Anomaly Detection

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.

Anomaly Detection publishes 8 API contracts indexed on the APIs.io network, including ChangePoint API, Multivariate API, Univariate API, and 5 more. Tagged areas include Anomaly Detection, Artificial Intelligence, Data Science, Fraud Prevention, and Machine Learning.

The Anomaly Detection catalog on APIs.io includes 1 JSON-LD context and 1 Spectral governance ruleset.

Anomaly Detection’s developer surface includes authentication, engineering blog, and 18 more developer resources.

📡 Profile data on GitHub

52.4/100 developing ▬ flat Agent 20/100 agent aware open core · MIT Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessFreemiumSelf serve⚡ Free to try

2 published contracts 6 Features 5 Use Cases

Anomaly Detection Artificial Intelligence Data Science Fraud Prevention Machine Learning Monitoring ObservabilityOutlier DetectionPattern RecognitionSecurity Time Series

What this lets a business do 2

Business capabilities this provider's published APIs can perform, derived from its own contracts. Browse all capabilities →

Information & Data Management

BC-610

Multivariate

AI Model Services

AE-110

ChangePoint

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 52.4/100 · developing

Contract Quality 7.0 / 16

Developer Ergonomics 6.5 / 13

Access Clarity 2.4 / 13

Operational Transparency 3.1 / 8

Contract Governance 1.8 / 8

Discoverability 4.3 / 7

Regulatory Posture 3.1 / 15

Open Source Surface 7.5 / 10

Create-or-Update Ergonomics 10.0 / 10

Agent readiness — 20/100 · agent aware

Machine-Readable Contract 18 / 18

Agentic Access Contract 10 / 10

Documented Reversibility 6 / 6

MCP Server 0 / 12

Machine-Readable Auth 10 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 7 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Open Source Surface applies to this provider. This product is open source and we read its repository directly, so Open Source Surface carries 10 points of the composite. It is scored from what the repository actually publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the provider rather than inferred from our own catalog pointers. This facet adds; nothing was taken away to make room for it. An open-source project is not excused from the commercial facets, because exemption would strip it of the points it does earn. If we have the wrong repository, or this product is not open source, say so on your provider repo and we will drop the facet rather than have you publish against it.

Create-or-Update Ergonomics applies to this provider. This API accepts writes, so it carries 10 points of the composite. It is scored from the published contracts themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in front of it, and the first time that check is skipped a duplicate record is created. Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet, not penalised by it.

The six quality facets above are damped to 65 points between them, because both conditional facets apply and carry 35 points together. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 65% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Request/Response Examples +7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link
Registration Without a Human +6 pts — a `registration_endpoint` in the served RFC 8414 / OIDC discovery document

8 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Access Clarity scores 18.4 — up to +16.3 composite points
Contract Quality scores 42.8 — up to +14.3 composite points
Developer Ergonomics scores 50.0 — up to +10.0 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/anomaly-detection/apis.yml: open an issue on github.com/api-evangelist/anomaly-detection to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Standards implemented 1

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

OpenAPI

measured — observed by APIs.io (published)

APIs 8

Individual APIs this provider publishes, each with its own machine-readable definition.

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.

Scroll for all 8

Open Collections 4

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

Azure AI Anomaly Detector ChangePoint API

OPEN COLLECTION

Azure AI Anomaly Detector ChangePoint Multivariate API

OPEN COLLECTION

Azure AI Anomaly Detector ChangePoint Univariate API

OPEN COLLECTION

Azure AI Anomaly Detector API

OPEN COLLECTION

Anomaly Detection API pricing

Not yet checked against Anomaly Detection's own pricing page. Compiled by API Evangelist on 2026-05-04; confirm before you rely on a figure. A free plan or tier is listed.

Plan	Price
Free freemium	Free
Professional paid	Free
Enterprise enterprise	—

Plan details, limits and sources →

Rate limits

Free Tier Default: 10 requests per minute
Free Tier Monthly Quota: 1000 requests per month
Professional Tier Default: 100 requests per minute

Compiled by API Evangelist, not yet checked against Anomaly Detection's documentation. All 5 limits →

Pricing Plans 1

Published pricing tiers and plan structures.

Anomaly Detection Plans Pricing

3 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Anomaly Detection Rate Limits

5 limits

RATE LIMITS

Service Levels 1

Uptime the provider promises or reports, as SLA4OAI. Read from its own pages; APIs.io runs no monitors.

Anomaly Detection

SLA

FinOps 1

Cost, billing, and metering signals for API financial operations.

Anomaly Detection Finops

FINOPS

Features 6

Notable capabilities this provider offers.

