Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot

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Run AI workloads across clouds with zero-cost data egress to Hugging Face.

8.1 (61.318 score)

open-sourceAPI Available

Overview

SkyPilot enables ML teams to execute training and inference jobs on any cloud provider while storing models and datasets on Hugging Face without egress charges. It abstracts cloud complexity, allowing researchers to focus on experiments rather than infrastructure management. The integration eliminates data transfer costs between compute and storage layers.

Pros

Runs workloads on AWS, GCP, Azure, or on-premise without vendor lock-in
Eliminates data egress fees when storing on Hugging Face
Automates cloud resource provisioning and management via simple YAML configs
Open-source project with active community support and documentation

✕ Cons

Requires familiarity with cloud infrastructure concepts and YAML syntax
Limited to Hugging Face storage integration; other storage requires egress costs
Setup and configuration overhead for teams new to infrastructure-as-code

Key Features

Multi-cloud workload execution

Zero-egress Hugging Face storage

YAML-based job configuration

Automatic resource provisioning

Cost optimization across clouds

Model and dataset management

Use Cases

ML researchers running distributed training experiments across multiple cloudsTeams managing large model artifacts on Hugging Face without egress costsOrganizations avoiding cloud vendor lock-in for AI workloadsData scientists automating infrastructure provisioning for batch inference jobs

Best For

ML EngineersMLOps TeamsAI ResearchersData ScientistsCloud Infrastructure Teams

Frequently Asked Questions

What are the pricing costs for using SkyPilot?▾

SkyPilot is open-source and free to use. You only pay for the underlying cloud compute resources (AWS, GCP, Azure) you provision, plus any storage costs on Hugging Face, though data egress between them is eliminated.

How difficult is it to get started with SkyPilot?▾

Setup is straightforward—you define workloads in simple YAML configuration files, and SkyPilot handles resource provisioning automatically. The learning curve is minimal if you're familiar with cloud platforms and basic configuration syntax.

Can SkyPilot integrate with other tools and services?▾

SkyPilot supports multiple cloud providers (AWS, GCP, Azure, on-premise) and integrates directly with Hugging Face for zero-egress storage. It also provides APIs and CLI interfaces for automation and pipeline integration.

What is the main limitation of SkyPilot?▾

SkyPilot's primary limitation is that it requires manual YAML configuration for workloads and is most beneficial when using Hugging Face for storage; teams with different storage strategies may see less value from the zero-egress advantage.

What is SkyPilot best used for?▾

SkyPilot is ideal for running large-scale AI and machine learning workloads across multiple cloud providers while minimizing data transfer costs through Hugging Face integration. It's best for teams wanting cloud flexibility without vendor lock-in.