API Gateway Setup

10 Steps

1. 1

   Choose an API Gateway: Select an API Gateway based on your infrastructure and requirements. Options include AWS API Gateway, Azure API Management, Google Cloud API Gateway, Kong, or Tyk. For this example, we'll assume AWS API Gateway.

2. 2

   Create an API Gateway: Create a new API Gateway in your chosen platform. In AWS, this involves navigating to the API Gateway service and creating a new REST API or HTTP API.

3. 3

   Define Resources and Methods: Define the resources (e.g., `/inference`) and HTTP methods (e.g., `POST`) that your API will expose. Each method will need an integration point.

4. 4

   Configure Integration: Configure the integration to your LLM inference endpoint. This could be an HTTP endpoint, a Lambda function, or another service. Specify the integration type, URI, and any necessary request/response mappings.

5. 5

   Implement Authentication: Implement authentication to secure your API. Options include API keys, IAM roles, or OAuth 2.0. Configure the authentication method in the API Gateway settings.

6. 6

   Apply Rate Limiting: Apply rate limiting to prevent abuse and ensure fair usage. Configure rate limits based on API keys, IP addresses, or other criteria.

7. 7

   Enable Request/Response Transformation: Use request/response transformation to adapt the API's input and output formats to match the LLM inference endpoint's requirements. This can involve mapping request parameters or transforming the response body.

8. 8

   Deploy the API: Deploy the API to a stage (e.g., `dev`, `prod`). This makes the API accessible to clients.

9. 9

   Test the API: Test the API to ensure it's working correctly. Send requests to the API endpoint and verify that the responses are as expected.

10. 10

    Monitor and Manage: Monitor the API's performance and usage. Use the API Gateway's monitoring tools to track metrics such as request latency, error rates, and API usage.