Usage-based pay-as-you-go pricing with hourly compute charges; committed contracts available for volume discounts. Hosted option with Anyscale-managed infrastructure or Bring Your Own Cloud deployment available.
Not given
Key features
Multimodal data curation at scale
Distributed model training with elastic scaling
Batch embedding generation at scale
Multi-cloud deployment and orchestration
Cluster-backed development environments
Workload-specific observability and debugging
Production-grade managed Ray clusters
Access control and governance
GPU budget controls and cost attribution
Post-training with SkyRL and veRL support
One user-owned context for every agent
Agents wake up to what changed since their last loop
Readable, traceable context with every change logged
End-to-end encryption with AES-256
Granular sharing controls with revocable access
Right to export, delete, and transparency
Context that travels across AI products and models
What makes it different
Built by creators of Ray, the world's most widely adopted AI compute engine
Multi-cloud deployment without code changes
Feels local but runs distributed
Unified GPU pooling across clouds and regions
User-owned context that travels with you across AI products
Traceable agent activity with full visibility and control
Agents coordinate through shared context without learning being trapped in silos