ZenLLM

AI Cost Per Customer for Teams That Need Better Unit Economics

ZenLLM helps teams attribute AI spend to customers, workflows, and product surfaces so pricing, margin, and showback decisions are based on real usage.

What ZenLLM surfaces first

These are the main cost patterns highlighted on the live landing page. They are designed to move a visitor from generic provider spend to route-level, workflow-level, and margin-relevant causes.

See which customers, features, and routes are driving disproportionate AI spend.

Support showback and chargeback with workflow-level cost attribution.

Compare unit economics before and after routing, caching, or prompt changes.

What to evaluate next

These next-step links are already part of the live page. They guide a visitor into adjacent cost, routing, or benchmark topics instead of leaving them stranded after the first click.

AI cost visibility: Start with workflow-level visibility before building showback models.

AI budget forecasting: Use per-customer cost visibility to improve forward budget assumptions.

AI chargeback and showback: Use customer attribution to support internal AI allocation models.