Datadog LLM Observability: Monitor & Trace AI in Production

<div>Are your LLM applications running blind in production?</div><div><br></div><div>You've deployed an AI agent, a RAG pipeline, or an LLM-powered chatbot.&nbsp;</div><div><br></div><div>But can you answer these questions?</div><div><ul><li>How much did that runaway agent loop cost before someone noticed?</li><li><span style="font-size: 1rem;">Why did hallucination rates spike last Tuesday?</span></li><li><span style="font-size: 1rem;">Which step in your RAG pipeline is returning irrelevant documents?</span></li><li><span style="font-size: 1rem;">How do you prove to compliance that you're protecting customer PII in LLM conversations?</span></li></ul></div><div><span style="font-size: 1rem;"><br></span></div><div><span style="font-size: 1rem;">If you can't answer these questions with data, you have a production problem.</span></div><div><br></div><div>Traditional APM tools see your LLM as a black box.&nbsp; They measure latency and error rates, but they can't show you token flows, prompt effectiveness, or quality degradation.</div><div><br></div><div>LLMs are fundamentally different—non-deterministic, multi-step, token-priced, and quality-sensitive.</div><div><br></div><div>You need LLM-native observability.</div><div><span style="font-size: 1rem;"><br></span></div><div><span style="font-size: 1rem;">Introducing Datadog LLM Observability</span></div><div><br></div><div>This course is the definitive guide to Datadog's LLM Observability platform for enterprise teams.&nbsp;</div><div><br></div><div>If you're already using Datadog for APM, infrastructure, or security, this integrates directly into your existing stack—no new tools to learn, no separate dashboards to monitor.</div><div><br></div><div>What you'll build:</div><div><br></div><div>Throughout this course, you'll instrument a production-grade Customer Support AI Agent with:</div><div><ul><li><span style="font-size: 1rem;">Multi-turn conversation tracing</span></li><li><span style="font-size: 1rem;">Tool integration (order lookup, refund processing)</span></li><li><span style="font-size: 1rem;">Custom quality evaluations</span></li><li><span style="font-size: 1rem;">Cost monitoring dashboard</span></li><li><span style="font-size: 1rem;">PII scrubbing compliance</span></li></ul></div><div><span style="font-size: 1rem;">This isn't a toy example—it's the architecture real enterprise teams deploy.</span></div>

What you'll learn:

• Instrument LLM applications with Datadog's ddtrace SDK for full visibility into prompts, completions, and token usage
• Trace complex AI agent workflows including multi-turn conversations, tool calls, and decision paths with enterprise-grade debugging
• Implement production evaluations using managed checks (toxicity, relevancy) and custom LLM-as-a-judge evaluators
• Monitor and optimize LLM costs with automated cost tracking, budget alerts, and model comparison dashboards
• Run experiments to test prompt and model changes before production deployment using Datadog's experimentation framework
• Build secure AI systems with PII scrubbing, compliance patterns, and security monitoring for enterprise requirements
• Instrument RAG pipelines with custom spans for embedding, retrieval, and generation steps for complete workflow visibility
• Integrate LLM observability with existing Datadog APM, infrastructure, and security tools for unified enterprise monitoring