SkyPilot is an AI infrastructure control plane that unifies fragmented compute resources across Kubernetes, Slurm, and 20+ cloud providers into a single, centrally managed pool. The platform enables AI teams to launch, manage, and scale training, batch, and inference workloads with a unified interface, reducing costs and accelerating development cycles. It supports native multi-cloud orchestration, gang scheduling, and SSH access, with customers like Shopify and H Company using it to scale workloads that were previously impossible on traditional schedulers.
Funding
Funding not disclosed
Founders
Product
Problem
AI teams face significant compute fragmentation, managing workloads across multiple cloud providers, on-premises clusters, and different schedulers like Kubernetes and Slurm. This fragmentation forces teams to navigate complex, provider-specific setups, leading to inefficient resource utilization, delayed development cycles, and high operational overhead.
Solution
SkyPilot provides a control plane that abstracts all AI compute—across neoclouds, hyperscalers, Kubernetes, and Slurm—into a single, unified pool of resources. The platform natively supports a wide range of AI workloads, including interactive development, batch jobs, and online reinforcement learning, allowing teams to launch thousands of jobs in seconds. SkyPilot enables gang scheduling, multi-node jobs, and SSH access through a unified interface, eliminating the need for multiple tools. It also offers production-ready inference endpoints and GPU Compass, a dashboard for browsing and comparing GPU offerings across 20+ clouds and 2,000+ instances, all while keeping code and data within the customer's own cloud environment.
Target Audience
Primary customers are frontier AI labs, enterprises, and research organizations running large-scale training, batch inference, or online RL workloads, including companies like Shopify, H Company, Nubank, and Covariant.
Features
- Unified control plane for managing compute across Kubernetes, Slurm, and 20+ cloud providers
- Native support for interactive development, batch jobs, and online reinforcement learning workloads
- Gang scheduling and multi-node job orchestration for distributed training
- GPU Compass dashboard for browsing, comparing pricing, and launching GPU instances across 2,000+ offerings
- Production-ready inference endpoints with one-YAML deployment, at up to one-tenth the cost of hosted alternatives
- Storage system tuning and object store optimization for distributed training performance