SkyPilot
by Community
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, Slurm, 20+ clouds, on-prem).
OSS
SkyPilot
Added 1 June 2026
Overview
SkyPilot is an open-source framework for running, managing, and scaling AI workloads across any infrastructure. It provides a unified interface to access and manage compute resources from Kubernetes, Slurm, 20+ cloud providers, and on-premises systems.
Best for
Best for
Teams that need to run AI workloads across diverse compute environments without being tied to a single provider
Use cases
- Launch and orchestrate distributed training jobs across multiple clouds
- Migrate workloads between on-prem and cloud without rewriting scripts
- Optimize cost by selecting the cheapest available GPU instance for a job
Notes
SkyPilot is an open-source framework for running, managing, and scaling AI workloads across any infrastructure. It provides a unified interface to access and manage compute resources from Kubernetes, Slurm, 20+ cloud providers, and on-premises systems.
10,051 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.
Use cases
- Launch and orchestrate distributed training jobs across multiple clouds
- Migrate workloads between on-prem and cloud without rewriting scripts
- Optimize cost by selecting the cheapest available GPU instance for a job
Pros
- Supports a wide range of backends including Kubernetes, Slurm, and major clouds
- Reduces vendor lock-in by abstracting infrastructure differences
- Active community with over 10,000 GitHub stars
Cons
- Requires Python and some infrastructure knowledge to set up
- May have a learning curve for teams new to multi-cloud orchestration
- Not a full MLOps platform; focuses on compute management only
Indexed from awesome-llm and enriched against its public facts.
Pros
- Supports a wide range of backends including Kubernetes, Slurm, and major clouds
- Reduces vendor lock-in by abstracting infrastructure differences
- Active community with over 10,000 GitHub stars
Cons
- Requires Python and some infrastructure knowledge to set up
- May have a learning curve for teams new to multi-cloud orchestration
- Not a full MLOps platform; focuses on compute management only
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