Rate Limiting for AI Inference Services

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Pulumi · Accepted Answer

Rate limiting is an important aspect of managing cloud services, particularly when dealing with resources that can be expensive, such as AI Inference services. It ensures that the service is used within set boundaries to prevent abuse and to manage costs. Most cloud providers offer mechanisms to handle rate limiting either at the API Gateway level or as part of the service quotas.

In the context of Pulumi, you can set up rate limiting by configuring the appropriate services of your cloud provider using Pulumi's Infrastructure as Code (IAC) approach. Below I'll demonstrate how to configure rate limiting on AWS for an AI inference service using the API Gateway and AWS Service Quotas.

For the AWS API Gateway, you can manage your rate limiting with "Usage Plans" and "API Keys". Usage Plans define who can access the deployment and at what rate they can do so. This means setting a rate limit (the steady-state request rate limit) and a burst limit (the short-duration maximum request rate limit).

For AWS Service Quotas, it’s about restricting resource usage within your AWS account, which can include setting limits on the number of inference units for services like Amazon SageMaker.

Here is a program that sets up rate limiting on AWS for an API Gateway which you could use for your AI inference service:

import pulumi
import pulumi_aws as aws

# API Gateway to serve the AI inference service
api_gateway = aws.apigateway.RestApi("apiGateway", 
    description="API Gateway for AI Inference Service")

# Resource attached to the API Gateway
resource = aws.apigateway.Resource("resource", 
    rest_api=api_gateway.id,
    path_part="inference")

# GET method configuration for the inference endpoint
get_method = aws.apigateway.Method("getMethod",
    rest_api=api_gateway.id,
    resource_id=resource.id,
    http_method="GET",
    authorization="NONE")

# Deploy the API
deployment = aws.apigateway.Deployment("deployment",
    rest_api=api_gateway.id,
    stage_name="prod")

# Create a Usage Plan to enforce rate limiting
usage_plan = aws.apigateway.UsagePlan("usagePlan",
    name="aiServiceUsagePlan",
    description="Usage plan for AI Inference Service",
    api_stages=[{
        "apiId": api_gateway.id,
        "stage": deployment.stage_name,
    }],
    throttle_settings={
        "rateLimit": 100,  # Average requests per second
        "burstLimit": 200  # Maximum requests at a peak
    },
    quota_settings={
        "limit": 10000,   # Maximum number of requests in a given time period
        "period": "WEEK"  # Time period for quota (e.g., DAY, WEEK, or MONTH)
    })

# Configure Service Quotas (if applicable for the AI Inference Service)
# Note: The 'quotaCode' and 'serviceCode' values will be specific to the service you're using.
service_quota = aws.servicequotas.ServiceQuota("serviceQuota",
    quota_code="<QuotaCode>",
    service_code="<ServiceCode>",
    value=100)  # Service-specific maximum value

# Output the URL to access the AI inference service
pulumi.export("invoke_url", pulumi.Output.concat(
    "https://", api_gateway.execution_arn, ".execute-api.",
    pulumi.Config("aws").require("region"), ".amazonaws.com/prod/inference"
))

In this code:

We create an API Gateway instance to serve as the access point for the AI inference service.
We define a Resource and Method that will handle the GET requests to our inference endpoint.
We deploy the REST API to make it accessible.
We create a Usage Plan to apply rate limiting to our service. This includes both a steady-state request rate limit and a burst limit, plus a quota that resets weekly.
Optionally, you can adjust Service Quotas if your AI service requires (uncomment and provide actual quotaCode and serviceCode values).
Finally, we export the URL that clients would use to access the AI inference service.

Remember to provide actual values for <QuotaCode> and <ServiceCode> for the Service Quota resource if needed. These are placeholders for the specific quota you want to manage for your AWS service.

When you run pulumi up, this code will provision the configured AWS resources. Make sure you have the correct AWS account settings and permissions before running the program.