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The Future of
Anomaly Detection

is Supervised AI

Learns. Detects. Alerts. Improves

Anomify AI

is an Advanced Anomaly Detection  engine.

See anomalies in the dashboard

Reducing noise

with Supervised Machine Learning.

Reduce false positives with pattern matches

Why it works

One-Click Supervision

Supercharged Machine Learning.

By training the model with one-click supervision, Anomify is able to supercharge the machine learning model, reduce false positives and only surface critical events.

It’s a better approach to detection,
saving you time and stress.

capture expected patterns with Anomify

More Features

Always on.

Analysing your data 24/7
so you don’t have to.

Real-time Alerts.

Fast analysis means fast alerts
straight to your workflow.

Custom Algorithms.

Anomify gives you the power to
implement custom algorithms.

Root Cause.

Reduce  de-bug time.
Get to the root of the  
problem quickly.

API Access.

Integrate with your existing dashboards.
Get direct access to our API.

Patented Tech.

Zero Point of Failure technology
means critical alerts don’t get missed.

How it works

1. Real-Time Analysis

Anomify is always analysing.

After ingesting data we learn normal patterns, detect any unusual behaviour, alert on anomalies in real-time, and constantly feed back to improve the model.

2. Cutting Edge Detection

Anomify acts on a consensus of cutting edge algorithms to determine all types of anomalies.

Add in, best in class Supervised Machine Learning, Pattern Matching, Correlation and Custom Algorithms, and Anomify has your critical events covered.

infographic showing Anomify's detection process

Who it's for

Dynamic Performance Monitoring for Teams

From Performance Engineers to the CEO, everyone has a stake in what impacts the organisation.

Anomify empowers teams with fresh data insights, while removing manual threshold and alert fatigue.

With Anomify the whole team gets real-time visibility on events like never before.

Network Engineers, Data Scientists, Site reliability engineers, Ops, Performance monitoring engineers

Experience Across Industry

Energy use case: Monitor IoT devices connected to a wind-farm


Infrastructure use case: monitoring network activity on a server


Use case: Monitor any KPI with Anomify

Business Metrics

Business Metrics

Anomify is helping organisations in Energy, Infrastructure, Ecommerce Fintech and more to unlock the power of real-time anomaly detection.

Workflow Ready

Anomify integrates with your existing data stack to analyse data in real-time, delivering alerts direct to your workflow via Slack, Email, SMS and more so you don’t miss a beat.

Slack Prometheus Graphite Pager Duty AWS Google Ads Google Analytics

There is really nothing like it in the industry, with this much functionality

Oscar, Switzerland

Frequently Asked Questions

Anomaly detection is the process of  identifying outliers or unexpected patterns in data.

Machine learning algorithms model the data in order to define a baseline of expected behaviour. Abnormal or unexpected patterns that deviate from this baseline are classified as anomalies.

It depends on the data source we are receiving metrics from. Each source varies in the types of data they support and frequency at which they are stored. 


Anomify is not a metric store. For efficiency Anomify preprocesses raw metric data it receives every minute. It stores the processed data for analysis and dumps the raw values.Therefore the metric values recorded by Anomify may not exactly match those recorded in your metric store, but behaviour and trends will be the same.

Anomify stores metric data at a frequency of one data point per minute. How we store data and for how long depends on the data source – it can be anywhere from 30 days with Prometheus/Influxdb/victoriametrics data to as long as 2 years with Graphite and other data sources. 


We also store the sections of time-series that have been trained on indefinitely for pattern matching. 


Your data will not be shared with any third parties.

Using the free account you can send metrics from anywhere to a dedicated endpoint directly via POST request. You can also send metrics directly using Telegraf, the InfluxDB agent, Graphite or Prometheus.

The Scale Up plan opens up a number of plugins and connectors for common time-series databases and tools like CollectD, StatsD, Google Analytics, Open Telemetry Collector, MySQL, SQL Server.

If we don’t support your datastore yet, please get in touch so we can add it to the roadmap.

Anomify adds an additional dimension to your monitoring/observability set up.  Where generally you have rules and thresholds set on things, Anomify monitors all your metrics in the background, keeping an eye on everything.


Rules, thresholds and SLO (Service Level Objective) calculations do not necessarily aid in pinpointing what changed and where – in fact they exclude most metrics.  Anomify monitors all metrics and identifies and records abnormal changes, giving you deep insights into your systems and applications when you need it.


Users in your organisation can train Anomify to recognise expected patterns, just as they would. This reduces false positive anomalies.

Most anomaly detection platforms use unsupervised learning, which creates a disconnect between the user and the model which makes the  anomaly assessment. Anomify’s transparent supervision provides a human explanation for the predictions it makes. Predictions can be amended to fit with your mental model of how the system should behave under normal conditions.

A time-series is a sequence of data points collected from one source at different points in time and ordered chronologically.

A metric is a piece of data that is tracked to form the time-series. For example, in the energy industry the temperature of a solar panel could be tracked as a metric. In infrastructure I/O from a server could be a metric.

Semi-supervised machine learning is a process for classifying data as normal or  anomalous. It involves some human intervention to guide machine learning algorithms.

Unsupervised learning is another machine learning method, requiring no human intervention, but is a poor fit for the anomaly detection problem space because it fails to take account of real-world context and produces more false positives.

Self-serve Integration: 1 or 2 hours depending on complexity
Learning phase: 5 minutes a day for 30 days

You will:

✔️   learn how to better manage alerts.
✔️   tune Anomify to interpret normal behavior for your systems.
✔️   spend less time interpreting false positive alerts.
✔️   understand your metrics like never before.
✔️   unlock more time and space in your day.

Free trial: if Anomify is not fulfilling your needs at the end of the learning phase is over then you pay nothing.

Simple and fair usage based SaaS Pricing.

At our core we are developers.

We built Anomify as we had a need to monitor complex infrastructure at scale. And we’ve done just that.

Over a period of more than two years running on production data, we developed and refined the supervised machine learning method that significantly reduced our alert noise, increasing our focus on the critical events.

Anomify allowed us to quickly detect performance issues across billions of daily events, far beyond what our team could keep an eye on manually.

Analyse 1,000 metrics for free

See how Anomify’s AI makes alerts
better for your organisation.

Self-serve access. No credit card details required.

Learns. Detects. Alerts. Improves.

Learn more about Anomaly Detection

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