Monitoring large language models (LLMs) after deployment is critical for long-term success, ensuring reliable performance, accuracy, and alignment with user expectations. It involves tracking key performance metrics such as latency, throughput, and factual correctness to prevent hallucinations and maintain high-quality outputs. Specialized tools like Galileo provide detailed analytics on various metrics, including token usage and GPU consumption, aiding in resource optimization and cost efficiency. AI-driven solutions detect anomalies in real time, analyzing patterns to identify deviations, while integration with DevOps practices ensures smooth operation with existing infrastructure, optimizing performance and supporting faster troubleshooting. Effective monitoring involves setting metrics, ongoing evaluations, and teamwork, ultimately driving continuous improvement and ensuring high-quality AI experiences.