Updated · Methodology: named formula library
LLM Fine-Tune ROI
Compare fine-tune training cost vs. monthly inference savings.
Training Cost to Monthly Savings = 4:1 (4 as decimal).
Fine-Tune Payback
Months to break even = training cost / monthly inference savings. A fine-tuned smaller model can replace a larger general-purpose one for 10–20% the inference cost; break even is usually 2–6 months for high-volume workloads.
Worked Example
500 Training Cost to 2000 Monthly Savings
- a
- 500
- b
- 2000
- Result
- 1:4 (0.25)
500 / 2000 = 0.25. Simplified: 1:4.
When to Use This Calculator
- Decide whether to fine-tune
- Compare to prompt engineering
Limitations & Common Mistakes
- Results are estimates based on the inputs you provide.
- Always verify with current data and consult a professional for major decisions.
Frequently Asked Questions
How is the LLM Fine-Tune ROI computed?
Training Cost divided by Monthly Savings, plus a simplified ratio (e.g., 4:3) using greatest common divisor. Both decimal and ratio forms are useful in different contexts: decimal for math, ratio form for comparisons or recipe scaling.
What does Training Cost:Monthly Savings mean?
It's a comparison: for every Monthly Savings unit, you have a corresponding amount of Training Cost. Useful when the absolute numbers matter less than the proportion (e.g., reading 8:1 LTV/CAC immediately tells you the unit economics are healthy without needing the dollar amounts).
Why simplify the ratio?
4:3 is more readable than 200:150. The simplified form (using greatest common divisor) preserves the proportion while making it easier to interpret. Common simplified ratios: 16:9 (widescreen), 4:3 (legacy displays), 3:1 (LTV:CAC for SaaS).
When is a ratio more useful than the absolute values?
Comparison across scales. A $1B company and a $1M company can both have a 3:1 LTV:CAC; the ratio reveals comparable unit economics regardless of scale. Use ratios for benchmarking; use absolute numbers for budgeting.
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