Cloud-Native LLMOps Meets DataOps: A Unified Framework for High-Volume Analytical Systems

The need to operate large language models (LLM) in real-time, at scale, and with explainability has finally brought Cloud-Native Operations (CloudOps), Data Operations (DataOps), and Large Language Model Operations (LLMOps) together at pace. This paper presents a coherent design that harmonises Cloud-Native LLMOps and DataOps to ensure the high-volume analytical systems. The framework builds on top of containerized microservices, event-driven data pipelines, and continuous model observability to scale data ingestion, transformation, LLM-based processing, and feedback loops. The proposed architecture, through embedded semantic metadata layers, policy-based automation, and lineage-aware monitoring, can increase reproducibility, compliance, and resilience of the systems. An enterprise-scale document intelligence case study shows a huge reduction in latency and throughput as well as model adaptability. The results indicate that the convergence of LLMOps and DataOps within a cloudnative landscape is a resilient route to operating AI in flexible, data-intensive workflows.