Managed AI Routing and Serverless Endpoint
Build one production-style lab that combines managed AI routing, Lambda packaging, and an API Gateway endpoint with fallback behavior.
Lab Overview
This hands-on lab teaches you to package managed AI access behind a reusable router and publish it through a serverless AWS endpoint.
You'll learn to:
- Call the TeKanAid AI gateway through an OpenAI-compatible client
- Route between a premium managed primary model and a lower-cost managed fallback model
- Package Python code for AWS Lambda using pinned dependencies
- Deploy a Lambda function with IAM and environment configuration
- Publish an HTTP API with API Gateway and validate fallback behavior end to end
This lab replaces the old Bedrock-specific workflow with a platform-safe pattern that still teaches managed-provider integration and serverless delivery.
What You'll Learn
Build a reusable router that selects a managed primary model and lower-cost fallback model through the TeKanAid AI gateway
Package a Python Lambda function with pinned dependencies for serverless AI delivery
Deploy and update Lambda infrastructure safely with IAM and AWS CLI automation
Publish an HTTP API endpoint that returns managed AI responses and demonstrates fallback behavior
Prerequisites
managed-ai-platform-architecture
managed-api-access-tradeoffs
multi-provider-integration
aws-lambda-basics
Technologies Covered
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