NannyML vs Radicalbit AI Monitoring

NannyML monitors deployed machine-learning models and estimates their performance when ground truth is delayed or unavailable. It detects concept drift as well as multivariate and univariate data drift, and connects alerts to changes in model performance. Data-quality checks and ranked drift alerts can help teams investigate features associated with performance issues. Users can relate model performance to monetary or other business outcomes. NannyML Cloud supports webhooks for retraining actions and an SDK for automating monitoring-data ingestion. Cloud is available through Azure and AWS marketplaces as SaaS or a managed application. With the managed-application option, the application and required infrastructure are provisioned within the customer's cloud subscription. For its Azure managed application, NannyML documents encrypted traffic and disks, but says automated backups were still being implemented and advises customers to safeguard model inputs and outputs elsewhere. The open-source library installs with pip or conda. Direct local dataset uploads through the Cloud UI must be smaller than 100 MB. Plans include a free self-managed Open Source option, Starter at $399 a month, and Scale at $999 a month.

Radicalbit AI Monitoring is a free, self-hosted platform for monitoring production LLM and machine-learning model effectiveness and reliability. It checks numerical and categorical data for anomalies, missing values, and outliers, then tracks metrics over time. Available model measures include Precision, Accuracy, Recall, F1, MSE, MAE, Perplexity, and Probability. Drift detection offers Kolmogorov-Smirnov, PSI, Wasserstein, Jensen-Shannon, Chi-square, Kullback-Leibler, and Hellinger methods. Reference and current datasets can be compared for data quality, model quality, and drift. LLM tracing records requests, prompts, and tools and displays sessions, traces, and spans with hierarchy and metadata. Documentation for version 1.4.0 focuses on binary and multiclass classification and regression, with more model types planned. A web UI, Python SDK, and REST API are provided. Local deployment uses Docker Compose with K3s and MinIO or AWS S3.