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Before moving an AI workload to another compute provider, prove five things: the destination can run your exact software and accelerator stack, your data can move within the required time and budget, security and compliance controls remain acceptable, the workload meets measurable performance and quality thresholds, and you can roll back without losing data. A provider’s advertised GPU model or capacity is only a starting point; compatibility and economics must be demonstrated with your workload.
Use a staged process: assess the current system, plan the target architecture and transfer, migrate a representative slice, validate it under realistic load, then cut over in controlled waves. Keep the original environment recoverable until the destination passes acceptance and recovery checks.
1. Define the decision before comparing providers
Write down why the move is being considered. Common drivers include a changed service level, a security or residency requirement, access to a particular machine-learning capability, resilience, portability, or a verified cost/performance target. None guarantees a benefit for your workload, so turn the motivation into a measurable decision.
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An “AI workload” is more than a model endpoint. Identify which of these components are in scope:
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- Model weights, configuration, tokenizer and preprocessing assets
- Training, fine-tuning and evaluation code
- Datasets, feature data, vector indexes and checkpoints, where applicable
- Inference endpoints, batch jobs and queues
- Container images, operating-system packages, frameworks and custom kernels
- Secrets, identities, certificates, orchestration and deployment definitions
- Logging, metrics, traces, alerting, backup and disaster-recovery systems
- Databases, object storage, APIs and other dependent services
Tailor the inventory to the actual application. Record what will move, what will remain at the source during a phased migration, and which system is authoritative for each piece of data.
Capture a baseline and acceptance thresholds
Measure the current environment before making changes. At minimum, record model-quality or correctness metrics, throughput, p95 and p99 latency where relevant, accelerator and CPU utilization, uptime, recovery objectives, storage and network consumption, and fully loaded end-to-end cost. Define the maximum acceptable regression and the minimum improvement for each metric. Include business outcomes such as completed jobs per hour or acceptable answer quality, not just infrastructure counters.
Name owners for data, model quality, infrastructure, security, application integration and business approval. Decide who can authorize a cutover and who can order a rollback.
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Do not select a destination from a headline accelerator name. Confirm that the required service, exact accelerator options and capacity are available in the intended region on the date you need them. Quotas, inventories and service catalogs change, so obtain current confirmation from the provider and check whether capacity reservations are possible.
Check hardware and topology
- Accelerator architecture, usable memory and supported precision modes
- Multi-GPU and multi-node topology, including interconnect bandwidth and latency
- CPU-to-accelerator balance and system memory
- Local and networked storage throughput, IOPS and latency
- Network bandwidth between workers, storage and serving clients
- Workload-specific requirements such as custom kernels, quantization or specialized operators
Specifications are screening information, not proof of performance. Run representative training or inference with production-like tensor shapes, sequence lengths, concurrency and data access patterns.
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Verify the software supply chain
Confirm the supported operating system, accelerator driver, runtime, framework and library versions. Check the container base image, device plugin or other scheduler integration, orchestration behavior, registry access and license terms. Establish which layers the provider patches and which your team must maintain. A model that loads on one driver or framework version may fail, produce different numerical results or lose performance on another.
Test model loading, preprocessing, one representative training or fine-tuning path and the serving path. Capture functional outputs as well as latency, throughput, utilization and failure behavior. This should use the same evaluation set and comparable random seeds where practical.
Identify provider-specific dependencies
List managed databases, queues, identity services, monitoring APIs, networking constructs and machine-learning features tied to the current provider. For each dependency, choose one of three paths: replace it with a destination equivalent, retain it across clouds temporarily or permanently, or refactor the application to remove it. A simple rehost can preserve portability but miss efficiencies available from a redesigned platform; a refactor can improve operations while increasing schedule and rollback risk.
3. Treat data movement as its own engineering project
Inventory every dataset and artifact by size, update rate, sensitivity, retention period, owner, format and authoritative copy. Include checkpoints, logs, backups, feature data and model artifacts when they are part of the workload. Record whether data can be regenerated or must be transferred exactly.
Build a transfer estimate
Estimate effective throughput and duration for the initial copy, then add repeated synchronization, validation and a final delta. Include source egress, destination ingress or storage, temporary duplicate copies, inter-provider connectivity and recurring charges while compute and data are split. There is no universal migration price: the amount depends on both providers, regions, route, contract and transfer pattern.
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Geographically closer regions can reduce latency and sometimes cost, but traffic traverses both providers’ networks and an intervening third-party facility. Validate the actual route, achievable throughput and published prices for the selected regions rather than assuming that a nearby region is automatically cheapest.
Choose a transfer method
- Bulk transfer: appropriate when a large initial copy can fit the deadline and network capacity.
- Incremental replication: useful when the source continues to change and downtime must be minimized.
- Physical transfer service: worth evaluating for very large volumes or constrained links; verify handling, encryption and chain of custody.
Encrypt data in transit, protect transfer credentials and keys, validate checksums or application-level integrity, and document who handled each copy. Define deletion and retention treatment for staging areas, temporary disks and failed transfers. Authorization procedures and applicable regulatory requirements should be documented before data leaves the source environment.
Test split-cloud operation
During a staged migration, some services will be separated by a provider boundary. Test cross-cloud API calls, data freshness, identity federation, DNS and routing, firewall rules, observability, latency-sensitive inference and failure handling. Measure the effect of a temporary network interruption or a stale replica, and decide whether the application should fail open, fail closed or queue work.
