Cisco Catalyst Center vs PySAD
Cisco Catalyst Center
cisco.comPySAD
github.comSide by side
| Figure | Cisco Catalyst Center | PySAD |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 2 | 1 |
| Spec score · Anomaly Detection Software | 7.7 (#7) | 7.4 (#14) |
| Purpose | Cisco Catalyst Center is a centralized network controller and management dashboard for enterprise network operations. | PySAD is an open-source Python framework for anomaly detection on streaming multivariate data. |
| Security | Catalyst Center supports FIPS 140-2-compliant cryptography modules, and Cisco recommends deploying it behind a firewall on a private internal network. | The repository links to a security policy, but the policy page could not be opened during this research. |
| Support | Cisco says Catalyst and Cisco DNA software subscriptions include embedded software support and downloads; opening a support case requires a Cisco Service Contract. | The maintainer says issues and pull requests are welcome and aims to reply within a few days; questions and show-and-tell go in GitHub Discussions. |
Plans
Cisco Catalyst Center
- Cisco DNA AdvantagePriced on request
- Cisco DNA EssentialsPriced on request
In their own words
Cisco Catalyst Center
Cisco Catalyst Center has no write-up on Specifiction yet; its figures are above.
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.