API Gateway as a Rate-Limited Interface for LLMs

Question

Pulumi · Accepted Answer

In cloud services, an API Gateway acts as the entry point for applications' client requests and can provide various features such as traffic management, authorization and access control, monitoring, and API version management. One useful feature is rate limiting, which controls the number of API requests a user can make in a given time period. This can be a critical component when interfacing with Language Learning Models (LLMs), where you might need to control the number of requests to prevent abuse and overuse of the model.

In the context of AWS, the Amazon API Gateway service can be used to create, publish, maintain, monitor, and secure APIs. Rate limiting in Amazon API Gateway is implemented using the Usage Plans and API Keys features. A Usage Plan specifies who can access one or more deployed API stages and methods—and also how much and how fast they can access them. The plan uses API keys to identify API clients and meters access to the API stages and methods according to the plan's configured throttle and quota limits.

Here's a Python program using Pulumi to create an AWS API Gateway with a rate-limited usage plan for an example LLM backend:

```python
import pulumi
import pulumi_aws as aws

# Create an API Gateway REST API resource.
api_gateway = aws.apigateway.RestApi('apiGateway',
    description="API Gateway for Language Learning Model",
    # Additional settings can be configured according to specific requirements.
)

# Define the API Gateway Deployment.
deployment = aws.apigateway.Deployment('apiDeployment',
    rest_api=api_gateway.id,
    # Setting the stage name for deployment (e.g., prod, dev, staging).
    stage_name="prod",
    # Indicate that the deployment is triggered once the API Gateway is ready.
    triggers={
        "redeployment": pulumi.Output.concat("API Gateway with ID ", api_gateway.id, " has been deployed"),
    }
)

# Define the API Gateway Stage.
stage = aws.apigateway.Stage('apiStage',
    deployment=deployment.id,
    rest_api=api_gateway.id,
    stage_name=deployment.stage_name,
)

# Create a Rate-Limited Usage Plan.
usage_plan = aws.apigateway.UsagePlan('apiUsagePlan',
    # Setting an associated rate limit and burst limit.
    throttle={
        "rate_limit": 10,   # The rate limit as the number of requests per second.
        "burst_limit": 20,  # The burst limit defines the maximum rate at which the tokens can be refilled.
    },
    quotas={
        "limit": 1000,       # The maximum number of requests that can be made in a given time period.
        "period": "DAY",     # The time period in which the quota resets; can be DAY, WEEK, or MONTH.
        "offset": 1,         # The day of the period when the quota resets.
    },
    # Linking usage plan to the API stage.
    api_stages=[{
        "api_id": api_gateway.id,
        "stage": stage.stage_name,
    }],
    # Configure settings as per requirements.
)

# Export the API endpoint as a stack output.
pulumi.export("api_endpoint", deployment.invoke_url.apply(lambda url: url + stage.stage_name))
```

In this program:

- `aws.apigateway.RestApi` creates a new API Gateway to route requests to the appropriate backend.
- The `aws.apigateway.Deployment` resource is created to deploy the API Gateway. It requires a trigger to redeploy when changes occur.
- The `aws.apigateway.Stage` resource represents a logical reference to where the API is deployed.
- `aws.apigateway.UsagePlan` is used to define who can access the API and at what rate and amount (rate limiting).
- We apply rate limiting with `throttle` to control the request rate and `quotas` to set the maximum number of requests in a given time period.

You can associate this Usage Plan with API keys and distribute them to clients, which will identify and enforce the allowed request patterns as per the plan when accessing the API Gateway.

By creating these resources using Pulumi, you declare your cloud infrastructure in code, which provides repeatability and version control for your infrastructure, just like for your application code. The deployment and management of these resources can be done through the Pulumi CLI or the Pulumi Service, which helps automate these processes.