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

# Machine Sizes

> Choose the CPU and memory available to workflow execution machines, and understand how each size affects credit consumption per run.

Every workflow run executes on a dedicated machine. The machine size determines the CPU and memory available to that machine, and how many credits the run consumes. Workflows that run heavy builds, large test suites, or memory-intensive analysis benefit from a larger size, while most workflows run comfortably on the default.

## Available sizes

| Size     | Resources for workspace commands | Credit cost   |
| -------- | -------------------------------- | ------------- |
| Standard | 2 vCPU / 1.5 GB                  | 1 credit/run  |
| Large    | 4 vCPU / 3 GB                    | 2 credits/run |
| X-Large  | 8 vCPU / 6 GB                    | 4 credits/run |

**Default:** Standard. Workflows without an explicit machine size run on Standard.

## Setting the machine size

Machine size is configured per workflow in [Workflow Metadata](/docs/how-to/workflow-builder#machine-size):

1. Open the workflow in Workflow Builder
2. Click the canvas background to show workflow metadata
3. Under **Settings**, choose a **Machine Size**

The selector shows each size with its credit cost, so you can weigh resources against consumption before committing. Leave the setting unset to use the default.

## Credits and billing

Credit cost is charged per run, based on the workflow's machine size. A workflow set to Large consumes twice the credits of the same workflow on Standard, and X-Large consumes four times as much.

Each run records the machine size it executed on. You can see it in the workflow runs list and in the run summary panel, alongside token usage.

<Tip>
  Machine size affects the compute credits of the execution machine, not LLM token usage. To analyze token consumption, see [Token Usage Analytics](/docs/workflows/token-usage-analytics).
</Tip>

## Choosing a size

Choose a larger size when the workflow:

* Runs heavy build or test commands
* Installs large dependency trees
* Analyzes large repositories or monorepos
* Uses memory-intensive steps

Keep the default for lightweight triage, comment, routing, or status workflows, especially when the workflow does not run expensive local commands.

<Tip>
  Start on Standard and move up only if runs fail or slow down on resource-heavy steps. Larger sizes multiply the credit cost of every run, including the ones that never needed the extra headroom.
</Tip>

## Related documentation

* [Workflow Metadata](/docs/how-to/workflow-builder): all workflow-level settings, including machine size
* [Token Usage Analytics](/docs/workflows/token-usage-analytics): analyze LLM token consumption per run