Cloud-Based Large Language Model Deployment: A Comparative Analysis of Serverless and Bring-Your-Own-Container Architectures

Abstract

Background: Large Language Models (LLMs) have transformed research and industry applications; however, cloud deployment decisions remain complex and poorly documented, particularly for academic researchers operating under budget constraints.Systematic guidance on infrastructure selection for LLM-based research is limited.Objective: This study provides a comprehensive empirical evaluation of cloud-based LLM deployment architectures, examining inference efficiency, serverless platform availability, and architectural tradeoffs across major cloud providers to deliver actionable guidance for budget-constrained researchers.Methods: The author evaluated 32 open-source LLMs ranging from 0.6 billion to 1 trillion parameters across serverless and Bring Your Own Container (BYOC) deployment configurations.Using the Belebele benchmark, we analyzed cost-efficiency relationships, serverless platform availability, and metrics exposure across Amazon SageMaker, Amazon Bedrock, Azure Serverless, and Hugging Facecompatible providers.Results: Model performance follows a logarithmic scaling relationship with parameter count (R=0.727)and deployment cost (R=0.639).Models in the 30-50B parameter range achieve 85-90% of maximum accuracy at a fraction of the cost of frontier models.However, serverless availability remains fragmented: only 34.4% of examined models are accessible via serverless endpoints, with minimal cross-platform redundancy (6.2%).Deployment architecture introduces a fundamental trade-off: serverless platforms expose 71% fewer metrics than BYOC approaches while eliminating infrastructure management overhead and idle costs.Conclusion: These findings provide practical guidance for researchers selecting cloud infrastructure under budget constraints.Models in the 7-14B range offer optimal cost efficiency, while the 30-50B range maximizes accuracy per dollar for demanding tasks.The results also challenge the prevailing emphasis on ever-larger models, as diminishing returns become substantial beyond 30B parameters.Persistent gaps in serverless availability and observability highlight the need for greater standardization in cloud platforms.

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