Anomaly Monitor

Anomaly Monitor


Anomaly detection is an algorithmic feature that identifies when a metric is behaving differently than it has in the past, taking into account trends, seasonal day-of-week, and time-of-day patterns. It is well-suited for metrics with strong trends and recurring patterns that are hard to monitor with threshold-based alerting.

For example, anomaly detection can help you discover when your web traffic is unusually low on a weekday afternoon—even though that same level of traffic is normal later in the evening. Or consider a metric measuring the number of logins to your steadily-growing site. Because the number increases daily, any threshold would be quickly outdated, whereas anomaly detection can alert you if there is an unexpected drop—potentially indicating an issue with the login system.

Monitor creation

To create an anomaly monitor in Datadog, use the main navigation: Monitors –> New Monitor –> Anomaly.

Define the metric

Any metric currently reporting to Datadog is available for monitors. For more information, see the Metric Monitor page. Note: The anomalies function uses the past to predict what is expected in the future, so using it on a new metric may yield poor results.

After defining the metric, the anomaly detection monitor provides two preview graphs in the editor:

  • The Historical View allows you to explore the monitored query at different time scales to better understand why data may be considered anomalous or non-anomalous.
  • The Evaluation Preview is longer than the alerting window and provides insight on what the anomalies algorithm takes into account when calculating the bounds.

Set alert conditions

Trigger an alert if the values have been above or below, above, or below the bounds for the last 15 minutes, 1 hour, etc. or custom to set a value between 15 minutes and 24 hours. Recover if the values are within the bounds for at least 15 minutes, 1 hour, etc. or custom to set a value between 15 minutes and 24 hours.

Anomaly detection
With the default option (above or below) a metric is considered to be anomalous if it is outside of the gray anomaly band. Optionally, you can specify whether being only above or below the bands is considered anomalous.
Trigger window
How much time is required for the metric to be anomalous before the alert triggers. Note: If the alert window is too short, you might get false alarms due to spurious noise.
Recovery window
How much time is required for the metric to not be considered anomalous so the alert recovers.

Advanced options

Datadog automatically analyzes your chosen metric and sets several parameters for you. However, the options are available for you to edit under Advanced Options.

The width of the gray band. This is equivalent to the bounds parameter used in the anomalies function.
The anomaly detection algorithm (basic, agile, or robust).
The seasonality (hourly, daily, or weekly) of the cycle for the agile or robust algorithm to analyze the metric.
Daylight savings
Available for agile or robust anomaly detection with weekly or daily seasonality. For more information, see Anomaly Detection and Time Zones.
The rollup interval.
The percentage of points that need to be anomalous for alerting, warning, and recovery.
The algorithm expects the same minute after the hour behaves like past minutes after the hour, for example 5:15 behaves like 4:15, 3:15, etc.
The algorithm expects the same time today behaves like past days, for example 5pm today behaves like 5pm yesterday.
The algorithm expects that a given day of the week behaves like past days of the week, for example this Tuesday behaves like past Tuesdays.

Note: Machine learning algorithms require at least twice as much historical data time as the chosen seasonality time to be fully efficient.

Anomaly detection algorithms
Use when metrics have no repeating seasonal pattern. Basic uses a simple lagging rolling quantile computation to determine the range of expected values. It uses very little data and adjusts quickly to changing conditions but has no knowledge of seasonal behavior or longer trends.
Use when metrics are seasonal and expected to shift. The algorithm quickly adjusts to metric level shifts. A robust version of the SARIMA algorithm, it incorporates the immediate past into its predictions, allowing quick updates for level shifts at the expense of being less robust to recent, long-lasting anomalies.
Use when seasonal metrics expected to be stable, and slow, level shifts are considered anomalies. A seasonal-trend decomposition algorithm, it is very stable and predictions remain constant even through long-lasting anomalies at the expense of taking longer to respond to intended level shifts (e.g. if the level of a metric shifts due to a code change.)

All of the seasonal algorithms may use up to a couple of months of historical data when calculating a metric’s expected normal range of behavior. By using a significant amount of past data, the algorithms can avoid giving too much weight to abnormal behavior that might have occurred in the recent past.

The graphs below illustrate how and when these three algorithms behave differently from one another.

In this example, basic successfully identifies anomalies that spike out of the normal range of values, but it does not incorporate the repeating, seasonal pattern into its predicted range of values. By contrast, robust and agile both recognize the seasonal pattern and can detect more nuanced anomalies, for example if the metric was to flat-line near its minimum value.

