Caching & Batching: Cutting the Token Bill
This lesson is part of the course
In this lesson
You calculate how much prompt caching and batch processing save on Larkspur Home's six monthly AI workloads, and the total saving in %.
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Lessons in this course
- Tokens & Cost per Request: Input vs Output Pricing Locked
- Monthly LLM Cost Forecast: Users × Requests × Tokens Locked
- Model Choice: Cost vs Quality vs Latency Scorecard Locked
- Automation Savings: Hours Saved × Loaded Rate − Tool Cost Locked
- AI Project ROI & Payback: Build Cost, Run Cost, Benefit Ramp Locked
- Human-in-the-Loop: Review Cost vs the Cost of Errors Locked
- Caching & Batching: Cutting the Token Bill Locked
- Risk-Adjusted Business Case: Scenarios, Expected NPV & Sensitivity Locked