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To keep AI workloads running through a cloud-region outage, prepare a recovery environment in another region, decide how inference traffic and interrupted jobs will move there, and test the complete recovery path. First set a recovery time objective (RTO)—how long recovery may take—and a recovery point objective (RPO)—how much recent data or work you can afford to lose. A second copy of your application alone is not enough: the model, data, credentials, network, configuration, compute capacity, and routing must also be ready.
What a regional outage plan must cover
Resilience within one region is not the same as recovery from losing that region. A service or cluster designed to tolerate a zone failure may still be unavailable if its entire region fails. Google Cloud distinguishes zonal, regional, and multi-regional resources; regional recovery requires a plan that reaches beyond the affected region. Its infrastructure guidance, last reviewed May 10, 2024, says to plan for failure. Check current service documentation before implementation because product behavior and regional availability can change.
For an AI workload, define what “running” means for each function. An inference API may need to keep accepting requests, while a training job may be allowed to restart later. Record the RTO and RPO for each function rather than assigning one target to a system with different needs.
- Inference: How long can the endpoint be unavailable, and can it serve requests with the model version available in the recovery region?
- Training and batch processing: Can an interrupted run restart, or must it continue from a checkpoint? What amount of completed work can be lost?
- Data and model state: Which datasets, model artifacts, checkpoints, and metadata must be available, and how recent must the recovery copies be?
Choose a recovery pattern against your RTO and RPO
Recovery patterns trade readiness and recovery speed against steady-state cost and operational complexity. The figures below are provider-published planning examples, not guarantees for an individual application or AI service. AWS’s Well-Architected guidance gives the following illustrative bands. Azure describes its own patterns separately; the descriptions are not measured results and should not be treated as directly comparable commitments.
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| Pattern | What is ready before the outage | AWS illustrative RPO / RTO | Trade-off |
|---|---|---|---|
| Backup and restore | Recoverable data and application definitions are stored for use in a recovery region; resources are provisioned and restored after the event. | RPO in hours; RTO of 24 hours or less. | Lower readiness and generally longer recovery. Repeatable infrastructure deployment can reduce setup work; backups and point-in-time recovery help recover from corruption or deletion. |
| Pilot light | Core infrastructure and replicated data are kept ready, while much of the application compute remains inactive. | RPO in minutes; RTO in tens of minutes. | Lower standing compute than a fully ready system, but recovery requires activating, deploying, or scaling resources. |
| Warm standby | A reduced but functional system is running in the recovery region and can be scaled up. | RPO in seconds; RTO in minutes. | Faster readiness than pilot light, with ongoing cost for a serving-ready recovery environment. Actual recovery depends on implementation and capacity. |
| Active-active | Production is served from multiple regions. | RPO near zero; RTO potentially zero. | Can reduce recovery time, but requires sufficient capacity in serving regions and careful data synchronization. Conflicting writes can be difficult to resolve; AWS describes this as its most complex and costly pattern. |
Azure’s cross-region guidance characterizes active-active as having an RTO of seconds to minutes, with full infrastructure in both regions and bidirectional data synchronization. It describes active-passive recovery as typically taking minutes to tens of minutes, depending on scaling and traffic failover, and pilot light as taking longer when compute must first start. These are general pattern descriptions, not workload-specific outcomes.
Plan recovery for each part of the AI workload
Inference endpoints and traffic
Do not assume a managed endpoint will send requests to another region when its region fails. Google documents Vertex AI online prediction as regional and recommends using multiple regions and directing traffic to an available region during a regional failure. Arrange an alternate endpoint or service and a traffic-steering mechanism, then validate that requests actually reach it under failure conditions.
Training and batch jobs
Google documents Vertex AI training jobs as region-scoped and recommends using another available region for jobs after a regional failure. Decide whether your recovery procedure will resubmit the job or attempt to resume from a checkpoint. The documentation cited here does not establish that a particular job transparently resumes at its last checkpoint, so verify that behavior for your job rather than assuming continuity.
Containers and orchestration
A regional GKE cluster can address zone failures within its region, but it does not by itself provide regional-outage recovery. Google describes regional mitigation as a customer-configured design using multiple regional clusters and a separate multi-region traffic path. Plan cluster recovery and traffic control as distinct parts of the design.
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Models, datasets, checkpoints, and metadata
Choose replication and backup based on the RPO and the kind of failure you need to recover from. Asynchronous replication can leave recent writes outside the recovery copy. Replication can also copy accidental deletion or corruption, so pair it with suitable point-in-time recovery or versioned backups. Google Cloud says its dual-region Cloud Storage turbo replication feature targets 100% of newly written objects being replicated and geo-redundant within 15 minutes. That is a target for this storage feature, not a general RPO guarantee for an AI workload.
Network, identity, configuration, and capacity
The recovery region needs usable network paths, routing, security policy, credentials, and application configuration—not merely a copy of the model-serving code. Azure guidance calls for consistent topology and policy and for validating secondary-region connectivity, routing, and security rules. Also verify that the target region supports the model and service configuration you need, and that your quota and compute capacity are sufficient; availability and capacity depend on the specific provider, service, and region.
Build and test a regional recovery runbook
Use a repeatable runbook that covers both inference and jobs. Infrastructure as code and explicit recovery steps can reduce manual setup, but only a recovery exercise can show whether the planned path meets your targets.
- Set workload-level targets. Write down RTO and RPO for inference, training, batch processing, and data. Specify which functions must stay live and which can recover later.
- Map regional dependencies. List the AI services, storage, clusters, network paths, identity and access settings, configuration, and routing each function needs. Classify dependencies as zonal, regional, or multi-regional, and check the failure guidance for each managed service.
- Select and provision the recovery pattern. Choose backup and restore, pilot light, warm standby, or active-active according to the targets and operational capacity you have. Make the recovery environment reproducible, including service configuration and access policy.
- Prepare data and model recovery. Replicate the required assets to meet the intended RPO, and maintain point-in-time or versioned recovery where replicated state alone would not protect against corruption or deletion. Define how checkpoints and metadata will be restored or used.
- Configure the recovery path. Validate traffic steering for inference and job submission or resubmission for training and batch work. Confirm network reachability, credentials, security rules, service configuration, quota, and capacity in the target region.
- Exercise regional loss and data recovery. Test traffic redirection, recovery-region load, backup restoration, and job recovery. Measure actual RTO and RPO, record what failed or required manual intervention, and update the runbook. Google Cloud and AWS guidance both recommend regular recovery testing.
How to decide whether the design is ready
A second region is useful only if it can perform the recovery tasks you expect of it. Before relying on the design, confirm that the recovery exercise demonstrated the required outcomes:
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- Inference requests reach a working endpoint in the recovery region within the intended RTO.
- The surviving environment can handle the expected load, rather than merely starting successfully.
- Restored data and model assets meet the intended RPO, and the team can recover a prior clean version if current state is damaged.
- Interrupted training and batch jobs have a documented, tested restart or checkpoint-recovery procedure.
- Routing, permissions, network rules, and service configuration work without relying on access to the failed region.
Choose active-active only when its lower potential recovery time justifies the synchronization, capacity, and operational burden. If a longer interruption is acceptable, a less continuously ready pattern may be a better fit. In every case, base the decision on measured recovery exercises and service-specific behavior rather than a provider’s planning band alone.
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