Improving Enterprise Application Reliability in Cloud Platforms through LLMOps and Automated  Model Observability

Ahmed Muhi Shantaf

doi:10.15662/IJAESIT.2026.0905001

Articles

Improving Enterprise Application Reliability in Cloud Platforms through LLMOps and Automated Model Observability

Ahmed Muhi Shantaf

Karabuk University, Turkey

Abstract

Enterprise applications deployed on cloud platforms increasingly rely on artificial intelligence and large language models (LLMs) to support decision-making, automate business processes, enhance customer interactions, and improve operational efficiency. However, integrating these models into enterprise environments introduces reliability challenges, including model drift, unpredictable outputs, latency variations, security vulnerabilities, and increased operational costs. This study examines how Large Language Model Operations (LLMOps) and automated model observability can improve the reliability, scalability, and performance of cloud-based enterprise applications. It explores the integration of continuous monitoring, automated evaluation, distributed tracing, anomaly detection, model versioning, and feedback-driven optimization into existing cloud infrastructure. The research methodology adopts a qualitative, literature-based approach to investigate established cloud reliability practices and emerging LLMOps frameworks. Particular attention is given to observability metrics, including response latency, error rates, output quality, token consumption, retrieval accuracy, and service availability. The study also examines how automated incident detection and model lifecycle management can reduce operational disruptions and support proactive maintenance. The proposed framework emphasizes continuous evaluation, transparent monitoring, secure deployment, and coordinated collaboration between development, operations, and machine learning teams. The findings are expected to demonstrate that combining LLMOps with automated model observability can strengthen operational resilience, improve service consistency, and support dependable AI adoption across complex enterprise cloud environments

Article Information

Journal	International Journal of Advanced Engineering Science and Information Technology (IJAESIT)
Volume (Issue)	Vol. 9 No. 5 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)
DOI	https://doi.org/10.15662/IJAESIT.2026.0905001
Pages	1671-1679
Published	October 9, 2026
Copyright	Creative Commons Attribution 4.0 International License
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Ahmed Muhi Shantaf (2026). Improving Enterprise Application Reliability in Cloud Platforms through LLMOps and Automated Model Observability. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 9 No. 5 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 1671-1679. https://doi.org/10.15662/IJAESIT.2026.0905001

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