> ## Documentation Index
> Fetch the complete documentation index at: https://elementary-devin-1782754750-bigquery-permissions-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Cloud Pipeline Task Performance

<Snippet file="cloud/cloud-feature-tag.mdx" />

The pipeline task performance monitor tracks the **execution duration** of dbt models, seeds, and snapshots on each run, and alerts when a node takes significantly longer than expected — or exceeds a fixed SLA threshold.

This is an automated monitor: Elementary creates it for every dbt node in your project, with no configuration required to get started.

## Monitoring modes

### Anomaly detection (default)

Elementary uses a z-score seasonal model to learn normal execution duration for each node. The model accounts for time-of-day and day-of-week patterns, so a model that reliably runs longer on Monday mornings will not be flagged during that window.

No configuration is required. Elementary builds the baseline from your pipeline run history automatically.

### Static SLA

Set a fixed duration threshold. The monitor fails whenever the observed execution time exceeds that threshold, regardless of historical norms.

Use static SLA when you have a hard operational or contractual limit (e.g., "this model must finish within 10 minutes").

## Understand the monitor result

The result shows the execution duration of the dbt node for the latest run alongside the historical baseline.

* **Anomaly detection** — data points outside the expected range (grey band) are flagged. The expected range is derived from the seasonal model trained on historical durations.
* **Static SLA** — a horizontal line marks the threshold. Any run that crosses it is a failure.

Use the **Anomaly settings** button to adjust mode, sensitivity, or training period after the monitor is created.

## Anomaly settings

| Setting                | Description                                                                          | Default     |
| ---------------------- | ------------------------------------------------------------------------------------ | ----------- |
| `mode`                 | `"anomaly"` or `"static"`                                                            | `"anomaly"` |
| `sensitivity`          | `"low"`, `"medium"`, or `"high"` (anomaly mode only)                                 | `"medium"`  |
| `training_period`      | Days of history used to train the model (anomaly mode only)                          | `14`        |
| `anomaly_direction`    | `"spike"` (duration longer than expected), `"drop"`, or `"both"` (anomaly mode only) | `"spike"`   |
| `excluded_time_ranges` | Time ranges excluded from training and detection (anomaly mode only)                 | `[]`        |
| `fixed_threshold`      | Max allowed execution duration in seconds (static SLA mode only)                     | —           |

<Snippet file="cloud/features/anomaly-detection/all-anomalies-configuration.mdx" />

## Alerts

Failures raise alerts in the **`pipeline_performance`** alert category, routed through [alert rules](/features/alerts-and-incidents/alert-rules) to all configured integrations (Slack, PagerDuty, Teams, OpsGenie, email).

By default, alerts are not active. To enable them, go to **Setup → Alert Rules** and add **Pipeline performance** to the relevant rule's alert categories.

## Related

* [Performance Alerts](/cloud/features/performance-monitoring/performance-alerts) — the legacy dbt-test method for performance alerts (Elementary OSS); not required in Cloud, where this monitor is created automatically
* [Automated monitors overview](/cloud/features/anomaly-detection/automated-monitors) — how automated monitors work across freshness, volume, and performance