Fine-tune ROI methodology

Open formula. Browser-based calculations. Source-dated rates. No API keys. No provider login. No tracing SDK. No account required. Input payloads are not stored.

Pricing source date: 2026-05-28 · Build verification: 2026-05-28 · Stale threshold: 30 days. This estimate depends primarily on the training and inference rates you enter, not a public price table. Provider fine-tune and hosting pricing must be verified directly, since fine-tune economics vary widely by model, dataset size, and serving pattern.

Compares one-time training spend against monthly inference savings from cheaper per-token rates and prompt reduction.

Formula

months_to_roi = training_cost / monthly_savings; breakeven_volume_12mo = training_cost / 12 / savings_per_token

Primary source register

Training and inference rates come from the pricing table. Dataset preparation, evals, and quality failure risk are separate costs.

Included assumptions

training tokens
training cost per 1M
baseline inference cost
fine-tuned inference cost
monthly token volume
prompt reduction

Excluded assumptions

data-labeling cost
eval suite cost unless modeled separately
deployment/integration cost
quality-regression risk

Architecture-cost audit

Fine-tune ROITraining cost, inference savings, prompt reduction, and volume needed to recover spend.

Eval-suite budgetSamples, candidate models, repeated trials, and optional LLM-as-judge pass costs.

Diagnostic output

This tool returns Cost classification, Dominant cost driver, Decision threshold, and Sensitivity. Diagnostic focus: Fine-tune crossover volume and payback sensitivity..

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