Artificial Intelligence in Cloud And Distributed Computing: Scalable And Cost-Efficient AI Systems

Abstract

Artificial Intelligence (AI) is an actively developing field that facilitates more sophisticated analytics, automation, and decision-making in a wide range of industries. However, the use of large-scale AI models is associated with significant computational resources and effective infrastructure, which is why cloud and distributed computing are considered parts of current AI systems. This systematic review evaluates existing studies on scalable and cost-effective AI architectures, frameworks and optimization strategies in the cloud and distributed settings. The database search was performed across IEEE Xplore, Springer, ScienceDirect, and Google Scholar and was limited to studies published in 2020-2025, dealing with scalability, performance, and cost optimization. The review identifies distributed training methods, such as data and model parallelism, containerization, orchestration software such as Kubernetes, and hybrid cloud-edge architectures in enabling high-performance AI deployment. The dynamic allocation of resources, auto-scaling, serverless computing, model design that is energy-efficient, and streamlined management of data make it cost-efficient. Regardless of such developments, there are still issues such as latency, communication overhead, data security, complexity of the infrastructure and high cost of operation. The gaps found in the standardized benchmarking, hybrid orchestration, sustainable AI, and awareness of privacy are recognized through comparative analysis. The future research directions will be green AI, AI-based resource management, federated learning, and standardized performance evaluation techniques. The review is a thorough synthesis of available literature, and it provides an idea on how to design scalable and affordable AI systems. These methods will allow the researchers and practitioners to create AI systems that are not only scalable and high-performing, but also economically viable, secure, and sustainable and address the evolving needs of the actual implementation in cloud computing and computational distributed computing systems.

Keywords: Artificial Intelligence, Cloud computing, Distributed computing, Scalability, Cost optimization, Hybrid cloud-edge, Federated learning.