1. API Throttling for AI Model Inference Endpoints

    Python

    To implement API throttling for AI model inference endpoints, you'll typically need to use services provided by cloud providers such as AWS, Azure, or GCP. Throttling is a critical technique to control the rate of incoming requests to ensure that your endpoint can handle traffic gracefully without being overwhelmed, which is particularly important for AI model inference endpoints that may require significant computational resources.

    Below is an example using AWS services, where we'll create an API Gateway with an associated usage plan that includes throttling settings. The API Gateway acts as the entry point for your inference endpoint, and the usage plan governs how clients can interact with it.

    The components involved in this setup will include the following:

    • aws.apigateway.RestApi: This resource represents your API in Amazon API Gateway.
    • aws.apigateway.Deployment: This is required to deploy the API so that it can be invoked.
    • aws.apigateway.Stage: Represents a stage, which is a snapshot of the API that you can call or manage.
    • aws.apigateway.ApiKey: An API Key for clients to use when calling your API.
    • aws.apigateway.UsagePlan: Links your resources to the API key and applies the throttling and quota limits.
    • aws.apigateway.UsagePlanKey: Associates the API key with the usage plan.

    This Pulumi program will outline the creation of these resources and configure the throttling to manage your AI inference endpoint effectively:

    import pulumi import pulumi_aws as aws # Create an API gateway rest_api = aws.apigateway.RestApi("my-api", description="API for AI Model Inference Endpoint") # Deploy the API to make it callable deployment = aws.apigateway.Deployment("my-api-deployment", rest_api=rest_api.id, # It's a best practice to redeploy the API whenever its configuration changes. triggers={ "redeployment": str(pulumi.Output.all(rest_api.id)) # Using the API id as a trigger for redeployments. }) # Create a Stage which is a logical reference to a lifecycle state of your API (like 'dev', 'prod', etc.) stage = aws.apigateway.Stage("my-api-stage", deployment=deployment.id, rest_api=rest_api.id, stage_name="prod") # Create an API key that clients will use to access the API api_key = aws.apigateway.ApiKey("my-api-key", description="API Key for AI Model Endpoint") # Define a usage plan to manage throttling and quota for the API usage_plan = aws.apigateway.UsagePlan("my-api-usage-plan", description="Usage plan for throttling of AI Model Endpoint", # Throttle settings for rate limiting. # Here rate_limit is the rate limit per second and burst_limit is the maximum burst for API calls. throttle_settings={ "rate_limit": 10, # The maximum request rate limit over a time ranging from one to a few seconds. "burst_limit": 5, # The maximum rate limit over a short burst of time, typically a few seconds at most. }, # Quota limits for the API calls. quota_settings

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