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The safest way to move an AI service away from a failing GPU cloud is not to move its running virtual machine. Build a fully tested replacement environment beside the old one, replicate models and mutable state, warm the new GPUs, send it a canary share of production traffic, and increase that share through a routing layer that can reverse instantly. Keep the original capacity active until health, latency, correctness, queue, and cost checks pass and the rollback window expires.
What “without downtime” means for a GPU workload
For an online inference API, zero visible downtime means clients continue receiving successful responses while requests are served by two environments during the transition. The old cloud remains the safety net while the replacement is proven. A brief increase in latency, a drained connection, or a restarted worker can still be acceptable if the service-level objective permits it; dropped requests, incorrect model output, lost jobs, or an unrecoverable state split are not.
This is an overlap-and-cutover operation, not usually a cross-provider live move of a running GPU virtual machine. A cloud provider may support live migration of a vGPU virtual machine between compatible physical hosts, but the documented requirements include the same physical GPU type, memory configuration, NVLink topology, compatible driver and hypervisor versions, and other restrictions. Unified memory, debuggers, and profilers are unsupported during that vGPU operation. Those conditions rarely exist across two unrelated providers, so plan for parallel deployment instead.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesInference and asynchronous workers can usually be designed for this approach. Distributed training is different: a multi-node job may need framework checkpoints and a restart or elastic recovery when capacity changes. Do not promise a training job will continue without interruption unless checkpoint recovery has been implemented and tested for that exact framework and topology.
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Pick the migration strategy before touching production
Azure’s migration guidance describes three useful levels of change. Choose according to the failure deadline, not according to an ideal future architecture.
| Strategy | What changes | Use it when | Main risk |
|---|---|---|---|
| Rehost | Reproduce the existing images, services, storage, and deployment with minimal application changes. | The current provider is failing and you need the fastest path to equivalent capacity. | Provider-specific networking, storage, identity, or GPU assumptions may be carried into the new environment. |
| Replatform | Make limited platform changes, such as moving to a portable Kubernetes deployment, a different storage class, or a managed load-balancing pattern. | A small operational change materially improves repeatability, observability, or rollback. | Compatibility work can expand the outage risk if it is allowed to become an application rewrite. |
| Refactor | Redesign the workload or data flow, for example by separating model serving, queues, and state services. | You have time for architectural work and the existing design cannot meet recovery or capacity requirements. | The largest change surface and the greatest chance of discovering new defects during the incident. |
For a failing provider, rehost or a tightly bounded replatform is normally the first move. Record refactoring ideas separately so they do not delay the safety migration.
1. Declare the failure boundary and freeze risky changes
Start with an incident owner who can make the cutover decision. Write down what is failing and what is not:
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- Hardware: repeated host or node failures are evicting workers.
- Data: volumes, object storage, databases, or queues are losing availability or integrity.
- Network: packet loss, route instability, DNS failure, or an overloaded gateway is affecting requests.
- Control plane: deployments, credentials, or provider APIs cannot reliably create and manage resources.
Freeze nonessential application releases, schema changes, model changes, and infrastructure experiments. Microsoft’s execution guidance calls for stakeholder preparation and a change freeze before cutover. Set a rollback deadline and name the person who can stop the migration if the old environment deteriorates faster than expected.
Capture the current service baseline before changing anything: success rate, p95 and p99 latency, throughput, queue age, model-output quality checks, GPU memory use, and saturation. A baseline lets you distinguish a target defect from a failure that was already present.
2. Inventory every dependency, not just the GPU count
Make the inventory executable: each item should identify its version, owner, location, and reproduction method. GPU scheduling on the target requires vendor drivers and device plugins, so a container image alone is not a complete environment.
Compute and acceleration
- GPU model, count per worker, VRAM, partitioning profile such as MIG or vGPU, and required topology.
- Host architecture, CPU and system memory, local scratch storage, and interconnect requirements.
- Driver version, CUDA version, framework and compiler versions, kernel modules, and container runtime.
