skypilot-org/skypilot

Health v2 · maintenance only

Adoption not part of the score

10529 stars · 1211 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

SkyPilot is an open-source AI compute platform that unifies fragmented infrastructure (Kubernetes, Slurm, VMs, 20+ clouds) into a single pool for running, managing, and scaling AI workloads. It provides job scheduling, distributed training, LLM serving, and cost optimization across heterogeneous compute.

Use cases

• run distributed training jobs across multiple clouds
• serve LLMs on my own GPUs
• find cheapest GPU cloud and launch there
• schedule ML jobs on spot instances
• manage a unified control plane over Kubernetes and Slurm clusters
• scale reinforcement learning inference on GPUs
• run batch inference jobs on any cloud

When to choose

• you need to run AI/ML workloads across multiple clouds, Kubernetes, or Slurm without vendor lock-in
• you want gang scheduling, multi-node jobs, and spot-instance cost savings out of the box
• you need to train or serve LLMs on heterogeneous GPU fleets

When to avoid

• you only deploy simple stateless web services on a single cloud
• you need a fully managed ML platform with no infrastructure to operate
• your workloads are non-AI batch jobs with no GPU or accelerator requirements

Facets

framework · maturity active

llm-training llm-inference scheduling deployment workflow-automation gpu-computing container-orchestration machine-learning deep-learning cloud-computing infrastructure-as-code gpu-computing large-language-models python cloud cli multicloud job-scheduler spot-instances distributed-training mlops slurm cost-optimization ai-infrastructure devops kubernetes linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="skypilot-org/skypilot")