Scalability and Error Handling in Azure OpenAI with API Management

🔑 Azure OpenAI can be deployed in an Enterprise grade resilient and secured way by using Azure API management as a gateway.

🌐 The proposed deployment model includes a central logging and monitoring framework for chargeback purposes.

🔒 API management uses Azure AD service principles to authenticate and authorize against Azure OpenAI.

💡 API management handles error handling and retry logic for OpenAI backends.

🔧 API management simplifies the process of creating a completion operation for the Azure OpenAI service.

📊 API management allows for integration with reporting solutions for data analysis.

🔑 The video discusses the configuration of the API Management service for Azure OpenAI scalability.

⚙️ The API operation in question does not have any retry logic and will throw an error if the call to OpenAI instance one fails.

🔒 Access for the service principle representing business unit 1 has been revoked, resulting in a permission denied error.

API Management allows for scalability and fault tolerance in Azure services.

Retry logic is implemented to handle errors and switch to a different backend service URL.

Clients need to use a subscription key issued by API Management to consume the API.

🔑 Using Azure AD for authentication and authorization instead of OpenAI API Keys.

🔍 Tracing the API call to identify the caller and understand the back end response.

🔄 Implementing retry logic to switch to a different backend instance in case of errors.

🔑 The video discusses how Azure OpenAI scalability can be achieved using API Management.

📈 By forwarding calls to multiple instances and logging necessary information to an event hub, a chargeback policy can be implemented based on business units, number of calls made, and tokens consumed.

💡 Stream Analytics can be used to create aggregations and query the data in the event hub to analyze the usage and make informed decisions.

📌 API Management can handle errors gracefully and implement retry logic for improved client experience.

🔒 Azure ID access tokens can be utilized to add security to the system.

🌐 By repeating the logic in another region and implementing a multi-regional load balancer, the deployment becomes multi-regional and active-active.