updates

Author: aahill

articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md

@@ -12,7 +12,7 @@ recommendations: false
 
 # Understanding costs associated with provisioned throughput units (PTU)
 
-Use this article to learn about calculating and undestanding costs assocaited with PTU. For an overview of PTU, see [What is provisioned throughput?](../concepts/provisioned-throughput.md). When you're ready to sign up for PTU, see the [getting started guide](./provisioned-get-started.md).
+Use this article to learn about calculating and understanding costs associated with PTU. For an overview of PTU, see [What is provisioned throughput?](../concepts/provisioned-throughput.md). When you're ready to sign up for PTU, see the [getting started guide](./provisioned-get-started.md).
 
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 ## When to use provisioned throughput units (PTU)
@@ -26,7 +26,7 @@ You should consider switching from standard deployments to provisioned deploymen
 
 ## Understanding the provisioned throughput purchase model
 
-Azure OpenAI Provisioned, Data Zone Provisioned, and Global Provisioned are purchased on-demand at an hourly basis based on the number of deployed PTUs, with substantial term discount available via the purchase of Azure Reservations.   
+Azure OpenAI [Provisioned](../how-to/deployment-types.md#provisioned), [Data Zone Provisioned](../how-to/deployment-types.md#data-zone-provisioned), and [Global Provisioned](../how-to/deployment-types.md#global-provisioned) are purchased on-demand at an hourly basis based on the number of deployed PTUs, with substantial term discount available via the purchase of Azure Reservations.   
 
 The hourly model is useful for short-term deployment needs, such as validating new models or acquiring capacity for a hackathon.  However, the discounts provided by the Azure Reservation for Azure OpenAI Provisioned, Data Zone Provisioned, and Global Provisioned are considerable and most customers with consistent long-term usage will find a reserved model to be a better value proposition. 
 
@@ -44,13 +44,13 @@ If the deployment size is changed, the costs of the deployment will adjust to ma
 
 Paying for provisioned, data zoned provisioned, and global provisioned deployments on an hourly basis is ideal for short-term deployment scenarios.  For example: Quality and performance benchmarking of new models, or temporarily increasing PTU capacity to cover an event such as a hackathon.  
 
-Customers that require long-term usage of provisioned, data zoned provisioned, and global provisioned deployments, however, might pay significantly less per month by purchasing a term discount via Azure Reservations as discussed in the next section. 
+Customers that require long-term usage of provisioned, data zoned provisioned, and global provisioned deployments, however, might pay significantly less per month by purchasing a term discount via [Azure Reservations](#azure-reservations-for-azure-openai-provisioned-deployments) as discussed later in the article. 
 
-> [!NOTE]
+> [!IMPORTANT]
 > It is not recommended to scale production deployments according to incoming traffic and pay for them purely on an hourly basis. There are two reasons for this:
 > * The cost savings achieved by purchasing Azure Reservations for Azure OpenAI Provisioned, Data Zone Provisioned, and Global Provisioned are significant, and it will be less expensive in many cases to maintain a deployment sized for full production volume paid for via a reservation than it would be to scale the deployment with incoming traffic.
 > * Having unused provisioned quota (PTUs) does not guarantee that capacity will be available to support an increase in the size of the deployment when required. Quota limits the maximum number of PTUs that can be deployed, but it is not a capacity guarantee. Provisioned capacity for each region and model dynamically changes throughout the day and might not be available when required. As a result, it is recommended to maintain a permanent deployment to cover your traffic needs (paid for via a reservation).
-> * Charges for deployments on a deleted resource will continue until the resource is purged.  To prevent this, delete a resource’s deployment before deleting the resource.  For more information, see [Recover or purge deleted Azure AI services resources](../../recover-purge-resources.md). 
+> Charges for deployments on a deleted resource will continue until the resource is purged.  To prevent this, delete a resource’s deployment before deleting the resource.  For more information, see [Recover or purge deleted Azure AI services resources](../../recover-purge-resources.md). 
 
 ## Determining the number of PTUs needed for a workload
 
@@ -66,12 +66,13 @@ A few high-level considerations:
 
 To get a quick estimate for your workload using input and output TPM, leverage the built-in capacity planner in the deployment details section of the deployment dialogue screen. The built-in capacity planner is part of the deployment workflow to help streamline the sizing and allocation of quota to a PTU deployment for a given workload. For more information on how to identify and estimate TPM data, review the recommendations in our [performance and latency documentation](./latency.md). 
 
-After filling out the input and output TPM data in the built-in capacity calculator, select the **Calculate** button to view your PTU allocation recommendation. 
-
-![Screenshot of deployment workflow PTU capacity calculator.](media/provisioned-throughput-onboarding/deployment-ptu-capacity-calculator.png)
+To use the capacity planner, go to the Azure AI Foundry Portal and select the **Deployments** button. Then select **Deploy model**.
 
+:::image type="content" source="../media/provisioned/deploy-model-button.png" alt-text="A screenshot of the model deployment screen" lightbox="../media/provisioned/deploy-model-button.png":::
 
+Choose a model, and click **Confirm**. Select a provision-managed deployment type. After filling out the input and output TPM data in the built-in capacity calculator, select the **Calculate** button to view your PTU allocation recommendation. 
 
+:::image type="content" source="../media/provisioned/deployment-ptu-capacity-calculator.png" alt-text="A screenshot of deployment workflow PTU capacity calculator." lightbox="../media/provisioned/deployment-ptu-capacity-calculator.png":::
 
 To estimate provisioned capacity using request level data, open the capacity planner in the [Azure AI Foundry](https://ai.azure.com). The capacity calculator is under **Shared resources** > **Model Quota** > **Azure OpenAI Provisioned**.