Case study / Product concepts
AI Token Cost Controller
Understand model spend by product feature and find practical savings
Period
Product concept
Role
Product direction and workflow design
Type
Independent product concept
Product preview
Illustrative concept screens · Select any image to zoom
Product overview: illustrative concept screen
01 / Overview
An AI operations concept that attributes language-model usage to product features, customers, prompts, and model choices. It highlights cost and latency patterns and proposes optimization experiments without changing production settings automatically.
02 / Challenge
AI usage costs can grow across several models and product features, but a single monthly total does not show which workflows drive spend or where caching, batching, or a smaller model might help.
- /Usage is difficult to attribute to a product workflow.
- /Model and prompt changes affect cost and latency differently.
- /Optimization needs evidence and a safe comparison path.
03 / Proposed capabilities
- 01Attribute requests by feature, tenant, model, and environment
- 02Compare token use, latency, and estimated cost over time
- 03Flag unusual usage and budget threshold changes
- 04Model caching, batching, and model-choice scenarios
- 05Track reviewed optimization experiments and results