> ## Documentation Index
> Fetch the complete documentation index at: https://docs.thoras.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Stopping Autonomous Scaling

> Options for halting Thoras autoscaling, from cluster-wide to per-workload

There are three ways to stop Thoras from autonomously scaling workloads, ordered
from broadest to narrowest scope. Choose the option that matches the blast
radius you need.

| Approach              | Scope               | Restarts Pods | Workload requests                                                                              | Model & data retention                                                                                |
| --------------------- | ------------------- | ------------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
| System-wide pause     | All autonomous ASTs | No            | Running pods keep the last applied suggestion; restarted pods come up with the controller spec | Thoras keeps forecasting and producing suggestions                                                    |
| `recommendation` mode | One AST             | Yes           | Reverts to the controller spec                                                                 | Thoras keeps forecasting and producing suggestions                                                    |
| Unenroll (delete AST) | One AST             | Yes           | Reverts to the controller spec                                                                 | History and model state for the AST is discarded; workload is no longer managed or observed by Thoras |

## 1. System-wide pause

Halts all autonomous scaling actions across the entire cluster. Thoras continues
collecting metrics and producing recommendations; only the apply step is
suspended. Workloads in `recommendation` mode are unaffected.

Pausing does **not** restart workload pods. Running pods keep the resource
requests Thoras last applied to them. When new pods are created they will use
the requests defined in the controller spec.

**When to use:** planned maintenance windows, incident response, or baseline
validation where you want to freeze allocations cluster-wide without changing
any `AIScaleTarget` definitions.

**How to do it:**

1. Open the **Manage Cluster** dropdown in the dashboard header.
2. Select **Pause autonomous scaling**.
3. Confirm in the flyout, which shows the count of autonomous targets that will
   be affected.

To resume, open the same dropdown and select **Resume autonomous scaling**.

The pause state persists across pod restarts and upgrades via the
`thoras-operator-system-config` ConfigMap. See
[Pausing Autonomous Scaling](/guides/pause-scaling) for pod behavior during
pause, visual indicators, and the ConfigMap-based advanced workflow.

## 2. Switch the AST from autonomous to recommendation mode

Stops autonomous scaling for a single workload while keeping the `AIScaleTarget`
enrolled. Thoras continues to forecast and surface suggestions in the dashboard,
but no scaling actions are applied.

<Note>
  Unlike a system-wide pause, switching to `recommendation` mode restarts the
  workload's pods and reverts it to the requests defined in the controller spec.
</Note>

**When to use:** you want to keep the workload monitored and the model warm (for
example, to compare suggestions against actual usage), but you don't want Thoras
applying changes for that specific workload.

**How to do it:**

Edit the `AIScaleTarget` and set the active scaling direction's `mode` to
`recommendation`:

```sh theme={null}
kubectl edit ast {{YOUR_AST_NAME}} -n {{YOUR_NAMESPACE}}
```

For a vertically autonomous workload:

```yaml theme={null}
spec:
  vertical:
    mode: recommendation
```

For a horizontally autonomous workload:

```yaml theme={null}
spec:
  horizontal:
    mode: recommendation
```

Recommendations continue to appear in the dashboard. To re-enable autonomous
scaling later, set `mode` back to `autonomous`. See
[Understanding Vertical and Horizontal Scaling Modes](/guides/configuring-asts#understanding-vertical-and-horizontal-scaling-modes)
for how the two directions interact, and the
[AIScaleTarget reference](/reference/ast-definition) for the full mode
specification.

## 3. Unenroll the workload (delete the AST)

Removes the workload from Thoras entirely. The workload is restarted and its
pods come up with the resource requests defined in the controller spec.

<Warning>
  **Deleting an AST is permanent.** Historical suggestions, utilization data,
  and model state for that AST will be lost. Re-creating an AST with the same
  name later produces a brand-new AST. If you want to stop scaling actions while
  keeping history intact, use option 1 or 2 above.
</Warning>

**When to use:** the workload should no longer be managed or observed by Thoras
at all. For example, when the workload is being decommissioned, moved to a
different scaling system, or you want a clean re-enrollment later.

**How to do it:**

```sh theme={null}
kubectl delete ast {{YOUR_AST_NAME}} -n {{YOUR_NAMESPACE}}
```