Monitor clutter builds up over time, resulting in noise, duplicated alerts, and increased operational friction. This guide outlines a clear approach to identifying and cleaning up cluttered monitors, with use cases to help you streamline your alerting workflows.
It also provides best practices to help maintain a clean monitoring environment, making it easier to scale and govern your monitoring strategy as your systems grow.
Monitors serve as an early warning system for failures, security threats, and performance issues. However, having monitors muted for a long period of time defeats that purpose, long-term muting often signals that a monitor is obsolete, irrelevant, or too noisy to be useful. These should be reviewed and either re-enabled with proper tuning or retired to reduce clutter and eliminate stale monitors from your alerting environment.
Clean up monitors that are not providing value and replace long-term mutes with time-bound schedules:
Audit monitors that have been muted for a long period of time to understand which are actually needed or useful. Some monitors might be muted for a good reason and you want to avoid deleting them.
After you have your list, you can either take action on each monitor from the Monitor Quality page or do a bulk deletion of monitors with steps 2 and 3.
Get a list of your monitor IDs to programmatically automate the changes. Start with the monitors that have been muted for over 60 days.
This gives you the details of your monitors in a CSV file for readability. You can refine the query to your specific use case.
With your list of monitors that have been muted for over 60 days (from Step 2), you can delete them with the following script. Before you run the script, put the monitor ID column first in the table.
Persistent alerts suggest one of two problems: either the issue is not actionable, or the monitor threshold is misconfigured. Both cases erode trust in alerts and contribute to alert fatigue. These monitors should be reviewed and edited, or removed.
Here is how to get the list of monitors which have been in ALERT state for more than 60 days:
Creating separate monitors that differ only by a tag, can lead to unnecessary duplication. For example, monitoring CPU usage with one monitor for prod
and another for staging
increases your monitor count.
Redundant monitors create unnecessary noise and confusion. In many cases, these can be consolidated into a single multi-alert monitor with proper scoping and tagging, reducing duplication and making alerts more manageable.
If you need to send different notifications depending on the tag value that triggered the alert, use monitor variables to dynamically customize the message based on the tag that breached the threshold.
Noisy monitors desensitize teams to real issues. Flapping (when a monitor frequently switches between alert and recovery states) often indicates unstable thresholds, missing evaluation delays, or underlying system volatility.
To reduce noise, review the monitor’s evaluation aggregation and the threshold configuration. Adjust the settings to stabilize alert behavior, or delete the monitor if it no longer provides value.
Here is how to get a list of monitors that are generating a high volume of alerts:
Misconfigured monitors are active monitors that may have a proper use, but are inefficient because you won’t be notified. These misconfigurations undermine the monitor’s reliability and make debugging or triaging harder. Cleaning these up ensures your alerts are accurate, actionable, and integrated into your observability workflows.
This issue mainly impacts monitors based on AWS metrics. Because Datadog retrieves AWS metrics through the API, there’s often a built-in delay before the data becomes available. If you don’t account for this, monitors can trigger false positives due to incomplete or delayed data.
Composite monitors evaluate their state based on the logical combination of two or more monitors (called constituents). If any of those constituent monitors are deleted or become unavailable, the composite monitor becomes invalid or unreliable.
A missing constituent typically means that at least one of the original input monitors has been removed after the composite monitor was created. This causes the composite to be incomplete and potentially misleading in alerting behavior.
To help you get started, import the following JSON dashboard definition directly into your Datadog account.
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"content": "This Monitor Quality dashboard provides a comprehensive view of monitor quality metrics, broken down by `team` and `service`. Its goal is to help you easily analyze and act on monitor quality data, enabling you to schedule reports, download insights as PDFs, and more.\n\n**Key Features:**\n- Team and Service Views: You can filter the dashboard either by team or by service, but not both simultaneously. If you filter by `team`, refer to the [Team Section](https://app.datadoghq.com/dashboard/u7b-4n7-gn5/monitor-quality-ootb-dashboard?fromUser=false&refresh_mode=paused&from_ts=1732107838741&to_ts=1732280638741&live=false&tile_focus=4548404374449802) for relevant insights. If you filter by `service`, explore the [Service Section](https://app.datadoghq.com/dashboard/u7b-4n7-gn5/monitor-quality-ootb-dashboard?fromUser=false&refresh_mode=paused&from_ts=1732107865224&to_ts=1732280665224&live=false&tile_focus=2841959907422822) for detailed information.\n- Monitor-Level Details: For a deeper dive into specific impacted monitors, navigate to the [Monitor Quality page](https://app.datadoghq.com/monitors/quality).\n- Seamless Navigation: Use the context links provided in the dashboard to jump directly to the [Monitor Quality page](https://app.datadoghq.com/monitors/quality), pre-filtered with the same criteria you've applied on the dashboard.\n\nThis dashboard is designed to give you both a high-level overview and actionable paths to improve your monitoring posture.",
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