1. Rate Limiting for AI API Endpoints


    Rate limiting is a crucial aspect of managing API endpoints, especially for AI services where heavy computation might be involved, or resources are limited. By rate limiting, you control the rate at which user requests are handled by your API endpoints. This is often critical to prevent abuse, manage load on your services, and ensure fair usage among users.

    Pulumi offers different ways to implement rate limiting, depending on the cloud provider and the service being used. For cloud-based AI APIs, you can typically apply rate limiting using API gateway services or dedicated rate-limiting resources provided by the cloud infrastructure.

    In this guide, we'll create a rate-limited AI API endpoint using AWS as our cloud provider. AWS offers the API Gateway service, which allows creating, publishing, maintaining, monitoring, and securing APIs at any scale. You can perform basic rate limiting using the API Gateway itself or with the help of AWS WAF (Web Application Firewall).

    Here's a Pulumi program in Python that demonstrates setting up an AWS API Gateway with rate limiting for a hypothetical AI endpoint:

    import pulumi import pulumi_aws as aws # Create an API Gateway REST API resource api = aws.apigateway.RestApi("myAPI", description="This is my API for demonstration purposes") # Create an API Gateway Resource (part of the API) resource = aws.apigateway.Resource("myResource", parent_id=api.root_resource_id, path_part="myendpoint", rest_api=api.id) # Create a mocked integration (this could be integrated with your lambda function or another service). integration = aws.apigateway.Integration("myIntegration", http_method="POST", resource_id=resource.id, rest_api=api.id, type="MOCK") # Create an API Gateway Method that responds to POST requests for the resource method = aws.apigateway.Method("myMethod", http_method="POST", authorization="NONE", # For demonstration, no auth resource_id=resource.id, rest_api=api.id, integration=integration, ) # Create a Stage which is a deployment of the Rest API stage = aws.apigateway.Stage("myStage", deployment=aws.apigateway.Deployment("myDeployment", rest_api=api.id, # The stage will keep the deployment up to date as new changes are made. stage_name="test", ).id, rest_api=api.id, stage_name="test") # Rate limiting settings # Set up default method throttling for specified stage api_settings = aws.apigateway.Stage("apiSettings", stage_name=stage.stage_name, rest_api=api.id, method_settings=[{ "methodPath": "*/*", # Applies to all methods in all resources "throttlingBurstLimit": 5, # The rate limit ceiling for individual IP "throttlingRateLimit": 10, # The steady-state request rate limit }]) # Output the HTTPS endpoint of the API pulumi.export("http_endpoint", stage.invoke_url)

    This program will set up an AWS API Gateway with a single /myendpoint resource that responds to POST requests. The endpoint is set to mock responses here for demonstration, but in a real-world scenario, you would have an AWS Lambda function or another backend service handling the requests.

    The api_settings variable includes the rate limiting configuration, where throttlingBurstLimit is the maximum rate limit over a short time, and throttlingRateLimit is the average steady-state rate limit.

    By default, in the provided code, the setup will rate limit every IP to 5 requests "burst" capacity and 10 requests per second. This means that a user may exceed the average rate of 10 requests per second in short bursts, but over time their average request rate must not cross the rate limit.

    Now, when you deploy this Pulumi stack, the API Gateway with these settings will be set up in your AWS environment, and you can access your rate-limited AI API at the URL outputted by pulumi.export("http_endpoint", stage.invoke_url).