skypilot: a unified control plane to manage and scale AI compute across multiple clouds and clusters

What it solves

SkyPilot addresses the complexity of managing AI compute across diverse environments. It eliminates vendor lock-in by providing a unified interface to run AI workloads on any cloud provider, Kubernetes cluster, or Slurm cluster, 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 needs, and setup/run commands. SkyPilot then automates the heavy lifting: finding the cheapest available infrastructure, provisioning the resources, syncing the codebase, installing dependencies, and executing the job. It also includes features like autostop for idle resources and binpacking for shared clusters to maximize GPU utilization.

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 scheduling, scaling, and orchestration of AI compute.

Highlights

• Multi-cloud and Multi-cluster Support: Works across 20+ clouds (AWS, GCP, Azure, etc.), Kubernetes, and Slurm.
• GPU Optimization: Features an intelligent scheduler for the cheapest available infra, autostop for idle resources, and binpacking.
• AI-Native Kubernetes: Simplifies interactive development on K8s via SSH and IDE connectivity, and adds advanced scheduling like gang scheduling.
• BYOC Model: Operates as a "Bring Your Own Cloud" system, launching everything within the user's own accounts and VPCs.
• Agent Integration: Provides a "SkyPilot Skill" for AI agents to manage GPU access and jobs.