The Tool Desk
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Use a staged process: assess the complete workload, design the target, move a non-production slice, validate it under representative load, then cut over with explicit gates and a tested recovery path.
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Set the decision boundary before comparing providers
Write down why the move is being considered and what result would justify the risk. Common objectives include a changed service-level requirement, a security or residency need, access to a particular machine-learning capability, improved resilience, portability, or a verified cost/performance target. None guarantees a benefit for a particular workload; each must become a measurable acceptance condition.
- Business owner: approves the risk, budget and final cutover.
- Model or data-science owner: defines quality, reproducibility and evaluation requirements.
- Platform owner: owns infrastructure, deployment, quotas and operations.
- Security and compliance owner: verifies controls, residency, contracts and evidence.
- Application owner: handles APIs, user traffic, integrations and downstream dependencies.
Record which systems will remain at the current provider during a phased move. A model endpoint may move first while a feature store, object archive, identity system or observability platform remains in place. Those cross-provider links become part of the workload and must be tested.
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Inventory everything the AI workload needs
Do not define the workload as “the model.” Build an inventory with an owner, location, sensitivity, size and dependency for every item that will have to run, move or remain reachable.
- Model weights, configuration, tokenizer files and preprocessing assets.
- Training, fine-tuning, evaluation and deployment code.
- Training data, inference data, labels, feature data and evaluation sets.
- Checkpoints, experiment metadata, logs, backups and model registries.
- Vector indexes, feature stores, caches or other retrieval components, if used.
- Container images, operating-system packages, accelerator libraries and license files.
- Secrets, keys, service accounts, identity federation and authorization policies.
- Schedulers, Kubernetes manifests, device plugins, service meshes and ingress rules.
- Monitoring, tracing, alerting, DNS, queues, databases and external APIs.
Mark the authoritative copy of each dataset and artifact. Also record update frequency, retention period, format, data owner and whether a temporary duplicate is allowed. This inventory exposes hidden work such as rebuilding a registry, reissuing credentials or reproducing a feature pipeline.
Establish a baseline and acceptance gates
Measure the current system before selecting a destination. Capture model correctness with a fixed evaluation set, throughput, p50/p95/p99 latency where relevant, accelerator and CPU utilization, memory pressure, uptime, recovery time, recovery point, storage consumption, network traffic and fully loaded cost. Include the traffic shape that matters: batch size, sequence length, concurrency, prompt or input distribution, training checkpoint frequency and peak demand.
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Prove destination compatibility, not just GPU availability
Confirm that the exact service and accelerator configuration can be provisioned in the intended region, at the required scale and on the date you need it. Catalogs, quotas and capacity change, so obtain current confirmation from the destination provider. Then test the entire software and hardware path.
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Compute and topology checks
- Accelerator architecture, usable memory and supported precision modes.
- Multi-GPU topology, interconnect bandwidth and multi-node behavior.
- CPU-to-accelerator balance, local storage performance and network bandwidth.
- Kernel, compiler and communication-library requirements for your framework.
- Capacity reservations, quota limits, autoscaling behavior and maintenance policy.
A card with the same product name can behave differently when attached to another host, virtualized service or interconnect. Treat specifications as screening information. Run representative training or inference rather than inferring performance from a model number.
Software, orchestration and licensing checks
- Operating-system image and kernel compatibility.
- Accelerator driver, runtime, framework and library versions.
- Container base image, registry access and image-scanning process.
- Kubernetes version, CNI, device plugin, scheduler and service-mesh behavior, if applicable.
- Commercial and open-source license terms, including redistribution and inference restrictions.
- Provider-managed versus customer-managed patching, upgrades and incident response.
Load the production model and run a representative path with production-like data shape and concurrency. Compare both functional outputs and performance. Check numerical tolerances, determinism expectations, tokenizer behavior, unsupported operators, kernel fallbacks, out-of-memory handling and checkpoint restore. This is an engineering validation exercise, not something a service catalog can prove.
