, network primitives, managed AI APIs, storage behavior, GPU shapes, quotas, and monitoring integrations generally require explicit target definitions and policy review. Infrastructure-as-code files rarely translate automatically: resource names, policy languages, subnet behavior, security-group rules, role assumptions, and managed-service settings may all change.
Trace hidden runtime dependencies
Inspect container entrypoints, environment variables, mounted volumes, startup scripts, certificate stores, DNS names, package indexes, telemetry agents, license servers, and callback URLs. Check whether code assumes a particular object-storage path format, metadata service, region name, GPU device order, filesystem behavior, or provider SDK. These assumptions often remain invisible in architecture diagrams but cause failures after deployment.
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Build the destination landing zone first
Prepare the destination before moving production data or traffic. Microsoft’s recommended sequence is to prepare the environment, execute the migration, evaluate the result against the baseline, and then decommission the source.
- Create the destination accounts, subscriptions, projects, or organizational boundaries and select regions that meet data-residency and latency requirements.
- Request GPU, CPU, storage, IP, and managed-service quotas early. A workload that fits technically can still stall because accelerator capacity or endpoint quotas are unavailable.
- Define IAM roles, service identities, least-privilege policies, break-glass access, key management, secret storage, certificate rotation, and audit logging.
- Build private networks, subnets, route tables, firewalls, private service endpoints, DNS zones, egress controls, and centralized logging.
- Create encrypted artifact storage, container registries, model registries, backup locations, and retention policies.
- Recreate CI/CD, infrastructure-as-code pipelines, image scanning, dependency scanning, promotion gates, and approval workflows.
- Implement monitoring, tracing, metrics, alerting, cost allocation, dashboards, on-call ownership, and recovery runbooks before enabling user traffic.
- Test backup restoration and a minimal failover path with non-production data.
Design cross-cloud networking from discovery, not from assumptions
Map every VPC or VNet, CIDR range, route, traffic flow, port, protocol, latency requirement, bandwidth requirement, encryption requirement, and name-resolution dependency. Microsoft warns that overlapping address ranges can prevent VPN connectivity and recommends translating security groups and firewall rules into the destination cloud’s constructs.
Select a transfer path
For data movement, compare an encrypted VPN, dedicated interconnect, partner interconnect, cross-cloud interconnect, and controlled public-IP transfer. Google’s architecture guidance identifies transfer speed, latency, reliability, service-level agreements, complexity, and cost as the deciding dimensions. Sensitive or high-volume transfers usually justify private connectivity, while a small, non-sensitive migration may be practical over a tightly controlled public path.
- Use non-overlapping address ranges whenever possible; otherwise plan address translation and verify that it works for every required protocol.
- Measure the available bandwidth and round-trip latency between the actual regions, not just between provider headquarters or nearby cities.
- Define which flows are allowed during replication, validation, dual-write, cutover, and rollback, then remove temporary rules afterward.
- Design private DNS resolution in both directions if services in the two clouds must communicate by name.
- Encrypt data in transit and at rest, and log connection attempts, transfer failures, and unexpected destinations.
Make the AI runtime portable where it pays off
A common portability pattern is to package the application and model server in Docker containers and orchestrate them with Kubernetes. Supporting tools cited in AWS guidance include MLflow for model tracking and versioning, Prometheus and Grafana for monitoring, Airflow for workflow orchestration, and scheduled synchronization with a service such as AWS DataSync. Equivalent tools can be selected in another cloud; the important point is to preserve interfaces and versioned artifacts.
Containers do not automatically port IAM, network policy, DNS, storage classes, GPU drivers, managed databases, vector indexes, provider model APIs, quotas, or observability semantics. Maintain a portability-boundary document with three columns: standards-based components, provider-specific components, and the adapter or replacement required in the destination.
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Match accelerator architecture, memory, driver, CUDA or ROCm stack, framework build, and precision support. A model that loads on one GPU family can fail, use a different kernel, or produce small numerical differences on another. Pin images and dependencies, run a known-good inference test, and record startup time and utilization before scaling out.
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Managed API replacements
If the workload calls a provider-hosted embedding, speech, vision, or large-language-model API, decide whether to keep that API through a private connection, switch to a destination equivalent, or self-host the model. API names, tokenization, rate limits, safety behavior, context limits, error codes, and billing units may differ. Treat the adapter as a versioned component and include it in the validation plan.
Move data, models, and indexes with integrity controls
Classify each data set as immutable, append-only, or mutable. Immutable training snapshots and model artifacts can be copied and verified independently. Mutable feature stores, event streams, operational databases, and vector indexes need a consistency plan.
Choose a synchronization pattern
- Bulk copy plus incremental replication: copy the historical base, then stream changes until the lag reaches the cutover threshold.
- Dual-write: write new records to both clouds while the destination is validated; use idempotent event identifiers and a reconciliation job.
- Freeze and drain: pause writes, drain queues, copy the final delta, verify counts and checksums, and then switch consumers.
- Rebuild: transfer source data and regenerate features, embeddings, or indexes in the destination when the target engine is incompatible.
