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

# Scaling

> Adjusting the computational resources allocated to your application

At Shuttle, you can adjust the vCPU and memory resources allocated to your application’s compute environment.
This functionality enables you to **vertically scale** your application to better suit your specific performance requirements and workload demands.

<Warning>
  Changing the instance size of your project is limited to the Pro tier and above, and will result in additional charges. These are calculated according to our [usage-based pricing](/pricing/billing#usage-based-pricing) model. Please ensure you review the pricing details before making adjustments.
</Warning>

### Example

The example below illustrates how to configure your application to use a **medium** instance size using **<Tooltip tip="Provisioning resources directly from your code, instead of having to deal with complex consoles and yaml files">Infrastructure from Code</Tooltip>**:

```rust main.rs theme={null}
#[shuttle_runtime::main(instance_size = "m")]
async fn main() -> shuttle_axum::ShuttleAxum {
    // Your application logic here
}
```

### Available Instance Sizes

The table below lists the available `instance_size` values, along with the corresponding instance type and minimum required Account Tier:

| Instance Size       | Value | vCPU | Memory (GB) | Account Tier |
| ------------------- | ----- | ---- | ----------- | ------------ |
| **Basic** (default) | `xs`  | 0.25 | 0.5         | Pro+         |
| **Small**           | `s`   | 0.5  | 1           | Pro+         |
| **Medium**          | `m`   | 1    | 2           | Pro+         |
| **Large**           | `l`   | 2    | 4           | Pro+         |
| **X Large**         | `xl`  | 4    | 8           | Pro+         |
| **XX Large**        | `xxl` | 8    | 16          | Growth+      |

*Ensure your selected instance size aligns with your application's current and anticipated workload.*