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
Find files across Finder, Gmail, Slack, and Google Calendar
Send emails and schedule meetings with voice commands
Review actions before execution or enable auto-execute
Handle multi-step workflows with a single voice request
Works across 20+ apps including Slack, Gmail, Cursor, and Notion
Automatic grammar correction and filler word removal
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
Turn voice into action across multiple apps without switching
Execute multi-step workflows with a single voice command
User maintains full control with review-before-running option
Integrates seamlessly with existing productivity tools and workflows