AI Observability is a crucial framework for gaining real-time insights into the operation and performance of AI systems, especially large language models (LLMs), which diverge significantly from traditional software in their probabilistic decision-making and dynamic behavior. As conventional monitoring tools fall short in addressing the complexities of LLMs, specialized observability tools become essential for tracking semantic drift, output quality, and emerging anomalies, thereby preventing customer-facing issues. The practice extends beyond monitoring CPU and memory usage to understanding model interactions, optimizing performance, and ensuring security. AI Gateways play a pivotal role by providing centralized control over AI traffic, enhancing observability through unified metrics collection, and enabling efficient troubleshooting. OpenTelemetry further supports this framework by standardizing data collection and ensuring interoperability across diverse systems. Essential metrics such as latency, throughput, error rates, and token usage are critical for maintaining efficient, cost-effective AI operations. By adopting AI Observability, organizations can manage complex LLM deployments effectively, ensuring reliability, reducing costs, and enhancing user satisfaction while staying agile in an evolving AI landscape.