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What are Projects?

Projects are JupyterLab servers running on cloud compute. They provide kernels for notebooks, shell access, file storage, and web app hosting — all in an isolated environment. Access: https://runalph.ai/{org-slug}/{project-slug}

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Automations

Schedule recurring tasks that run in your project — notebook cells or AI agents on a cron. See guides for notebook automations and agent automations.

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AI Editor

Every project includes an AI-powered IDE. The agent can create and execute notebook cells, read outputs, create and edit files, debug errors, and iterate — all from a chat interface. See AI Agents for details. Access: https://runalph.ai/{org}/{project}/ide

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Creating a Project

  1. Go to Projects in your organization
  2. Click New Project
  3. Enter a name and slug
  4. Select a compute type
  5. Click Create

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Compute Types

TypeCPURAMUse Case
Micro0.51 GBLight tasks, testing
Small24 GBData analysis
Medium48 GBML training
Large816 GBLarge datasets
XLarge1632 GBHeavy workloads
GPU instances are also available. Change compute type anytime in Settings > Compute (restarts the server).

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Terminals

Full shell access in your browser. Install packages, run scripts, manage files. Claude Code comes pre-installed in every project — open a terminal and run claude to start. Create multiple terminal sessions and switch between them. Sessions persist until the project stops.

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Kernels

Monitor running kernels at Kernels in your project:
  • See kernel status (idle, busy, starting)
  • View attached notebook sessions
  • Shut down individual kernels or all at once

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Web App Hosting

Run any web framework and get a public URL automatically.
  1. Start your app on port 5000, bound to 0.0.0.0
  2. Your app is live at https://{org}-{project}.runalph.dev
Works with Streamlit, Gradio, Flask, FastAPI, and anything else that serves HTTP. Add a custom domain in Settings > Domains.

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Local Projects

Use your own hardware instead of cloud compute: 
alphai jupyter lab --org my-org --project my-project
This connects a local JupyterLab to Alph. No compute charges — use your laptop, workstation, or cloud VM. Add --app-port 5000 to expose a web app through the tunnel. See the Bring Your Own GPU guide.