MonaLabs

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.

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.

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.

Stores and analyzes historical performance data to identify trends, patterns, and anomalies over time. Enables retrospective analysis of model behavior and performance evolution.

Captures detailed logs of inference requests and responses for debugging and analysis. Enables replay of specific requests to understand model behavior and troubleshoot issues.

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.

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.

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.

Tracks token consumption across LLM API calls and monitors usage against quota limits. Provides visibility into token spending patterns and alerts when approaching quota thresholds.

Tracks end-to-end latency of model inference requests and identifies performance bottlenecks. Measures latency across different components and provides percentile distributions to understand tail performance.

Provides a minimal-footprint SDK that integrates into existing production deployments with minimal code changes. Enables rapid instrumentation of AI applications without requiring architectural modifications.

Evaluates and scores the quality of individual predictions or batches of predictions. Provides metrics on prediction confidence, uncertainty, and correctness when ground truth labels become available.

Consolidates monitoring and metrics across multiple LLM providers (OpenAI, Anthropic, etc.) in a single unified dashboard. Enables consistent tracking regardless of which provider is used for inference.