What is OpenPAI used for?

OpenPAI is Microsoft's Open Platform for AI, a Kubernetes-based resource scheduler and cluster manager for GPU training workloads; now in stable, read-only mode. It is a good fit for sharing a GPU cluster among a research team and running model training jobs on-premises.

Is OpenPAI open source?

Yes. OpenPAI is open source under the MIT licence. Its source code is on GitHub at microsoft/pai and is written mainly in JavaScript.

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

Can I self-host OpenPAI?

Yes. OpenPAI can be self-hosted on your own server or infrastructure; there is no official hosted version.

What is OpenPAI an alternative to?

OpenPAI is an open-source alternative to Azure Machine Learning. Other open-source alternatives to Azure Machine Learning include KubeDL, MLflow and Backend.AI.

Is OpenPAI actively maintained?

Yes. The most recent commit to OpenPAI was on 29 September 2026, and the latest release is v1.8.0, published on 16 July 2021. The project has 2.7k stars on GitHub.

OpenPAI: open-source alternative to Azure Machine Learning

About OpenPAI

OpenPAI, or Open Platform for AI, is a cluster management and resource scheduling platform for AI workloads, created by Microsoft. It helps administrators share GPU clusters among users and lets data scientists submit training jobs on-premises or in the cloud. It runs on Kubernetes and is released under the MIT license.

Since version 1.0 the platform has used a lighter and more modular architecture that can be customized and expanded. It includes features aimed at AI users, such as running popular frameworks like TensorFlow, PyTorch, and Chainer, notebook environments like Jupyter, and scheduling for GPU resources.

The README notes that after the 1.8.1 release OpenPAI entered a stable mode with no major feature releases planned, and the repository was set to read-only to save maintenance effort. Collaboration requires contacting the repository admin. It remains relevant for teams that operate their own GPU clusters and want an existing, documented scheduler.

Key features

GPU cluster resource scheduling
Kubernetes-based architecture
Job submission for TensorFlow and PyTorch
Jupyter notebook support
Modular design for customization
On-premises and cloud deployment

Good fit for

→Sharing a GPU cluster among a research team
→Running model training jobs on-premises

Built with: JavaScript; Kubernetes
Tags: ai; gpu; scheduling; kubernetes; cluster-management; machine-learning; model-training; microsoft

OpenPAI: questions and answers

What is OpenPAI used for?: OpenPAI is Microsoft's Open Platform for AI, a Kubernetes-based resource scheduler and cluster manager for GPU training workloads; now in stable, read-only mode. It is a good fit for sharing a GPU cluster among a research team and running model training jobs on-premises.
Is OpenPAI open source?: Yes. OpenPAI is open source under the MIT licence. Its source code is on GitHub at microsoft/pai and is written mainly in JavaScript.
Is OpenPAI free?: Yes. OpenPAI is open source, so the software itself is free to use.
Can I self-host OpenPAI?: Yes. OpenPAI can be self-hosted on your own server or infrastructure; there is no official hosted version.
What is OpenPAI an alternative to?: OpenPAI is an open-source alternative to Azure Machine Learning. Other open-source alternatives to Azure Machine Learning include KubeDL, MLflow and Backend.AI.
Is OpenPAI actively maintained?: Yes. The most recent commit to OpenPAI was on 29 September 2026, and the latest release is v1.8.0, published on 16 July 2021. The project has 2.7k stars on GitHub.