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You can use a model beyond the region where your Microsoft Foundry resource is located by choosing a deployment type that permits cross-region inference, deploying regionally in another supported region, or—in supported cases—using Model Router. These choices differ in where prompts and responses may be processed, how capacity is provided, and which models and regions are available. The Foundry resource’s region alone does not define the processing boundary for global or data zone deployments.
How can you use an Azure model in another region?
First decide what “another region” means for your workload. Azure may select a processing region from a broad set, route only within a Microsoft-defined data zone, or run inference in the region where you place a regional deployment. Model Router is a separate option with its own supported-region and underlying-model limits.
For global and data zone deployments, the deployment type—not simply the Foundry resource location—sets the inference-processing scope. Microsoft says data at rest remains in the designated Azure geography, while inference may be processed according to the deployment type’s broader routing scope. Review the current deployment type documentation and model availability information for the exact model, deployment type, cloud, and target region.
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1. Global Standard: let Azure route requests across regions
Global Standard is a pay-per-token deployment type. Azure dynamically routes inference to available datacenters, and Microsoft’s documentation says prompts and responses may be processed in any Azure region where the model is deployed. This is the broadest general-purpose choice when your organization permits processing across that scope.
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Microsoft notes that Global Standard can have greater latency variability at high, consistent volume. It does not promise a specific latency. Global Batch is a distinct asynchronous deployment type, not a real-time substitute; Microsoft lists a 50% lower cost than Global Standard and a 24-hour target turnaround, but jobs may take longer than that target. See Microsoft’s deployment-type documentation.
2. Global Provisioned: globally routed inference with reserved throughput
Global Provisioned retains global cross-region routing while reserving throughput. Consider it when a workload needs dedicated capacity and more predictable throughput behavior than a standard deployment can provide. Reserved capacity does not make processing regional: the global routing scope still applies. Consult Microsoft’s provisioned throughput guidance for deployment details and current availability.
3. Data Zone Standard or Data Zone Provisioned: route within a defined zone
Data Zone deployment types limit cross-region processing to a Microsoft-defined data zone rather than allowing the broader global scope. A zone can include more than one region, so this is not equivalent to keeping inference in a single region. Standard is pay-per-token; Provisioned reserves throughput. Confirm which zone applies to your requirements and whether the model and deployment type are supported there in the current deployment-type documentation.
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A regional deployment runs inference in its deployment region. Regional Standard uses pay-per-token billing, while Regional Provisioned reserves capacity in that region. This is the relevant path when the goal is to select a particular supported region rather than accept cross-region routing. It is only possible if the chosen model and deployment type are available there.
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Model Router is a related alternative, not another name for a regional deployment. It routes among supported underlying models, and both its supported regions and model choices are limited; it is not an unrestricted proxy to any model in any region. Check the current model availability table before relying on it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare processing scope, capacity, and billing
| Choice | Inference processing scope | Capacity and billing | Key consideration |
|---|---|---|---|
| Global Standard | Any Azure region where the model is deployed | Pay per token | Broad routing scope; latency variability may rise at high consistent volume |
| Global Provisioned | Global cross-region routing | Reserved throughput | Dedicated capacity does not narrow the processing scope |
| Data Zone Standard | Within the applicable Microsoft-defined data zone | Pay per token | A zone may span multiple regions |
| Data Zone Provisioned | Within the applicable Microsoft-defined data zone | Reserved throughput | Confirm zone and model support |
| Regional Standard | Deployment region | Pay per token | Model and deployment type must be available in that region |
| Regional Provisioned | Deployment region | Reserved throughput | Reserves capacity in the selected region |
| Model Router | Depends on its supported region and underlying-model choices | Not stated as a single capacity or billing arrangement; see the applicable model and deployment documentation | Not an unrestricted cross-region route |
Batch deployments are intended for asynchronous work, so their turnaround target should not be compared with real-time latency. Provisioned types reserve throughput and can offer more predictable throughput behavior, but no option in this comparison carries a universal latency guarantee.
Quick Recap
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Choose the deployment type against your requirements
- Set the permitted inference geography. Decide whether your policy allows processing in any Azure region where the model is deployed, permits processing within a particular Microsoft-defined zone, or requires a deployment in a specific region. Do not infer the processing boundary from the Foundry resource’s region alone.
- Pick a capacity and billing model. Choose Standard for pay-per-token billing or Provisioned when reserved throughput is needed. Use Batch only for asynchronous workloads, not as a real-time path.
- Check the exact availability combination. In Microsoft’s current model availability table, verify the model, deployment type, target region, and Azure cloud environment. Availability can differ across public, government, and other sovereign clouds.
- Validate operating expectations. Treat routing scope, throughput, and latency as separate questions. Global routing can increase latency variability under high consistent volume; provisioned capacity supports more predictable throughput, but does not guarantee a particular response time.
What to verify before deployment
- Whether the model supports the deployment type you plan to use.
- Whether that deployment type is available in the intended region or applicable data zone.
- Whether the region and model combination is supported in your Azure cloud environment.
- Whether your data-handling rules permit the deployment’s inference-processing scope, separately from where data at rest is kept.
- Whether a regional deployment, data zone, or global route best matches your latency and capacity needs without assuming a specific latency outcome.
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