Challenges of Routing LLM Calls and Lessons from Building AI Gateway

The article discusses the complexities of building a routing layer for large language models (LLMs) to handle different types of requests efficiently. It covers the author's experience in developing a self-hostable gateway that supports multi-provider integration, intent-based routing, semantic caching, and health-aware failover.

Details

The article describes the author's experience in building a routing layer for LLMs, called 'ai-gateway', to address common issues like simple queries hitting expensive models, provider outages, and lack of cost vs. quality control. The core idea is to have a router that decides which model (cheap, reasoning, or fallback) should handle each request based on the prompt. The system supports multi-provider integration, intent-based routing using embedding similarity, semantic caching, and health-aware failover. However, the author found that routing decisions based on heuristics and embedding similarity can be challenging due to ambiguous prompts. Running embeddings locally also has trade-offs like cold start latency and scaling challenges. The article suggests that the next step could be a learning-based routing approach that adapts over time based on signals like retries and failures.