Cost-Aware and Scalable Approaches for Large-Scale Model Evaluation in Enterprise Systems

Abstract

The fast pace of development in Large Language Models (LLMs) has resulted in their adoption in enterprise systems for applications such as automated customer service, decision-making, and knowledge discovery. However, the assessment of these models in a cost-effective and scalable way is a challenge, especially when the models are complex. The conventional assessment techniques, which depend on static benchmarks and test data, fail to capture the dynamic nature of the enterprise environment. Additionally, the computational complexity of exhaustive assessment grows with the size of the models and the diversity of tasks. We present a cost-effective and scalable architecture for the assessment of large-scale language models and enterprise text analysis systems, which overcomes the above-mentioned challenges by leveraging adaptive sampling, decentralized assessment protocols, and enterprise-specific benchmarks. The proposed 1D-CNN model used in this architecture demonstrates very high classification performance on the evaluated dataset, supporting efficient large-scale model assessment. The proposed framework achieves a balance between fidelity and cost Thus, providing a practical framework for scalable evaluation and deployment of LLM-based systems in enterprise environments. Based on recent research and adjusted to fit enterprise constraints, this solution provides an effective platform for continuous model evaluation and decision-making.