Fine‑Tuning Price Estimator

Training token drivers and scaling

Training cost is primarily driven by effective tokens, which grow with dataset size, epochs, and overhead. If a dataset contains five million tokens and you train for three epochs with a 1.05 multiplier, effective tokens reach 15.75 million. At a rate of 0.008 per 1K tokens, that portion is 126.00. Doubling epochs doubles training usage, while small multiplier changes compound across large runs. Pruning duplicates and shortening long examples often reduces spend fast.

Evaluation and quality assurance spend

Evaluation tokens represent validation sweeps, regression tests, and safety checks. Teams often run repeated test suites after each tuning iteration, so evaluation can expand quickly. Budgeting 400 thousand evaluation tokens at 0.002 per 1K adds 0.80, but frequent re-testing raises this line item. Tracking evaluation spend separately encourages disciplined experiment design and helps justify quality gates to stakeholders. Use a fixed test set to keep comparisons consistent.

Labor, iteration cycles, and hidden effort

Engineering labor is usually the largest controllable component. Hours include data cleaning, prompt and label audits, run monitoring, error analysis, and improvements to training data. For 30 hours at 35 per hour, labor is 1,050.00. Reducing rework through clear labeling guidelines and automated checks can cut hours more effectively than chasing marginal token savings, especially on smaller datasets. Document decisions to avoid repeating investigations.

Deployment operations and runtime usage

Deployment costs combine hosting, monitoring, and support over the chosen months. A one‑month pilot with 20 hosting, 10 monitoring, and 15 support totals 45.00. Production plans typically add inference usage as demand grows. Estimating 800 thousand inference tokens at 0.0025 per 1K adds 2.00. Separating pilot and scale phases makes it easier to align budgets with rollout milestones. Add alert thresholds so incidents stay bounded.

Contingency planning and decision readiness

Contingency converts uncertainty into an explicit reserve. A 10% contingency on the subtotal covers re-runs, scope shifts, and extra evaluation passes. This calculator reports subtotal, contingency, and total so reviewers can see what is baseline versus buffer. For approvals, export CSV to compare scenarios or download a PDF to attach to procurement and finance requests with consistent, auditable numbers. Review totals after each major dataset refresh. Consider separate contingencies for data risk, schedule risk, and vendor pricing changes later too.

What rates should I enter in the token fields?

Use your provider’s published per‑1K token prices for training, evaluation, and inference. If pricing differs by model, enter the rate that matches your selected tier and region.

How do I estimate training tokens accurately?

Sample your dataset, count tokens per example, then multiply by the number of examples. Add an overhead multiplier for formatting, system text, and occasional retries.

Why are evaluation tokens separated from inference tokens?

Evaluation captures testing during development, while inference reflects production usage after deployment. Keeping them separate clarifies where spend occurs and supports better optimization decisions.

Does this calculator include data labeling and licensing?

Yes, you can add fixed data preparation costs. Use that field for labeling, acquisition, storage, and quality checks that are not billed per token.

How should I choose contingency percentage?

Start with 10% for stable scopes. Increase it when requirements are uncertain, data quality is unknown, or multiple re‑runs are likely due to strict performance targets.

Can I compare multiple scenarios quickly?

Run the calculator for each scenario, download CSV files, and combine them in a spreadsheet. The consistent breakdown makes side‑by‑side comparisons straightforward.