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AI Tools›Infrastructure & MLOps›Skypilot
Infrastructure & MLOps

Skypilot

SkyPilot is an open-source system designed to run, manage, and scale AI workloads across a wide range of AI infrastructures. It provides AI teams with a unified interface to execute machine learning training and inference jobs on multiple cloud providers and on-premises Kubernetes clusters. Users define their environments and jobs as code using YAML or command-line interface, enabling portability and automation of compute provisioning, job submission, and resource management. SkyPilot supports over 20 cloud providers including AWS, GCP, Azure, and specialized AI infrastructure providers such as CoreWeave and Lambda Cloud. The tool automates complex tasks such as GPU and region selection, including the use of spot or preemptible instances to optimize costs. It manages job queuing, execution, and auto-recovery, facilitating multi-job workflows without requiring users to directly manage infrastructure. SkyPilot is installed via pip with modular cloud provider support and is actively maintained with a strong community presence on GitHub.

Updated Jan 22, 2026open-source
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Overview

SkyPilot enables AI teams to run and scale machine learning workloads across diverse cloud and on-premises infrastructures using a portable, code-defined approach.

Pricing
open-source
Category
Infrastructure & MLOps
Company
Visual Guide
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Key Features
01
Supports running AI workloads on over 20 cloud providers and Kubernetes clusters without rewriting job configurations.
02
Users define environments and jobs in YAML or via CLI, enabling portable and reproducible execution across infrastructures.
03
Automates compute selection, provisioning, and management including spot/preemptible instance usage for cost efficiency.
04
Manages multiple jobs with queuing, running, and automatic recovery to handle failures without manual intervention.
05
Installable via pip with cloud-specific extras to include only needed providers, e.g., `skypilot[kubernetes,aws,gcp]`.
Real-World Use Cases

Multi-Cloud AI Training

AI teams need to run large-scale training jobs across different cloud providers to optimize cost and availability.

On-Premises Kubernetes AI Workloads

Organizations want to run AI workloads on their own Kubernetes clusters alongside cloud resources.

Quick Start
1
Install SkyPilot
Run pip install -U "skypilot[clouds]" replacing [clouds] with needed providers like aws,gcp.
2
Define Your Job
Create a YAML file specifying resources, environment, and commands for your AI workload.
3
Submit the Job
Use the SkyPilot CLI command sky launch job.yaml to start your job.
4
Manage Jobs
Monitor and control your jobs via the CLI, which supports queuing, running, and auto-recovery.
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Pricing
Model: open-source

SkyPilot is freely available under the Apache-2.0 license and can be installed via pip.

Assessment
Strengths
  • Supports over 20 cloud providers and on-prem Kubernetes with portable job definitions.
  • Automates GPU and region selection including spot/preemptible instances for cost savings.
  • Simple pip installation with modular cloud provider support.
  • Open-source with an active community and frequent updates.
  • Manages job queuing and auto-recovery for multi-job workflows.
Limitations
  • Requires specifying cloud provider extras during installation, which may require multiple installs for full coverage.
  • Documentation and support are primarily hosted on GitHub with no dedicated standalone website.
  • Latest release indicates ongoing development rather than a fully mature product.
Alternatives
Ray→Kubeflow→