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 global batch processing for large-scale inference jobs.	Efficient for processing large datasets.	Not suitable for real-time applications.
Data Zone Standard	Shared infrastructure within a specific data zone for compliance needs.	Meets data residency requirements affordably.	Limited performance consistency.
Data Zone Provisioned	Dedicated infrastructure in a data zone for high-performance workloads.	Combines compliance with consistent performance.	More expensive than shared options.
Data Zone Batch	Batch processing within a data zone for regulated data workflows.	Ideal for compliant, large-scale processing.	Slower response times; not real-time.
Standard	Default shared deployment for general use across Azure AI Foundry.	Simple setup and broad compatibility.	May lack advanced performance or compliance features.
Regional Provisioned	Dedicated infrastructure in a specific region for localized performance.	Optimized for regional latency and control.	Higher cost and limited to regional availability.
Developer (Fine-tuned)	Lightweight deployment for testing and iterating fine-tuned models.	Fast iteration and low cost for development.	Not suitable for production-scale workloads.