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 compute across different cloud providers and on-premises clusters. It eliminates vendor lock-in by providing a unified interface to launch and scale AI workloads, while optimizing for cost and resource availability (such as GPU/TPU availability).

How it works

Users define their AI tasks using a unified YAML or Python API that specifies resource requirements (e.g., 8x A100 GPUs), data synchronization, setup commands, and the execution commands. SkyPilot then automatically finds the cheapest and available infrastructure across supported clouds or Kubernetes/Slurm clusters, provisions the resources, syncs the code, installs dependencies, and runs the job.

Who it’s for

AI teams who need to run jobs on various infrastructure providers and infrastructure teams who require a unified control plane to manage AI compute, scheduling, and scaling.

Highlights

  • Multi-cloud and Multi-cluster Support: Supports 20+ clouds (AWS, GCP, Azure, etc.), Kubernetes, and Slurm clusters.
  • GPU Fleet Optimization: Includes features like Autostop for idle resources, binpacking for shared clusters, and an intelligent scheduler for availability.
  • Kubernetes Enhancement: Adds AI-native capabilities to Kubernetes, including gang scheduling, multi-node jobs, and simplified interactive development (SSH/IDE connection).
  • BYOC (Bring Your Own Cloud): Everything is launched within the user's own cloud accounts and VPCs.
  • Auto-failover: Automatically handles capacity errors by failing over to other available infrastructure.

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