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

Expedite troubleshooting of LLM applications

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
Assess accuracy and identify errors in embedding and retrieval steps to improve the quality and relevance of information retrieved via Retrieval-Augmented Generation (RAG)

Improve performance and reduce cost of LLM applications

Efficiently monitor key operational metrics for LLM applications, including cost and latency trends, across all major LLMs (GPT, Azure OpenAI, Amazon Bedrock, Anthropic, etc.) in a unified dashboard
Instantly uncover opportunities for performance and cost optimization with visibility into latency and token usage metrics for each call in the LLM chain
Swiftly take action to maintain optimal performance of LLM applications with real-time alerts on anomalies, such as spikes in latency or errors

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
Enhance business-critical KPIs, including user feedback, by implementing custom evaluations to evaluate the performance of your LLM applications
Tune-up LLMs by uncovering drifts in production by isolating semantically similar low-quality prompt-response clusters

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