Cisco Catalyst Center vs PyOD
Cisco Catalyst Center
cisco.com
PyOD
pyod.readthedocs.ioSide by side
| Figure | Cisco Catalyst Center | PyOD |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · Anomaly Detection Software | 7.7 (#7) | 8.4 (#3) |
| Founded | 1984 | 2017 |
| Integrations | Cisco documents integrations with ISE, ThousandEyes, Webex, Microsoft Teams, ServiceNow, Infoblox, and BlueCat, plus generic APIs for other ITSM and IPAM systems. | Optional pip extras enable PyTorch, SUOD, XGBoost, model combination, thresholding, embeddings, OpenAI embeddings, Hugging Face encoders, graph models, MCP, and audio features. |
| Purpose | Cisco Catalyst Center is a centralized network controller and management dashboard for enterprise network operations. | PyOD is a Python library for anomaly detection. |
| 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 FAQ invites users to open an issue or contact the maintainer at [email protected]. |
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.
PyOD
PyOD is a free Python library for anomaly detection, with documented detectors for tabular, time-series, graph, text, image, and audio data. Its documentation lists 61 detectors available through a common API. Users can work with the classic detector API or ADEngine, which profiles data, selects benchmark-backed detectors, runs multiple detectors in parallel, computes consensus scores, and reports diagnostics. ADEngine also works as a standalone Python API without an LLM. PyOD includes an agentic investigation workflow and activation paths for Claude Code, Codex, and MCP-compatible agents, including an od-expert skill and optional MCP server. Installation is available through pip, conda-forge, or source, and requires Python 3.9 or higher. Optional pip extras enable capabilities including PyTorch, graph detectors, embeddings, audio, and MCP support. The project serves academic research and commercial products. Its model persistence guidance warns that pickle and joblib can execute arbitrary Python code during deserialization, requiring callers to pass trusted=True when loading artifacts. ADEngine describes its quality verdict as a heuristic rather than a guarantee and recommends validation against held-out labels or domain review.