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AI Service Load Balancing

1 post · Apr 2026

What this trail covers

Pinecone Assistant routes requests across embeddings, rerankers, and LLMs using a service-aware load balancer based on the 'power of two choices' algorithm. This provides adaptive routing without a global coordinator, automatic failover during upstream incidents, and reduced latency. Different scoring policies are used for different service types (embeddings/rerankers on latency+reliability, LLMs on reliability+load) to optimize performance and availability.

All Pinecone trails
  1. Load Balancing AI Services for Availability and SpeedThis 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.