Univariate Time Series Detection

Detect anomalies in a single time series metric using statistical algorithms, SARIMA models, and SR-CNN approaches for both batch and real-time streaming use cases.

Multivariate Detection

Identify anomalies across multiple correlated metrics simultaneously using graph attention networks and correlation analysis, capturing system-level failures invisible in indivi...

Streaming and Batch Modes

Support for both real-time streaming anomaly detection on incoming data points and batch retrospective analysis across historical datasets.

Change Point Detection

Identify structural breaks and trend changes in time series data beyond point anomalies, enabling detection of regime shifts and concept drift.

Root Cause Analysis

Group related anomalies and surface likely contributing factors to accelerate diagnosis and response.

Algorithm Diversity

Access to a wide range of detection algorithms from statistical methods to deep learning, including IForest, LOF, OCSVM, AutoEncoder, VAE, and SARIMA.

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Anomaly Detection Context

6 classes · 23 properties

JSON-LD

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs.

Anomaly Detection — jsonschema rules

5 rules · 4 warnings 1 info

SPECTRAL

JSON Schema 3

Standalone JSON Schema definitions for this provider's data models.

Anomaly

13 properties

JSON SCHEMA

DetectionJob

11 properties

JSON SCHEMA

TimeSeries

10 properties

JSON SCHEMA

JSON Structure 3

JSON Structure definitions describing this provider's data shapes.

Anomaly Detection Anomaly Structure

13 properties

JSON STRUCTURE

Anomaly Detection Detection Job Structure

11 properties

JSON STRUCTURE

Anomaly Detection Time Series Structure

10 properties

JSON STRUCTURE

Examples 3

Example request and response payloads for these APIs.

Anomaly Detection Anomaly Example

13 fields

EXAMPLE

Anomaly Detection Detection Job Example

11 fields

EXAMPLE

Anomaly Detection Time Series Example

10 fields

EXAMPLE

Security Posture 3

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Anomaly Detection Authentication

apiKey · 1 scheme

SECURITY

Anomaly Detection Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Anomaly Detection Vulnerability Disclosure

security.txt · contact published

SECURITY

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Anomaly Detection Agentic Access

9 operations · 7 acting

AGENTIC

Use Cases 5

What developers build with this provider.

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.

Integrations 5

Pre-built integrations with other platforms and tools.

Amazon S3

Connect anomaly detection pipelines to S3 data lakes for batch analysis of historical metric data.

Elasticsearch / OpenSearch

Use Elasticsearch ML datafeeds to continuously analyze indices for anomalous patterns using built-in anomaly detection jobs.

Amazon CloudWatch

Pipe CloudWatch metrics into AWS Lookout for Metrics for automated operational anomaly alerting.

Microsoft Fabric / Real-Time Intelligence

Migration target for Azure Anomaly Detector users, providing integrated real-time anomaly detection within the Microsoft Fabric analytics platform.

Grafana

Visualize anomaly scores and detected anomalies from Elasticsearch ML and Datadog within Grafana dashboards.

Resources

Documentation 3

Reference material describing how the API behaves

Anomaly Schema

JSONSchema

Time Series Schema

JSONSchema

Detection Job Schema

JSONSchema

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

AgenticAccess

AgenticAccess

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Vocabulary

Vocabulary

Build 2

SDKs, sample code, and the tooling you integrate with

CodeOfConduct

CodeOfConduct

GitHubOrganization

GitHubOrganization

Access & Security 4

Authentication, authorization, and security posture

SecurityPolicy

SecurityPolicy

VulnerabilityDisclosure

VulnerabilityDisclosure

DomainSecurity

DomainSecurity

Authentication

Authentication

Operate 3

Status, limits, changes, and where to get help

RateLimits

RateLimits

IssueTracker

IssueTracker

Releases

Releases

Commercial 3

Pricing, plans, and the legal terms of use

Plans

Plans

FinOps

FinOps

License

License

Company 1

The organization behind the API

Blog

Blog

Other 2

Properties that don't map to a standard resource type

CapabilityMap

CapabilityMap

BestPractices

BestPractices

Source (apis.yml)

