Progress Flowmon vs PySAD
Progress Flowmon
progress.comPySAD
github.comSide by side
| Figure | Progress Flowmon | PySAD |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 1 | 1 |
| Spec score · Anomaly Detection Software | 7.6 (#9) | 7.4 (#14) |
| Purpose | Flowmon is a network and security monitoring platform that uses AI-based detection to identify cyber threats and anomalies and provide network and application performance insights. | PySAD is an open-source Python framework for anomaly detection on streaming multivariate data. |
Plans
In their own words
Progress Flowmon
Progress Flowmon is a network and security monitoring platform for detecting threats and examining network and application performance. Flowmon ADS applies behavior analysis to traffic to expose malicious activity, attacks, data breaches, and indicators of compromise, including in encrypted traffic. Network operations tools cover visibility, troubleshooting, capacity planning, dashboards, reports, root-cause analysis, and SLA measurement. Supported flow formats include NetFlow, IPFIX, sFlow, jFlow, cflowd, NetStream, and RFC formats, plus cloud-native FlowLogs from Azure, AWS, and Google Cloud Platform. Flowmon integrates with network access control, authentication, firewall, and incident-response tools, and provides a documented REST API. Collector and Probe can run as virtual, cloud, or hardware appliances, with hybrid deployment available. Progress describes scaling from small and midsize businesses to large enterprises. A free, expert-guided 30-day Proof of Concept precedes final sizing and pricing through Progress’s partner network.
PySAD
PySAD is a free, open-source Python framework for online anomaly detection in streaming data. Its models update when each new data instance arrives, supporting sequential detection for univariate and multivariate data. The project describes 16 online detectors, including xStream, LODA, RS-Hash, Half-Space Trees, and Robust Random Cut Forest. It also provides stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, and probability calibrators. Integrations allow batch anomaly detectors from PyOD to run in a streaming setting. Experiments can use supervised, semi-supervised, or unsupervised settings. The documentation says streaming methods may retain one instance or a small recent window to meet memory and processing constraints. Installation is available through pip or the GitHub source; the current README lists Python 3.10 or newer on Linux, macOS, and Windows. PySAD is self-hosted and distributed under a BSD 3-Clause license. Its plan costs nothing, and 17 labelled benchmark datasets are downloaded on first use.