Large language models (LLMs) are at the core of many financial applications, where accuracy, reliability, and adaptability are essential for success. However, deploying an LLM is just the beginning—ensuring its long-term effectiveness requires continuous monitoring and maintenance. Over time, performance degradation, data drift, and evolving user needs can impact model accuracy and relevance. If left unchecked, these issues can lead to financial losses, compliance risks, and reduced trust in AI-driven decisions. This chapter explores key strategies for monitoring LLM performance, the importance of regular updates, and best practices for handling model drift. By implementing robust maintenance workflows, organizations can proactively detect performance issues, retrain models with fresh data, and optimize their LLMs for long-term efficiency. With a structured approach to LLM monitoring and maintenance, financial institutions can extend model lifespan, enhance decision-making accuracy, and ensure compliance with evolving regulatory standards.