> ## 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

> Deploy and manage model endpoints for real-time inference from the SeekrFlow Deployment Dashboard.

## Deployment Dashboard

The Deployment Dashboard provides a real-time view of all your deployments and their performance. At the top of the page:

* **Status** — A donut chart showing total deployment count broken down by status: Active, Pending, Failed, and Inactive.
* **Active Deployments** — Count of currently active deployments and total active hours to date.
* **Inference Tokens** — Total input and output tokens consumed across all deployments to date.

The deployments table lists all deployments with the following columns: **Deployment Name**, **Status**, **Model Type**, **Total Input Tokens**, **Total Output Tokens**, and **Last Modified**.

Hover a row to reveal inline actions. Available actions depend on status:

| Status   | Meaning                                      | Available actions |
| -------- | -------------------------------------------- | ----------------- |
| Pending  | Deployment requested, provisioning underway. | Pause · Delete    |
| Active   | Serving traffic.                             | Pause · Delete    |
| Inactive | Paused; no inference traffic.                | Resume · Delete   |
| Failed   | Error during start-up or runtime.            | Delete            |

## Create a deployment

Click **+ Create Deployment** to open a four-step wizard.

<Steps>
  <Step>
    **Deployment Details** — Name (up to 100 characters) and describe (up to 1000 characters) the deployment.
  </Step>

  <Step>
    **Select Model** — Choose a model to make available for inference. Use the **Fine-Tuned Models** tab to select from your completed fine-tuning jobs, or the **Base Models** tab for open-source and partner models. Each card shows the provider, model name, and license tags (Open Source, Warning).
  </Step>

  <Step>
    **Select Hardware Configuration** — Set the number of AMD Instinct MI300X instances for your deployment. You can choose up to 50 instances.
  </Step>

  <Step>
    **Confirm and Start Deployment** — Review your deployment details and model and hardware configuration. Accept the selected model's license terms, then click **Start Deployment**.
  </Step>
</Steps>

After submitting, a success screen confirms "Your model is deploying." The process may take a few minutes; track status in the Deployment Summary. Click **Go to Deployment** to navigate there directly, or navigate away — the deployment continues in the background.

## Deployment Summary

Click any deployment name to open its summary page. The summary includes:

* **Deployment Details** — Name (editable), ID, Model Type, Status, Date Created, Date Deployed, and description (editable).
* **Integrating Deployment for Inference** — Quickstart Guide with instructions for setting up the SDK, sending API requests, and integrating your deployment.
* **Data Sources & Training Prompt** — Training file and prompt from the originating fine-tuning job.
* **Model, Hardware, and Hyperparameters** — The exact configuration running for this deployment.
* **Fine-Tuning Job Details** — Link back to the source fine-tuning job.
* **Agent Details** — Agents linked to this deployment and their statuses, if applicable.
* **Event Timeline** — Right-hand feed of deployment lifecycle events (e.g., Deployment Pending, Deployment Complete).

## Pause and resume a deployment

Click **Pause** on the summary page or from the inline row action in the dashboard to stop traffic without deleting the endpoint. Resume from the deployments table when ready to serve traffic again.

## Delete a deployment

Deployments can be deleted from the inline row actions in the deployments table.