Shared vs. Dedicated AI Inference: Embed & Rerank

This post introduces a decision framework for choosing between shared and dedicated AI inference for Embed and Rerank models. It emphasizes that request shape (size and complexity of data per request) and traffic patterns (sustained vs. bursty) are more critical than request volume alone for determining cost-efficiency. The post details how different request profiles (e.g., batch catalog embedding vs. single query embedding, or varying numbers of rerank candidates) lead to different break-even points for dedicated infrastructure. It also highlights the importance of considering latency requirements alongside throughput when sizing infrastructure.