Identify provider-specific dependencies
List managed training jobs, proprietary inference endpoints, databases, queues, identity APIs, monitoring integrations and storage features in the current design. For each, choose one of three paths: replace it with a destination equivalent, retain it across providers, or refactor the application to remove the dependency. A simple rehost may move quickly but can preserve coupling and miss performance or operational improvements available through a replatform or redesign.
Plan data movement as a separate workstream
Data transfer often determines the schedule and cost more than compute provisioning. For every dataset and artifact, record size, growth or update rate, sensitivity, retention, owner, format and authoritative location. Include checkpoints, logs, backups and derived features where they are required to reproduce or serve the workload.
Estimate time and total transfer cost
Estimate transfer duration from the volume that must move, sustained throughput, protocol overhead, concurrency and the time needed for validation. Add repeated synchronization and a final delta before cutover. Budget for source egress, destination ingress or storage, temporary duplication, inter-provider connectivity and any recurring charge while compute and data are split between clouds. No universal migration price exists; the providers, regions, route and contract determine the result.
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| Cost or constraint | What to measure |
|---|---|
| Bulk copy | Dataset and artifact volume, achievable sustained throughput and transfer window |
| Ongoing synchronization | Update rate, replication frequency, changed-data volume and duration of split operation |
| Validation and staging | Temporary storage, duplicate copies, checksums and replay or reprocessing time |
| Network charges | Source egress, destination charges, inter-provider connectivity and the actual route |
| Runtime proximity | Latency and transfer cost while services and data reside in different regions or providers |
Geographically closer regions can reduce latency and sometimes transfer cost, but traffic traverses both providers’ networks and an intervening third-party facility. Validate the route, achievable throughput and current prices with both providers instead of assuming that geographic distance alone predicts the bill.
Choose a transfer method
- Bulk transfer: suitable when a large initial copy can finish before the test and cutover windows.
- Incremental replication: suitable when the source continues changing and downtime must be minimized.
- Physical transfer service: worth evaluating when volume or network limits make an online copy too slow.
Encrypt data in transit, protect transfer credentials and keys, verify checksums or application-level integrity, and keep chain-of-custody records where required. Define how temporary copies will be retained and deleted. Industry security guidance recommends documented authorization and procedures for alternate-location transfers, with applicable regulatory requirements considered explicitly.
Test split operation
Before production cutover, exercise cross-provider API calls, data freshness, identity federation, DNS and routing, firewall rules, observability, latency-sensitive inference and failure handling. Confirm what happens when replication lags, a route is unavailable or the destination reads a stale feature. A design that works only after every dependency moves at once is not a staged migration.
Reassess security, governance and provider responsibilities
A new provider does not inherit the security posture of the old environment. Re-evaluate data classification, residency, sovereignty, privacy obligations, industry rules and contractual restrictions for the target region and every transfer path. The answer depends on your jurisdiction and data; obtain legal and compliance review for the specific deployment.
Controls to verify
- Encryption at rest and in transit, including customer-managed key import, rotation and revocation.
- Secret storage, workload identity, federation, least privilege and break-glass access.
- Administrative access records, audit-log retention, incident notification and forensic access.
- Vulnerability remediation, image scanning, patch ownership and end-of-life software handling.
- Backup scope, restore testing, disaster-recovery regions and deletion guarantees.
- Service limits, quotas, capacity reservations, maintenance windows and support escalation.
- SLA definitions, exclusions, export rights and termination or exit procedures.
Inventory vulnerable or unsupported software before copying it. UK National Cyber Security Centre guidance recommends considering clean virtual-machine or image rebuilds instead of transferring snapshots that may carry existing weaknesses. Availability must be designed with redundancy, recovery and tested procedures; hosting in a cloud does not create high availability automatically.