4. Revalidate security, compliance and resilience
A move does not automatically inherit the security posture or availability design of the original environment. Reassess data classification, residency, sovereignty, privacy obligations, industry rules and contractual restrictions for the target region and transfer path. Applicability depends on your jurisdiction, service and data; obtain legal advice for organization-specific conclusions.
Compare control planes and responsibilities
For both providers, review encryption at rest and in transit, customer-managed key support and key migration, secrets handling, identity federation, least privilege, administrative access, audit logs, incident notification, vulnerability remediation, backups and deletion. Compare shared-responsibility boundaries, service limits, quotas, maintenance windows, support escalation, service-level definitions, disaster recovery and export or exit terms. Require current commitments in documentation and contract rather than relying on marketing descriptions.
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Harden Kubernetes and host images
If the workload runs on Kubernetes, verify ingress and egress network policies, control-plane and etcd exposure, and access to the cloud metadata API. Check the destination’s container network interface, service mesh, admission controls, workload identity and device-plugin behavior; equivalent product names do not guarantee equivalent defaults.
Inventory unsupported, end-of-life and vulnerable software. Rebuilding clean virtual-machine and container images is often safer than copying snapshots that may preserve existing weaknesses. Availability must be designed with redundancy, tested recovery and capacity planning; hosting in a cloud does not create high availability by itself.
5. Choose a migration strategy deliberately
Compare approaches against engineering effort, schedule, provider dependence, portability, achievable performance, operational burden, migration and ongoing cost, security fit and rollback complexity.
| Approach | What changes | Typical strengths | Risks to test |
|---|---|---|---|
| Rehost | Move images, data and deployment with few code changes | Shortest path when the stack is portable; preserves familiar operations | Provider-specific dependencies, inefficient hardware use and hidden compatibility problems can remain |
| Replatform | Adopt destination-managed runtimes, storage or orchestration with limited application changes | Can reduce operations work and improve integration | New limits, APIs, licensing and rollback dependencies |
| Refactor | Redesign services, data paths or model serving around the target platform | Potentially better performance, resilience and long-term portability | Highest engineering effort, longer schedule and the most complex rollback |
There is no universal winner. Select the least disruptive approach that meets your measured quality, performance, security and cost thresholds, while accounting for the time your team can sustain two environments.
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- Build the landing zone. Prepare accounts or projects, identities, network segmentation, policy, registries, storage, monitoring, quotas and recovery paths before moving production components.
- Rehearse deployment and synchronization. Automate image builds, infrastructure, secrets delivery and data replication. Record elapsed time and failure points.
- Move a representative slice. Use realistic data shape, traffic and concurrency. A tiny sample can hide memory, network and scheduling problems.
- Validate against the baseline. Compare correctness, model quality, throughput, p95/p99 latency, reliability, resource utilization and fully loaded cost.
- Run a controlled pilot. Route a small, observable share of traffic or a bounded batch queue while retaining the source as the recovery path.
- Execute the cutover gates. Decide how to route traffic, freeze or reconcile writes, preserve in-flight jobs, complete a final data delta and record the decision owner.
- Monitor and accept. Track errors, quality, latency, cost, security alerts and capacity for the agreed observation period. Keep the source recoverable until acceptance and recovery tests pass.
- Retire deliberately. Revoke old credentials, delete transferred copies where appropriate, confirm retention obligations and document decommissioning only after the rollback window closes.
Define rollback triggers before the pilot
Triggers might include a quality regression beyond the approved threshold, sustained latency or error violations, failed integrity checks, unexpected spend, a security control failure or an inability to recover a dependency. Specify who declares rollback, how traffic returns, how writes are reconciled and how the team prevents data loss. A rollback plan that has never been rehearsed is only an assumption.
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7. Use a workload-specific destination scorecard
When comparing providers, score the same workload and regions on these axes:
- Accelerator availability, usable memory and multi-node interconnect
- Framework, driver, runtime, container and scheduler compatibility
- Data location, transfer route, throughput and split-operation latency
- Service limits, quotas, reservations and scaling behavior
- Identity, encryption, key management, audit and policy controls
- Support, maintenance, incident response and recovery capabilities
- Service-level commitments and export or exit provisions
- Total cost at measured utilization, traffic, storage, replication and support levels
Record evidence for every score and mark unknowns as unresolved requirements. Do not substitute a provider’s peak specification for a benchmark on your model.
8. EU switching-charge timing
The European Commission’s Data Act explainer describes switching measures for providers of data-processing services. It includes open interfaces and machine-readable export requirements for platform and software services, plus functional-equivalence measures for infrastructure services when switching between services of the same type.
The Commission identifies 12 January 2027 as the date from which switching charges, including data-egress charges, are to be removed. Before that date, transitional charges related to switching and egress may still apply. Scope, contractual implementation and the parties covered depend on the service and circumstances, so counsel should determine whether the rule applies to your arrangement. The date does not remove the need to budget transfer capacity, engineering time or temporary duplication.
Quick Recap
Final pre-move checklist
- Business objective, baseline and acceptance thresholds are documented.
- Every model, dataset, dependency, identity, secret and operational process has an owner.
- Destination capacity, region, quota and software compatibility are confirmed.
- Representative workload tests cover quality, performance, reliability and cost.
- Transfer volume, update rate, route, duration and all charges are estimated.
- Integrity, encryption, chain of custody and temporary-copy deletion are defined.
- Residency, privacy, contractual and industry requirements are reviewed.
- Security responsibilities, vulnerabilities, backups and recovery are reassessed.
- Split-cloud behavior and failure modes are tested.
- Cutover gates, rollback triggers, decision owners and observation periods are agreed.
- The source environment remains recoverable until acceptance is complete.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
