Cross-Cloud Network Hops Can Dominate AI Inference Latency, Study Finds

A DEV Community article argues that teams often measure inference latency as a single number, which hides where time is spent. In multi-cloud setups, network crossings such as DNS resolution, connection setup, and retrieval calls can consume more time than the model itself. The author recommends assigning a latency budget to each hop in a retrieval-augmented request so slow components can be identified. It also advises tracking first-token latency and reducing the number of cross-cloud crossings, for example by batching document fetches and placing vector stores near the applications that query them most.