Fine-Tune Budget Estimator Calculator

Formula Used

How to Use This Calculator

1. Pick a pricing scenario that matches your deployment approach.
2. Enter token counts for training, validation, and evaluation per run.
3. Set epochs and runs to reflect iterations and model tuning cycles.
4. Add labor, labeling, and tooling values for end-to-end budgeting.
5. Apply overhead, contingency, and optional tax to match governance needs.
6. Click Estimate Budget, then export CSV or PDF for sharing.

Example Data Table

Sample inputs and a typical output shape for quick validation.

Token volumes and iteration planning

Budget accuracy starts with realistic token counts. Use the calculator’s training, validation, and evaluation tokens per run to model data growth across experiments. Many teams increase training tokens by 20–50% after the first baseline run because they add hard negatives, expand instruction variety, and rebalance classes. When you set runs and epochs, treat them as “planned iterations,” not a promise; two smaller cycles often outperform one large cycle with the same tokens. For early pilots, assume at least one rerun for prompt cleanup and data fixes, plus 5–10% extra tokens for formatting, system messages, and separators between batches often.

Pricing mode selection and unit economics

Select API token pricing when you pay per processed token and want transparent marginal cost per experiment. Select compute-based mode when you reserve GPUs or pay hourly. In compute mode, focus on throughput (tokens/second) and utilization; a 15% idle rate can erase negotiated savings. Keep rates in a single currency, then let overhead and contingency capture governance and procurement variance.

People cost and data operations realism

Fine-tuning budgets are frequently dominated by people-hours rather than compute. Include data engineering for extraction, schema alignment, and redaction; add QA for rubric checks and rejection sampling; and add project coordination for review cycles. A practical baseline is 0.5–2.0 minutes of review per example for simple tasks, and 3–8 minutes for complex reasoning or safety-sensitive domains.

Labeling, tooling, and hidden line items

Labeling costs vary by difficulty, language coverage, and audit requirements. Track both labeling per item and acceptance rate; if only 80% pass QA, effective labeling cost rises by 25%. Tooling costs include storage for datasets, experiment tracking, vector search, and security scanning. Use the calculator’s “tooling monthly” and “tooling months” fields to model these recurring charges.

Risk buffers, contingency, and stakeholder reporting

Overhead and contingency are not padding; they reflect uncertainty in requirements, vendor lead times, and rework. Common ranges are 2–6% overhead for administration and 8–20% contingency for iteration and scope risk. Export the CSV for finance review, and use the PDF summary for stakeholders so each scenario is traceable to inputs, assumptions, and a single total cost figure.

FAQs

1) What is the difference between runs and epochs?

Runs represent separate experiments or tuning cycles. Epochs represent how many passes you make over the training data within each run. Total training tokens scale with runs × epochs × tokens per run.

2) Should validation and evaluation tokens be included?

Yes. Validation supports selection and early stopping, while evaluation supports final reporting. They may be smaller than training, but repeated runs can make them a material portion of total token spend.

3) How do I estimate compute throughput for self-hosted work?

Start with a pilot job and measure tokens/second at typical batch sizes. Multiply by training hours to estimate tokens processed. Add a utilization discount for queue time, checkpoints, and data loading overhead.

4) Why does labeling sometimes cost more than compute?

High-quality examples require writing, review, and QA. Complex domains need expert annotators and auditing. If the acceptance rate is below 100%, rework increases effective cost per usable example.

5) What contingency percentage is reasonable?

Many teams use 10–15% for iterative projects with stable scope, and 15–20% for new domains or strict compliance. If your plan includes multiple baselines, higher contingency is usually justified.

6) Can I compare scenarios in one export?

Yes. Run multiple estimates and export each as CSV to keep a clear trail of assumptions. For side-by-side comparison, paste CSV rows into a spreadsheet and compute deltas across total and category subtotals.