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Radicalbit AI Monitoring
What it is
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 metrics over time. Listed model metrics include Precision, Accuracy, Recall, F1, MSE, MAE, Perplexity, and Probability. Drift checks use several statistical methods, including Kolmogorov-Smirnov, PSI, Wasserstein, and Jensen-Shannon. The platform can compare reference and current datasets to evaluate data quality, model quality, and drift. Its LLM tracing records requests, prompts, and tools, presenting sessions, traces, and spans with hierarchy, duration, and metadata. Version 1.4.0 documentation focuses on binary and multiclass classification and regression models, with additional types planned. A web interface and Python SDK are available, as is a REST API. 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. LLM tracing uses OpenLLMetry, an OpenTelemetry collector, and the Traceloop SDK.
For and against
For
- Free plan uses an Apache 2.0 license
- Checks for anomalies, missing values, and outliers
- Compares reference and current datasets
- Provides a web UI and Python SDK
Against
- Documented model scope focuses on classification and regression
- REST API requires PostgreSQL and distributed storage
- Spark metric jobs require Kubernetes
The verdict
Radicalbit AI Monitoring combines data-quality checks, model metrics, drift analysis, and LLM tracing in a self-hosted package. Check its documented model scope and deployment requirements against your needs.
Who it suits
Radicalbit AI Monitoring may suit teams monitoring production LLM or machine-learning systems that can manage a self-hosted deployment. Its documented model scope is focused on classification and regression.
Plans and prices
Free / Open Source
Apache 2.0 license · self-hosted deployment
Read 1 Oct 2026 · radicalbit.ai
On the record
- Data quality
- It detects anomalies, missing values and outliers in numerical and categorical data and tracks metrics over time.radicalbit.ai · read 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 · read 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 · read 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 · read 1 Oct 2026
- Drift detection
- Drift detection includes Kolmogorov-Smirnov, PSI, Wasserstein, Jensen-Shannon, Chi-square, Kullback-Leibler and Hellinger algorithms.radicalbit.ai · read 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 · read 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 · read 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 · read 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 · read 1 Oct 2026
- Purpose
- Radicalbit AI Monitoring monitors the effectiveness and reliability of production LLM and machine-learning models.radicalbit.ai · read 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 · read 1 Oct 2026
- Support
- The project directs users to a Discord community for help and discussion.github.com · read 1 Oct 2026
- Tracing integration
- LLM tracing uses OpenLLMetry, an OpenTelemetry collector and the Traceloop SDK.docs.oss-monitoring.radicalbit.ai · read 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 · read 1 Oct 2026
