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| Model | Offer Availability Region | Hub/Project Region for Deployment | Hub/Project Region for Fine tuning |
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|---------|---------|---------|---------|
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Phi-4 | Not applicable | East US 2 <br> Sweden Central | Not available |
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Phi-4 | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3 | Not available |
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Phi-3.5-vision-Instruct | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3 | Not available |
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Phi-3.5-MoE-Instruct | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3 | East US 2 |
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Phi-3.5-Mini-Instruct | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3 | East US 2 | East US 2 |
Azure AI Foundry brings together various Azure AI capabilities that previously were only available as standalone Azure services. While we strive to make all features available in all regions where Azure AI Foundry is supported at the same time, feature availability may vary by region. In this article, you'll learn what Azure AI Foundry features are available across cloud regions.
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## Azure Public regions
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## Azure AI Foundry projects
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Azure AI Foundry is currently available in the following Azure regions. You can create [projects in Azure AI Foundry portal](../how-to/create-projects.md) in these regions.
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@@ -44,30 +44,22 @@ Azure AI Foundry is currently available in the following Azure regions. You can
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- West US
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- West US 3
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### Azure Government regions
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Azure AI Foundry is currently not available in Azure Government regions or air-gap regions.
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## Azure OpenAI
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For information on the availability of Azure OpenAI models, see [Azure OpenAI Model summary table and region availability](../../ai-services/openai/concepts/models.md#model-summary-table-and-region-availability).
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> [!NOTE]
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> Some models might not be available within the Azure AI Foundry model catalog.
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For more information, see [Azure OpenAI quotas and limits](/azure/ai-services/openai/quotas-limits).
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## Speech capabilities
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Azure AI Speech capabilities including custom neural voice vary in regional availability due to underlying hardware availability. See [Speech service supported regions](../../ai-services/speech-service/regions.md) for an overview.
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## Serverless API deployments
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> Azure AI Foundry is currently not available in Azure Government regions or air-gap regions.
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Some models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. For information on the regions where each model is available, see [Region availability for models in Serverless API endpoints](../how-to/deploy-models-serverless-availability.md).
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## Azure AI Foundry features
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You can add features from different regions to your project. You may need to use a different region for a particular feature, based on the region availability of that feature.
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## Azure AI Content Safety
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The following table lists the availability of Azure AI Foundry features across Azure regions.
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To use the Content Safety APIs, you must create your Azure AI Content Safety resource in a supported region. For a list of supported regions, see [What is Azure AI Content Safety?](../../ai-services/content-safety/overview.md#region-availability)
| Azure OpenAI | Note that some models might not be available within the Azure AI Foundry model catalog. | [Azure OpenAI quotas and limits](/azure/ai-services/openai/quotas-limits)
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| Speech capabilities | Azure AI Speech capabilities including custom neural voice vary in regional availability due to underlying hardware availability. |[Speech service supported regions](../../ai-services/speech-service/regions.md)|
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| Serverless API deployments | Some models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. |[Region availability for models in Serverless API endpoints](../how-to/deploy-models-serverless-availability.md)|
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| Azure AI Content Safety | To use the Content Safety APIs, you must create your Azure AI Content Safety resource in a supported region. |[What is Azure AI Content Safety?](../../ai-services/content-safety/overview.md#region-availability)|
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| Azure AI Agent Service | Azure AI Agent Service supports the same models as the chat completions API in Azure OpenAI. |[Azure AI Agent Service region availability](../../ai-services/agents/concepts/model-region-support.md#azure-openai-models)|
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As you scale up your training on larger datasets or perform [distributed training](how-to-train-distributed-gpu.md), use Azure Machine Learning compute to create a single- or multi-node cluster that autoscales each time you submit a job. You can also attach your own compute resource, although support for different scenarios might vary.
**Compute targets can be reused from one training job to the next.** For example, after you attach a remote VM to your workspace, you can reuse it for multiple jobs.
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:::moniker range="azureml-api-1"
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For machine learning pipelines, use the appropriate [pipeline step](/python/api/azureml-pipeline-steps/azureml.pipeline.steps) for each compute target.
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:::moniker-end
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You can use any of the following resources for a training compute target for most jobs. Not all resources can be used for automated machine learning, machine learning pipelines, or designer. Azure Databricks can be used as a training resource for local runs and machine learning pipelines, but not as a remote target for other training.
> The compute instance has 120GB OS disk. If you run out of disk space, [use the terminal](~/articles/machine-learning/how-to-access-terminal.md) to clear at least 1-2 GB before you [stop or restart](~/articles/machine-learning/how-to-manage-compute-instance.md#manage) the compute instance.
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- The availability of **Preview features** in Azure Machine Learning isn't guaranteed.
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- Models (including the foundational model) from the **Model Catalog** and **Registry** aren't supported on Kubernetes online endpoints.
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- The process of creating a model inference deployment inside the cluster has a timeout limit of **20 minutes**. This includes downloading the image, downloading the model, and initializing the user scripts.
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