AI video analytics multiply required storage capacity per camera
Deploying object detection, facial recognition, or license plate recognition analytics onto an existing camera network typically increases the metadata and higher-resolution recording associated with each triggered event, multiplying the effective storage footprint well beyond what the raw video stream alone would ever require. Operators upgrading analytics capability increasingly discover their existing storage arrays reach capacity far sooner than originally planned, forcing an unplanned hardware or cloud capacity expansion mid-contract. Storage vendors are responding with tiered architectures that separate raw footage from analytics-flagged event clips, each retained on different cost profiles.
Market Impact: 600 million cameras under public deployment








