Ensure AI performs as intended and at the right cost
Out-of-the-box quality evaluations
Evaluate model and agent performance, quality, and behavior in real time
Track and measure output quality with scores that detect issues like hallucinations, bias, relevance, toxicity, sentiment, and other out-of-the-box evaluators.
Token usage and cost
Control costs and optimize resources across the AI stack
Track token usage, operational expenses, GPU and memory utilization, and other tokenomics and GPU-related metrics from specific requests, models, agents, workflows, and other AI infrastructure components over time.
Built-in guardrails and controls
Pinpoint and mitigate AI security risks in real time
Safeguard and improve models with security, privacy, and safety guardrails for PII, PHI, and PCI leakage, tool misuse, and prompt injection. Comply with AI security standards to confidently build and deploy trustworthy AI applications and systems.
Agent performance analysis
Observe agent performance
Track the requests, errors, latency, token usage, and quality scores of individual agents over time to establish baselines, detect outliers, and make data-driven decisions.
Agent workflow analysis
Visualize the sequence of steps, dependencies, and handoffs
See the associated tool calls, models, and retrieval steps of an agent workflow — from request to response — to investigate failures.
Interaction-centric trace views
Get trace-level visibility with AI details, tags, and span details
View inputs, outputs, and system prompts alongside quality metrics that are associated with each step of the agent workflow for end-to-end root cause.
