freshness updates deployment overview

AB#482225
AB#475803

Author: msakande

articles/ai-foundry/concepts/deployments-overview.md

@@ -4,16 +4,16 @@ titleSuffix: Azure AI Foundry
 description: Learn about deployment options for Azure AI Foundry Models.
 ms.service: azure-ai-foundry
 ms.topic: concept-article
-ms.date: 06/30/2025
-ms.reviewer: fasantia
+ms.date: 09/22/2025
 ms.author: mopeakande
+author: msakande
 manager: nitinme
 author: msakande
 ---
 
 # Deployment overview for Azure AI Foundry Models
 
-The model catalog in Azure AI Foundry is the hub to discover and use a wide range of Foundry Models for building generative AI applications. Models need to be deployed to make them available for receiving inference requests. Azure AI Foundry offers a comprehensive suite of deployment options for Foundry Models, depending on your needs and model requirements.
+The model catalog in Azure AI Foundry is the hub to discover and use a wide range of Foundry Models for building generative AI applications. You need to deploy models to make them available for receiving inference requests. Azure AI Foundry offers a comprehensive suite of deployment options for Foundry Models, depending on your needs and model requirements.
 
 ## Deployment options
 
@@ -31,23 +31,21 @@ This deployment option is available in:
 
 * Azure AI Foundry resources
 * Azure OpenAI resources<sup>1</sup>
-* Azure AI hub, when connected to an Azure AI Foundry resource (requires the [Deploy models to Azure AI Foundry resources](#configure-azure-ai-foundry-portal-for-deployment-options) feature to be turned on).
+* Azure AI hub, when connected to an Azure AI Foundry resource
 
-<sup>1</sup>If you're using Azure OpenAI resources, the model catalog shows only Azure OpenAI in Foundry Models for deployment. You can get the full list of Foundry Models by upgrading to an Azure AI Foundry resource.
+<sup>1</sup>If you use Azure OpenAI resources, the model catalog shows only Azure OpenAI in Foundry Models for deployment. You can get the full list of Foundry Models by upgrading to an Azure AI Foundry resource.
 
 To get started with standard deployment in Azure AI Foundry resources, see [How-to: Deploy models to Azure AI Foundry Models](../foundry-models/how-to/create-model-deployments.md).
 
 ### Serverless API endpoint
 
-This deployment option is available **only in** [Azure AI hub resources](ai-resources.md) and it allows the creation of dedicated endpoints to host the model, accessible via API. Azure AI Foundry Models support serverless API endpoints with pay-as-you-go billing. 
-
-Only regional deployments can be created for serverless API endpoints, and to use it, you _must_ **turn off** the "Deploy models to Azure AI Foundry resources" option.
+This deployment option is available **only in** [Azure AI hub resources](ai-resources.md). It allows you to create dedicated endpoints to host the model, accessible through an API. Azure AI Foundry Models support serverless API endpoints with pay-as-you-go billing, and you can create only regional deployments for serverless API endpoints.
 
 To get started with deployment to a serverless API endpoint, see [Deploy models as serverless API deployments](../how-to/deploy-models-serverless.md).
 
 ### Managed compute
 
-This deployment option is available **only in** [Azure AI hub resources](ai-resources.md) and it allows the creation of a dedicated endpoint to host the model in a **dedicated compute**. You need to have compute quota in your subscription to host the model, and you're billed per compute uptime. 
+This deployment option is available **only in** [Azure AI hub resources](ai-resources.md). It allows you to create a dedicated endpoint to host the model in a **dedicated compute**. You need to have compute quota in your subscription to host the model, and you're billed per compute uptime. 
 
 Managed compute deployment is required for model collections that include:
 
@@ -61,7 +59,7 @@ To get started, see [How to deploy and inference a managed compute deployment](.
 
 ## Capabilities for the deployment options
 
-We recommend using [Standard deployments in Azure AI Foundry resources](#standard-deployment-in-azure-ai-foundry-resources) whenever possible, as it offers the largest set of capabilities among the available deployment options. The following table lists details about specific capabilities available for each deployment option:
+Use [Standard deployments in Azure AI Foundry resources](#standard-deployment-in-azure-ai-foundry-resources) whenever possible. This deployment option provides the most capabilities among the available deployment options. The following table lists details about specific capabilities for each deployment option:
 
 | Capability                    | Standard deployment in Azure AI Foundry resources | Serverless API Endpoint | Managed compute |
 |-------------------------------|--------------------------------------------------|------------------------|-----------------|
@@ -73,19 +71,16 @@ We recommend using [Standard deployments in Azure AI Foundry resources](#standar
 | Content filtering             | Yes                                              | Yes                   | No             |
 | Custom content filtering      | Yes                                              | No                    | No             |
 | Key-less authentication       | Yes                                              | No                    | No             |
-| Billing bases                 | Token usage & [provisioned throughput units](../../ai-services/openai/concepts/provisioned-throughput.md) | Token usage<sup>1</sup> | Compute core hours<sup>2</sup> |
+| Billing bases                 | Token usage & [provisioned throughput units](../../ai-services/openai/concepts/provisioned-throughput.md) | Token usage<sup>2</sup> | Compute core hours<sup>3</sup> |
 
-<sup>1</sup> A minimal endpoint infrastructure is billed per minute. You aren't billed for the infrastructure that hosts the model in standard deployment. After you delete the endpoint, no further charges accrue.
+<sup>2</sup> A minimal endpoint infrastructure is billed per minute. You aren't billed for the infrastructure that hosts the model in serverless deployment. After you delete the endpoint, no further charges accrue.
 
-<sup>2</sup> Billing is on a per-minute basis, depending on the product tier and the number of instances used in the deployment since the moment of creation. After you delete the endpoint, no further charges accrue.
+<sup>3</sup> Billing is on a per-minute basis, depending on the product tier and the number of instances used in the deployment since the moment of creation. After you delete the endpoint, no further charges accrue.
 
 ## Configure Azure AI Foundry portal for deployment options
 
-Azure AI Foundry portal might automatically pick up a deployment option based on your environment and configuration. We recommend using Azure AI Foundry resources for deployment whenever possible. To do that, ensure that the **Deploy models to Azure AI Foundry resources** feature is **turned on**. 
-
-:::image type="content" source="../media/concepts/deployments-overview/docs-flag-enable-foundry.png" alt-text="A screenshot showing the steps to enable deployment to Azure AI Foundry resources in the Azure AI Foundry portal." lightbox="../media/concepts/deployments-overview/docs-flag-enable-foundry.png":::
-
-Once the **Deploy models to Azure AI Foundry resources** feature is enabled, models that support multiple deployment options default to deploy to Azure AI Foundry resources for deployment. To access other deployment options, either disable the feature or use the Azure CLI or Azure Machine Learning SDK for deployment. You can disable and enable the feature as many times as needed without affecting existing deployments.
+Azure AI Foundry portal might automatically pick a deployment option based on your environment and configuration. Use Azure AI Foundry resources for deployment whenever possible. 
+Models that support multiple deployment options default to Azure AI Foundry resources for deployment. To access other deployment options, use the Azure CLI or Azure Machine Learning SDK for deployment.
 
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