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
Request routing across multiple LLM providers
Real-time monitoring and analytics dashboard
Prompt management and playground
Rate limiting and alerts
Automatic fallbacks for LLM requests
Response caching
User tracking and sessions
HQL query language
Webhooks integration
Data export capabilities
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
Works with multiple LLM providers unlike competitors focused on single ecosystems