What Is the Best Architecture for Real-Time Vision AI Systems?

In real-time vision AI pipelines, a hybrid approach often emerges as the most effective solution, integrating both edge and cloud processing to balance latency, privacy, and reliability. Commonly, light models run on edge devices to meet constraints like low latency, privacy, and occasional offline operations, while more complex tasks are deferred to cloud models when higher accuracy is needed. The pipeline usually involves stages such as capture, decode, preprocess, inference, post-process, tracking, and action, with latency predominantly arising from sensor exposure and network issues rather than inference itself. For detection, tracking, and segmentation, models such as YOLO, RT-DETR, and SAM 2 are recommended, with vision-language models reserved for specific tasks requiring open-vocabulary or contextual reasoning. Architectural pitfalls often include improper balancing between edge and cloud processing, inadequate handling of video codec conversion, and neglecting privacy considerations. Dynamic batching and pre-warming techniques are emphasized to prevent latency spikes and ensure smooth operation.