1. Managed API Throttling for AI Model Serving


    When serving AI models, it's crucial to manage the rate at which requests are processed to ensure consistent performance and prevent overloading the backend services that power the model. API throttling is a technique used to control the amount of incoming requests to a service. It can be especially important for AI models that require significant computational resources to return predictions.

    To implement managed API throttling for AI model serving, you might use cloud services like AWS API Gateway or Azure API Management. These services provide features to set up throttling rules that can limit the rate at which API endpoints can be called.

    The example below is for setting up an API with managed throttling using AWS API Gateway. AWS API Gateway allows you to create, publish, maintain, monitor, and secure APIs. The aws.apigatewayv2.Api resource creates an API that acts as a "front door" for applications to access data, business logic, or functionality from back-end services. The Stage resource, which is a child of Api, represents a deployment of the API and allows you to specify settings such as throttling and logging.

    The program will:

    1. Create an HTTP API endpoint using AWS API Gateway V2.
    2. Define a stage for this API, where we'll specify the throttling limits.
    3. Export the URL for the API endpoint.

    Here's how you could set it up in Pulumi using Python:

    import pulumi import pulumi_aws as aws # Create an HTTP API for AI model serving. http_api = aws.apigatewayv2.Api("aiModelHttpApi", protocol_type="HTTP", route_selection_expression="$request.method $request.path") # Define a stage with throttling settings for the HTTP API. # Throttle settings are defined per route and HTTP method. # An overall rate limit and burst capacity are set to control the traffic. stage = aws.apigatewayv2.Stage("aiModelStage", api_id=http_api.id, auto_deploy=True, route_settings={ "defaultRouteSettings": { "throttling_burst_limit": 5, "throttling_rate_limit": 10 } }) # Export the invoke URL of the API stage to access the AI model. pulumi.export("api_invoke_url", stage.invoke_url)

    In this program:

    • We start by importing Pulumi and the AWS SDK for Pulumi.
    • We define an Api which is the logical API. The protocol_type is set to HTTP, and route_selection_expression is used to determine the route for incoming requests.
    • We create a Stage that references the Api by its ID. The auto_deploy attribute is set to True for automatic deployment of updates.
    • In route_settings, we use defaultRouteSettings to apply the same throttling limits across all routes and methods by default. In this case, throttling_burst_limit is the maximum rate limit over a time ranging and throttling_rate_limit is the steady-state rate limit.
    • Finally, we export the invoke_url of the API stage, which can be used to access the AI model.

    This is a simple example, but real-world scenarios often involve setting up authentication, logging, request validation, and integration with backend services that perform the AI model serving. These services then sit behind the API Gateway, receiving throttled requests and returning predictions or other data as needed.