Edge AI vs Cloud CCTV: A Side-by-Side Comparison

Two approaches to intelligent video

As AI-powered security becomes mainstream, retailers face a fundamental architecture choice: should video analytics run in the cloud, or on-premises at the edge?

Both approaches use AI models to detect events. The difference is where that processing happens — and it affects everything from alert speed to privacy compliance.

Speed: edge AI wins decisively

Cloud-based systems follow this path:

1. 1.Camera captures frame → 2. Compress and upload to cloud → 3. Queue for processing → 4. AI inference in data centre → 5. Result sent back → 6. Alert triggered

This round-trip typically takes 5–15 seconds depending on network conditions, server load, and upload bandwidth. In a concealment scenario, 15 seconds is the difference between intervention and a walk-out.

Edge AI processes locally:

1. 1.Camera captures frame → 2. AI inference on local device → 3. Alert triggered

Total latency: under 2 seconds. The person is still in the aisle.

Privacy: not even a contest

With cloud processing, video frames leave the building. They travel across the internet to data centres that may be in another country, managed by a third party, subject to different privacy laws.

Edge AI keeps everything local. Video is processed in the store. Frames never leave the network. There's no upload, no third-party access, no jurisdictional risk. For Australian pharmacies subject to the Privacy Act 1988, this isn't just a preference — it's a compliance advantage.

Reliability: what happens when the internet drops?

Cloud systems fail when connectivity fails. No upload means no detection. For a regional pharmacy with an ADSL connection, this is a real vulnerability.

Edge systems continue operating regardless of internet status. Detection, alerting (to local devices), and floor display all function entirely offline. Internet is only needed for remote monitoring and AI model updates.

Cost structure

For multi-camera deployments (10–15 cameras), cloud costs can exceed $500/month in compute and bandwidth alone. Edge-based services like IntelliGuard charge a flat fee per store regardless of camera count.

When cloud still makes sense

Cloud analytics can be appropriate for:

• →Low-camera installations (1–3 cameras) where bandwidth costs are minimal
• →Multi-site dashboards where centralised reporting is the primary use case
• →Post-event analysis where real-time alerting isn't critical

But for loss prevention — where speed, privacy, and reliability are table stakes — edge AI is the clear architecture for the job.

IntelliGuard uses on-premises edge AI for all detection. No video is uploaded to any cloud server. Learn more at neuraiq.com.au/intelliguard.