This article presents a comprehensive analysis of the strategic considerations in choosing between dedicated machine learning models and Large Language Models (LLMs) for various applications. The article examines the performance metrics, resource requirements, and cost-benefit relationships of both approaches through multiple case studies, including inventory optimization and content generation scenarios. Through empirical evidence and comparative analysis, the article demonstrates that while LLMs offer remarkable versatility in handling diverse tasks, dedicated ML models often provide superior performance and resource efficiency for specialized applications. The article highlights the importance of aligning technological choices with specific use cases and operational requirements, providing organizations with a framework for making informed decisions about their machine learning implementations.
Keywords: dedicated ml models, large language models, machine learning strategy, model selection framework, resource optimization
