Beam

Beta9 is the open source runtime for serverless AI workloads that powers Beam, the fully managed platform built on top of it. It presents a Pythonic interface for deploying and scaling AI applications, so the container image, the autoscaler, and the retry policy become decorator arguments rather than infrastructure you maintain on the side.

A custom container runtime, scheduler, and embedded caching bring containers up in under a second. An endpoint decorator publishes an autoscaling inference endpoint with a chosen GPU, CPU and memory allocation, a task queue decorator turns a function into a resilient background job that can replace a Celery queue, and a Sandbox class spins up an isolated container for running code an LLM generated.

The runtime covers the parts of a deployment you would otherwise assemble yourself.

• Fast cold starts: containers launch in under a second on the purpose-built runtime and scheduler.
• Parallelization: a single workload fans out across hundreds of containers.
• Scale to zero: workloads are serverless by default rather than always on.
• Developer experience: hot reloading, webhooks, and scheduled jobs come with the runtime.
• Volume storage: distributed storage volumes mount into running containers.
• GPU support: run on Beam's cloud with 4090s, H100s and others, or bring your own GPUs.

Installation is a pip install of the client package, followed by a getting started guide for the first deployment. Beta9 can be self-hosted for free or run as managed cloud hosting through Beam, and the code is AGPL licensed.