> For the complete documentation index, see [llms.txt](https://docs.datasaur.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datasaur.ai/llm-projects/deployment.md).
# 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 all members 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](/llm-projects/sandbox.md).
{% endhint %}
1. After creating and configuring your model on the Sandbox page, click the **Deploy** button.
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.
3. Once you’ve deployed the model, you will be redirected to the deployed model's 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 deployed model. Deleting a deployed model 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.
4. You can also manage your list of previously created deployments on the **Deployed models** page.
{% hint style="info" %}
To learn more about using our Deployment API, please refer to this [Deployment API](/llm-projects/deployment/deployment-api.md) page.
{% endhint %}
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