Edge AI vs. Cloud: Why Latency Matters in Physical Operations

Physical operations move in real time: a customer walks away, a machine drifts out of tolerance, a safety violation happens and ends within seconds. Analysis that depends on a round trip to a distant cloud server before returning a decision is often too slow to act on the moment it describes.

Edge AI runs inference on-premise, on GPU hardware installed at the location itself, so detection and the resulting decision happen with minimal delay and without depending on facility bandwidth to a remote data center. For a PLC reject signal or a safety alert, that local decision loop is the point.

This doesn't mean every deployment has to run this way, and it doesn't mean the cloud has no role. Aggregation, multi-location analytics, and model management always belong in the cloud. For the inference itself, Proglint supports two deployment models: GPU hardware on-premise at each location for the lowest latency, or a lighter on-site VMS (Video Management System) that streams camera feeds to a cloud-hosted GPU inference engine, for customers who would rather not invest in edge GPU hardware at every site.

Which model fits depends on what's actually at stake at that location. A safety interlock that can't tolerate a network hiccup argues for edge. An enterprise rolling out to dozens of new sites a quarter, where deployment speed matters more than shaving the last few hundred milliseconds off a shelf-availability alert, may reasonably choose cloud GPU inference instead. Proglint doesn't mandate one architecture — the deployment model is chosen per customer, per site.