What is the difference between edge compute and cloud computing?

The core difference between edge compute and cloud computing lies in the location, delay, bandwidth utilization and application scenarios of data processing.
edge compute sinks data processing tasks to devices or local nodes close to data sources (such as factory sensors, cameras, and on-board systems) to achieve low latency and high real-time, and is suitable for scenarios requiring high response speed, such as automatic driving, industrial control, and intelligent security.
Cloud computing relies on a centralized data center and provides powerful computing and storage capabilities through the Internet. It is suitable for scenarios requiring large-scale data analysis, long-term model training and resource elastic expansion, such as AI training, big data mining, enterprise application hosting, etc.
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Contrast dimension edge compute cloud computing
The data processing location is close to the data source (device end, local edge node) and remote data center
Extremely low latency (millisecond level response) relatively high latency (affected by network transmission)
Low bandwidth usage (only uploading processed results or critical data) and high (requiring uploading the original data stream)
Strong real-time performance, weak support for real-time decision-making, suitable for non real time batch processing
High reliability, local operation still depends on network connection when disconnected, service interruption occurs when disconnected
Typical applications include autonomous driving, intelligent manufacturing, real-time video analysis, big data analysis, AI training, and enterprise SaaS services
The two are not in opposition, but complementary. For example, in an intelligent factory, edge compute is responsible for real-time monitoring of equipment status and triggering alarms, while cloud computing is responsible for in-depth analysis of historical data, optimization of production processes and prediction of maintenance cycles.