LLM Observability | Datadog
LLM Observability

LLM Observability

Monitor, Troubleshoot, Improve, and Secure Your LLM Applications

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
Improve performance and reduce cost 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
  • 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
Evaluate and enhance the response quality of LLM applications

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
Safeguard LLM applications from security and privacy risks

Resources

products/llm-observability/llm-observability-product-hero-240612-desktop

official docs

LLM Observability
/blog/datadog-llm-observability/llm-obs-hero

BLOG

Monitor, troubleshoot, improve, and secure your LLM applications with Datadog LLM Observability
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BLOG

Integration roundup: Monitoring your AI stack
/blog/ml-model-monitoring-in-production-best-practices/ml-model-monitoring-hero

BLOG

Machine learning model monitoring: Best practices
Get started with LLM Observability today with a 14-day free-trial