rrcf
rrcf: A focused, self-hosted Python method for unsupervised streaming anomaly scoring. Ranked #19 of 21 in Anomaly Detection Software by our editors (6.6/10); pricing: Free plan; best for developers needing a focused streaming anomaly method.
At a glance
- Editor score6.6 / 10
- PricingFree plan
- Best forDevelopers needing a focused streaming anomaly method
- Free planYes
- Paid fromFree
- Real-time detectionYes
- Facts checked24 Sep 2026
Where it wins
- Open-source Python package for local, self-managed execution
- Unsupervised scoring for multidimensional and streaming numerical data
- Measures co-displacement and collusive anomalies alongside scores
Where it doesn't
- Narrowly focused on one tree-based detection method
- Self-hosted deployment puts operations and retention on your team
- Docs and community are the listed support channels
Our verdict on rrcf
rrcf is an open-source Python package implementing Random Cut Forest for unsupervised anomaly detection. It targets developers and data scientists working with multidimensional numerical data, especially streaming time series, who want to calculate anomaly scores within their own applications. The package runs locally in a self-managed environment rather than as a hosted service, making it a practical fit for solo developers and small or mid-market teams that can manage their own Python deployment.
Its main strength is method depth within a focused scope. rrcf constructs random-cut forests, updates them with streaming observations, and handles multidimensional points without labeled training data. In addition to anomaly scoring, it exposes co-displacement and collusive-anomaly measurements, giving technical users more tree-based signals to inspect. Real-time detection and machine-learning classification in the category specs align with workloads where observations arrive continuously and scoring needs to happen inside an application or pipeline.
The trade-off is breadth. rrcf supplies an algorithmic building block, not a broad detection suite with a hosted console, managed storage, retention controls, or multiple detection approaches. Self-hosting also leaves deployment, storage, monitoring, and operational upkeep to the adopting team. Documentation and community support are the stated support paths, so teams seeking managed onboarding or a wider platform ecosystem may prefer another product. Choose rrcf when a Python-native Random Cut Forest implementation and local control are the priority; look elsewhere when you need an end-to-end service or broader method coverage.
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rrcf pricing
rrcf fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Detection method | Machine-learning |
| Real-time detection | Yes |
| Supported data | multidimensional numerical and streaming time-series data |
| Deployment options | Self-hosted |
| Anomaly explanations | Not verified |
| Data retention | Not verified |
| Deployment | Self-hosted |
| Support | Docs, Community |
| Built for | Solo, Small business, Mid-market (editorial estimate) |
| Pricing | Free plan |
| Website | klabum.github.io |
| Facts checked | 24 Sep 2026 |
Alternatives to rrcf
- Progress FlowmonA network-focused platform for traffic visibility, anomaly detection and capacity planning.9.0
- ManageEngine NetFlow AnalyzerAffordable on-premises flow monitoring with alerts, reporting, and security analytics.8.6
- AnodotBroad real-time anomaly detection with forecasting, correlation, and root-cause analysis.8.4
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Featured on iTechGuides
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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