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Axomaly vs PySAD

Axomaly

axomaly.com

PySAD

github.com

Side by side

FigureAxomalyPySAD
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
WebYes—
Windows—Yes
macOS—Yes
Linux—Yes
APIYes—
Plans published11
Spec score · Anomaly Detection Software7.8 (#5)7.4 (#14)
PurposeAxomaly 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

Axomaly

  • Axomaly AlphaFree

Axomaly pricing →

PySAD

  • PySADFree

PySAD pricing →

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.

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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.

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