Use encrypted channels, manifests, checksums, object counts, schema validation, and sampled record comparisons. Preserve dataset lineage, model hashes, feature definitions, index parameters, and retention metadata. AWS states that AWS Transform communications use TLS 1.2 or higher and can use Direct Connect or Site-to-Site VPN instead of the public internet for replication; apply equivalent transport and key-management requirements when using another transfer design.
Vector databases and feature stores
Export the original documents or feature source, embedding-model identifier, preprocessing version, vector dimension, distance metric, metadata schema, filters, shard layout, and index parameters. If the target cannot import the index format, rebuild it from a verified source snapshot. Compare nearest-neighbor recall on a fixed query set and test filtering, deletes, updates, and time-to-consistency rather than checking only that a row count matches.
Secrets and credentials
Do not copy plaintext secrets in configuration files or images. Create destination secrets, rotate credentials where possible, update workload identities, and verify access using short-lived test credentials. Keep source credentials active only for the replication and rollback window.
Execute the migration in controlled waves
- Discover: inventory components, dependencies, owners, versions, data classifications, and operational baselines.
- Design: select a strategy per component, map replacements, choose regions and connectivity, and define acceptance thresholds.
- Mobilize: build the landing zone, quotas, policies, registries, pipelines, dashboards, and recovery paths.
- Pilot: migrate a low-risk model or non-production slice using the same controls planned for production.
- Replicate: transfer immutable artifacts, establish incremental synchronization, and monitor lag, errors, checksums, and cost.
- Deploy in parallel: run the destination service with production-shaped traffic that does not yet affect user-visible results.
- Validate: compare quality, latency, throughput, reliability, security, data integrity, and cost against the baseline.
- Cut over: shift traffic gradually, watch acceptance dashboards, and keep the source ready for rollback.
- Observe: complete the agreed stability window, reconcile late events, and obtain service-owner acceptance.
- Decommission: revoke obsolete access, archive required evidence, remove replication, cancel unused resources, and update runbooks.
Validate model quality and operations before cutover
Run shadow or parallel inference and representative training jobs. Use the same evaluation set, data distributions, prompts, thresholds, and business decision rules used for the baseline.
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| Area | What to compare | Useful failure tests |
|---|---|---|
| Functional behavior | API schemas, status codes, serialization, preprocessing, postprocessing, and deterministic test cases. | Malformed input, missing features, oversized requests, timeout, and retry behavior. |
| Model quality | Accuracy, precision, recall, calibration, ranking metrics, generation safety checks, and output-diff distributions appropriate to the model. | Boundary cases, out-of-distribution data, stale features, tokenizer changes, and alternate GPU paths. |
| Performance | p50 and p95 latency, throughput, queueing, cold-start time, GPU utilization, memory pressure, and autoscaling response. | Peak concurrency, burst traffic, quota exhaustion, slow dependencies, and node replacement. |
| Data integrity | Counts, checksums, schema, lineage, feature freshness, event ordering, duplicate handling, and vector-index recall. | Interrupted transfer, late events, replay, partial writes, and corrupted artifact detection. |
| Reliability | Availability, retry safety, failover, backup restoration, RPO, RTO, and alert delivery. | Region loss, database outage, queue backlog, expired credentials, and failed deployment. |
| Security and compliance | Identity decisions, encryption, network boundaries, audit events, vulnerability findings, retention, and residency. | Unauthorized access, public exposure, key rotation, policy denial, and log-pipeline failure. |
| Cost | Compute, storage, managed services, network transfer, replication, licensing, and idle resources at expected load. | Autoscaling maximum, retry storms, cross-region traffic, and runaway batch jobs. |
Test degraded modes explicitly: provider API errors, unavailable GPUs, exhausted quotas, delayed feature data, duplicate messages, partial vector indexes, and observability outages. A migration is not accepted merely because the endpoint returns a prediction.
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Plan the cutover and rollback
Lower DNS time-to-live values ahead of the change, but do not assume every client honors them immediately. Microsoft’s cross-cloud guidance also calls for DNS cutover planning and private name-resolution design.
- Announce a change window and freeze nonessential deployments.
- Confirm replication health, zero or agreed lag, checksum results, queue state, destination capacity, dashboards, and on-call coverage.
- Send a small percentage of traffic to the destination or enable shadow comparison, depending on the workload’s risk profile.
- Increase traffic in deliberate steps while watching quality, p50 and p95 latency, errors, saturation, cost, and business KPIs.
- Keep the source serving or able to resume service until the observation window and acceptance sign-off are complete.
- If a threshold is breached, stop the increase, route traffic back, preserve logs and failing inputs, reconcile writes, and investigate before another attempt.
Define rollback ownership and authority in advance. For mutable systems, specify whether rollback means replaying events, reversing DNS, restoring a database snapshot, or continuing from a dual-write stream. A DNS change alone cannot undo writes already accepted by the destination.
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No universal duration, cost, or success-rate statistic applies to AI migrations. The workload-specific baseline and transfer design determine the answer. Estimate the full migration run rate rather than only accelerator pricing:
- Source and destination compute during parallel operation.
