Axomaly vs PySAD
Axomaly
axomaly.comPySAD
github.comSide by side
| Figure | Axomaly | PySAD |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | — |
| Plans published | 1 | 1 |
| Spec score · Anomaly Detection Software | 7.8 (#5) | 7.4 (#14) |
| Purpose | Axomaly learns patterns in SaaS product and business metrics, then flags meaningful changes without relying on static thresholds. | PySAD is an open-source Python framework for anomaly detection on streaming multivariate data. |
Plans
In their own words
Axomaly
Axomaly is a cloud service that learns patterns in SaaS product and business metrics and flags meaningful changes without fixed thresholds. It accepts timestamped time-series values, with optional metadata, for business, operational, and application metrics. Before creation, Smart Metric Creation prepares a detection configuration and baseline requirements for review. Values are submitted through a REST API, one timestamped value per POST request. Detection begins once the required history is available; the amount depends on metric cadence and detector configuration. Results include an anomaly decision, confidence, expected range, direction of change, and detector evidence, which can feed existing workflows alongside logs, traces, dashboards, and alerting. The alpha starts with one metric and includes guided onboarding and direct support. It is free during alpha with no credit card required. During that period, data and results are hosted in the EU, isolated by organisation, encrypted in transit using HTTPS/TLS, and kept for up to 12 months before rolling deletion.
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.