Estimate the total budget required to train an AI model from compute and data costs.
A realistic AI training budget covers more than raw GPU-hours: total = gpu_cost + storage_cost + overhead(15%), where gpu_cost = hourly_rate × gpu_count × training_hours, storage_cost = storage_GB × price_per_GB_month × months (for datasets, checkpoints, and logs), and a 15% overhead buffer accounts for failed/restarted runs, hyperparameter search, evaluation passes, and miscellaneous cloud fees (networking, snapshotting) that aren't captured in the raw compute and storage line items. Budgeting with this buffer up front avoids underestimating real project cost, which commonly runs 10-20% over the 'pure compute' estimate.
total_cost = (gpu_rate * gpu_count * hours + storage_GB * price_per_GB_mo * months) * 1.15
Real training projects rarely go perfectly on the first attempt — failed runs, hyperparameter sweeps, evaluation passes, and incidental cloud costs (data egress, snapshots, idle time between runs) typically add 10-20% on top of the 'ideal' compute and storage cost, so 15% is a reasonable planning buffer.
It should — factor in your dataset size plus periodic model checkpoints (which can be substantial for large models saved multiple times during training) and any logs or intermediate artifacts you plan to retain for the storage duration.
Spot instances can cut GPU cost by 50-70% compared to on-demand pricing, but introduce risk of interruption requiring checkpoint/resume logic — if using spot instances, consider using a lower effective hourly rate and possibly increasing the overhead buffer to account for interruption-related re-runs.
It's best to estimate training hours for a single successful run and let the 15% overhead buffer absorb typical retry costs; if you expect substantial iteration (e.g. many hyperparameter trials), multiply training hours by your expected number of runs instead.