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.
Free plan at $0 USD; Pro plan at $5 USD per month.
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
Entity-graph spreading activation for cross-vocabulary retrieval
Works with any LLM model
No LLM or GPU in retrieval path
Point-in-time and windowed recall
Timestamped memories
Fixed recall cost as memory grows
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
Reaches answers across unrelated documents that share no keywords, where vector search scores zero
Deterministic retrieval with no model in the path, so results never vary between queries
Constant per-query cost regardless of memory size, even at 32,000 words
No infrastructure to run—no vector database, cluster, or GPU needed
Works with any model, so small local models gain the most from better retrieval