AI systems in production need observability that goes beyond traditional monitoring. Track model quality, data drift, latency, token costs, and failure patterns across your AI fleet.
Track accuracy, precision, recall, and drift over time. Detect degradation before it impacts users — with alerts when quality drops.
Monitor input data for anomalies, schema changes, and quality issues. Catch problems at the source before they affect model outputs.
Break down AI spend by model, endpoint, team, and use case. Identify waste, forecast budgets, and optimize where every dollar goes.
Track time-to-first-token, total response time, error rates, and availability across all AI services. Build SLOs and alert on violations.