Merge pull request #695 from ChrisHMSFT/docs-editor/deployment-types-1728349493

Update data residency to data processing

Author: prmerger-automator[bot]

articles/ai-services/openai/how-to/deployment-types.md

@@ -15,7 +15,7 @@ ms.author: mbullwin
 
 Azure OpenAI provides customers with choices on the hosting structure that fits their business and usage patterns. The service offers two main types of deployment: **standard** and **provisioned**. Standard is offered with a global deployment option, routing traffic globally to provide higher throughput. Provisioned is also offered with a global deployment option, allowing customers to purchase and deploy provisioned throughput units across Azure global infrastructure. All deployments can perform the exact same inference operations, however the billing, scale and performance are substantially different. As part of your solution design, you will need to make two key decisions:
 
-- **Data residency needs**: global vs. regional resources  
+- **Data processing needs**: global vs. regional resources  
 - **Call volume**: standard vs. provisioned
 
 ## Global versus regional deployment types
@@ -32,7 +32,7 @@ Azure OpenAI offers three types of deployments. These provide a varied level of
 
 | **Offering** | **Global-Batch** | **Global-Standard**|  **Global-Provisioned**  | **Standard** | **Provisioned**  |
 |---|:---|:---| -------- |:---|:---|
-| **Best suited for**      | Offline scoring <br><br> Workloads that are not latency sensitive and can be completed in hours.<br><br> For use cases that do not have data processing residency requirements.|Recommended starting place for customers. <br><br>Global-Standard will have the higher default quota and larger number of models available than Standard. |Real-time scoring for large consistent volume. Includes the highest commitments and limits.                                                                                               For use cases that do not have data residency requirements.| For customers with data residency requirements. Optimized for low to medium volume. |Real-time scoring for large consistent volume. Includes the highest commitments and limits.                                                                                          For use cases with data residency requirements|
+| **Best suited for**      | Offline scoring <br><br> Workloads that are not latency sensitive and can be completed in hours.<br><br>|Recommended starting place for customers. <br><br>Global-Standard will have the higher default quota and larger number of models available than Standard. |Real-time scoring for large consistent volume. Includes the highest commitments and limits.                                                                                             | For customers with data residency requirements. Optimized for low to medium volume. |Real-time scoring for large consistent volume. Includes the highest commitments and limits.                                                                                          For use cases with data residency requirements|
 | **How it works**         | Offline processing via files |Traffic may be routed anywhere in the world |Traffic may be routed anywhere in the world| | |
 | **Getting started**      | [Global-Batch](./batch.md) | [Model deployment](./create-resource.md) |[Provisioned onboarding](/azure/ai-services/openai/how-to/provisioned-throughput-onboarding)| [Model deployment](./create-resource.md) | [Provisioned onboarding](./provisioned-throughput-onboarding.md) |
 | **Cost**                 | [Least expensive option](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) <br> 50% less cost compared to Global Standard prices. Access to all new models with larger quota allocations.  | [Global deployment pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |May experience cost savings for consistent usage|  [Regional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |May experience cost savings for consistent usage |