skypilot

separate

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

When to use

Running ML workloads across multiple clouds (AWS, GCP, Azure, etc.)
Need cost optimization with automatic cloud/region selection
Running long jobs on spot instances with auto-recovery
Managing distributed multi-node training
Want unified interface for 20+ cloud providers
Need to avoid vendor lock-in
**Multi-cloud**: AWS, GCP, Azure, Kubernetes, Lambda, RunPod, 20+ providers
**Cost optimization**: Automatic cheapest cloud/region selection

When not to use

Simpler serverless GPU with Python-native API
Single-cloud persistent pods
Existing K8s infrastructure
Pure Ray-based orchestration

Skill page

Source

Repository	Path
Orchestra-Research/AI-Research-SKILLs	`09-infrastructure/skypilot/SKILL.md`