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

# Billing

> How on-demand Hosted Training runs are metered and billed

On-demand Hosted Training is billed **per GPU-hour**, for the GPUs your run occupies.

## Where to find rates

Rates are set per GPU type and change over time, so they live in the CLI rather than in these docs:

```bash theme={null}
prime train models
```

The **On-Demand Capacity** table lists each purchasable GPU type with its current `$/GPU-hour`. A GPU type with no active rate isn't sold on-demand and won't appear.

When you dispatch, the CLI echoes the GPU type it resolved and the rate it will bill at, so the cost is visible before the run starts rather than first appearing on an invoice:

```bash theme={null}
prime train configs/full-ft.toml
```

<Note>
  The rate is snapshotted onto your run at dispatch. If rates change while your run is in flight, your run keeps the rate it started with.
</Note>

## What gets billed

| | |
| - | - |
| **Meter starts** | When your pods are **Ready** |
| **Meter stops** | When the run reaches a terminal state |
| **Granularity** | Prorated to the second, no minimum |

Queueing, scheduling, and image pull are **not** billed. A cold node pulling a multi-gigabyte prime-rl image can take many minutes — that time is on us.

## Dispatch requirements

A run is admitted against your wallet, so before dispatch you need:

* **On-demand enabled** on your account or team. On-demand training is in beta, so [request access](mailto:contact@primeintellect.ai) if it is not enabled yet
* **Spendable balance** — your balance plus any overdraft allowance and training credit grants
* **A run within your per-run GPU cap**

Credits and discounts are applied as grants against your wallet, and grants are consumed before your balance.

## Capacity and queueing

If the on-demand pool has no headroom, your run **queues** rather than being rejected, and is promoted automatically as capacity frees. Your wallet is re-checked at promotion.

`prime train models` shows a **Capacity** column per GPU type, either available or busy. That is a coarse hint about pool headroom, not an admission decision: whether your run starts immediately or queues also depends on how many GPUs you ask for and their topology, which the discovery endpoint does not know.

<Note>
  If you already hold reserved capacity for a matching GPU type, your run is placed there instead of on-demand — that hardware is already paid for.
</Note>

<CardGroup cols={2}>
  <Card title="Models" icon="list" href="/hosted-training/models">
    Which models and GPU types you can dispatch on.
  </Card>

  <Card title="Full Fine-Tuning" icon="wand-sparkles" href="/hosted-training/full-finetuning">
    Config schema, dispatch knobs, and run lifecycle.
  </Card>
</CardGroup>


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