# Deployment

### Overview

The deployed models page in LLM Labs provides a centralized place to manage all deployed model APIs efficiently. It serves as a hub where users can oversee their deployed models, track their status, and perform necessary maintenance operations. By offering a user-friendly interface, this feature streamlines the deployment workflow, making it simple to monitor and manage models at scale.

### Get started

{% hint style="info" %}
To deploy a model, you will need to create a Sandbox first. [Learn more about Sandbox](https://docs.datasaur.ai/llm-projects/sandbox).
{% endhint %}

1. After creating and configuring your model on the Sandbox page, click the **Deploy** button.

   <figure><img src="/files/Rw6iyMdQJD1l4VmmpdRQ" alt=""><figcaption></figcaption></figure>
2. A dialog box will appear, allowing you to review and name your model before deployment. Once you have reviewed the details and are ready to proceed, click the **Deploy model** button.

   <figure><img src="/files/OIbilIdIbg7ljspWZcC7" alt=""><figcaption></figcaption></figure>
3. Once you’ve deployed the model, you will be redirected to the deployment details page. Here you can:

   1. **Create API Key**: Generate an API key for use when calling the API endpoint.
   2. **Suspend**: Temporarily pause the API endpoint. Systems using these suspended endpoints may not function until it is resumed.
   3. **Delete**: Delete the deployment. Deleting a deployment permanently removes its configurations and active endpoints. Any systems using these endpoints will stop functioning properly.
   4. **Use in Sandbox**: Choose the sandbox environment where you want to create a copy of the deployed model for iteration. After making changes, you can redeploy it to the same deployment.
   5. Review the statistics of the deployment.

   <figure><img src="/files/gKIntyPNUT1HutiEfQ69" alt=""><figcaption></figcaption></figure>
4. You can also manage your list of previously created deployments on the **Deployed models** page.

   <figure><img src="/files/FwaXlPbxGsjpJDlQ3pLN" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
To learn more about how to use our Deployment API, please refer to this [Deployment API](https://docs.datasaur.ai/llm-projects/deployment/deployment-api) page.
{% endhint %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.datasaur.ai/llm-projects/deployment.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
