Open-source Crusoe alternatives

Beam, whose open-source engine lives in the beta9 repository, is a fast runtime for serverless AI workloads. It gives developers a Pythonic interface to deploy and scale AI applications without managing infrastructure. The code is written in Go and released under the AGPL-3.0 license, and topics include GPU, LLM inference, autoscaling, and functions as a service.

Features include sub-second container cold starts using a custom runtime, scheduler, and embedded caching, parallel fan-out to hundreds of containers, hot reloading, webhooks and scheduled jobs, scale-to-zero by default, and mounted distributed storage volumes. GPU support runs on the Beam cloud, with cards such as the RTX 4090 and H100 mentioned, or on your own GPUs.

The project can be self-hosted or used through the hosted platform, which requires creating an account. Beam suits machine learning engineers and startups that want to serve models, run batch jobs, or sandbox untrusted code without operating a Kubernetes GPU cluster themselves.

Backend.AI, from Lablup, is a streamlined, container-based computing cluster platform that hosts popular machine learning frameworks and many programming languages. Its distinguishing feature is pluggable support for heterogeneous accelerators, including CUDA and ROCm GPUs, Intel Gaudi, Google TPU, Graphcore IPU and NPUs from vendors such as Rebellions, FuriosaAI, HyperAccel and Tenstorrent.

It allocates and isolates computing resources for multi-tenant sessions on demand or in batches, using customizable job schedulers and its own orchestrator named Sokovan. All functions are exposed through REST and GraphQL APIs. The README lists requirements including Python 3.13 with Pantsbuild, Docker 20.10 or newer or Podman 5.4 or newer, Docker Compose v2, PostgreSQL 16 or newer, Valkey (Redis-compatible), etcd and Prometheus, with Grafana recommended for observability.

Backend.AI is written in Python and licensed under LGPL-3.0. It is aimed at organizations running shared GPU or accelerator clusters for research and ML workloads, and you operate it on your own infrastructure.