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
Search for web pages, images, and news
Crawl pages into Markdown, HTML, links, and screenshots
Map discovery using website sitemaps
Extract structured JSON from pages
Index with scheduled refreshes and retrieval
TypeScript and Python SDKs
Model Context Protocol (MCP) integration
REST API for all capabilities
CLI for terminal and script access
Private GitHub repository indexing
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
Maintains indexed knowledge with automatic scheduled refreshes
Provides source references for verifiable answers
Supports both public and private knowledge sources
Bulkgrid