name: Anomaly Detection
description: 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.
deliveryModel:
  model: open-core
  license: MIT
  open_source: true
  commercial: true
  callable_host: true
  label: Open core · an OSS project plus a commercial hosted product
  confidence: high
  source:
  - license
  - openapi
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: self-serve
  trial: false
  try_now: true
  public: false
  gated: none
  gated_source:
  - onboarding
  label: Freemium · Self-serve signup
  confidence: medium
  source:
  - plans
  - authentication
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/anomaly-detection.png
created: 2024-01-15 00:00:00+00:00
modified: 2026-04-19 00:00:00+00:00
specificationVersion: '0.23'
url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/apis.yml
apis:
- name: Azure AI Anomaly Detector
  description: 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. Supports univariate batch and streaming detection, multivariate
    detection using Graph Attention Networks for up to 300 correlated signals, and change-point detection. The service is
    being retired on 1 October 2026 in favor of Microsoft Fabric real-time intelligence.
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  humanURL: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview
  baseURL: https://api.cognitive.microsoft.com
  tags:
  - Anomaly Detection
  - Microsoft Azure
  - Machine Learning
  - Microsoft
  - Multivariate
  - Time Series
  - Univariate
  tags_raw:
  - Anomaly Detection
  - Azure
  - Machine Learning
  - Microsoft
  - Multivariate
  - Time Series
  - Univariate
  - Machine-Learning
  properties:
  - type: Documentation
    url: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview
  - type: APIReference
    url: https://learn.microsoft.com/en-us/rest/api/anomalydetector/
  - type: GettingStarted
    url: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/quickstarts/client-libraries
  - type: Tutorials
    url: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/tutorials/batch-anomaly-detection-powerbi
  - type: GitHubRepository
    url: https://github.com/microsoft/anomaly-detector
  contact:
  - FN: Microsoft Azure Support
    url: https://azure.microsoft.com/en-us/support/
- name: Elasticsearch Anomaly Detection API
  description: Elasticsearch Machine Learning APIs provide a comprehensive suite of anomaly detection capabilities for time
    series data stored in Elasticsearch indices. Supports creating and managing anomaly detection jobs and datafeeds, accessing
    bucket, record, category, and influencer results, model snapshots, calendars, scheduled events, and forecasting. Part
    of the Elastic Stack ML feature set available in subscriptions.
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  humanURL: https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-apis.html
  baseURL: https://your-elasticsearch-host:9200
  tags:
  - Anomaly Detection
  - Elasticsearch
  - Machine Learning
  - Monitoring
  - Time Series
  tags_raw:
  - Anomaly Detection
  - Elasticsearch
  - Machine Learning
  - Monitoring
  - Time Series
  - Machine-Learning
  properties:
  - type: Documentation
    url: https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-apis.html
  - type: APIReference
    url: https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-ad-apis.html
  - type: GettingStarted
    url: https://www.elastic.co/guide/en/machine-learning/current/ml-ad-overview.html
  - type: GitHubOrganization
    url: https://github.com/elastic
  contact:
  - FN: Elastic Support
    url: https://www.elastic.co/support
- name: Datadog Anomaly Monitor API
  description: 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 seasonality. Offers three detection algorithms — Basic,
    Agile (SARIMA), and Robust (seasonal-trend decomposition) — configurable via REST API. Available across regional endpoints
    for US, EU, AP1, AP2, GOV, US3, and US5 deployments.
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  humanURL: https://docs.datadoghq.com/monitors/types/anomaly/
  baseURL: https://api.datadoghq.com
  tags:
  - Anomaly Detection
  - Datadog
  - Monitoring
  - Observability
  - Time Series
  properties:
  - type: Documentation
    url: https://docs.datadoghq.com/monitors/types/anomaly/
  - type: APIReference
    url: https://docs.datadoghq.com/api/latest/monitors/
  - type: Authentication
    url: https://docs.datadoghq.com/api/latest/authentication/
  - type: GitHubOrganization
    url: https://github.com/DataDog
  contact:
  - FN: Datadog Support
    url: https://www.datadoghq.com/support/
- name: AWS Lookout for Metrics
  description: 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, Amazon Redshift, Amazon CloudWatch, and SaaS applications,
    learns each metric's normal behavior, and sends alerts when anomalies are detected. Provides root cause analysis grouping
    related anomalies for faster diagnosis.
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  humanURL: https://aws.amazon.com/lookout-for-metrics/
  baseURL: https://lookoutmetrics.us-east-1.amazonaws.com
  tags:
  - Amazon Web Services
  - Anomaly Detection
  - Business Metrics
  - Machine Learning
  tags_raw:
  - Amazon Web Services
  - Anomaly Detection
  - AWS
  - Business Metrics
  - Machine Learning
  - Machine-Learning
  properties:
  - type: Documentation
    url: https://docs.aws.amazon.com/lookoutmetrics/latest/dev/lookoutmetrics-welcome.html
  - type: APIReference
    url: https://docs.aws.amazon.com/lookoutmetrics/latest/api/Welcome.html
  - type: GettingStarted
    url: https://docs.aws.amazon.com/lookoutmetrics/latest/dev/lookoutmetrics-gettingstarted.html
  - type: Pricing
    url: https://aws.amazon.com/lookout-for-metrics/pricing/
  contact:
  - FN: AWS Support
    url: https://aws.amazon.com/contact-us/
- name: PyOD (Python Outlier Detection)
  description: 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 approaches (AutoEncoder, VAE), proximity-based methods
    (LOF, CBLOF), linear models (PCA, OCSVM), and ensemble methods (IForest, LOCI). Widely used in research and production
    for fraud detection, intrusion detection, medical anomaly detection, and data quality monitoring.
  image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
  humanURL: https://pyod.readthedocs.io/
  baseURL: https://pypi.org/project/pyod/
  tags:
  - Anomaly Detection
  - Data Science
  - Machine Learning
  - Open Source
  - Outlier Detection
  - Python
  tags_raw:
  - Anomaly Detection
  - Data Science
  - Machine Learning
  - Open Source
  - Outlier Detection
  - Python
  - Machine-Learning
  - Open-Source
  properties:
  - type: Documentation
    url: https://pyod.readthedocs.io/en/latest/
  - type: APIReference
    url: https://pyod.readthedocs.io/en/latest/pyod.html
  - type: GitHubRepository
    url: https://github.com/yzhao062/pyod
