The rapid growth of Large Language Models (LLMs) has revolutionized business operations across sectors such as finance, healthcare, customer service, and more. These models are no longer experimental but are becoming core components in streamlining workflows and decision-making. However, the integration of LLMs brings new challenges: ensuring the accuracy of outputs, monitoring model performance, and maintaining strict compliance with industry regulations[1]. This white paper provides an in-depth review of leading LLM tracing and evaluation tools, examining their strengths, limitations, and relevance for real-world use cases. We aim to guide organizations in choosing the right tools to enhance model performance, ensure compliance with data security protocols, and generate actionable insights.