Services
AI cost is a moving target — token prices change quarterly, GPU spot capacity flexes, vendor pricing rebases. We put cost telemetry at the workload level, not the cloud-bill level, so engineering can see the cost of every agent run and every model call.
How it works
Cost telemetry instrumented at the workload and model-call level.
Capacity tuning — GPU scheduling, batch vs real-time, spot vs on-demand.
Vendor cost-model review with renegotiation hooks.
Exit-readiness work where the engagement crosses into deal prep.
Output
A cost dashboard at the workload, model, and agent-run level.
A capacity tuning report with quantified savings opportunities.
A vendor cost-narrative memo for the deal team (where in scope).
A monitored-egress baseline — because an unpredictable bill is often an unmonitored egress problem in disguise.
Cost: TBC — engagement-based; exit-readiness component priced separately
