Running AI at scale is expensive. The problem isn't the GPUs — it's that most platforms give you utilization dashboards instead of actual cost controls. We built the missing layer: a system that tracks what every model, team, and feature is spending in real time, enforces limits, and eliminates the structural waste that quietly inflates your bills. Think of it as financial discipline for your GPU fleet.
tokens/sec/$ by model, tenant, and workload class — tied to cost attribution you can defend.
Fairness, routing, isolation, and cost envelopes enforced by a control plane — not tribal process.
Reduce drift and regressions through measurable guardrails and continuous economic optimization.
Unit economics, fairness, leakage, and p99 stability — shown as CFO-defensible metrics (mock telemetry).
Structural waste signal over time (mock).
Tail latency tightening under policy (mock).
Activate token, GPU-second, and workload telemetry to generate a fleet-wide economic baseline.
Enable fairness, routing, and cost guardrails across controlled production slices.
Expand runtime governance across inference + training surfaces with deterministic p99 controls.
Continuously tune runtime + scheduling using live economic + performance signals.
