Radicalbit AI Monitoring vs STAMM

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.

STAMM is an open-source MLOps framework for deploying and maintaining machine-learning soft sensors in industrial processes. It monitors live process data for concept drift and changes in operating regimes, and its registry records model versions, configurations, artifacts, validation results, and metadata. A dashboard shows process measurements, soft-sensor outputs, drift signals, and historical context, with support for human-in-the-loop labelling. STAMM serves Python and R soft sensors through REST inference. Its event-driven orchestrator calls the registry over REST and links predictions to the data snapshot that produced them. The drift detector package offers 10 detectors through a common interface and can also be used separately. Data can enter through equipment REST hooks, MQTT applications, or frameworks such as LEAF. InfluxDB is the reference time-series store; a PostgreSQL adapter is described as in progress. The reference Docker Compose installation supports Linux, macOS, or Windows with Docker 24 or later, and 8 GB RAM or more is recommended. Released under Apache License 2.0, STAMM is intended for process modelers, ML engineers, operators, and production managers.