# Anomaly Detection

**Canonical:** https://apis.io/providers/anomaly-detection/  
**APIs profiled:** 8

A curated collection of APIs, tools, and platforms for detecting anomalies in data streams, time series, and multivariate metrics. Covers cloud ML services, observability platforms, and open-source frameworks used for fraud detection, predictive maintenance, IoT monitoring, and security analytics.

## Kin Score — 37.2 / 100 (thin)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 37.2).

| Facet | Score |
|---|---|
| Discoverability | 74.1 |
| Contract Quality | 63.9 |
| Governance | 25.0 |
| Contract Governance | 25.0 |
| Operational Transparency | 36.8 |
| Developer Ergonomics | 14.3 |
| Commercial Clarity | 15.8 |
| Access Clarity | 15.8 |

## Agent readiness — 29.1 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | yes |
| Agentic Access | derived |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | yes |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | documented |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Freemium · Self-serve signup — onboarding: self-serve, pricing: freemium, trial: no (confidence: high).

## APIs (8)

- **Azure AI Anomaly Detector** — Azure AI Anomaly Detector is a managed REST API service that enables monitoring and detection of anomalies in time series data without requiring machine learning expertise. Supp...
- **Elasticsearch Anomaly Detection API** — Elasticsearch Machine Learning APIs provide a comprehensive suite of anomaly detection capabilities for time series data stored in Elasticsearch indices. Supports creating and m...
- **Datadog Anomaly Monitor API** — Datadog's Monitors API supports anomaly detection monitors that identify unusual metric behavior using historical pattern analysis including trends, day-of-week, and time-of-day...
- **AWS Lookout for Metrics** — Amazon Lookout for Metrics is a fully managed ML service that automatically detects anomalies in business and operational data. It connects to data sources including Amazon S3, ...
- **PyOD (Python Outlier Detection)** — PyOD is a comprehensive and scalable Python library for detecting outliers/anomalies in multivariate data. It includes more than 40 detection algorithms including deep learning ...
- **Anomaly Detection ChangePoint API** — Trend change-point detection.
- **Anomaly Detection Multivariate API** — Multivariate anomaly detection across correlated signals.
- **Anomaly Detection Univariate API** — Anomaly detection on a single time series.

## Agentic access (1)

- **Anomaly Detection Agentic Access** — 9 operations · 7 acting

## Security (3)

- **Anomaly Detection Authentication** — apiKey · 1 scheme
- **Anomaly Detection Domain Security** — TLSv1.3 · HSTS · DMARC
- **Anomaly Detection Vulnerability Disclosure** — security.txt · contact published

## Plans (1)

- **Anomaly Detection Plans Pricing**

## Use cases (5)

- **Fraud Detection** — Identify fraudulent transactions, account takeovers, and suspicious behavioral patterns in financial and e-commerce systems.
- **Predictive Maintenance** — Detect early signs of equipment failure in industrial IoT systems by identifying anomalous sensor readings before breakdowns occur.
- **IT and Security Operations** — Detect unusual network traffic, unauthorized access patterns, and security incidents in real time using behavioral baselines.
- **Business Metrics Monitoring** — Alert on unexpected drops or spikes in KPIs such as revenue, conversion rates, user engagement, or API error rates.
- **Healthcare Monitoring** — Monitor patient vitals, lab values, and medical device readings for out-of-range or clinically significant anomalies.

## Tags

Anomaly Detection, Artificial Intelligence, Data Science, Fraud Detection, Machine-Learning, Monitoring, Observability, Outlier Detection, Pattern Recognition, Security, Time Series

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/anomaly-detection/). Scores are computed from the provider's own public artifacts under a published rubric.