  - type: SDKs
    url: https://pypi.org/project/pyod/
  contact:
  - FN: PyOD Maintainers
    url: https://github.com/yzhao062/pyod/issues
- aid: anomaly-detection:anomaly-detection-changepoint-api
  name: Anomaly Detection ChangePoint API
  description: Trend change-point detection.
  humanURL: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview
  baseURL: https://api.cognitive.microsoft.com
  tags:
  - ChangePoint
  properties:
  - type: OpenAPI
    url: openapi/anomaly-detection-changepoint-api-openapi.yml
- aid: anomaly-detection:anomaly-detection-multivariate-api
  name: Anomaly Detection Multivariate API
  description: Multivariate anomaly detection across correlated signals.
  humanURL: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview
  baseURL: https://api.cognitive.microsoft.com
  tags:
  - Multivariate
  properties:
  - type: OpenAPI
    url: openapi/anomaly-detection-multivariate-api-openapi.yml
- aid: anomaly-detection:anomaly-detection-univariate-api
  name: Anomaly Detection Univariate API
  description: Anomaly detection on a single time series.
  humanURL: https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview
  baseURL: https://api.cognitive.microsoft.com
  tags:
  - Univariate
  properties:
  - type: OpenAPI
    url: openapi/anomaly-detection-univariate-api-openapi.yml
maintainers:
- FN: Kin Lane
  email: info@apievangelist.com
  X: apievangelist
  url: https://apievangelist.com
tags:
- Anomaly Detection
- Artificial Intelligence
- Data Science
- Fraud Prevention
- Machine Learning
- Monitoring
- Observability
- Outlier Detection
- Pattern Recognition
- Security
- Time Series
tags_raw:
- Anomaly Detection
- Artificial Intelligence
- Data Science
- Fraud Detection
- Machine Learning
- Monitoring
- Observability
- Outlier Detection
- Pattern Recognition
- Security
- Time Series
- Machine-Learning
include: []
common:
- type: Plans
  url: plans/anomaly-detection-plans-pricing.yml
- type: RateLimits
  url: rate-limits/anomaly-detection-rate-limits.yml
- type: FinOps
  url: finops/anomaly-detection-finops.yml
- type: CapabilityMap
  url: capabilities/anomaly-detection-capability-edges.yml
  name: Anomaly Detection Business Capability Map
- type: IssueTracker
  url: https://github.com/microsoft/anomaly-detector/issues
- type: Releases
  url: https://github.com/microsoft/anomaly-detector/releases
- type: SecurityPolicy
  url: https://github.com/microsoft/anomaly-detector/blob/main/SECURITY.md
- type: CodeOfConduct
  url: https://github.com/microsoft/anomaly-detector/blob/main/CODE_OF_CONDUCT.md
- type: License
  name: MIT
  url: https://github.com/microsoft/anomaly-detector/blob/main/LICENSE
- type: AgenticAccess
  url: agentic-access/anomaly-detection-agentic-access.yml
- type: VulnerabilityDisclosure
  url: security/anomaly-detection-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/anomaly-detection-domain-security.yml
- type: Authentication
  url: authentication/anomaly-detection-authentication.yml
- type: GitHubOrganization
  url: https://github.com/api-evangelist/anomaly-detection
- type: BestPractices
  url: https://pyod.readthedocs.io/en/latest/faq.html
- type: Blog
  url: https://techcommunity.microsoft.com/t5/AI-Customer-Engineering-Team/Introducing-Azure-Anomaly-Detector-API/ba-p/490162
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/json-schema/anomaly-detection-anomaly-schema.json
  title: Anomaly Schema
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/json-schema/anomaly-detection-time-series-schema.json
  title: Time Series Schema
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/json-schema/anomaly-detection-detection-job-schema.json
  title: Detection Job Schema
- type: Vocabulary
  url: https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/vocabulary/anomaly-detection-vocabulary.yaml
- type: Features
  data:
  - name: Univariate Time Series Detection
    description: Detect anomalies in a single time series metric using statistical algorithms, SARIMA models, and SR-CNN approaches
      for both batch and real-time streaming use cases.
  - name: Multivariate Detection
    description: Identify anomalies across multiple correlated metrics simultaneously using graph attention networks and correlation
      analysis, capturing system-level failures invisible in individual metrics.
  - name: Streaming and Batch Modes
    description: Support for both real-time streaming anomaly detection on incoming data points and batch retrospective analysis
      across historical datasets.
  - name: Change Point Detection
    description: Identify structural breaks and trend changes in time series data beyond point anomalies, enabling detection
      of regime shifts and concept drift.
  - name: Root Cause Analysis
    description: Group related anomalies and surface likely contributing factors to accelerate diagnosis and response.
  - name: Algorithm Diversity
    description: Access to a wide range of detection algorithms from statistical methods to deep learning, including IForest,
      LOF, OCSVM, AutoEncoder, VAE, and SARIMA.
- type: UseCases
  data:
  - name: Fraud Detection
    description: Identify fraudulent transactions, account takeovers, and suspicious behavioral patterns in financial and
      e-commerce systems.
  - name: Predictive Maintenance
    description: Detect early signs of equipment failure in industrial IoT systems by identifying anomalous sensor readings
      before breakdowns occur.
  - name: IT and Security Operations
    description: Detect unusual network traffic, unauthorized access patterns, and security incidents in real time using behavioral
      baselines.
  - name: Business Metrics Monitoring
    description: Alert on unexpected drops or spikes in KPIs such as revenue, conversion rates, user engagement, or API error
      rates.
  - name: Healthcare Monitoring
    description: Monitor patient vitals, lab values, and medical device readings for out-of-range or clinically significant
      anomalies.
- type: Integrations
  data:
  - name: Amazon S3
    description: Connect anomaly detection pipelines to S3 data lakes for batch analysis of historical metric data.
  - name: Elasticsearch / OpenSearch
    description: Use Elasticsearch ML datafeeds to continuously analyze indices for anomalous patterns using built-in anomaly
      detection jobs.
  - name: Amazon CloudWatch
    description: Pipe CloudWatch metrics into AWS Lookout for Metrics for automated operational anomaly alerting.
  - name: Microsoft Fabric / Real-Time Intelligence
    description: Migration target for Azure Anomaly Detector users, providing integrated real-time anomaly detection within
      the Microsoft Fabric analytics platform.
  - name: Grafana
    description: Visualize anomaly scores and detected anomalies from Elasticsearch ML and Datadog within Grafana dashboards.

