You can create the following types of Monitors in Observe:

Threshold Monitors

Threshold Monitors alert when a value crosses a threshold over a period of time. Thresholds are ideal for metrics data, where a numeric value is set as part of the Dataset definition. You can also use other Datasets, such as logs, traces, or resources, by selecting a single numeric column as the metric value.

For example, here are some threshold Monitor use cases:

• Alert when the CrashLoopBackOff metric in Kubernetes Pod Metrics is high.
• Alert when the bytesSent in AWS S3 access logs is higher or lower than expected.

Count Monitors

Count Monitors alert when the number of rows in a monitored set cross a threshold over a period of time. Counts are ideal for measuring volumes of data instead of contents of data and are good for negative monitors .

For instance, a count Monitor use case would be to alert on the number of user access logs matching an error condition and a URL regular expression.

Promote Monitors

Promote Monitors send the matching data in a monitored set to the destination. Promotes are ideal for sending actionable alerts to human operators or analysts, because you can include all relevant data directly into the message.

Some example promote Monitor use cases include:

• Crash reports that link in the affected customer, responsible engineer, and triggering condition from other Datasets.
• Customer feedback alerts that include contextual data or links to investigative tools.

Anomaly Monitors

Anomaly Monitors alert you when recent behavior deviates from an established baseline in a sustained way. They’re best for signals that have evolving behavior, such as slow trends or evolutions, where static thresholds are either too noisy or must be constantly adapted. Consider the following uses cases where anomaly monitors are effective:

• Traffic patterns, such as RPS or throughput.
• Error rates or failure ratios.
• Latency or resource utilization.
• Group by:
  • Team‑owned units, such as service or cluster.
  • Environments, such as prod or staging.

Conversion from earlier Monitor types

Monitors from legacy monitoring are not automatically converted or migrated. To plan a migration of existing monitors, contact your Observe data engineer.