LLM observability tools are essential for monitoring, tracing, and evaluating LLM applications, providing insights into their non-deterministic behavior and helping prevent unpredictable failures. These platforms enable users to trace requests, assess output quality, and monitor performance, ensuring issues are detected before affecting users. Choosing the right observability tool depends on the specific needs of the application, including whether it is in development or production, and whether it requires evaluation-centric or observability-centric features. Popular tools in 2025 include open-source options like Opik and Langfuse, which offer comprehensive observability and evaluation capabilities, and commercial platforms like Datadog and LangSmith, which provide integration with existing systems and advanced features such as real-time guardrails and automated prompt optimization. The choice between self-hosted and managed services also depends on factors like control, compliance, and cost considerations. Regardless of the platform chosen, implementing observability is crucial for ensuring reliable and performant LLM applications.