Datadog LLM Observability provides end-to-end tracing, monitoring and diagnostics for large language models and agentic application workflows. It maps each prompt, tool call and intermediate step into spans and traces; tracks latency, token usage, errors and quality metrics; and integrates with Datadog’s broader APM and observability suite.
Organizations already using Datadog — and familiar with its cost structure — may find the LLM observability feature a straightforward way to gain visibility into AI workloads, assuming those workloads can be instrumented. However, configuring and using LLM instrumentation requires care and a solid understanding of both the workloads and their implementation. We recommend data engineers and operations staff collaborate closely when deploying it. See also our advice on avoiding standalone data engineering teams..
