Evidently AI

Evidently AI is an open-source powerhouse that slashes ML monitoring time by 10X. In 200 words: it spots data drift, model decay, and LLM issues with one Python line, then auto-generates interactive dashboards you can embed or email. Loved by DeepL, Wise, and Realtor.com, its statistical engine and LLM-as-judge evaluators turn silent failures into instant Slack alerts. Zero infra bloat, forever-free core, and a new cloud tier at $0.05 per 1k predictions make it the fastest, cheapest route to bulletproof ML observability.

Tabular Data Drift & Quality Suite

Evidently started here, and it remains the most battle-tested module. Users can compare training versus production datasets, receive feature-level drift scores, and obtain granular quality indicators such as missing-value spikes, cardinality explosions, or unexpected new categories.

ML Model Performance Monitoring

Regression, classification, and ranking metrics—MAE, RMSE, ROC-AUC, Precision@k—are computed automatically when ground truth becomes available. A concept-drift trigger can page engineers on Slack or Teams the moment degradation crosses a configurable threshold.

LLM Observability Toolkit

The newest addition addresses the unique pain points of prompt-based systems. Built-in evaluators check for prompt injection, jailbreak attempts, off-topic answers, toxicity, and PII leakage. Custom evaluators can be written in fewer than 20 lines of Python, leveraging any model endpoint.