Edge AI vs cloud AI for security cameras: the bandwidth math that settles the debate

1. The bandwidth math: why cloud AI fails at scale

Let's start with the numbers that should end most cloud AI conversations for security. A single 1080p camera at 30fps with H.264 encoding generates 3-5 Mbps of data. That's the bitrate after compression. Raw video would be 10x higher, but nobody streams raw.

Now multiply by your camera count:

• 4 cameras: 12-20 Mbps sustained upload
• 8 cameras: 24-40 Mbps sustained upload
• 16 cameras: 48-80 Mbps sustained upload
• 25 cameras: 75-125 Mbps sustained upload

Compare this to what commercial properties typically have. A standard business internet plan provides 100-500 Mbps download but only 20-30 Mbps upload. Even fiber plans often cap upload at 50-100 Mbps. And that bandwidth is shared across the entire property: tenant WiFi, point-of-sale systems, building management systems, access control panels, and everything else that needs internet.

Dedicating 80% of your upload capacity to camera feeds is not a viable architecture. Your tenants lose functional internet, your smart building systems start failing, and your cloud AI pipeline still drops frames because TCP congestion throttles the upload.

Some cloud providers suggest reducing resolution or frame rate to save bandwidth. Drop to 720p at 15fps and you cut bandwidth by 60-70%. But you also destroy the image quality that makes AI analysis reliable. Trying to detect a person loitering at 720p/15fps in a dimly lit parking garage is a fundamentally different (harder) problem than doing it at full resolution and frame rate.

2. Latency: when seconds matter for security

Security monitoring has a latency budget that cloud architectures struggle to meet. When someone is breaking into a vehicle in your parking garage, the difference between a 2-second alert and a 30-second alert determines whether you can intervene or just review footage after the fact.

The cloud AI pipeline looks like this: capture frame, encode, buffer (typically 2-5 seconds for network efficiency), upload to cloud endpoint, queue for processing, run inference, generate alert, send notification back. Total round-trip: 10-45 seconds depending on network conditions and processing queue depth.

Edge AI cuts this to: capture frame, run inference locally, generate alert. Total time: 1-3 seconds. The video never leaves the building until there's something worth sending. When an alert fires, the edge device sends a short clip or screenshot (kilobytes, not gigabytes) to the operator.

That latency gap matters most during active incidents. A trespasser who trips an alert in 2 seconds can be confronted before they reach the building interior. At 30 seconds, they're already inside. For property managers, this is the difference between prevention and documentation.

3. Monthly cost comparison: cloud vs edge

Cloud AI monitoring carries costs that compound with camera count. Here's what the monthly bill looks like:

• Bandwidth upgrade:Upgrading from 30 Mbps to 100+ Mbps upload (if available) costs $200-$800/month depending on your market and provider. Many properties can't get symmetric fiber at all.
• Cloud compute: Running continuous inference on 16 camera streams costs $1,500-$4,000/month in cloud GPU time. Video AI models are computationally expensive compared to text or image classification.
• Storage: Cloud video retention at 30 days for 16 cameras: $300-$600/month depending on resolution and compression.
• Total cloud cost:$2,000-$5,400/month for a 16-camera system, before counting the value of the internet bandwidth you're consuming from other building operations.

Edge AI devices flip the economics. You pay for the hardware once ($300-$1,500 depending on the solution) plus a monitoring subscription ($150-$400/month). The device uses your existing cameras, your existing DVR/NVR for storage, and near-zero internet bandwidth because all processing happens locally.

For a 16-camera property, edge AI typically costs 85-95% less per month than equivalent cloud processing. The payback period on the hardware is usually 1-2 months compared to what you'd spend on cloud compute alone.

4. Reliability and internet dependency

Here's a scenario that kills cloud-only security: your internet goes down. It might be a fiber cut, a router failure, or your ISP having an outage. With cloud AI, your entire security monitoring layer disappears instantly. Cameras still record locally (your DVR/NVR handles that), but the AI that watches those feeds and generates alerts is gone.

Edge AI continues operating regardless of internet connectivity. The device sits between your cameras and your DVR, processing feeds locally. Alerts can still be sent via cellular backup, SMS, or queued for delivery when connectivity returns. But the detection itself never stops.

For properties in areas with unreliable internet (rural multifamily, construction sites, properties in storm-prone regions), edge processing is not a preference. It's a requirement.

5. How edge AI solutions work in practice

Edge AI for security cameras works by placing a small compute device on-premise that connects to your existing camera infrastructure. The device intercepts or mirrors video feeds, runs AI models locally, and only sends alerts (with short clips or screenshots) over the internet when it detects something worth flagging.

The implementation varies by vendor. Some solutions require IP camera integration (connecting directly to each camera via RTSP streams). Others, like Cyrano, take a different approach: plugging into the HDMI output of your existing DVR/NVR. This means you don't need to reconfigure any cameras or network settings. If you can see your cameras on a monitor, Cyrano can process them.

The key differentiators between edge solutions:

• Camera capacity: How many feeds can the device handle simultaneously? Budget devices max out at 4-8 cameras. Higher-end solutions like Cyrano support up to 25 feeds per unit.
• Detection capabilities: Basic edge devices do person/vehicle detection only. More sophisticated models can assess behavior (loitering, tailgating, fighting) and assign threat levels.
• Alert quality:The difference between “motion detected in zone 3” and “person loitering near rear gate for 4 minutes, LOW THREAT” is what determines whether operators actually respond to alerts.
• Integration method: RTSP (requires network changes and camera credentials) vs HDMI (plug and play, works with any DVR/NVR brand).

The HDMI approach is particularly relevant for properties with legacy camera systems. Many multifamily properties have analog cameras from 5-10 years ago connected to a basic DVR. These cameras are perfectly functional for monitoring, but they don't support RTSP or modern IP protocols. An HDMI-based edge device works with whatever your DVR outputs to a monitor, regardless of camera age or type.

6. Decision framework: when to use what

Cloud AI makes sense in a narrow set of scenarios: when you have 1-4 cameras, abundant symmetric bandwidth (100+ Mbps upload), and don't need sub-5-second alert latency. Think a small retail store with gigabit fiber and a couple of cameras at the door.

Edge AI is the better fit when:

• You have 8+ cameras and limited upload bandwidth
• Real-time alerting (under 5 seconds) is important
• Internet reliability is not guaranteed
• You want to keep video data on-premise for privacy or compliance
• Monthly cloud compute costs exceed your security budget
• Your cameras are analog or legacy systems without IP capabilities

For most commercial properties, multifamily communities, and construction sites, edge AI is the only architecture that works within the constraints of existing infrastructure. The bandwidth limitations alone rule out cloud for any system with more than a handful of cameras.

The good news: you do not need to replace your cameras to get AI monitoring. Edge devices work with what you already have, installed on top of your existing DVR/NVR. No new cabling, no IP reconfiguration, no bandwidth upgrades.