ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.
Open source
24 alternativesPAI
PAI schedules resources and manages clusters for AI workloads across distributed computing environments.
About PAI
PAI (OpenPAI) is Microsoft's open-source platform for scheduling resources and managing clusters for AI workloads across distributed computing environments. It is optimized for deep learning use cases, including shared GPU/FPGA resource allocation for teams, distributed training for common AI frameworks, and end-to-end training pipeline execution.
The platform supports on-premises, hybrid, public cloud, and single-box trial deployments, and is built with a modular architecture compatible with the Kubernetes ecosystem. As of the v1.8.1 release, OpenPAI has entered stable mode with no major feature releases planned, and the public repository is set to read-only for maintenance.
Key features
- Schedules shared GPU/FPGA computing resources for team use
- Supports distributed training for common AI frameworks
- Executes end-to-end AI training pipelines in a single cluster
- Compatible with Kubernetes ecosystem for cluster operations
- Supports on-premises, hybrid, cloud, and single-box trial deployments
- Modular architecture for customization and extension
Best for: Organizations and teams that need to share AI computing resources, run distributed AI training, and execute end-to-end deep learning pipelines.
Good for
- Design proven in Microsoft's large-scale production environment
- Full-stack solution supporting multiple deployment scenarios
- Modular architecture that is easy to customize and extend
Watch out for
- No major new feature releases are planned after the v1.8.1 stable release
- The public repository is read-only, requiring direct admin contact for collaboration
PAI at a glance
- Website
- github.com/microsoft/pai
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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PAI FAQ
What deployment scenarios does OpenPAI support?
It supports on-premises, hybrid, public cloud, and single-box trial deployments.
What AI workloads is OpenPAI designed for?
It is optimized for deep learning, including distributed training and end-to-end training pipelines.
Is OpenPAI compatible with Kubernetes?
Yes, it is compatible with the Kubernetes ecosystem for cluster management.
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