Tech riderRev. 1 Oct 2026

Radicalbit AI Monitoring

Ch 126.9of 20Machine Learning Model Monitoring Software

1Runs onAPI, self-hosted, Web
2CostsFree plan
3Drift monitoringYes
4Model performance metricsYes
5Data quality checksYes
6Deployment optionsself-hosted

6 lines stated Written from the maker's own pages: radicalbit.ai

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Overview

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.

Who it is for

It suits teams running self-hosted monitoring for classification, regression, and LLM applications.

What is good

Free Apache 2.0 self-hosted plan
Multiple drift detection algorithms
LLM request and tool tracing
Web UI and Python SDK
Compares reference and current datasets

What to know first

Documented scope focuses on classification and regression
REST API requires PostgreSQL and Kubernetes
Self-hosted deployment requires infrastructure

Verdict

Radicalbit AI Monitoring provides broad data, model, drift, and LLM tracing capabilities without a listed charge. Its current documented model scope and infrastructure requirements matter when planning deployment.

Radicalbit AI Monitoring plans and pricing

All plans

Free / Open Source Free free Apache 2.0 license · self-hosted deployment radicalbit.ai · 1 Oct 2026

Compared on machine learning model monitoring software

Free plan: Yesradicalbit.ai
Drift monitoring: Yesradicalbit.ai
Model performance metrics: Yesradicalbit.ai
Data quality checks: Yesradicalbit.ai
Deployment options: self-hostedradicalbit.ai

Facts

Purpose: Radicalbit AI Monitoring monitors the effectiveness and reliability of production LLM and machine-learning models.radicalbit.ai · 1 Oct 2026
Data quality: It detects anomalies, missing values and outliers in numerical and categorical data and tracks metrics over time.radicalbit.ai · 1 Oct 2026
Model metrics: It provides metrics including Precision, Accuracy, Recall, F1, MSE, MAE, Perplexity and Probability for LLM, classification and regression models.radicalbit.ai · 1 Oct 2026
Drift detection: Drift detection includes Kolmogorov-Smirnov, PSI, Wasserstein, Jensen-Shannon, Chi-square, Kullback-Leibler and Hellinger algorithms.radicalbit.ai · 1 Oct 2026
LLM tracing: LLM application tracing records requests, prompts and tools and displays sessions, traces and spans with hierarchy, duration and metadata.radicalbit.ai · 1 Oct 2026
Dataset comparison: The platform analyzes reference and current datasets to evaluate data quality, model quality and model drift.docs.oss-monitoring.radicalbit.ai · 1 Oct 2026
Documented scope: The v1.4.0 documentation says the current scope focuses on binary and multiclass classification and regression models, with additional model types planned.docs.oss-monitoring.radicalbit.ai · 1 Oct 2026
REST API: The API exposes platform functionality through REST APIs and requires PostgreSQL, Kubernetes for Spark metric jobs and distributed storage.docs.oss-monitoring.radicalbit.ai · 1 Oct 2026
User interface and SDK: A web UI covers model creation through metric visualization, and a Python SDK implements the REST API functionality.docs.oss-monitoring.radicalbit.ai · 1 Oct 2026
Deployment: The repository provides Docker Compose installation for a local K3s-based deployment and supports MinIO or real AWS S3 storage.github.com · 1 Oct 2026
Tracing integration: LLM tracing uses OpenLLMetry, an OpenTelemetry collector and the Traceloop SDK.docs.oss-monitoring.radicalbit.ai · 1 Oct 2026
Privacy: The project collects anonymous usage data only, collects no personally identifiable information and asks users to opt in or out at first use.github.com · 1 Oct 2026
Support: The project directs users to a Discord community for help and discussion.github.com · 1 Oct 2026
Maker: Radicalbit is brought to market by Fortitude Group, an innovative technology holding company providing consulting, system integration and AI solutions.radicalbit.ai · 1 Oct 2026