Feature Overview

Datadog LLM Observability provides end-to-end tracing across AI agents with visibility into inputs, outputs, latency, token usage, and errors at each step, along with structured experiments and robust quality and security evaluations. By correlating LLM traces with APM and utilizing cluster visualization to identify drifts, Datadog LLM Observability helps teams rapidly test and validate changes in development and confidently scale AI applications in production while ensuring quality, safety, and cost-efficiency.

Improve AI agent behavior and operational performance

Track how AI agents and LLMs behave and why by tracing prompts, responses, and intermediate steps across AI agents
Improve performance and cost efficiency by monitoring latency, token usage, and errors throughout agentic workflows and LLM chains
Ensure consistent and reliable user experiences by identifying and troubleshooting production bottlenecks like slow response times

Expedite troubleshooting of LLM applications

Balance performance, cost, and quality with structured experiments

Generate datasets directly from production traces to test changes against real-world scenarios
Validate and compare experiments in minutes using Playground to test prompt tweaks, swap models, or fine-tune parameters
Experiment with configurations, benchmark performance, and select your preferred iteration to confidently move to production

Datadog LLM Observability experiments dashboard showing accuracy, cost, token count, duration, and evaluation metrics for GPT-4.

Evaluate and safeguard output quality, security, and safety

Detect issues like hallucinations with out-of-the-box evaluation frameworks or build custom evaluations for your KPIs
Enhance quality with prompt-response cluster visualizations that isolate low-quality outputs to identify drifts
Prevent leaks with built-in scanners and flag prompt injection attempts automatically

Datadog LLM Observability clusters view showing grouped AI traces, failure-to-answer metrics, and detailed input-output analysis

Unify visibility across your entire application stack

Improve full application performance and cost by tying LLM workloads to backend service and infrastructure metrics with APM
Connect LLM performance to user impact by linking response times and quality to real user sessions in RUM
Ship performant and reliable AI applications faster by accessing full stack visibility in one platform