Large Language Models (LLMs) have quickly become integral to modern software applications, enhancing capabilities across industries by providing AI-driven functionalities such as customer support and code generation. While these advancements offer significant business value, they also introduce complexities and risks, including performance volatility, cost unpredictability, quality drift, and security concerns. Traditional Application Performance Monitoring (APM) methods, which rely on heavy instrumentation, are inadequate for the dynamic nature of LLMs. In response, groundcover has developed a zero-instrumentation observability solution using eBPF technology, allowing organizations to monitor LLM interactions without code changes, ensuring security and compliance by keeping all data within the organization's cloud environment. This approach provides comprehensive insights into token usage, latency, and error patterns, helping teams optimize performance and manage costs effectively.