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
Connect once through MCP endpoint across Claude, ChatGPT, Cursor and Claude Code
Shared memory automatically synced across all connected AI tools
Plain markdown storage with YAML frontmatter
EU-hosted data with no vendor lock-in
Lexical search across memory
No API keys or complex configuration needed
Account created automatically on first connection
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
Cross-vendor memory through MCP instead of locked to one assistant
Plain readable markdown you own forever, not black-box vectors or rented format
EU-hosted by architecture so data stays in your region
Survives AI tool changes and deprecations unlike built-in assistant memory
No maintenance needed unlike DIY scripts and cron jobs
Agentage Memory