What is Backend.AI used for?

Backend.AI is a container-based computing cluster platform that allocates GPUs and other accelerators for multi-tenant ML sessions through REST and GraphQL APIs. It is a good fit for shared GPU clusters for ML teams, and batch and interactive training sessions.

Is Backend.AI open source?

Yes. Backend.AI is open source under the LGPL-3.0 licence. Its source code is on GitHub at lablup/backend.ai and is written mainly in Python.

Yes. Backend.AI is open source, so the software itself is free to use.

Can I self-host Backend.AI?

Yes. Backend.AI can be self-hosted on your own server or infrastructure.

What is Backend.AI an alternative to?

Backend.AI is an open-source alternative to RunPod, Amazon SageMaker, Lambda and CoreWeave. Other open-source alternatives to RunPod include beta9.

Is Backend.AI actively maintained?

Yes. The most recent commit to Backend.AI was on 2 October 2026, and the latest release is 26.4.12, published on 2 October 2026. The project has 673 stars on GitHub.

Backend.AI: open-source alternative to RunPod

About Backend.AI

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.

Key features

Container-based compute sessions
Support for many accelerator types
Multi-tenant resource isolation
Sokovan job scheduler and orchestrator
REST and GraphQL APIs
Docker and Podman container engines

Good fit for

→Shared GPU clusters for ML teams
→Batch and interactive training sessions

Built with: Python; Docker
Tags: gpu-cluster; machine-learning; containers; orchestration; python; hpc; paas; accelerators

Backend.AI: questions and answers

What is Backend.AI used for?: Backend.AI is a container-based computing cluster platform that allocates GPUs and other accelerators for multi-tenant ML sessions through REST and GraphQL APIs. It is a good fit for shared GPU clusters for ML teams, and batch and interactive training sessions.
Is Backend.AI open source?: Yes. Backend.AI is open source under the LGPL-3.0 licence. Its source code is on GitHub at lablup/backend.ai and is written mainly in Python.
Is Backend.AI free?: Yes. Backend.AI is open source, so the software itself is free to use.
Can I self-host Backend.AI?: Yes. Backend.AI can be self-hosted on your own server or infrastructure.
What is Backend.AI an alternative to?: Backend.AI is an open-source alternative to RunPod, Amazon SageMaker, Lambda and CoreWeave. Other open-source alternatives to RunPod include beta9.
Is Backend.AI actively maintained?: Yes. The most recent commit to Backend.AI was on 2 October 2026, and the latest release is 26.4.12, published on 2 October 2026. The project has 673 stars on GitHub.