- Whether the service depends on a particular GPU generation, NVLink arrangement, or peer-to-peer behavior.
Application and model artifacts
- Container image digests, startup commands, health probes, model weights, tokenizer files, custom kernels, and inference configuration.
- Feature data, vector indexes, preprocessing rules, prompt or policy files, and any licensed assets required at runtime.
- Environment variables, certificates, API keys, service-account permissions, and secret-rotation procedures.
State and integration points
- Persistent volumes, object-storage buckets, databases, queues, caches, and their retention or consistency rules.
- DNS names, private routes, firewall rules, service discovery, webhooks, payment or identity integrations, and client allowlists.
- Metrics, logs, traces, alerts, dashboards, SLO checks, and on-call escalation paths.
Export the inventory into version-controlled manifests or an equivalent declarative format. If an item cannot be recreated from those records, it is a migration risk that needs an owner before the first canary.
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3. Build an isolated target environment in parallel
Provision the replacement in a separate account or project, region, network, identity boundary, and GPU node pool. Isolation prevents an incident in the old environment from consuming target capacity or credentials. It also gives you a clean rollback path.
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Prefer standard Kubernetes APIs and declarative infrastructure when the workload already uses Kubernetes. NVIDIA’s AI-cloud requirements emphasize standard APIs, node-pool lifecycle control, explicit GPU node-type selection, rolling node upgrades, and zero-downtime control-plane upgrades. Portability still depends on more than a Deployment manifest: reproduce node labels and taints, storage classes, ingress behavior, identity bindings, image-registry access, network policy, and observability agents.
Use separate target credentials with only the permissions needed to operate the service. Keep source and target secrets distinct so that a compromised or failing provider cannot automatically reach the new environment. Restrict administrative access and log every change made during the incident.
4. Prove GPU and software compatibility before sending traffic
Run a compatibility test on the actual target node type, not a smaller or different accelerator. Check the following in order:
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- Install the supported host driver and device plugin, then verify that the scheduler advertises the expected resources.
- Run a container-level CUDA and framework smoke test that allocates the intended memory and executes representative kernels.
- Load the production model and tokenizer, exercise preprocessing and postprocessing, and compare outputs with the source baseline.
- Measure cold-start time, compilation time, steady-state throughput, p95 and p99 latency, GPU memory headroom, and behavior under queue pressure.
Do not infer portability from a successful container pull. A framework can start while silently falling back to the CPU, selecting a different precision, or failing to use peer-to-peer transfers. Make GPU utilization and memory allocation explicit health signals.
Google Cloud documents that Compute Engine machine types with attached GPUs do not support host live migration. After a host event, a GKE node may be terminated and its Pods restarted. Design inference workers to restart safely and keep durable state outside the node. A restart-tolerant worker is a more portable availability mechanism than relying on a provider’s host migration behavior.
5. Replicate artifacts and mutable state
Copy immutable artifacts first, then synchronize changing data continuously until the cutover.
Immutable artifacts
- Push the exact image digest to a registry reachable by the target, or transfer it through a controlled export and import process.
- Copy model weights, tokenizer files, custom extensions, feature snapshots, and vector indexes. Record checksums and verify them after transfer.
- Recreate configuration and secret references in the target identity system rather than embedding credentials in images or manifests.
Mutable data
For databases and queues, choose continuous replication, dual writes, or another mechanism that meets the workload’s recovery point objective (RPO) and recovery time objective (RTO). The correct technology depends on the database, message semantics, and consistency requirements; no single provider feature makes this safe automatically.
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Define a consistency check before enabling target writes. Examples include matching row or object counts, comparing a recent event window, verifying queue offsets, checking index document identifiers, and running application-level reconciliation. If dual writes are used, make operations idempotent and record a request or event identifier so retries cannot create duplicates.
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Keep the source authoritative until the cutover decision. A target that has copied files but is missing the last minutes of orders, features, or queue messages is not ready for production traffic.
