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
Governance: checks proposed AI actions against defined rules before execution
Recorded decision records that can be reviewed, replayed and verified later
Evaluation: compares AI agent performance using representative test cases and expected outcomes
Optimization: recommends model and cache candidates based on usage, prices and evaluation evidence
Regress detection identifies changed action paths and quality regressions
On-premises and private cloud deployment
Integration with existing AI tools via connectors and wrappers
No model training on customer data; separate data boundaries from external AI providers
Cost comparison and optimization across model and cache configurations
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
Independent accountability: checks run inside your systems, not asking another AI to audit AI
Your people keep authority: Forge produces decision evidence, your business enforces results
Recorded decision evidence that auditors and regulators can examine
Designed for consequential work in regulated industries like financial services
Not another dashboard: integrates into your workflow without replacing your AI tools
Forge Orbital