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Kubernetes-specific checks
If the workload uses Kubernetes, verify the destination’s network and control-plane behavior rather than assuming equivalent defaults. Apply ingress and egress network policies, avoid exposing Kubernetes APIs or etcd publicly, and filter access to the cloud metadata API. Check CNI, service-mesh, load-balancer, storage-class and device-plugin behavior under failure. These controls are a baseline, not an exhaustive security assessment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a migration approach deliberately
Compare approaches against the same decision criteria: engineering effort and schedule, dependence on provider-specific services, portability, achievable performance, operational burden, migration and ongoing cost, security/compliance fit and rollback complexity.
| Approach | Typical trade-off | Questions to answer |
|---|---|---|
| Rehost | Few application changes and a shorter initial schedule, but existing coupling and inefficiencies may remain | Will drivers, images, networking and licenses run unchanged? What technical debt is being carried? |
| Replatform | Adapts selected services or deployment layers for the destination, requiring moderate engineering | Which managed dependencies are replaced, and can the team operate both versions during rollback? |
| Refactor or re-architect | Highest effort and longest schedule, with potential for better portability, resilience or utilization | Which redesign benefits are measurable, and how will compatibility and rollback be maintained? |
There is no universal winner. A time-critical move may favor a controlled rehost, while long-lived strategic infrastructure may justify refactoring after the workload and target have been proven.
Use a staged migration and validation runbook
- Build the landing zone. Create destination accounts or projects, identities, network segmentation, policy, storage, logging, monitoring, quotas and recovery resources before moving production components.
- Rebuild and scan images. Pin tested driver, runtime and framework versions; scan images; record provenance; and remove unsupported packages.
- Copy a representative slice. Move a controlled dataset and required artifacts. Verify checksums, permissions, metadata, retention and deletion handling.
- Deploy a parallel workload. Run the same model and code against the same evaluation set. Test model loading, checkpoint restore, training or inference, autoscaling and failure behavior.
- Apply representative load. Reproduce batch sizes, concurrency, input lengths, peak patterns and synchronization frequency. Measure quality, throughput, tail latency, reliability, utilization and fully loaded cost.
- Run a pilot wave. Shift a bounded internal or low-risk workload while retaining the source path. Monitor errors, quality drift, capacity, security alerts and cross-cloud latency.
- Review the gate. The named decision owner compares results with the preapproved thresholds. A failed gate produces a remediation plan or rollback, not an informal exception.
- Repeat by dependency group. Move related services in waves so that each wave has a known data owner, dependency map, test set and recovery point.
Define cutover and rollback before production traffic moves
Write the cutover procedure as an executable checklist. Decide how traffic will be routed, whether writes are frozen or reconciled, how in-flight jobs finish, how the final data delta is copied, and who can authorize the switch.
- Set a maintenance or change window and a single decision owner.
- Keep the source deployment recoverable and credentials usable until acceptance checks pass.
- Record the exact DNS, gateway, feature-flag or service-mesh change that shifts traffic.
- Specify rollback triggers for quality regression, latency, error rate, data divergence, security alerts or cost anomalies.
- Test returning traffic to the source without overwriting newer data or losing jobs.
- Confirm backups, checkpoints, logs and audit trails are available after the switch.
After cutover, monitor model quality as well as infrastructure health. Watch errors, latency, cost, security alerts, capacity and data freshness. Retain the source until acceptance and recovery checks pass. Then revoke obsolete credentials, delete transferred copies where appropriate, and confirm source-side retention and decommissioning.
EU switching rules: understand the 12 January 2027 date
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.
Switching charges, including data-egress charges, are scheduled to be removed from 12 January 2027. Before that date, transitional charges related to switching and egress may still apply. Scope depends on the service, provider, customer and contractual situation, so treat the date as a legal implementation milestone rather than a promise that every migration is free. Have counsel determine whether the rule applies to your parties and service.
When outside migration help is justified
A team can self-migrate, work with its current or destination provider, or engage an independent migration partner. Outside help is most useful when the workload spans regulated data, multiple regions, complex Kubernetes networking, large-scale replication, or a short cutover window. Require a concrete deliverable: dependency inventory, target architecture, test plan, cost model, security review, runbook and rollback rehearsal. Do not accept a generic promise of compatibility or savings without workload-specific measurements.
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Approve the move only when the destination is available in the required region and scale, the complete software stack has passed representative tests, data transfer and split-operation costs are understood, security and contractual controls are accepted, and rollback has been rehearsed. If any gate is unproven, narrow the pilot, resolve the gap or defer the cutover. This approach turns a provider switch from a speculative GPU purchase into a controlled engineering change.
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