In this example, the metric exhibits a sudden level shift. Agile adjusts more quickly to the level shift than robust. Also, the width of robust’s bounds increases to reflect greater uncertainty after the level shift; the width of agile’s bounds remains unchanged. Basic is clearly a poor fit for this scenario, where the metric exhibits a strong weekly seasonal pattern.

This example shows how the algorithms react to an hour-long anomaly. Robust does not adjust the bounds for the anomaly in this scenario since it reacts more slowly to abrupt changes. The other algorithms start to behave as if the anomaly is the new normal. Agile even identifies the metric’s return to its original level as an anomaly.

The algorithms deal with scale differently. Basic and robust are scale-insensitive, while agile is not. The graphs on the left below show agile and robust mark the level-shift as being anomalous. On the right, we add 1000 to the same metric, and agile no longer calls out the level-shift as being anomalous whereas robust continues do so.

This example shows how each algorithm handles a new metric. Robust and agile won’t show any bounds during the first few seasons (weekly). Basic starts showing bounds shortly after the metric first appears.


For detailed instructions on the Say what’s happening and Notify your team sections, see the Notifications page.


Enterprise-level customers can create anomaly detection monitors using the create-monitor API endpoint. Datadog strongly recommends exporting a monitor’s JSON to build the query for the API. By using the monitor creation page in Datadog, customers benefit from the preview graph and automatic parameter tuning to help avoid a poorly configured monitor.

Note: Anomaly detection monitors are only available to enterprise-level customers. Pro-level customers interested in anomaly detection monitors should reach out to their customer success representative or email the Datadog billing team.

Anomaly monitors are managed using the same API as other monitors. These fields are unique for anomaly monitors:


The query property in the request body should contain a query string in the following format:

avg(<query_window>):anomalies(<metric_query>, '<algorithm>', <deviations>, direction='<direction>', alert_window='<alert_window>', interval=<interval>, count_default_zero='<count_default_zero>' [, seasonality='<seasonality>']) >= <threshold>
A timeframe like last_4h or last_7d. The time window displayed in graphs in notifications. Must be at least as large as the alert_window and is recommended to be around 5 times the alert_window.
A standard Datadog metric query (e.g., sum:trace.flask.request.hits{service:web-app}.as_count()).
basic, agile, or robust.
A positive number; controls the sensitivity of the anomaly detection.
The directionality of anomalies that should trigger an alert: above, below, or both.
The timeframe which will be checked for anomalies (e.g., last_5m, last_1h).
A positive integer representing the number of seconds in the rollup interval. We recommend that the interval be at least a fifth of the alert_window duration.
Use true for most monitors. Set to false only if submitting a count metric in which the lack of a value should not be interpreted as a zero.
hourly, daily, or weekly. Exclude this parameter when using the basic algorithm.
A positive number no larger than 1. The fraction of points in the alert_window that must be anomalous in order for a critical alert to trigger.

Below is an example query for an anomaly detection monitor, which alerts when the average Cassandra node’s CPU is three standard deviations above the ordinary value over the last 5 minutes:

avg(last_1h):anomalies(avg:system.cpu.system{name:cassandra}, 'basic', 3, direction='above', alert_window='last_5m', interval=20, count_default_zero='true') >= 1


Most of the properties under options in the request body are the same as for other query alerts, except for thresholds and threshold_windows.

Anomaly monitors support critical, critical_recovery, warning, and warning_recovery thresholds. Thresholds are expressed as numbers from 0 to 1, and are interpreted as the fraction of the associated window that is anomalous. For example, an critical threshold value of 0.9 means that a critical alert triggers when at least 90% of the points in the trigger_window (described below) are anomalous. Or, a warning_recovery value of 0 means that the monitor recovers from the warning state only when 0% of the points in the recovery_window are anomalous.
The critical threshold should match the threshold used in the query.
Anomaly monitors have a threshold_windows property in options. threshold_windows must include both two properties—trigger_window and recovery_window. These windows are expressed as timeframe strings, such as last_10m or last_1h. The trigger_window must match the alert_window from the query. The trigger_window is the time range which is analyzed for anomalies when evaluating whether a monitor should trigger. The recovery_window is the time range that analyzed for anomalies when evaluating whether a triggered monitor should recover.

A standard configuration of thresholds and threshold window looks like:

"options": {
  "thresholds": {
    "critical": 1,
    "critical_recovery": 0
  "threshold_windows": {
    "trigger_window": "last_30m",
    "recovery_window": "last_30m"


Further Reading