# Autotune

> For AI agents: a documentation index is available at [/llms.txt](/llms.txt). Append `.md` to any page URL for markdown, or send `Accept: text/markdown`.

Autotune uses a multi-armed bandit to automatically shift traffic toward the best-performing variant for a single goal metric. In Warehouse Native, Statsig pulls Autotune success events from a Metric Source and computes results in your warehouse. Setup is similar to cloud [Autotune](https://docs.statsig.com/autotune/overview).

## Creating an Autotune

To set up an Autotune on Warehouse Native, start by defining your objective. The objective can be an _event_ or a _value_ from a Metric Source. For detailed instructions, refer to [how to set up a Metric Source](https://docs.statsig.com/statsig-warehouse-native/configuration/metric-sources).

You can also specify the optimization direction (Maximize or Minimize) to determine how Autotune evaluates variant performance.

![Autotune configuration interface](https://docs.statsig.com/images/statsig-warehouse-native/features/autotune/autotune-config.png)

1. Navigate to the [Experiments section](https://console.statsig.com/experiments) in the sidebar of the Statsig Console.
2. Click on the [Autotune tab](https://console.statsig.com/autotune) at the top.
3. Click the Create button and enter the name and description of the Autotune Experiment that you want to create.
4. Select an ID Type for your Experiment.
5. Create and name your variants for your Autotune Experiment. Statsig returns the variant listed as Control/Default when the Autotune Experiment isn't running.
6. Select your Metric Source that you defined earlier as shown below.

![Autotune metric source configuration interface](https://docs.statsig.com/images/statsig-warehouse-native/features/autotune/autotune-metric-source.png)

You can configure the following parameters:

| Parameter | Description |
| --- | --- |
| Exploration Window | The initial time period where Autotune splits traffic equally. Useful for noisy or temporal metrics where hourly swings can bias initial measurements. |
| Attribution Window | The maximum duration between the exposure and success event that counts as a success. Statsig recommends 1 hour for most applications; adjust if you expect the success event to lag the exposure event by several hours. |
| Winner Threshold | The "probability of best" threshold a variant must reach for Autotune to declare it the winner, stop collecting data, and direct all traffic. A lower value results in faster decisions but increases the probability of selecting the wrong winner. |

Click **Create** to finalize the setup.

7. You've configured the Autotune, and it's ready. Click **Start** when you're ready to launch your Autotune test.