Groundcover addresses the challenges of monitoring Large Language Models (LLMs) in production environments by providing comprehensive observability tools that offer insights into aspects like request volume, latency, errors, and costs. Traditional monitoring tools fail to provide the necessary visibility into LLMs, which often function as black boxes in the technology stack. Groundcover's solution leverages eBPF technology, enabling developers to monitor LLM activity without the need for additional instrumentation or SDKs, thus eliminating blind spots. The platform supports AWS Bedrock alongside OpenAI and Anthropic, providing a unified view of LLM performance across different providers, and ensures data residency and security within an AWS instance. This observability is crucial for understanding and controlling costs, as LLMs can quickly become expensive if not monitored properly. Moreover, the integration of LLM observability with other monitoring metrics like CPU, memory, and network allows teams to identify and address issues efficiently, preventing costly outages and ensuring data safety by flagging sensitive information. As the AI landscape rapidly evolves, having such visibility is essential for teams to manage AI systems effectively and maintain operational integrity from the onset of LLM adoption.