Implement an AI Gateway for Intelligent Model Routing

Stop hardcoding model choices. Use a gateway to automatically route LLM calls based on cost, speed, and quality, reducing expenses and improving performance while avoiding vendor lock-in.

intermediate2 Hours4 steps

The play

Audit Your Current LLM Calls
Identify all services making direct API calls to providers like OpenAI or Anthropic. Use codebase search and cloud cost reports to map use cases (e.g., summarization, classification) to specific models and their associated costs. This establishes your baseline for cost and performance.
Create a Central Gateway Function
Refactor your code to route all LLM calls through a single, unified function or service endpoint. Initially, this gateway will just pass requests to your default model, but it establishes the critical abstraction layer for all future logic.
Implement a Cost-Based Routing Rule
Enhance your gateway with its first routing rule. For a specific, low-complexity use case identified in your audit (e.g., 'simple_classification'), route the request to a cheaper, faster model like Claude 3 Haiku instead of a premium one like GPT-4 Turbo. Monitor your 'Blended Cost Per Million Tokens' benchmark to measure the impact.
Add Failover and Explore Advanced Routing
Make your system more resilient by adding a try/except block to your gateway that reroutes a request to a backup model from a different provider if the primary one fails. This is the first step towards a robust, multi-provider strategy. To build out a production-grade gateway with semantic caching and dynamic routing, follow the step-by-step instructions in our DIY package.

Starter code

A Python script with a placeholder `call_llm_gateway` function and mock API calls for two different use cases (one simple, one complex) to demonstrate routing logic.