# Deployments

The **Deployments** section manages your live inference endpoints. It mirrors `client.deployments` in the [SDK](/docs/sdk). Each endpoint serves a single model behind an HTTPS API.

## Create a deployment

From the [Models](/docs/dashboard-models) section, click **Deploy** on a ready model. Choose the hardware tier and Fleet provisions a serverless endpoint. Inference scales up with demand and back down to zero when idle, so an idle endpoint costs you nothing. See [Billing](/docs/billing) for how inference is metered.

## Deployment status

| Status | Meaning |
| --- | --- |
| `starting` | The endpoint is being provisioned. |
| `active` | Live and serving requests. |
| `failed` | Provisioning or startup failed. |
| `stopped` | Shut down; no longer serving. |

## Test inference

Click **Test** (or open the endpoint detail) to send a prompt straight from the dashboard and see the response, without writing any code. This is the quickest way to confirm a freshly deployed model behaves as expected.

For production use, copy the endpoint's URL and call it from your app, or use `endpoint.infer(...)` in the [SDK](/docs/sdk). Responses can be streamed.

## Manage deployments

Each endpoint row offers actions to copy its URL, test it, and delete it. Deleting an endpoint stops it serving and releases its resources immediately.