Load Balancing AI Services for Availability and Speed

This post details the implementation of a service-aware load balancer for Pinecone Assistant, utilizing the 'power of two choices' algorithm to dynamically route requests to embeddings, rerankers, and LLMs. It contrasts this approach with less effective methods like 'least loaded' and 'latency-aware routing', highlighting the importance of service-specific scoring policies (latency+reliability for embeddings/rerankers, reliability+load for LLMs). The post presents metrics showing significant reductions in p95/p99 latency for rerankers and embedders, and discusses availability improvements during upstream incidents.