6. Warm the target and validate it under realistic load
Start target workers before the traffic shift. Load the model into GPU memory, compile kernels, populate caches, build or open indexes, and establish connections to required services. Run representative requests rather than only a readiness endpoint.
Validate five dimensions:
- Health: workers start, probes pass, dependencies resolve, and restarts recover without manual repair.
- Correctness: deterministic test cases and sampled production-like inputs produce equivalent or explicitly approved output.
- Performance: throughput, p95 and p99 latency, batch behavior, and cold-start time stay within the agreed envelope.
- Capacity: GPU memory, utilization, CPU, network, storage throughput, and queue lag retain headroom during a burst.
- Operations: logs, traces, metrics, alerts, deployment rollback, and access controls work from the target account.
Run a soak period long enough to expose memory growth, cache exhaustion, token or connection leaks, and delayed queue effects. Keep the source serving normally during this test.
7. Send a canary through a reversible routing layer
Put traffic control in front of both environments. Suitable mechanisms include weighted DNS, a global load balancer, a service mesh, or an API gateway. The important property is a fast, independently tested weight change—not the product name.
| Routing method | Strength | Watch for |
|---|---|---|
| Weighted DNS | Simple and widely available for coarse distribution. | Resolver and client caching can delay a rollback. |
| Global load balancer | Central health checks and rapid weight changes at the edge. | Provider-specific health semantics and cross-region data paths. |
| Service mesh | Fine-grained percentage, header, tenant, or region routing. | Added control-plane and sidecar complexity during an incident. |
| API gateway | Useful when authentication, quotas, and request transformation already run there. | Gateway limits, connection draining, and retry behavior can hide target errors. |
Begin with a small canary share or a controlled tenant cohort. Compare source and target for error rate, p95 and p99 latency, output-quality checks, queue lag, retries, GPU saturation, and cost signals. Increase the target weight only after the agreed observation window passes without a threshold breach.
Azure describes “hot relocation” as appropriate for workloads requiring downtime measured in seconds, minutes, or zero. The exact implementation remains workload-specific: your routing layer, state model, and client behavior determine whether a weight change is truly reversible.
8. Define automatic rollback before the first shift
Write rollback thresholds and an owner before production traffic reaches the target. Examples are a sustained error-rate breach, a p99 latency breach, a correctness mismatch, rising queue age, failed replication checks, or GPU memory exhaustion. Use a time window so one transient request does not trigger an unnecessary flip.
When a threshold is met:
- Set the target weight to zero or the last known-safe value.
- Stop new writes to the target if it can create state that the source does not have.
- Drain or cancel target requests according to the API’s retry and idempotency rules.
- Reconcile events, records, and object versions created during the canary.
- Preserve logs, traces, and node diagnostics before repairing or deleting the target.
Keep source capacity, credentials, images, and replicated state available throughout the stabilization window. Microsoft recommends maintaining a fallback option and validating migration success before decommissioning the source. A rollback plan that requires rebuilding the old environment is not a live rollback.
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Training and batch workloads need a different safety plan
Inference can split traffic request by request; distributed training usually cannot. Before moving a training job, verify that checkpoints contain optimizer state, scheduler state, random seeds, data position, and any framework-specific sharding metadata needed for a restart. Test restoring a checkpoint on the target GPU type and node count.
Choose a checkpoint cadence that fits the acceptable loss of work, then document whether recovery is a manual restart or an elastic job that can add and remove workers. Validate that the target has the same dataset visibility, object-storage throughput, interconnect behavior, and collective-communication support. If those assumptions are not proven, finish the current run on the source while moving the next run, or accept a controlled interruption rather than claiming zero downtime.
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Assuming a VM can move between providers
Cross-provider hypervisor, GPU, driver, topology, and storage differences make transparent migration unlikely. Use a reproducible image and deployment, then run both environments concurrently.
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Weights without tokenizers, custom kernels, feature data, indexes, or configuration can produce startup failures or incorrect output. Track checksums and run application-level correctness tests.
