Cloud-Native Application Development for AI-Conducive Architectures.

Abstract

The integration of AI technologies into cloud-native application development is reshaping the landscape of software engineering by enhancing scalability, flexibility, and efficiency. This paper explores the evolution of development processes driven by advancements in AI and cloud technologies, emphasizing the importance of adopting an AI mindset. It discusses how cloud-native architectures, characterized by microservices and containerization, align well with AI requirements, enabling organizations to deploy and scale AI-driven applications effectively. Key guidelines for balancing innovation with cost-efficiency are outlined, including leveraging generative AI tools, optimizing resource management, and implementing observability practices. The paper also examines the impact of AI on architectural decision-making, such as data management and real-time processing, and highlights the role of databases in managing extensive data required for AI tasks. Additionally, the paper compares legacy tools with AI-driven tools in accelerating development processes and addresses security considerations in AI-enabled cloud-native applications. The future direction of AI-driven cloud-native development is discussed, focusing on advancements in efficiency, scalability, and security.