Cloud-Native Continuous Integration and Delivery as a Performance Catalyst for Large Language Models: A Systems-Theoretic and Empirical Synthesis
Year: 2026
Type: article
Abstract: Large language models have moved from laboratory artifacts to mission-critical digital infrastructures that power search, analytics, medical triage, software development, and enterprise decision support. This transition has exposed a structural tension between the static nature of pre-trained neural representations and the dynamic, continuously evolving requirements of production-grade artificial intelligence systems. While the literature has traditionally focused on architectural innovations, training regimes, and prompt-level optimization, a parallel but under-theorized dimension has emerged around the operationalization of model lifecycles through cloud-native continuous integration and continuous delivery pipelines. This article develops a comprehensive research synthesis that positions CI/CD not as an auxiliary software engineering practice but as a primary determinant of large language model performance, reliability, and epistemic alignment in real-world deployment. Drawing on recent theoretical and empirical work, especially the cloud-based CI/CD framework proposed by Chandra et al. (2025), this study conceptualizes LLM performance as an emergent property of iterative deployment cycles, automated evaluation feedback, and distributed cloud orchestration. The methodological contribution of this work lies in its text-based, theory-driven synthesis of cloud engineering and machine learning scholarship. Rather than relying on numerical experiments, the study uses comparative interpretive reasoning to examine how CI/CD pipelines mediate between model training, inference-time prompting, and user interaction. The results indicate that models embedded in well-designed CI/CD environments exhibit lower hallucination rates, faster alignment to domain shifts, and more stable downstream task performance, corroborating the systems-level framework articulated by Chandra et al. (2025). The discussion extends these findings into broader debates about automation, governance, and epistemic trust in artificial intelligence, arguing that CI/CD represents a new paradigm of algorithmic accountability.
Author: Dr. Mateo Alvarez
Institution: Universidad de Buenos Aires
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7220112027