Anomaly Detection API — frequently asked questions

Does Anomaly Detection have an API?

Yes. APIs.io lists 8 APIs from Anomaly Detection, including Azure AI Anomaly Detector, Elasticsearch Anomaly Detection API and Datadog Anomaly Monitor API, and 5 more.

Is there an OpenAPI specification for the Anomaly Detection API?

Yes. APIs.io indexes 3 OpenAPI specifications for Anomaly Detection, each linked for download from this page.

How do I authenticate with the Anomaly Detection API?

Anomaly Detection uses bearer-token authentication (apiKey · 1 scheme). The details are profiled on APIs.io as Anomaly Detection Authentication.

How much does the Anomaly Detection API cost?

APIs.io lists 3 pricing plans for Anomaly Detection, with each plan's price, limits and inclusions under Pricing on this page.

Is the Anomaly Detection API ready for AI agents?

Anomaly Detection has a Kin Score of 52.4 out of 100 (developing) and an Agent Readiness score of 19.6 (agent-aware), scored by API Evangelist on 2026-11-06.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

All 92 tools →

Call it yourself

curl for this page

This provider

curl "https://apis.io/api/v1/providers/anomaly-detection"

All providers

curl "https://apis.io/api/v1/providers?limit=25"

Every operation they expose

curl "https://apis.io/api/v1/providers/anomaly-detection/operations?limit=25"

How their score was established

curl "https://apis.io/api/v1/providers/anomaly-detection/evidence"

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