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.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Anomaly Detection the way a machine reads it — 43 machine-readable artifacts across 8 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Anomaly Detection scores 52.2/100 (developing), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Anomaly Detection
Each block below is one kind of artifact we hold for Anomaly Detection. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 8
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
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 within the panel for all 8 ·
Open Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Azure AI Anomaly Detector API
OPEN COLLECTIONPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Anomaly Detection Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Anomaly Detection Finops
FINOPSFeatures 6
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
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 give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Anomaly Detection Context
JSON-LDSpectral Rules 1
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Anomaly Detection API Rules
SPECTRALJSON Schema 3
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
JSON Structure 3
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Anomaly Detection Anomaly Structure
JSON STRUCTUREAnomaly Detection Detection Job Structure
JSON STRUCTUREAnomaly Detection Time Series Structure
JSON STRUCTUREExamples 3
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 3
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
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 tell you where this provider already fits in a stack.
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
Every other property we hold for Anomaly Detection — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Documentation 3
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 1
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 3
Authentication, authorization, and security posture
Company 1
The organization behind the API
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/anomaly-detection · machine-readable index on apis.io