1. Monitoring AI APIs with Usage Plan Metrics


    When integrating monitoring for AI APIs with usage plan metrics, it is vital to have a usage plan in place that can keep track of and throttle the API usage as needed. Usage plans are linked to API keys and can specify who can access an API with what conditions. For this purpose, a usage plan resource in the appropriate cloud provider can be used to monitor the API usage and enforce throttling rules to manage API traffic.

    Let's say you're using AWS as your cloud provider, and you want to monitor an AI API with usage plan metrics using Pulumi. You would use AWS API Gateway to create your API and define the usage plan. The aws.apigateway.UsagePlan resource can help you to track the use of your APIs by defining plans that throttle and limit the request rates for individual API keys.

    Here's a program written in Python using Pulumi to create an AWS API Gateway Usage Plan to monitor API usage. We will define an API and a usage plan with specific throttle and quota limits associated with an API key.

    import pulumi import pulumi_aws as aws # Create an API Gateway Rest API, which will host our AI API. api = aws.apigateway.RestApi("myapi", description="This is my API for demonstration purposes") # Deploy the API to make it accessible. deployment = aws.apigateway.Deployment("myapi_deployment", rest_api=api.id, stage_name="prod") # Create the usage plan to monitor and limit API usage. usage_plan = aws.apigateway.UsagePlan("myusageplan", name="MyUsagePlan", description="A usage plan to monitor and limit API usage rates", api_stages=[aws.apigateway.UsagePlanApiStageArgs( api_id=api.id, stage=deployment.stage_name )], # Define throttle and quota limits; adjust the values based on your needs. throttle_settings=aws.apigateway.UsagePlanThrottleSettingsArgs( rate_limit=1, burst_limit=2 ), quota_settings=aws.apigateway.UsagePlanQuotaSettingsArgs( limit=1000, period="WEEK" )) # Create an API key to be used with the usage plan. api_key = aws.apigateway.ApiKey("myapikey", description="API Key for external clients", enabled=True) # Associate the API key with the usage plan. usage_plan_key = aws.apigateway.UsagePlanKey("myusageplan_key", key_id=api_key.id, key_type="API_KEY", usage_plan_id=usage_plan.id) # Export the API endpoint to access the AI API. pulumi.export("api_endpoint", deployment.invoke_url) # Export the API key value for clients to use when making requests. pulumi.export("api_key_value", api_key.value)

    In this program:

    • A REST API is created using aws.apigateway.RestApi, which acts as a container for the AI API resources and methods.
    • The aws.apigateway.Deployment resource deploys the API to a stage, making it externally accessible.
    • An aws.apigateway.UsagePlan specifies how customers access the APIs and controls API request and rate limits. We set throttle and quota limits to manage the request rates and overall usage.
    • An aws.apigateway.ApiKey is generated, which will be used to authorize requests to the APIs associated with the usage plan.
    • The API key is associated with the usage plan using the aws.apigateway.UsagePlanKey resource.
    • Finally, the API endpoint URL and the API key value are exported as outputs of the stack, using pulumi.export, which will be necessary for clients to access the AI API.

    To verify the usage plan metrics, you can use AWS CloudWatch associated with API Gateway to monitor API calls and cache hits and misses. You can track this data in the AWS Management Console or use AWS SDKs / CLI for more granular and automated monitoring solutions.

    Remember, before running this Pulumi program, you need to have your AWS credentials configured either through the AWS CLI or by setting the relevant environment variables. Once the credentials are in place, you can run the program using the pulumi up command, which will provision the resources as per the configuration specified in the code.