# Deployments

> Dedicated capacity for a base or custom model: pick a performance tier and Sference provisions and runs it for you.

Source: https://sference.com/docs/deployments

A **deployment** gives a model **dedicated capacity**: reserved throughput and predictable performance on a private endpoint, separate from the shared serverless pool. Deploy any **catalog** model or one of your own [custom models](https://sference.com/docs/custom-models), with the same OpenAI-compatible API, same pinned versions, same audit trail.

## Performance tiers

Instead of choosing hardware, you choose a **performance tier**. Sference maps the tier to the right capacity and provisions it for you, so you describe the outcome you need, not the GPUs behind it.

| Tier | Best for |
| --- | --- |
| **Cost saving** | Lowest cost, for async and latency-tolerant workloads. |
| **Standard** | Balanced cost and speed. The default. |
| **Turbo** | Highest throughput and lowest latency. |

> **Hardware is handled for you**
>
> You never pick or manage GPUs. Sference selects the hardware behind your chosen tier, provisions it, and keeps it running.

## Request a deployment

1. In the [console](https://app.sference.com), open **Deployments** and choose **Create deployment**.
2. Select the **model**: a base catalog model or one of your **available** custom models.
3. Choose a **region**, a **performance tier**, and the **replica** range (min/max), then give the deployment a name.
4. Submit. Sference provisions and activates it; the status moves from **pending** through **deploying** to **available**.

## Use it

Once the deployment is **available**, send traffic to it the same way you call any model on the API: streaming, tool calling, and structured output all work unchanged. Because the model id and request shape are identical to serverless, moving a hot path onto dedicated capacity needs no code changes beyond routing.

## Pricing

Dedicated capacity is arranged with your account. **[Talk to us](mailto:hello@sference.com)** to set up a plan that fits your throughput and commitment.

## Next steps

- [Custom Models](https://sference.com/docs/custom-models): Bring your own weights, then deploy them.
- [Models](https://sference.com/docs/models): Catalog, pinned versions, and size classes.