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Open-source framework to run AI workloads on any cloud, cluster, or GPU
SkyPilot is an open-source framework that runs AI workloads across any cloud, neocloud, or Kubernetes cluster, auto-provisioning the cheapest available GPUs; its 2026 SkyPilot Platform adds enterprise fleet management.
SkyPilot addresses the fragmentation of modern AI compute: teams juggle multiple clouds, neoclouds, Kubernetes clusters, and accelerator types to find capacity. SkyPilot abstracts these away, automatically selecting, provisioning, and managing compute and storage on whatever infrastructure is available and cheapest, so a single job definition can run anywhere. It handles spot-instance recovery, data sync, and multi-cloud orchestration for training, fine-tuning, RL, inference, and evaluations. The open-source project has been downloaded over 14 million times and is used by hundreds of organizations, including Nubank, Abridge, and H Company, to turn scattered resources into a unified 'AI supercomputer.' It supports development, agentic workloads, batch jobs, and multi-cluster production serving. In July 2026, SkyPilot launched from stealth with $20M in seed funding and introduced SkyPilot Platform, a unified AI compute platform built on the open-source core. The Platform adds large GPU fleet operations, standardized cluster management, workload orchestration, governance, and enterprise controls for frontier AI teams, while the framework itself remains free and open-source.
SkyPilot is an open-source framework for running AI workloads across any cloud, neocloud, or Kubernetes with automatic cheapest-GPU provisioning; its 2026 Platform adds enterprise fleet management, backed by $20M seed funding.
SkyPilot originated at UC Berkeley's Sky Computing Lab and grew into a widely adopted open-source project with over 14 million downloads. In July 2026 the team launched a company and platform out of stealth.
Backed by $20M in seed funding, SkyPilot Platform commercializes the open-source core with enterprise fleet management for frontier AI teams, while keeping the framework free and open.
SkyPilot abstracts multi-cloud complexity, auto-provisioning compute and storage on the cheapest available infrastructure and handling spot recovery, data sync, and orchestration for training, RL, inference, and agentic workloads.
SkyPilot Platform adds large GPU fleet operations, standardized cluster management, workload orchestration, governance, and enterprise controls on top of the open-source engine.
SkyPilot serves AI and ML teams — from individual researchers to frontier labs — that run GPU-heavy workloads across multiple clouds and clusters and want cost optimization and unified orchestration.
ML researchers and engineers launching training, fine-tuning, and inference jobs.
AI infrastructure and platform leaders managing GPU fleets and cloud spend.
MLOps and infra practitioners, plus the open-source community.
AI teams with multi-cloud GPU needs seeking cost-optimized, unified compute orchestration and, at scale, enterprise fleet governance.
Launched in July 2026 with $20M in seed funding. Verify the latest details via public sources or the vendor.
Yes. The SkyPilot open-source framework is free; you pay your underlying cloud or GPU providers directly. SkyPilot Platform is a separate enterprise offering.
It abstracts cloud complexity and automatically provisions compute and storage across any cloud, neocloud, or Kubernetes cluster, finding the cheapest available GPUs for your AI workloads.
It supports major hyperscalers (AWS, GCP, Azure), neoclouds like CoreWeave and Lambda, and Kubernetes clusters, unifying them into one compute pool.
Launched in July 2026 with $20M in seed funding, it is a unified AI compute platform built on the open-source core, adding fleet operations, orchestration, governance, and enterprise controls.
Yes. It manages spot-instance provisioning with automatic recovery and data sync to reduce cost while maintaining reliability.
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Run open LLMs locally with a single command.
GPU cloud for training and serverless AI inference with zero egress fees
Open-source AI gateway to call 100+ LLM APIs in one format
The open hub for machine learning models, datasets, and demos.