LLM Observability

Measure and Validate LLM Quality with Built-In Evaluation Frameworks

Get clear, automated evaluations for every model run with built-in accuracy, precision, recall, and F1 scoring
Compare models, prompts, and configurations side-by-side using benchmarking dashboards and experiment results
Validate outputs in context against expected patterns and monitor drift in accuracy, topic relevancy, and sentiment over time
Catch quality issues early, including hallucinations and off-topic responses, with custom KPI-based evaluations
Apply a complete evaluation framework across pre-production and production with retrieval testing, faithfulness scoring, and relevancy analysis

Automatically catch inaccurate responses before they reach your users by flagging contradictions and unsupported claims using Datadog’s hallucination detection engine
Customize detection sensitivity for your use case by flagging only critical contradictions or both contradictions and unsupported claims
Pinpoint root causes by drilling into full traces to see the exact hallucinated claim and where it failed in the chain
Continuously improve models by tracking hallucination trends over time by model, tool call, or environment

Quickly investigate the root cause of hallucinations, low-quality outputs, and other anomalies with complete trace visibility across your LLM chain
Fix issues at the source, whether in embeddings, retrieval settings, or prompt construction, to improve reliability before you scale
Debug complex RAG workflows by pinpointing and correcting errors in embeddings, retrieval, and context injection steps
Feed resolved issues into performance monitoring to ensure improvements are reflected in cost, latency, and accuracy metrics over time

Keep costs under control by tracking key operational metrics like tokens, usage patterns, and latency trends across all major LLMs in one place
Take action instantly as issues arise with real-time alerts on anomalies such as latency spikes, error surges, or unexpected usage changes
Instantly uncover opportunities for performance and cost optimization by drilling into detailed end-to-end data on token usage and latency across the entire LLM chain

Protect user privacy by preventing the exposure of sensitive data, including PII, emails, IP addresses, and API keys, through built-in security measures
Defend against direct and indirect prompt injection attacks by scanning prompts, responses, and retrieved content for malicious patterns before they can be executed
Monitor MCP server interactions to detect unauthorized tool changes, credential exposure, and unusual activity patterns, and protect against threats such as tool poisoning, rug pulls, and consent fatigue exploitation
Secure your RAG pipelines by detecting and tracing malicious instructions seeded in vector databases and identifying the exact documents used in each model response

Get full visibility into every experiment run with automatic tracing that captures evaluation scores, latency, errors, and token usage
Resolve regressions faster by isolating low-scoring test cases and inspecting tool calls, retrieval steps, and intermediate outputs in the execution trace
Keep testing repeatable across teams with versioned datasets, experiment runs, and shared performance analysis in one place
Compare experiment outcomes alongside production telemetry and evaluation signals from the same platform