Workstation

A workstation is a dedicated cloud-hosted environment where you can develop, test, and deploy your data workflows and applications. Workstations provide isolated resources, allowing you to manage dependencies and configurations specific to your projects.

A default workstation is created for you when you sign up for Datatailr. Administrators can also create additional workstations as needed.

A workstation is based on an environment definition that specifies the software dependencies and compute resources required for your development process. You can open and manage your workstations from the workstations page of the Datatailr dashboard. Open it from the left sidebar by clicking the Workstations icon.

Workstations in sidebar

The workstation page has two collapsible panels:

Workstation — your cloud-hosted development environment with resource controls and connect options.
Local IDE — a setup guide for developing from your own machine using the Datatailr Python SDK.

Using a Workstation

Click the Workstation panel to expand it. You will see your currently selected workstation with its resource allocation and connection options.

Workstation page

Resources

Each workstation has configurable vCPU and Memory allocations. You can adjust these values inline and click Save to apply. If your workstation is running, restart it for the changes to take effect.

Starting and Stopping

Use the toggle switch next to the resource controls to start or stop your workstation.

Connecting

Once your workstation is built and started, you can connect using one of the three options:

VS Code — opens a browser-based IDE.
Jupyter — opens a notebook environment.
SSH — access your workstation from a local terminal (see below).

It is best to start with VS Code, as it provides a full-featured IDE experience and allows you to quickly initialize new Datatailr projects using the integrated terminal.

The first thing you'll see when you open a workstation is a README file with instructions on how to initialize a new demo project and deploy it to the Datatailr platform. See the Quick Start guide to get up and running.

Switching Workstations

If multiple workstations are available, use the dropdown next to the workstation name to switch between them.

SSH Access

Click the SSH row in the connect section to expand the setup steps:

Install the Datatailr Python package (if not already installed):
```
pip install datatailr
```
Run the SSH setup command (only needs to be done once per workstation):
```
datatailr setup-ssh <workstation-name>
```
If you call datatailr setup-ssh without specifying the workstation name, you will be prompted to choose from available workstations.
Connect:
```
ssh dt-<workstation-name>
```

You can also use the SSH connection functionality in your local IDE (Cursor, VS Code, PyCharm, etc.)

Local IDE

The Local IDE panel provides a step-by-step guide for setting up your local machine to build and deploy workflows directly to the Datatailr platform using the Python SDK — without opening a cloud-hosted workstation.

AI Agent Skills

Datatailr ships with a set of AI agent skills that teach coding assistants (Cursor, Claude Code, Codex, Copilot, etc.) how to work with the platform. These skills are automatically available inside every workstation — no setup needed. Any supported AI agent will discover them and use them to help you deploy apps, workflows, services, and more.

To use the same skills on your local machine, see the Python SDK — Agent Skills section.

Administration

Administrators can create, edit, copy, and delete workstations. See Workstation Management for details. For an introduction to permissions on workstations and other resources, see Permissions.