Edge AI vs Cloud AI for Factory Video Processing

Set the response-time requirement

An inspection tied to a nearby reject mechanism needs a bounded end-to-end delay. Reporting hourly production trends has a different tolerance. Measure capture, transfer, inference and application delay together. Internet-dependent processing must include realistic jitter and outages rather than using a laboratory network result as the operating promise.

Calculate data movement and retention

Estimate camera count, stream bitrate and the hours uploaded. Distinguish continuous video from event images and compact results. Check who may access footage and how long it is needed. Edge processing can reduce uploads, but local storage and remote support access still require appropriate controls.

Plan the operational burden

Edge hardware needs power, cooling, monitoring and a replacement procedure. Cloud services need capacity management, connectivity and cost controls. In both cases, maintain model versions and rollback capability. Test the selected hardware under all camera streams at once, including thermal conditions, rather than relying on a single-model benchmark.

Define the outage behaviour

A hybrid design may keep local inspection running while uploading summaries later. Specify buffer limits, synchronisation and what happens when storage fills. Show processing status to operators and mark gaps in reports. Verify recovery without duplicated events or silent loss before choosing the architecture for multiple sites.

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