real-time model performance monitoring
Continuously tracks and displays key performance metrics for deployed AI models including accuracy, latency, throughput, and inference quality. Provides live dashboards that update as models process requests in production.
automated data drift detection
Automatically identifies when input data distributions shift away from training data without requiring manual threshold configuration. Detects statistical anomalies in feature distributions that could indicate model degradation.
custom metric definition and tracking
Allows definition of custom metrics specific to application needs beyond standard performance metrics. Enables tracking of business metrics, domain-specific quality measures, and application-level KPIs.
historical performance analytics
Stores and analyzes historical performance data to identify trends, patterns, and anomalies over time. Enables retrospective analysis of model behavior and performance evolution.
inference request logging and replay
Captures detailed logs of inference requests and responses for debugging and analysis. Enables replay of specific requests to understand model behavior and troubleshoot issues.
model degradation alerting
Proactively detects when model performance metrics decline below acceptable levels and triggers alerts. Identifies performance regressions caused by data drift, concept drift, or other factors affecting prediction quality.
unified llm cost tracking and analysis
Aggregates and analyzes costs across multiple language models and API providers in a single dashboard. Tracks token usage, API call costs, and provides cost breakdowns by model, endpoint, and time period.
multi-model performance comparison
Displays performance metrics side-by-side across multiple deployed models or model versions. Enables comparison of latency, accuracy, cost, and other metrics to evaluate model variants in production.
+5 more capabilities