Edge vs Cloud Inference: The Key Trade-offs for Live Sports Highlight Systems

Building a live sports highlight detection system requires a foundational architectural choice: whether to run AI inference at the edge, close to the video source, or in the cloud after the stream is ingested. Edge inference reduces latency and bandwidth by processing footage locally and transmitting only relevant clips, but is limited by fixed hardware capacity, smaller model sizes, and complex distributed update management. Cloud inference offers elastic compute, easier model updates, and the ability to handle multiple concurrent streams, but adds latency and bandwidth costs due to the longer data travel path. In practice, most production systems adopt a hybrid approach, using lightweight edge models for time-critical first-pass detection and cloud infrastructure for richer downstream analysis like ranking and clip assembly. Experts recommend defining a latency budget first and then assigning each pipeline stage to edge or cloud accordingly.