Merge pull request #5750 from ssalgadodev/patch-695684

Update monitor-models.md

Author: JamesJBarnett

articles/ai-foundry/foundry-models/how-to/monitor-models.md

@@ -5,7 +5,7 @@ author: ssalgadodev
 ms.author: ssalgado
 ms.service: azure-ai-model-inference
 ms.topic: how-to
-ms.date: 4/30/2025
+ms.date: 06/30/2025
 manager: scottpolly
 ms.reviewer: fasantia
 reviewer: santiagxf
@@ -26,7 +26,7 @@ To use monitoring capabilities for model deployments in Foundry Models, you need
 * An Azure AI services resource. For more information, see [Create an Azure AI Services resource](../../model-inference/how-to/quickstart-create-resources.md).
 
     > [!TIP]
-    > If you are using Serverless API Endpoints and you want to take advantage of monitoring capabilities explained in this document, [migrate your Serverless API Endpoints to Foundry Models](../../model-inference/how-to/quickstart-ai-project.md).
+    > If you're using Serverless API Endpoints and you want to take advantage of monitoring capabilities explained in this document, [migrate your Serverless API Endpoints to Foundry Models](../../model-inference/how-to/quickstart-ai-project.md).
 
 * At least one model deployment.
 
@@ -54,11 +54,15 @@ You can view metrics within Azure AI Foundry portal. To view them, follow these
 
 1. Select the tab **Metrics**.
 
-1. You can see a high level view of the most common metrics you may be interested about.
+1. You can access an overview of common metrics that might be of interest. For cost-related metrics, use the Azure Cost Management deep link, which provides access to detailed post-consumption cost metrics in the Cost analysis section located in the Azure portal. Cost data in Azure portal displays actual post-consumption charges for model consumption, including other AI resources within Azure AI Foundry. Follow this link for a full list of [AI resources](https://azure.microsoft.com/products/ai-services#tabs-pill-bar-oc14f0_tab0). There's approximately a five hour delay from the billing event to when it can be viewed in Azure portal cost analysis.
 
     :::image type="content" source="../media/monitor-models/deployment-metrics.png" alt-text="Screenshot showing the metrics displayed for model deployments in Azure AI Foundry portal." lightbox="../media/monitor-models/deployment-metrics.png":::
 
-1. To slice, filter, or view model details about the metrics you can **Open in Azure Monitor**, where you have more advanced options.
+    > [!IMPORTANT]
+    > The **Azure Cost Management deep link**  provides a direct link within the Azure portal, allowing users to access detailed cost metrics for deployed AI models. This deep link integrates with the Azure Cost Analysis service view, offering transparent and actionable insights into model-level costs.
+    > The deep link directs users to the Cost Analysis view in the Azure portal, providing a one-click experience to view deployments per resource, including input/output token cost/consumption. To view cost data, you need at least read access for an Azure account. For information about assigning access to Microsoft Cost Management data, see [Assign access to data](/azure/cost-management-billing/costs/assign-access-acm-data). 
+
+1. You can **view and analyze metrics with Azure Monitor metrics explorer** to further slice and filter your model deployment metrics.
 
     :::image type="content" source="../media/monitor-models/deployment-metrics-azmonitor.png" alt-text="Screenshot showing the option to open model deployment metrics in Azure Monitor." lightbox="../media/monitor-models/deployment-metrics-azmonitor.png":::
 
@@ -91,7 +95,7 @@ To use Azure Monitor, follow these steps:
 
     :::image type="content" source="../media/monitor-models/azmon-add-filter.png" alt-text="Screenshot showing how to apply a filter to a metric." lightbox="../media/monitor-models/azmon-add-filter.png":::
 
-1. It is useful to break down specific metrics by some of the dimensions. The following example shows how to break down the number of requests made to the resource by model by using the option **Add splitting**:
+1. It's useful to break down specific metrics by some of the dimensions. The following example shows how to break down the number of requests made to the resource by model by using the option **Add splitting**:
 
     :::image type="content" source="../media/monitor-models/azmon-add-splitting.png" alt-text="Screenshot showing how to split the metric by a given dimension." lightbox="../media/monitor-models/azmon-add-splitting.png":::