Getting started

  1. Create an account
  2. Download the CLI
  3. Start developing and deploying apps - that’s it!

Beam is currently in private beta. If you’re reading this, you’re one of the first people to try it.

📦 Setup remote development environments in code

Configure your runtime in Python - tell us how many GPUs you need and which libraries you want installed, and Beam will spawn a remote environment for you.

🛰 Develop locally on remote hardware

You can write and run your code locally - except when you enter your shell, your code will run on Beam instead of your local machine.

🚀 Deploy apps as serverless functions

Deploy your apps as serverless REST APIs, scheduled cron jobs, or webhooks - all in just four lines of Python.

What can you do with Beam?

  • Deploy ML models on serverless runtimes. Your app will scale automatically with traffic and spin down when idle.

  • Develop on your local IDE, while running on remote GPUs

  • Instantly jump from hacking on a script to serving an API in production

  • Save money on your cloud bill. Beam is serverless and charges simple usage-based, per-second pricing

Tutorial: Deploying Stable Diffusion on GPU

Get started with an example

Stable Diffusion on GPU

Deploy Stable Diffusion using a GPU

Web Scraping

Scraping a website and running the results through an ML model

Huggingface REST API

Deploying a pre-trained ML model as a REST API

Core Concepts

  • Apps. Each project in Beam is called an app. When you first start Beam, you’ll be prompted to define your environment through the Beam.App() method in the SDK.

  • Triggers. Triggers are actions that can invoke your Beam apps. For example, a REST API trigger allows your Beam app to be invoked via a REST API. A webhook trigger will allow your Beam app to be invoked asyncronously using a webhook, and so on.

  • Outputs. Outputs are file paths that can be used to save files created when your functions are run.