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 context layer connecting all GTM tools and AI agents
Agent observability with end-to-end tracing
Source citations for every answer
Permission-aware answers scoped to user access levels
Playbook grounding to enforce business semantics
Corrections inbox for agent-generated changes
Integration with Claude, ChatGPT, Slack, Salesforce and other tools
Open Model Context Protocol (MCP) for any client
SOC 2 Type II compliance and GDPR/CCPA support
140x faster answers via pre-compiled context
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
40x lower cost through pre-compiled context vs raw data processing
98% answer accuracy with no hallucinations through checked data
Agent-agnostic: works with any AI tool your team uses
Never trains on customer data with enterprise-grade security
Set up within days with forward-deployed engineer support
Endgame