Decision Analysis of System Architecture in Artificial Intelligence Cloud Service Model

Abstract

The artificial intelligence cloud service model is gradually becoming the core carrier to support the implementation of intelligent applications, and the rationality of its system architecture decision directly affects service efficiency and resource utilization. This paper focuses on the architecture design challenges in the integration scenario of artificial intelligence and cloud services, and explores the multi-dimensional balance mechanism of technology selection, cost control, and performance optimization. The research points out that system architecture decisions need to take into account algorithm complexity, data dynamics, and business flexibility requirements, and traditional architecture evaluation methods have limitations in the cloud-native environment. By introducing a multi-dimensional decision-making framework and combining cost-benefit analysis with simulation modeling tools, the comprehensive value of different architecture solutions can be quantitatively evaluated. Practical cases show that the dynamically scalable microservice architecture and serverless computing model have significant advantages in real-time inference scenarios, but one needs to be vigilant against the security risks caused by fragmented deployment. Future research should focus on the collaborative architecture design between edge intelligence and the cloud to meet the industrial needs of low latency and high concurrency.