update deployment type language

Author: PatrickFarley

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

@@ -24,8 +24,13 @@ For standard and provisioned deployments, you have an option of two types of con
 
 Global deployments leverage Azure's global infrastructure, dynamically route customer traffic to the data center with best availability for the customer’s inference requests. This means you will get the highest initial throughput limits and best model availability with Global while still providing our uptime SLA and low latency. For high volume workloads above the specified usage tiers on standard and global standard, you may experience increased latency variation. For customers that require the lower latency variance at large workload usage, we recommend purchasing provisioned throughput.
 
-Our global deployments will be the first location for all new models and features. Customers with very large throughput requirements should consider our provisioned deployment offering.
+Global deployments leverage Azure's global infrastructure to dynamically route customer traffic to the data center with the best availability for the customer’s inference requests. This means you will get the highest initial throughput limits and best model availability with Global while still providing our uptime SLA and low latency. For high volume workloads above the specified usage tiers on standard and global standard, you may experience increased latency variation. For customers that require the lower latency variance at large workload usage, we recommend leveraging our provisioned deployment types.
 
+Our global deployments will be the first location for all new models and features. Depending on call volume, customers with large volume and low latency variance requirements should consider our provisioned deployment types.
+
+Data zone deployments leverage Azure's global infrastructure to dynamically route customer traffic to the data center with the best availability for the customer's inference requests within the data zone defined by Microsoft. Positioned between our Azure geography and Global deployment offerings, data zone deployments provide elevated quota limits while keeping data processing within the Microsoft specified data zone. Data stored at rest will continue to remain in the geography of the Azure OpenAI resource (e.g., for an Azure OpenAI resource created in the Sweden Central Azure region, the Azure geography is Sweden).
+
+For any [deployment type](/azure/ai-services/openai/how-to/deployment-types) labeled 'Global,' prompts and responses may be processed in any geography where the relevant Azure OpenAI model is deployed (learn more about [region availability of models](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For any deployment type labeled as 'DataZone,' prompts and responses may be processed in any geography within the specified data zone, as defined by Microsoft. If you create a DataZone deployment in an Azure OpenAI resource located in the United States, prompts and responses may be processed anywhere within the United States. If you create a DataZone deployment in an Azure OpenAI resource located in a European Union Member Nation, prompts and responses may be processed in that or any other European Union Member Nation. For both Global and DataZone deployment types, any data stored at rest, such as uploaded data, is stored in the customer-designated geography. Only the location of processing is affected when a customer uses a Global deployment type or DataZone deployment type in Azure OpenAI Service; Azure data processing and compliance commitments remain applicable.
 ## Deployment types
 
 Azure OpenAI offers three types of deployments. These provide a varied level of capabilities that provide trade-offs on: throughput, SLAs, and price. Below is a summary of the options followed by a deeper description of each.