LLM Observability

Resolve Quality and Reliability Issues Before They Impact Performance

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

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