Skypilot Multi Cloud Orchestration
SkyPilot gives you a unified interface to run ML workloads across 20+ cloud providers with automatic cost optimization and spot instance management. The real value is in the multi-cloud orchestration: it picks the cheapest GPU across AWS, GCP, Azure, or smaller providers, handles spot preemption with auto-recovery, and lets you run distributed training without vendor lock-in. The YAML-based task definitions are straightforward, with built-in support for checkpointing to cloud storage and managed job queues. It's overkill if you're happy with a single cloud provider, but if you're burning through GPU budgets or need resilience across providers, this is the tool that actually delivers on the multi-cloud promise without adding kubernetes-level complexity.
- skills.sh installs
- 1K
- GitHub stars
- 14.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add orchestra-research/ai-research-skills --skill skypilot-multi-cloud-orchestration --agent claude-codeInstalls into .claude/skills of the current project.
Files
SkyPilot Multi-Cloud Orchestration
Comprehensive guide to running ML workloads across clouds with automatic cost optimization using SkyPilot.
When to use SkyPilot
Use SkyPilot when:
- 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
Key features:
- Multi-cloud: AWS, GCP, Azure, Kubernetes, Lambda, RunPod, 20+ providers
- Cost optimization: Automatic cheapest cloud/region selection
- Spot instances: 3-6x cost savings with automatic recovery
- Distributed training: Multi-node jobs with gang scheduling
- Managed jobs: Auto-recovery, checkpointing, fault tolerance
- Sky Serve: Model serving with autoscaling
Use alternatives instead:
- Modal: For simpler serverless GPU with Python-native API
- RunPod: For single-cloud persistent pods
- Kubernetes: For existing K8s infrastructure
- Ray: For pure Ray-based orchestration
Quick start
Installation
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- Repository
- github.com/orchestra-research/ai-research-skills
- Categories
- AI & Agent Building, Data Science & ML, Automation & Workflows, Cloud & Infrastructure
- First seen
- Jun 3, 2026
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