Meridian is deciding where to deploy its computer-vision ramp-safety monitoring system, which must process live camera feeds with minimal delay to flag foreign-object debris before ground crew are exposed to danger, and which touches operational data Meridian is reluctant to send off-site. What deployment approach best fits these requirements?

CPMAI explicitly frames on-premise deployment as the fit for sensitive-data or high-performance latency needs, and this scenario has both: safety-critical, real-time debris detection where network round-trip delay to a distant cloud region could mean the difference between an alert arriving in time or too late, plus operational camera-feed data the airline prefers to keep in its own environment rather than routing externally. On-premise infrastructure, sited close to the ramp cameras, minimizes network hops and gives Meridian direct control over where the data lives. Choosing a distant public cloud region purely on cost ignores the latency requirement entirely and could introduce exactly the delay a safety-critical system cannot tolerate. Assuming deployment location doesn't matter contradicts a core Managing AI concept — where a model runs materially affects latency, data control, and reliability, which is why deployment strategy is its own evaluated task. A generic serverless, pay-per-invocation setup optimizes for cost and elastic scaling, not for the deterministic low latency this safety use case demands, and ignoring latency when choosing it is a planning failure.