Skypilot allows AI training and reasoning to automatically select economical GPU solutions

SkyPilot is an open-source framework for running AI and batch workloads on any infrastructure, including Kubernetes and over 16 cloud providers. It uses “environment as code” and “job as code” paradigms to streamline compute provisioning, job queuing, and auto-recovery. SkyPilot unifies GPU, TPU, and CPU resource management, enabling seamless migration of existing scripts across clouds and on-premises clusters. Cost optimizations include 3–6× savings with spot instances, automatic cleanup of idle resources, and intelligent selection of optimal VM types and regions for efficient, low-cost AI deployments.

Source code: https://github.com/skypilot-org/skypilot
Document:  https://docs.skypilot.co/en/latest/docs/index.html

SkyPilot is easy to use for AI users:

• Quickly spin up compute on your own infra
• Environment and job as code — simple and portable
• Easy job management: queue, run, and auto-recover many jobs

SkyPilot unifies multiple clusters, clouds, and hardware:

• One interface to use reserved GPUs, Kubernetes clusters, or 16+ clouds
• Flexible provisioning of GPUs, TPUs, CPUs, with auto-retry
• Team deployment and resource sharing

SkyPilot cuts your cloud costs & maximizes GPU availability:

• Autostop: automatic cleanup of idle resources
• Spot instance support: 3-6x cost savings, with preemption auto-recovery
• Intelligent scheduling: automatically run on the cheapest & most available infra

SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.