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AI Evaluation & Observability

Radicalbit AI Monitoring

Radicalbit AI Monitoring is an open-source platform for monitoring machine-learning and large language models in production. It analyzes a reference dataset alongside current datasets to track data quality, model quality and model drift. Its repository contains UI, API, SDK and Spark components, and provides a local Docker Compose setup with a K3s cluster for Spark jobs.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Radicalbit AI Monitoring — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need to inspect production model data and quality against a reference dataset, or want to explore the platform locally before considering its documented monitoring workflow.

Who it is for

  • Teams responsible for monitoring machine-learning models after deployment.
  • Practitioners investigating data shifts or changes in production model quality.
  • Developers evaluating an open-source model-monitoring platform through a local Compose setup.

Documented uses

  • Compare current datasets with a pre-production reference dataset.
  • Inspect data quality, model quality and model drift.
  • Explore the platform locally with demo models.
Before you choose

Requirements & limits

The repository provides a local Docker Compose setup and directs users to a UI at localhost:5173 after startup.

  • The repository identifies Python as its primary language and Apache-2.0 as its license.
  • The local setup uses Docker Compose with a K3s cluster and Spark jobs.
  • The README marks batch LLM model-quality monitoring and the listed real-time workloads as planned rather than available.
  • The README documents using real AWS instead of MinIO by setting AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION and S3_BUCKET_NAME for the API container and removing S3_ENDPOINT_URL.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start the local platform with demo models

Bring up the local Docker Compose platform, include its UI and initialize demo models.

Before you start

  • The project repository and its Docker Compose setup.
  • A local environment able to run Docker Compose.

Steps

  1. From the repository, run the command below.

    Documented command
    docker compose --profile ui --profile init-data up

  2. Wait for the containers to start, then open http://localhost:5173.

Expected outcome

The README describes a local platform with the UI and demo models available to explore.

Guide limitations

  • This is the documented local development/testing setup, not a hosted deployment.
  • The README does not specify hardware or resource requirements.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
12 October 2026
Original website title
radicalbit/radicalbit-ai-monitoring · Official homepage title · retained in its source language
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