# Add Azure AI Studio Model

Prerequisites for Using Azure AI Foundry Portal and Hosting Serverless LLM, Embedding, or Reranker Models

General Prerequisites

Azure Subscription - An active Azure subscription is required to access and use Azure AI Foundry services.
User Permissions - Ensure that your Azure account has the necessary permissions:
Contributor or Owner Role: Provides full access to projects, including the ability to assign permissions to other users.
Azure AI Developer Role: Allows users to perform most actions, such as creating deployments, but does not permit assigning permissions to project users.

For more details, refer to Microsoft's documentation on Role-based access control in Azure AI Foundry.

Resource Group Permissions

Your account must have appropriate permissions on the resource group containing the AI project.
Deploying Models as Serverless APIs - Azure AI Foundry enables you to deploy models as serverless API endpoints, allowing you to consume models without managing infrastructure.

Steps to Deploy a Model as a Serverless API:

Model Subscription -

Non-Microsoft models: Subscribe to the desired model via Azure Marketplace.
Microsoft models (e.g., Phi-3 models): No subscription required.

Deployment Process

Navigate to the Azure AI Foundry portal and select the model to deploy.
Click on 'Deploy' → Select 'Serverless API with Azure AI Content Safety (preview)'.
Choose the appropriate project and region.
Assign a unique name to your deployment (part of the API endpoint URL).
Configure content filtering settings if needed.
Click 'Deploy' and wait for the process to complete.

Accessing the Deployment

After deployment, access the Target URI and authentication keys via the Azure AI Foundry portal. Use these details to integrate the deployed model into your applications. For detailed guidance, refer to Deploy models as serverless APIs - Azure AI Foundry.

Additional Considerations

Region Availability
Ensure the selected Azure region supports your model and deployment type.
Content Filtering
Utilize Azure AI Content Safety to detect and manage harmful content.
Cost Management
Be aware of the billing structure based on token usage and minimal infrastructure costs.
By fulfilling these prerequisites and following the outlined steps, you can effectively deploy serverless LLMs, embeddings, or reranker models using Azure AI Foundry.

Post the prerequisites are met, you will need to add the following details for adding a model by Azure AI Studio -

LLMs/ReRanker

Select the type of model you want to configure.

LLMs: Large Language Models used for tasks within the Expert Agent Studio
Reranker: Select this if the model is used to reorder a list of results based on relevance

Model ID

A unique identifier assigned to the model within Azure AI Studio tells the platform exactly which model to route requests to.

Display Name

A user-friendly name that will appear in the platform UI

API Key

A secure token provided by Azure AI Studio to authenticate API requests. Ensures only authorized applications can access the model.

Endpoint

The full URL where the model can be accessed for inference. This is where the platform sends requests when invoking the model