LLM Observability Best Practices

As organizations expand their use of generative AI and large language models (LLMs), gaining visibility into model performance, reliability, and cost has become critical. Traditional observability practices must evolve to address the unique challenges of LLM applications—tracking prompts, latency, accuracy, and resource utilization to ensure efficient and trustworthy AI-driven systems.

In this eBook, you’ll learn how to extend observability to every layer of your LLM stack. Explore real-world use cases from Datadog customers, an actionable framework for implementing LLM observability, and key criteria for selecting the right solution to monitor and optimize your AI workloads at scale.