• GIREESH KAMBALA

Cloud-native architectures have emerged as a fundamental approach to building scalable, resilient, and cost-efficient applications in modern cloud computing environments. Among the dominant paradigms in cloud-native development, Kubernetes and Serverless Computing represent two distinct yet influential models that enable organizations to deploy, manage, and scale applications efficiently. This paper provides a comprehensive comparative analysis of Kubernetes and Serverless Computing, evaluating their architectural differences, scalability mechanisms, deployment strategies, cost implications, performance characteristics, and suitability for different workloads. Kubernetes, an open-source container orchestration platform, offers robust workload management, automated scaling, self-healing capabilities, and seamless deployment strategies for microservices-based applications. It enables greater control over infrastructure resources and is particularly suited for applications requiring complex orchestration, persistent state management, and hybrid-cloud or multi-cloud deployments. However, Kubernetes introduces challenges such as increased operational complexity, higher management overhead, and upfront infrastructure costs. In contrast, Serverless Computing, often implemented through Function-as-a-Service (FaaS) or Backend-as-a-Service (BaaS), abstracts infrastructure management and enables event-driven execution. Serverless platforms scale automatically based on demand, reducing operational overhead and offering a cost-efficient, pay-per-use pricing model. Despite these advantages, Serverless Computing is often limited by execution constraints, potential vendor lock-in, cold-start latencies, and difficulties in handling long-running processes and stateful applications. This study compares Kubernetes and Serverless Computing across multiple dimensions, including scalability, cost-effectiveness, performance, maintenance complexity, and use case suitability. Through an in-depth evaluation, we present empirical benchmarks demonstrating how both architectures perform under different workload conditions. We analyze real-world adoption trends and discuss industry use cases where each approach excels. Additionally, hybrid models that integrate Kubernetes with Serverless solutions are explored, offering a balance between control and automation. Our findings indicate that while Kubernetes is well-suited for large-scale, microservices-oriented applications requiring fine-grained resource control, Serverless Computing excels in scenarios where demand fluctuates unpredictably, requiring rapid, automatic scaling with minimal infrastructure management. The paper concludes with insights into future trends in cloud-native computing, such as AI-driven workload optimization, edge computing integration, and the convergence of Kubernetes and Serverless paradigms to enable more efficient, flexible, and scalable cloud deployments. By providing a comparative framework, this study aims to assist organizations, software architects, and cloud engineers in selecting the most appropriate cloud-native architecture for their application needs.

Cloud-native computing, Kubernetes, Serverless computing, Function-as-a-Service (FaaS), Container orchestration, Microservices, Cloud scalability, Cost efficiency, Deployment strategies, Performance benchmarking.

"Cloud-Native Architectures: A Comparative Analysis of Kubernetes and Serverless Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.n208-n233, April-2023, Available :http://www.jetir.org/papers/JETIR2304F29.pdf

"Cloud-Native Architectures: A Comparative Analysis of Kubernetes and Serverless Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppn208-n233, April-2023, Available at : http://www.jetir.org/papers/JETIR2304F29.pdf