Queue-driven execution
On-demand AI workloads run through a message queue and isolated containers, supporting predictable scheduling on shared infrastructure.
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Project · TNO
AI Model Orchestration & Distributed Access Control
A distributed AI model orchestration platform that enables secure, on-demand execution of AI workloads via a queue-driven execution pipeline on shared infrastructure.
What it is
Secure, isolated execution for shared AI workloads.
The system architects model execution as queue-driven jobs that land in isolated containers on Kubernetes. That keeps tenants separated, lets the platform scale model deployment across shared infrastructure, and gives every run a clear audit trail.
Distributed access control is layered on with Ory Kratos and Ory Keto, so identity flows and authorization decisions stay separate. Execution policies make model usage auditable across system components, not just at the entry point.
Kubernetes isolation
Container orchestration with execution isolation keeps tenants separate while supporting scalable model deployment.
Distributed access control
Ory Kratos and Ory Keto handle identity and authorization, with policies that make model usage secure and auditable across components.