Scaling LLMs in the Cloud: Data Engineering Strategies That Work

Abstract

Large Language Models (LLMs) are transforming multiple industries with their unprecedented language capabilities, but effectively deploying these models in production environments requires sophisticated data engineering infrastructure. This article examines architectural patterns and operational strategies enabling organizations to overcome deployment challenges in cloud-native ecosystems. From Kubernetes-based model hosting to vector databases and specialized memory optimization techniques, the article presents comprehensive mechanisms for scaling LLMs while balancing performance, cost, and reliability. The evaluation explores how tensor parallelism and quantization techniques address memory constraints, while event-driven architectures handle variable workloads efficiently. Special attention is given to enterprise considerations including multi-tenant architectures, security controls, and governance frameworks essential for regulated environments. By leveraging modern infrastructure components like container orchestration, serverless computing, and distributed data processing frameworks, organizations can build robust LLM systems that scale to meet diverse business needs while maintaining security and compliance requirements. The strategies presented serve as a practical roadmap for data engineers and machine learning practitioners tasked with delivering production-ready LLM applications in increasingly complex technical landscapes.