Edge vs Cloud Inference Trade-offs for Video ML

Edge Inference Advantages

Low latency: No network round-trip. Results in milliseconds rather than tens of milliseconds. Critical for time-sensitive applications like collision avoidance.

Bandwidth savings: Process locally, send only results. A camera producing 10 Mbps of video might generate only 1 Kbps of detection metadata. Reduces network infrastructure costs dramatically.

Privacy preservation: Raw video never leaves the device. Only processed metadata is transmitted. Addresses regulatory concerns about video data handling.

Edge Inference Limitations

Model capability: Edge devices have limited compute. MobileNet-class models work; large transformers do not. Accuracy is 5-15% lower than cloud models on challenging cases.

Update complexity: Thousands of distributed devices require over-the-air updates. Rolling out a new model takes days or weeks, not minutes.

Hardware costs: Each device needs ML-capable hardware. At scale, per-device costs add up significantly.

Cloud Inference Advantages

Model power: Use the largest, most accurate models available. No hardware constraints. Easy to upgrade models instantly across all streams.

Centralized management: All compute in one place. Easier monitoring, debugging, and optimization.

Decision Framework

Choose edge when: Latency below 50ms required. Bandwidth is expensive or unreliable. Privacy regulations restrict video transmission.

Choose cloud when: Accuracy is paramount. Models change frequently. Centralized analysis of multiple cameras is needed.