When deploying AI video analytics, one of the first architectural decisions is where inference runs. Edge AI processes video near the camera. Cloud analytics sends video or metadata to a remote service. Hybrid designs combine both.
Edge AI video analytics
BAV Edge runs approved AI models on site or near the cameras. This can reduce dependence on video backhaul, support data-control requirements and allow continued local operation when connectivity is limited. Results still depend on available compute, network design and operational acceptance.
Cloud video analytics
Cloud processing centralizes inference and management. It can simplify scaling and reduce on-site hardware requirements. It requires reliable connectivity and may not suit environments with strict data-residency requirements.
Hybrid architecture
A hybrid design runs inference at the edge while synchronizing selected events and operational data with central services. AVEKSHA can provide authorized multi-site operational views while BAV Edge continues site-level connectivity and inference.
How to choose
Consider bandwidth availability, data-control requirements, site resilience needs, compute budget and operational latency requirements. A camera and workflow assessment helps determine the right architecture before deployment.
What edge AI does not mean
Edge AI does not mean zero bandwidth, unlimited offline operation or guaranteed lower latency in every scenario. The actual performance depends on the hardware, workload, network design and operating conditions.