Edge AI vs Cloud AI: A Comparative Study of Performance Latency and Scalability

The increasing need for real-time decision-making and low-latency processing in AI applications has created interest in the comparative study of Edge AI and Cloud AI. Cloud AI, with its high computational power and scalability, has been the choice for data-intensive applications. But with the introduction of IoT devices and the growing need for instant data processing, Edge AI, which processes data locally on devices close to the data source, has become a potential alternative. This shift in paradigm, however, introduces new challenges in balancing the computational power of edge devices with the processing and storage of cloud infrastructure. Despite the extensive amount of work in Edge and Cloud AI, to this point, there has been limited documentation in the form of an end-to-end literature that investigates the trade-offs in the performance, latency, and scalability of Edge and Cloud AI across a wide range of applications. Although Edge AI excels in latency reduction through compressing data transfer time, it generally does not have scalability and computational power, especially in handling complex AI models. On the other hand, Cloud AI excels in scalability and resource-intensive tasks but is limited by latency constraints on data transfer. This work tries to fill this gap by comparatively evaluating the strengths and limitations of Edge AI and Cloud AI, i.e., performance, scalability, and latency, in different application fields like healthcare, autonomous vehicles, smart cities, and industrial IoT. The research points toward the predominance of hybrid systems combining Edge and Cloud AI to combat these limitations and provide a balanced solution to new AI applications.