SkyPilot abstracts away cloud-specific complexity to let AI teams run workloads on whichever cloud offers the best price and availability at any given moment. With over 6,000 GitHub stars, it provides a unified interface for launching training jobs, serving endpoints, and batch inference across AWS, GCP, Azure, Lambda Cloud, RunPod, and other GPU providers. Teams define their resource requirements — GPU type, count, memory — and SkyPilot's optimizer automatically selects the cheapest region and instance type that meets the specification.
The managed spot instance feature is particularly valuable for GPU-heavy AI workloads where costs can be substantial. SkyPilot automatically provisions spot or preemptible instances at 50-70% cost savings, handles preemption by checkpointing and relaunching on available capacity, and supports failover across multiple clouds and regions. The cluster management system handles autoscaling, SSH access, file sync, and job queuing, providing a serverless-like experience while giving teams full control over their compute environment.
SkyPilot is open-source under Apache 2.0, developed primarily at UC Berkeley's Sky Computing Lab. It integrates with popular ML tools including vLLM for model serving, Hugging Face for model downloads, and supports Kubernetes clusters alongside cloud providers. For organizations running significant GPU workloads, SkyPilot provides the multi-cloud orchestration layer that prevents vendor lock-in and captures cost savings that are difficult to achieve with single-cloud deployments.
