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
Offline verification in milliseconds
Cryptographic passports for agents
Revocation under 60 seconds
Authority classes and assurance tiers
No central authority calls required
Local registrar lifecycle
Request signing with short-lived keys
Public conformance testing
Fail-closed HTTP gating
Appeal mechanism for revocation
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
Neutral non-profit root barred by charter from issuing credentials or computing scores
Fully open standard with published conformance vectors and reproducible releases
Verification runs offline without phoning home to any authority
Four independent implementations agreeing on all conformance vectors
Public recorded ceremony to establish the root across independent custodians
AINRA