skypilot-org/skypilot

The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.

What it solves

SkyPilot addresses the complexity of managing AI workloads across diverse infrastructure. It eliminates vendor lock-in by providing a unified interface to run jobs on any cloud provider, Kubernetes, or Slurm clusters, while optimizing for cost and resource availability.

How it works

Users define their AI tasks using a unified YAML or Python API, specifying resource requirements (like GPUs/TPUs), data synchronization, and setup/run commands. SkyPilot then handles the provisioning of the cheapest available infrastructure, automatic failover if capacity is unavailable, syncing the codebase, installing dependencies, and executing the job.

Who it’s for

It is designed for AI teams who need a simple, portable way to launch and manage jobs, and infrastructure teams who require a unified control plane for advanced scheduling, scaling, and GPU fleet utilization.

Highlights

  • Multi-cloud and Multi-cluster: Supports 20+ clouds (including AWS, GCP, Azure) and Kubernetes/Slurm clusters under one interface.
  • GPU Optimization: Features autostop for idle resources, binpacking for shared clusters, and an intelligent scheduler for maximum utilization.
  • Kubernetes Enhancement: Adds AI-native capabilities to Kubernetes, such as gang scheduling, multi-node jobs, and simplified interactive development (SSH/IDE connection).
  • Agent Integration: Provides a "SkyPilot Skill" for AI agents to manage GPU access and jobs.
  • BYOC Model: Operates on a Bring Your Own Cloud (BYOC) basis, launching everything within the user's own accounts and VPCs.

Related

  • Project
    dstackai/dstack

    A unified control plane for GPU provisioning and orchestration that streamlines AI development, training, and inference across any GPU cloud, Kubernetes, or on-prem cluster.

  • Project
    nebius/soperator

    A Kubernetes operator that automates the deployment and management of Slurm clusters for AI training and HPC, providing automated health checks and efficient GPU utilization.

  • Project
    raids-lab/crater

    A Kubernetes-native control plane for shared AI computing clusters that simplifies GPU resource governance, workload scheduling, and asset management.

  • Project
    GoogleCloudPlatform/cluster-toolkit

    An open-source toolkit by Google Cloud for deploying and managing AI/ML and high performance computing (HPC) environments using customizable blueprints.

  • Project
    transformerlab/transformerlab-app

    An open-source machine learning platform that unifies training, fine-tuning, inference, and evaluation into a single interface for individuals and research labs.