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Radicalbit AI Monitoring

APIyesFREEyesDOCS4/5
RA7.3#10of 20
outWeb

Ranked in Machine Learning Model Monitoring Software·Free plan

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About

Radicalbit AI Monitoring is a free, self-hosted platform for monitoring production LLM and machine-learning models. It checks numerical and categorical data for anomalies, missing values and outliers, and tracks model metrics over time. Available metrics 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. Teams can compare reference and current datasets to assess data quality, model quality and drift. For LLM applications, tracing records requests, prompts and tools, then shows sessions, traces and spans with hierarchy, duration and metadata. Documentation for v1.4.0 focuses on binary and multiclass classification and regression models, with other model types planned. A web UI and Python SDK are provided, and platform functionality is also exposed through REST APIs. The API setup requires PostgreSQL, Kubernetes for Spark metric jobs and distributed storage. Local deployment uses Docker Compose with K3s and supports MinIO or AWS S3; tracing uses OpenLLMetry, an OpenTelemetry collector and the Traceloop SDK.

Who it is for

Radicalbit AI Monitoring suits teams that can self-host and need model metrics, data-quality checks, drift detection or LLM tracing. Its documented scope is centered on binary and multiclass classification and regression models.

What is good

  • Tracks anomalies, missing values and outliers.
  • Includes multiple model and drift metrics.
  • Compares reference and current datasets.
  • Provides a web UI and Python SDK.
  • Free under the Apache 2.0 license.

What to know first

  • Documented scope focuses on classification and regression.
  • REST API setup requires PostgreSQL and distributed storage.
  • Spark metric jobs require Kubernetes.

Verdict

Radicalbit AI Monitoring offers a broad monitoring surface for teams prepared to operate its self-hosted stack. Check the documented model scope and infrastructure requirements against the intended deployment before adopting it.

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

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