Radicalbit AI Monitoring
Radicalbit AI Monitoring: A self-hosted monitor covering data quality, drift, model performance, and LLM tracing. Ranked #12 of 20 in Machine Learning Model Monitoring Software by our editors (7.7/10); pricing: Free plan; best for teams wanting a self-hosted open-source monitor.
At a glance
- Editor score7.7 / 10
- PricingFree plan
- Best forTeams wanting a self-hosted open-source monitor
- Free planYes
- Paid fromFree
- Drift monitoringYes
- Facts checked21 Sep 2026
Where it wins
- Covers data quality, model performance, and drift detection
- Supports classification, regression, and LLM application monitoring
- Includes dashboards plus Docker Compose and local K3s deployment
Where it doesn't
- Self-hosted deployment places infrastructure responsibility on the team
- Drift analysis depends on reference-versus-current dataset comparisons
- Teams needing managed cloud deployment should consider alternatives
Our verdict on Radicalbit AI Monitoring
Radicalbit AI Monitoring is an open-source platform for monitoring machine learning models and large language models in production. It is designed for teams that want to analyze data quality, model quality, and drift while retaining a self-hosted deployment model. The platform supports classification and regression monitoring alongside LLM application tracing, making it relevant to organizations operating both traditional machine learning systems and newer language-model applications.
Its monitoring coverage is broad within this category. Data quality analysis handles numerical and categorical variables, while model performance metrics use predictions and ground truth. Drift detection also covers numerical and categorical features, and reference-versus-current dataset comparison provides a basis for examining changes over time. Launchpad dashboards summarize data quality, model quality, and drift, while LLM tracing follows sessions, traces, and spans. This combination suits teams that want one monitoring layer across model and LLM workloads rather than a tool limited to a single model type.
Deployment is a defining consideration. Radicalbit AI Monitoring is available under the Apache 2.0 license and can run locally through Docker Compose with a local K3s cluster. That approach fits engineering teams comfortable owning their monitoring environment, configuration, and operational setup. It is less suitable for buyers seeking a managed cloud service with minimal infrastructure responsibility. Choose it when open-source access, self-hosting, dataset comparison, and combined ML and LLM monitoring are priorities; consider an alternative when a fully managed deployment is central to the purchasing decision.
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Radicalbit AI Monitoring pricing
Radicalbit AI Monitoring fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Drift monitoring | Yes |
| Model performance metrics | Yes |
| Data quality checks | Yes |
| Bias monitoring | Not verified |
| Alert channels | Not verified |
| Deployment options | Self-hosted |
| Included model limit | Not verified |
| Deployment | Self-hosted |
| Platforms | Web |
| Support | Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan |
| Website | radicalbit.ai |
| Facts checked | 21 Sep 2026 |
Alternatives to Radicalbit AI Monitoring
- DeepchecksDeepchecks brings open-source validation and hosted monitoring into one ML quality workflow.9.3
- SUPERWISEBroad ML monitoring and governance from $10 per month, with production features on Business.9.1
- ArthurA flexible monitoring platform for teams that need fairness, drift, and explainability controls.8.9
See all Radicalbit AI Monitoring alternatives →
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Featured on iTechGuides
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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