
Get more out of every GPU
Assign exactly the right capacity per job and cut idle spend.


Assign exactly the right capacity per job and cut idle spend.
Scale training, inference, and burst workloads without rewriting pipelines.
Run training and serving together, extend hardware life, and save on capex.
Unlike stock Kubernetes, Ori’s GPU-aware scheduler dynamically assigns resources at the job or container level.
Minimizes node fragmentation by intelligently placing jobs and ensuring GPUs are fully utilized.
Run up to 7 workloads per GPU securely and efficiently, all without extra configuration.

Train today, serve tomorrow, all on the same cluster.
Securely segment and share GPUs across workloads.
One scheduling engine that evolves with your AI workloads and customer needs.