2 posts tagged with "edge-inference"

Most teams make the cloud-vs-edge decision by gut instinct: cloud is easier, so they default to cloud. Then a HIPAA audit hits, or the latency SLO slips by 400ms, or the monthly invoice arrives. Only then do they ask whether some of that inference should have been local all along.

The answer is almost never "all cloud" or "all edge." The teams running production AI at scale have settled on a tiered architecture: an on-device or on-premise model handles the majority of requests, and a cloud frontier model catches what the smaller model can't. Getting that routing right is an engineering decision, not an intuition.

This is the decision framework for making it rigorously.

Most teams treat the cloud-vs-edge decision as binary: either you pay per token to a cloud provider or you run everything locally. In practice, the interesting architecture is the one in between — a routing layer that sends each query to the cheapest compute tier that can handle it correctly. The teams getting this right are cutting inference costs 60–80% while improving both latency and privacy compliance. The teams getting it wrong are running frontier models on every autocomplete suggestion.

The hybrid cloud-edge pattern has matured significantly over the past two years, driven by two converging trends: small language models (SLMs) that fit on consumer hardware without embarrassing themselves, and routing systems sophisticated enough to split traffic intelligently. This article covers the architecture, the decision framework, and the failure modes that make hybrid harder than it looks.