ManageEngine NetFlow Analyzer vs OpenUBA
ManageEngine NetFlow Analyzer
manageengine.comOpenUBA
openuba.orgSide by side
| Figure | ManageEngine NetFlow Analyzer | OpenUBA |
|---|---|---|
| Price from | $172/mo | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| Linux | Yes | — |
| iOS | Yes | — |
| Android | Yes | — |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 0 |
| Spec score · Anomaly Detection Software | 7.5 (#11) | 7.4 (#13) |
Plans
ManageEngine NetFlow Analyzer
- Free EditionFree
- Standard Edition$172 a month
- Professional Edition$245 a month
- Enterprise Edition$3,795 a month
In their own words
ManageEngine NetFlow Analyzer
ManageEngine NetFlow Analyzer monitors network traffic and bandwidth, with application visibility and analysis of conversations between network endpoints. It supports NetFlow, sFlow, IPFIX, J-Flow, NetStream, cflowd, and AppFlow. The product is deployed on premises and lists Android, iOS, Linux, web, and Windows platforms. Its Free Edition costs nothing and covers 2 interfaces. Standard Edition costs $172 a month for 10 interfaces; Professional Edition costs $245 a month for 10 interfaces. Enterprise Edition costs $3,795 a month for 100 interfaces. The listed pricing model is freemium, with a 30-day trial. Interface capacity differs by edition, so the number of interfaces to monitor is central to choosing an option. ManageEngine was founded in 1996 and is headquartered in Pleasanton, California, United States.
OpenUBA
OpenUBA is a free, open-source User and Entity Behavior Analytics framework for security analytics. It analyzes behavior with machine-learning models to identify anomalies, assign risk scores, and produce classifications. The dashboard covers entity monitoring, risk trends, job orchestration, case management, and real-time behavioral baselines for tracking 10,000+ users. Data ingestion supports Spark, Elasticsearch, and CSV, and the project describes the platform as SIEM agnostic. Rule Studio provides a visual flow-graph builder for detection logic, while matching rules can trigger real-time alerts. Cases can be triaged, escalated, and resolved in built-in workflows. Models use a model.yaml configuration and MODEL.py interface with train() and infer() methods; examples use scikit-learn, TensorFlow, PyTorch, Keras, and NetworkX. A Kubernetes-native operator runs jobs in ephemeral containers, with GraphQL logs and monitoring. Deployment is self-hosted on Kind or production Kubernetes clusters.