SkyPilot
Cloud-agnostic framework for running LLM workloads, AI training, and batch jobs across any cloud (AWS, GCP, Azure, Lambda Labs, RunPod, and more) with automatic spot instance provisioning, failover, and cost optimization. SkyPilot abstracts cloud-specific APIs behind a simple YAML task definition and CLI — launch GPU jobs on the cheapest available cloud, automatically retry on preemption, and mount storage from any cloud. Developed at UC Berkeley SKY Computing Lab.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Apache 2.0, UC Berkeley research. Cloud credentials passed to SkyPilot — follow cloud credential best practices. SSH keys auto-generated and rotated per cluster. No central credential storage. Compute runs in your cloud account.
⚡ Reliability
Best When
You want to run GPU ML workloads on spot instances across multiple clouds to minimize cost, with automatic failover and simple YAML job definitions.
Avoid When
You need persistent stateful services, dedicated hardware guarantees, or compliance-controlled single-cloud environments.
Use Cases
- • Launch fine-tuning or training jobs on the cheapest available GPU across multiple clouds without cloud-specific configuration
- • Run spot/preemptible GPU instances with automatic failover to other clouds when instances are reclaimed — maximize cost savings
- • Serve LLMs across clouds using SkyPilot's managed spot serving with automatic failover and load balancing
- • Scale up distributed training across multiple GPU nodes with automatic cluster provisioning, SSH setup, and teardown
- • Run agent ML workloads across cloud providers with a single YAML spec — no cloud-specific code required
Not For
- • Long-running stateful services — SkyPilot is optimized for batch and spot workloads, not persistent services
- • Teams without cloud accounts — SkyPilot requires configured cloud credentials for each target cloud provider
- • Production inference serving at scale — use managed services (AWS SageMaker, GCP Vertex AI) for production SLA requirements
Interface
Authentication
SkyPilot authenticates to each cloud via cloud-native credentials (AWS_ACCESS_KEY, GCP service account, Azure CLI auth). SkyPilot itself has no central auth — it acts on behalf of the configured cloud credentials. SSH key management for cluster access handled automatically.
Pricing
Apache 2.0 licensed. SkyPilot itself is free — costs come from cloud GPU instances you provision. Spot instances via SkyPilot can reduce GPU costs by 60-90% vs on-demand.
Agent Metadata
Known Gotchas
- ⚠ Cloud GPU availability is not guaranteed — spot instances may not be available in specific regions; configure multiple cloud/region fallbacks
- ⚠ Cluster startup time can be 5-15 minutes including cloud provisioning, Docker pull, and setup scripts — agents must account for this in workflow timing
- ⚠ Data transfer between clouds incurs egress costs — avoid mounting data from one cloud while running compute on another
- ⚠ SSH key rotation or cloud credential changes can break connectivity to running clusters — keys are generated at launch time
- ⚠ Spot instance preemption is silent — agents must poll job status to detect preemption; logs may indicate interrupted runs
- ⚠ SkyPilot YAML task spec format changes between versions — check version compatibility when upgrading
- ⚠ GPU quota limits on cloud accounts are a common blocker — request quota increases before planning large-scale training
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for SkyPilot.
Scores are editorial opinions as of 2026-03-06.