As organizations increasingly utilize large language models (LLMs), LiteLLM has emerged as a tool to streamline access to various LLM providers and models, albeit with challenges in understanding model selection and performance due to its abstraction layer. To tackle these issues, Datadog has introduced an Agent integration and SDK with LiteLLM, providing comprehensive observability across LLM workflows. This integration allows users to monitor, troubleshoot, and optimize applications by tracing every LLM request, offering insights into model performance, token usage, latency, and cost. The Datadog Agent further enhances this by capturing high-level metrics about the LiteLLM proxy service, aiding in performance trend tracking and ensuring reliability. Together, these tools offer full-stack visibility, empowering teams to make informed decisions about host sizing, model selection, and resource allocation, ultimately supporting data-driven optimization of LLM-powered applications.