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

# Deployments

> Create and manage model endpoints for real-time inference with the Python SDK.

Deployments host a model on dedicated compute infrastructure and make it available for inference requests and agent usage. For conceptual background, see [Deployments](/flow/components/deployments).

## Create a deployment

### Base model

<CodeGroup>
  ```python Python theme={null}
  from seekrai import SeekrFlow
  from seekrai.types.deployments import DeploymentType

  client = SeekrFlow()

  deployment = client.deployments.create(
      name="my-base-model-deployment",
      description="Base model deployment for inference.",
      model_type=DeploymentType.BASE_MODEL,
      model_id="meta-llama/Llama-3.3-70B-Instruct",
      n_instances=1
  )
  print(f"Deployment ID: {deployment.id}")
  print(f"Status: {deployment.status}")
  ```
</CodeGroup>

### Fine-tuned model

<CodeGroup>
  ```python Python theme={null}
  deployment = client.deployments.create(
      name="my-fine-tuned-deployment",
      description="Fine-tuned model deployment for inference.",
      model_type=DeploymentType.FINE_TUNED_RUN,
      model_id="ft-1234567890",
      n_instances=1
  )
  print(f"Deployment ID: {deployment.id}")
  print(f"Status: {deployment.status}")
  ```
</CodeGroup>

## Parameters

| Parameter     | Required | Description                                                                                             |
| ------------- | -------- | ------------------------------------------------------------------------------------------------------- |
| `name`        | Yes      | A name for the deployment. Must be 5–100 characters.                                                    |
| `description` | Yes      | A description of the deployment. Must be 5–1000 characters.                                             |
| `model_type`  | Yes      | `DeploymentType.BASE_MODEL` for a base model or `DeploymentType.FINE_TUNED_RUN` for a fine-tuned model. |
| `model_id`    | Yes      | The model ID (base model name or fine-tuning job ID) to deploy.                                         |
| `n_instances` | Yes      | Number of dedicated instances to provision. Must be between 1 and 50.                                   |

## Deployment status

| Status     | Description                                                    |
| ---------- | -------------------------------------------------------------- |
| `Pending`  | Deployment requested, infrastructure provisioning in progress. |
| `Active`   | Serving inference traffic.                                     |
| `Inactive` | Paused, not serving requests.                                  |
| `Failed`   | Error during startup or runtime.                               |

## Promote a deployment

Promote a deployment to make it active and ready to serve inference requests.

<CodeGroup>
  ```python Python theme={null}
  deployment = client.deployments.promote(deployment.id)
  print(f"Status: {deployment.status}")
  ```
</CodeGroup>

## Demote a deployment

Demote a deployment to pause it without deleting the endpoint.

<CodeGroup>
  ```python Python theme={null}
  deployment = client.deployments.demote(deployment.id)
  print(f"Status: {deployment.status}")
  ```
</CodeGroup>

## List deployments

<CodeGroup>
  ```python Python theme={null}
  deployments = client.deployments.list()
  for d in deployments.data:
      print(f"{d.name} ({d.status}): {d.id}")
  ```
</CodeGroup>

## Retrieve a deployment

<CodeGroup>
  ```python Python theme={null}
  deployment = client.deployments.retrieve("<deployment-id>")
  print(f"{deployment.name}: {deployment.status}")
  ```
</CodeGroup>