Relying on node-local state
A GPU host restart can remove local caches and temporary files. Store durable state in replicated external services and make workers rebuild caches during startup.
Changing the schema during cutover
Schema changes can make source and target incompatible. Freeze them, or use backward-compatible expand-and-contract changes with an explicit rollback path.
Using DNS as if it were an instant switch
Resolvers and clients may cache answers. If rollback must be fast, use a load balancer, mesh, or gateway with tested weight controls, and account for existing keep-alive connections.
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Ignoring retries and duplicate work
Retries during a canary can duplicate writes or charge a downstream service twice. Use idempotency keys, bounded retries, and a reconciliation process.
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Deleting the source as soon as the canary looks good
Delayed failures often appear after cache expiry, a traffic peak, a model refresh, or a scheduled batch. Retain the source until the agreed stabilization window and reconciliation checks are complete.
Compare target environments on recoverability, not hourly GPU price
Record each candidate’s answer to the following decision axes before committing:
- GPU model, VRAM, partitioning, topology, and availability guarantees.
- Supported driver and CUDA combinations and the process for rolling upgrades.
- Region, network latency, private connectivity, and bandwidth to required data.
- Persistent-storage latency and throughput, snapshot behavior, and replication options.
- Data-egress pricing and transfer limits for the initial copy and ongoing replication.
- Portability of orchestration APIs, node pools, identity, policy, and image registries.
- Observability, support response, incident communication, and control-plane resilience.
- How quickly capacity can be recreated and how traffic can be returned to the source.
For a mission-critical service, rank recoverability and guaranteed capacity ahead of nominal hourly price. A cheaper GPU that cannot be acquired during an incident or cannot reproduce the model’s topology is not a lower-risk target.
Production cutover checklist
- Incident owner, stakeholders, rollback deadline, and change freeze are recorded.
- Source baseline and acceptance thresholds are captured.
- Target account, network, identity boundary, GPU pool, drivers, and device plugins are ready.
- Images, weights, tokenizers, indexes, configuration, and secrets are verified by checksum or equivalent checks.
- Database and queue replication is current, and consistency checks pass.
- Target workers are warm, observable, restart-safe, and passing correctness and load tests.
- Routing controls have been tested in both directions.
- Automatic rollback thresholds and the decision owner are active.
- Canary traffic is observed for the agreed window before each weight increase.
- Source capacity remains available until stabilization and reconciliation finish.
Stabilize, then retire the failing cloud
After the target carries all traffic, continue monitoring for delayed failures: memory leaks, cache churn, scheduled jobs, replica lag, storage throttling, certificate expiry, and cost anomalies. Reconcile data one final time and document any records or events that were replayed.
Rotate credentials that were exposed to the old provider, remove obsolete firewall rules and access grants, update deployment and incident runbooks, and add cost and capacity alerts for the new environment. Keep a tested recovery artifact—images, manifests, model versions, and state procedures—so the next provider failure is a controlled exercise rather than an emergency rebuild.
Only after the rollback window closes, acceptance checks remain green, and all owners agree should you drain and decommission the source GPU nodes, storage, routing entries, and credentials. Preserve the incident timeline and migration decisions for the next capacity or outage review.
Frequently Asked Questions
Can I live-migrate a GPU VM from one cloud provider to another?
Generally no. Documented vGPU live migration transfers a VM between compatible physical hosts and requires matching GPU type, memory configuration, topology, drivers, and hypervisors. Across providers, parallel deployment and traffic shifting is the safer design.
How small should the first canary be?
Use a small, controlled share that is large enough to exercise real authentication, model paths, state writes, and GPU behavior. Set the percentage according to your request volume and rollback thresholds rather than using a universal number.
When is it safe to delete the old GPU environment?
After the target passes health, correctness, latency, queue, capacity, replication, and observability checks through the agreed stabilization and rollback window, and after data reconciliation and credential rotation are complete.
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