Enter training tokens and cost, your baseline and fine-tuned inference rates, monthly volume, and any prompt reduction; Model Ruler computes months to payback and the volume needed to recover the training spend. Data prep, evals, and quality risk are separate costs not modeled here.
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
Audit coverage: Fine-tune ROI · Eval-suite budget
What does this calculator compute?
It compares one-time fine-tuning training cost against monthly inference savings from cheaper per-token rates and shorter prompts, and reports the payback period.
What should I check before committing to a workload shape?
Compare the one-time training cost plus ongoing fine-tuned-inference cost against what you'd spend achieving the same result with prompting on a base model. The ROI depends on the rates you enter and your request volume — fine-tuning pays off mainly at sustained scale or where it removes expensive prompt overhead.
Is this a quote or a benchmark?
Neither. It is a computed estimate. Real-world cost varies by rate-limit shape, caching, token-count precision, and provider-specific discounts not reflected in the public pricing table.
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. View methodology.