Managed API Throttling for AI Model Serving
PythonWhen 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. TheStage
resource, which is a child ofApi
, represents a deployment of the API and allows you to specify settings such as throttling and logging.The program will:
- Create an HTTP API endpoint using AWS API Gateway V2.
- Define a stage for this API, where we'll specify the throttling limits.
- 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. Theprotocol_type
is set toHTTP
, androute_selection_expression
is used to determine the route for incoming requests. - We create a
Stage
that references theApi
by its ID. Theauto_deploy
attribute is set toTrue
for automatic deployment of updates. - In
route_settings
, we usedefaultRouteSettings
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 andthrottling_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.