Performance‑Aware Lifecycle Framework for Deployment of Large Language Models in Cloud‑Native and Serverless Environments

Abstract

Large Language Models (LLMs) have rapidly evolved to become central components in contemporary artificial intelligence applications, promising sophisticated natural language understanding, generation, and decision-making capabilities. However, their deployment at scale—especially within cloud-native and serverless infrastructures—poses significant challenges in terms of performance, scalability, cost-efficiency, and lifecycle management. Existing literature offers detailed surveys on LLM capabilities, optimization techniques, and deep learning model compression (Hadi et al., 2023; Raiaan et al., 2024; Patil & Gudivada, 2024; Menghani, 2023), as well as broader concerns and methodologies regarding cloud-native architectures, serverless latency, and machine learning (ML) lifecycle orchestration (Henning & Hasselbring, 2022; Golec et al., 2024; Ashmore et al., 2021; Kodakandla, 2021; Buyya et al., 2018; Nigenda et al., 2022). Yet, limited work integrates these threads into a unified deployment and evaluation framework tailored for LLM-driven services. In this article, we propose a comprehensive, performance-aware lifecycle framework for LLM deployment in cloud-native and serverless environments. The framework systematically addresses scalability benchmarking, resource optimization, latency mitigation (particularly cold-start issues), continuous testing and monitoring, cost optimization, and compliance with ML lifecycle best practices. We elaborate on the theoretical underpinnings of the framework, describe a methodology for its adoption, present hypothetical results illustrating potential gains, discuss limitations, and outline avenues for future research. Our goal is to equip ML engineers, system architects, and academic researchers with a cohesive, practical, and theoretically grounded guideline for deploying LLM-based systems under real-world constraints.