Deployment Types in AI Foundry
Deploying a model in Azure AI Foundry can be done in 9 different ways. Depending on the type of deployment chosen, it may impact one of more factors, such as cost, latency, efficiency for processing large datasets, compliance. Listed below is a description of each deployment type, along with advantages and disadvantages. For more details, please visit https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/deployment-types Deployment Type Description Advantage Disadvantage Global Standard Shared global infrastructure for general-purpose model inference. Cost-effective and easy to scale. Performance may vary under high demand. Global Provisioned Dedicated global infrastructure for consistent performance. Reliable throughput and latency. Higher cost due to dedicated resources. Global Batch Asynchronous globa...