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

Mateusz Ploskonka

Acta Informatica Pragensia, vol. preprint

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 trade-offs 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 Face-compatible providers.Results: Model performance follows a logarithmic scaling relationship with parameter count (R 2 =0.727) and deployment cost (R 2 =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.

Keywords: LLMs; Cloud computing; Serverless architecture; Cost optimization; Performance evaluation; Model deployment; Infrastructure selection (search for similar items in EconPapers)
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DOI: 10.18267/j.aip.313

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