LLM Observability

Datadog LLM Observability provides end-to-end tracing of LLM chains with visibility into input-output, errors, token usage, and latency at each step, along with robust output quality and security evaluations. By seamlessly correlating LLM traces with APM and utilizing cluster visualization to identify drifts, Datadog LLM Observability enables you to swiftly resolve issues and scale AI applications in production, all while ensuring accuracy and safety.

Expedite troubleshooting of erroneous and inaccurate responses

Quickly pinpoint root causes of errors and failures in the LLM chain with full visibility into end-to-end traces for each user request
Resolve issues like failed LLM calls, tasks, and service interactions by analyzing inputs and outputs at each step of the LLM chain
Enhance the relevance of information obtained through Retrieval-Augmented Generation (RAG) by evaluating accuracy and identifying errors in the embedding and retrieval steps

Expedite troubleshooting of LLM applications

Evaluate and enhance the response quality of LLM applications

Easily detect and mitigate quality issues, such as failure to answer and off-topic responses, with out-of-the-box quality evaluations
Uncover hallucinations, boost critical KPIs like user feedback, and perform comprehensive LLM assessments with your custom evaluations
Refine your LLM app by isolating semantically similar low-quality prompt-response clusters to uncover and address drifts in production.

Improve performance and reduce cost of LLM applications

Easily monitor key operational metrics for LLM applications like cost, latency, and usage trends with the out-of-the-box unified dashboard
Swiftly detect anomalies such as spike in errors, latency and token usage with real-time alerts to maintain optimal performance
Instantly uncover cost optimization opportunities by pinpointing the most token-intensive calls in the LLM chain

Safeguard LLM applications from security and privacy risks

Prevent leaks of sensitive data—such as PII, emails, and IP addresses—with built-in security and privacy scanners powered by Sensitive Data Scanner
Safeguard your LLM applications from response manipulation attacks with automated flagging of prompt injection attempts

Setup in seconds with our SDK:

WHOOP Coach is powered by the latest and greatest in LLM AI. Datadog's LLM Observability allows our engineering teams to evaluate performance of model changes, monitor production performance and increase quality of Coach interactions. LLM Observability allows WHOOP to provide and maintain coaching for all our members 24/7

Bobby Johansen Senior Director Software, WHOOP

The Datadog LLM Observability solution helps our team understand, debug and evaluate the usage and performance of our GenAI applications. With it, we are able to address real-world issues, including monitoring response quality to prevent negative interactions and performance degradations, while ensuring we are providing our end users with positive experiences