- GPU reservations, idle capacity, autoscaling headroom, and batch scheduling.
- Object storage, snapshots, databases, registries, vector indexes, logs, metrics, traces, and backup copies.
- Inter-region and cross-cloud egress, private interconnect or VPN charges, and replication traffic.
- Temporary duplicate environments, data-conversion jobs, licenses, support, and engineering time.
Set budgets and alerts for both clouds, tag resources by migration wave, and create a retirement date for temporary copies. Confirm GPU and managed-service quotas before the pilot; quota increases can take longer than code changes.
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Do not select a provider from a generic feature checklist. Score each candidate against the workload’s measured requirements.
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| Comparison axis | Questions to answer |
|---|---|
| Model and data compatibility | Can the destination load the framework, model format, tokenizer, feature schema, embedding dimension, and index format without retraining or rebuilding? |
| GPU availability and performance | Are the required accelerator families, memory sizes, drivers, regions, quotas, and autoscaling behaviors available at the needed times? |
| Migration effort | How many services, adapters, data conversions, policies, tests, and runbooks must change? |
| Managed-service lock-in | Which APIs, registries, feature stores, vector engines, or orchestration features would make a future move harder? |
| Network and egress cost | What are the measured bandwidth, latency, private-connectivity options, transfer charges, and replication costs between selected regions? |
| Security and compliance | Can the destination satisfy encryption, identity, audit, residency, retention, and regulatory controls? |
| Identity and policy model | How will roles, service identities, key management, firewall rules, and policy-as-code be translated and audited? |
| Observability | Can existing metrics, traces, logs, alerts, and model-quality monitors be reproduced with comparable retention and sensitivity? |
| Reliability and recovery | Do regions, backups, failover procedures, RPO, RTO, and service-level commitments meet the acceptance criteria? |
| Regional availability and quotas | Are the required GPUs, managed services, and private-network features available in the target region under realistic quotas? |
| Operational skills | Does the team have the expertise to operate the target platform, or is additional training and support required? |
Common failure modes and fixes
The model loads but predictions change
Check framework and driver versions, floating-point precision, tokenizer or preprocessing changes, feature freshness, random seeds, and provider API defaults. Re-run a fixed evaluation set, compare intermediate tensors or features where practical, and decide whether the difference is within the preapproved tolerance.
Inference is slower or more expensive
Check GPU type and memory, batch size, concurrency limits, cold starts, autoscaling thresholds, network hops, serialization, and logging volume. The target may need a different instance shape, model-optimization build, or placement strategy rather than more replicas.
Replication never reaches zero lag
Measure source write rate, transfer bandwidth, serialization overhead, retries, and destination write capacity. Throttle nonessential writes, increase transfer capacity, parallelize independent partitions, or extend the dual-write period. Do not cut over while the unreplicated delta exceeds the stated data-loss tolerance.
Private connectivity fails
Look for overlapping CIDRs, missing routes, asymmetric paths, blocked ports, incorrect security-group translation, and DNS resolution across clouds. Validate each required flow from both directions with packet and flow logs.
Autoscaling or quotas cause outages
Load-test the destination at peak concurrency, request quota increases, set bounded scaling policies, reserve critical capacity where appropriate, and define a graceful queue or degraded response when capacity is unavailable.
Rollback creates duplicates or lost events
Use idempotency keys, durable offsets, replayable logs, reconciliation reports, and an explicit source-of-truth rule. Test an interrupted cutover before production so operators know which events to replay and which to ignore.
Security controls are weaker after the move
Compare effective permissions rather than policy-file appearance. Test public exposure, cross-account access, secret rotation, key use, audit-log delivery, image vulnerabilities, and data-residency rules in the destination.
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After the observation window closes, obtain sign-off from application, data, security, and operations owners. Then revoke obsolete credentials, remove temporary firewall rules and replication jobs, archive required logs and model versions, preserve legal or compliance records, cancel unused source resources, and update architecture diagrams, inventories, cost allocation, and runbooks.
Keep a documented recovery option for the period required by the organization’s risk policy. Decommissioning is a separate stage, not an automatic consequence of sending the first request to the destination. The final record should state what moved, what remained, which versions are authoritative, how to restore service, and who owns each component.
Quick Recap
A practical readiness checklist
- Baseline measurements and acceptance thresholds are approved.
- Every model, dataset, feature pipeline, vector index, prompt, secret, API, queue, database, and monitoring component has an owner.
- The target landing zone, quotas, IAM, encryption, networking, DNS, registries, backups, and observability are tested.
- Replication uses encrypted transport, manifests, checksums, and a documented consistency model.
- GPU, framework, driver, model-server, and provider-API compatibility tests pass.
- Shadow or parallel runs meet model-quality, latency, throughput, reliability, security, and cost criteria.
- Cutover steps, rollback triggers, event reconciliation, DNS behavior, and on-call responsibilities are written and rehearsed.
- Source retention, credential revocation, log archiving, and decommission dates are approved.
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