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
Video captioning and detection
Image and video embeddings
Question answering on visual content
Video search and retrieval
Audio transcription and analysis
Real-time model inference
Model distillation for efficiency
Model quantization
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
Natively multimodal architecture processing video, image, audio, and text in one model
Real-time inference optimized for existing hardware
Scalable video infrastructure for reasoning beyond detection
Enterprise deployment infrastructure with customization options