LLM Observability

Expedite Troubleshooting of Your OpenAI Applications

Gain full visibility into end-to-end traces for each user request, allowing you to quickly pinpoint the root causes of errors and failures in your LLM chain
Analyze inputs and outputs at each step of the LLM chain to quickly resolve issues such as failed LLM calls, task interruptions, and service interactions
Improve the accuracy and relevance of your LLM outputs by identifying and correcting errors in the embedding and retrieval steps, and optimize the performance of Retrieval-Augmented Generation (RAG) systems

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

Easily spot and address quality concerns, such as missing responses or off-topic content, with turnkey quality evaluations
Enhance business-critical KPIs, and detect hallucinations, by implementing custom evaluations that accurately assess and improve the performance of your LLM applications
Automatically detect drifts in production and optimize your LLMs by isolating and addressing low-quality prompt-response clusters with similar semantics

Visualize every decision and action in your multi-agent workflows, from planning steps to tool usage, to understand exactly how outcomes are produced
Pinpoint issues fast by tracing interactions between agents, tools, and models to find the root cause of errors, latency spikes, or poor responses
Optimize agent behavior with detailed insights into performance metrics, memory usage, and decision-making paths
Correlate agentic monitoring with broader application performance by connecting LLM traces to microservice, API, and user experience data—all in one place

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