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Radicalbit AI Monitoring
What Radicalbit AI Monitoring is
Radicalbit AI Monitoring is a free, self-hosted platform for tracking the reliability and effectiveness of 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 includes methods such as Kolmogorov-Smirnov, PSI, Wasserstein, Jensen-Shannon, Chi-square, Kullback-Leibler, and Hellinger. The platform compares reference and current datasets to assess data quality, model quality, and drift. For LLM applications, tracing records requests, prompts, and tools, then displays sessions, traces, and spans with hierarchy, duration, and metadata. Documentation for version 1.4.0 focuses on binary and multiclass classification and regression, with additional model types planned. A web interface and Python SDK are available. REST API use requires PostgreSQL, Kubernetes for Spark metric jobs, and distributed storage. Local deployment uses Docker Compose with K3s and supports MinIO or AWS S3 storage.
Who it suits
It suits teams monitoring production models who want self-hosted data quality checks, model metrics, drift detection, or LLM tracing. Its documented model scope focuses on binary and multiclass classification and regression.
What it does well
- Free and self-hosted under Apache 2.0.
- Checks data quality and tracks model metrics.
- Offers several drift detection methods.
- Compares reference and current datasets.
- Includes a web UI and Python SDK.
What to know first
- Documented scope focuses on classification and regression.
- REST API requires PostgreSQL and distributed storage.
- Spark metric jobs require Kubernetes.
Limits
The Free / Open Source plan is self-hosted. The documented v1.4.0 scope focuses on binary and multiclass classification and regression models, with additional model types planned.
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| Free / Open Source | free · Apache 2.0 license · self-hosted deployment | Free |
As radicalbit.ai lists them, read 1 Oct 2026. The maker's own page has the final word.
Getting started
- Visit https://radicalbit.ai/ai-monitoring-oss/.
- Choose the self-hosted deployment under the Free / Open Source plan.
- Use the repository's Docker Compose installation for a local K3s-based deployment.
- Configure MinIO or AWS S3 for storage.
- Use the web interface or Python SDK to access platform functionality.
- For LLM tracing, use OpenLLMetry, an OpenTelemetry collector and the Traceloop SDK.
Verdict
Radicalbit AI Monitoring combines data checks, model metrics, drift analysis, and LLM tracing in a free self-hosted package. Check the documented model scope and deployment requirements against your setup.
Questions
How much does Radicalbit AI Monitoring cost?
The Free / Open Source plan costs nothing, billed free.
Is Radicalbit AI Monitoring open source?
Yes. The plan is under the Apache 2.0 license.
Which platforms does it support?
Its listed platforms are API, self-hosted and web.
Which model types are in its documented scope?
The v1.4.0 documentation focuses on binary and multiclass classification and regression models.
What does the REST API deployment require?
It requires PostgreSQL, Kubernetes for Spark metric jobs and distributed storage.
Who brings Radicalbit AI Monitoring to market?
It is brought to market by Fortitude Group, a technology holding company providing consulting, system integration and AI solutions.
Facts from Radicalbit AI Monitoring's own pages
- Data quality
- It detects anomalies, missing values and outliers in numerical and categorical data and tracks metrics over time. · 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
- 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
- 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
- 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
- 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
- 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
- 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
- Purpose
- Radicalbit AI Monitoring monitors the effectiveness and reliability of production LLM and machine-learning models. · 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
- Support
- The project directs users to a Discord community for help and discussion. · 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
- 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