SkyPilot

Run AI workloads seamlessly across any cloud infrastructure.

Hosting & Deployment Infrastructure as Code AI Model Deployment

What is SkyPilot?

SkyPilot is a hosting & deployment tool. SkyPilot is an open-source framework designed to simplify the execution of AI and machine learning workloads across diverse cloud environments. It abstracts away the complexities of cloud infrastructure management, allowing users to define their AI tasks once and run them on various cloud providers like AWS, Azure, GCP, and others, without vendor lock-in. Key capabilities: Multi-cloud support (AWS, Azure, GCP, OCI, Lambda Labs, etc.), Automatic provisioning and deprovisioning of resources, Cost optimization through spot instance utilization, Data synchronization across clouds, Unified interface for job submission and management. SkyPilot is free to use with no paid tier. Buyers most often compare SkyPilot against Helm, Baseten, Replicate.

TL;DR - SkyPilot

Orchestrates AI workloads across multiple cloud providers.
Automates resource provisioning and data management for AI.
Optimizes cost and performance by leveraging diverse cloud options.

Pricing: Free forever

Best for: Individuals & startups

Pros & Cons

Pros

Eliminates vendor lock-in by supporting multiple cloud providers.
Reduces cloud computing costs through intelligent resource selection.
Simplifies complex cloud infrastructure management for AI workloads.
Enhances reproducibility of AI experiments across different environments.

Cons

Requires familiarity with cloud concepts for advanced configurations.
Initial setup and configuration might have a learning curve.

Key Features

Multi-cloud support (AWS, Azure, GCP, OCI, Lambda Labs, etc.)Automatic provisioning and deprovisioning of resourcesCost optimization through spot instance utilizationData synchronization across cloudsUnified interface for job submission and managementSupport for various AI frameworks and environmentsReproducible experiment setup

Pricing

Free

SkyPilot is completely free to use with no hidden costs.

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About SkyPilot

LCLouis CorneloupUpdated Apr 23, 2026

SkyPilot is an open-source framework designed to simplify the execution of AI and machine learning workloads across diverse cloud environments. It abstracts away the complexities of cloud infrastructure management, allowing users to define their AI tasks once and run them on various cloud providers like AWS, Azure, GCP, and others, without vendor lock-in. This enables researchers and developers to leverage the best available resources, optimize costs, and improve the efficiency of their AI development lifecycle. The tool is ideal for AI practitioners, researchers, and MLOps engineers who need flexibility and cost-effectiveness in their cloud compute strategy. It helps in dynamically provisioning and managing compute resources, handling data transfer, and ensuring reproducibility of experiments across different cloud setups. By automating much of the infrastructure orchestration, SkyPilot allows users to focus more on model development and less on cloud configuration.

How we evaluate tools →Source: skypilot.readthedocs.io

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SkyPilot FAQ

How does SkyPilot handle data transfer and synchronization when running a job across different cloud providers?

SkyPilot includes built-in mechanisms for data synchronization. It can automatically transfer necessary data to the chosen cloud environment before a job starts and retrieve results afterward, ensuring that your AI workloads have access to the required datasets regardless of the underlying cloud provider.

Can SkyPilot automatically select the most cost-effective cloud provider for a given AI workload?

Yes, SkyPilot is designed with cost optimization in mind. It can intelligently identify and utilize the cheapest available compute resources, including spot instances, across supported cloud providers to minimize the cost of running your AI workloads.

What types of AI frameworks and environments does SkyPilot support for running jobs?

SkyPilot is framework-agnostic and supports a wide range of AI frameworks and environments. Users can define their desired environment, including specific Python packages, Docker images, and custom setup scripts, allowing for flexibility with frameworks like TensorFlow, PyTorch, JAX, and more.

Is it possible to use SkyPilot to manage long-running AI training jobs that might require preemption handling on spot instances?

SkyPilot can manage long-running jobs and is capable of utilizing spot instances for cost savings. While it orchestrates the provisioning, users typically integrate their own checkpointing and resumption logic within their AI applications to handle potential preemptions gracefully, ensuring job progress is not lost.

How does SkyPilot ensure the reproducibility of AI experiments when running them on different cloud infrastructures?

SkyPilot promotes reproducibility by allowing users to define their environment and dependencies explicitly. By specifying the exact software stack, data sources, and execution commands, it helps ensure that the same experiment yields consistent results regardless of which supported cloud provider it runs on.

Source: skypilot.readthedocs.io