AllocateFine-tuning cost calculator
Enter your dataset size, epochs, and training method. Get GPU-hours and a dollar estimate, so you know what training your own model costs before you start.
That buys one run. A build session quotes the whole project flat: training, serving, and the compounding loop that keeps the model improving after launch.
Prices checked 21 Jul 2026 against published provider rates.
How it works
Common questions
How accurate is the estimate?
It assumes H100-class GPUs at current market rental rates with realistic multi-GPU scaling efficiency. Real runs vary with sequence length, batch size, and checkpointing, but the estimate lands in the right bracket for planning.
LoRA or full fine-tuning?
LoRA trains a small adapter on top of frozen weights and captures most task gains at a fraction of the cost; it is the production default. Full fine-tuning updates every weight and only pays off for deep domain shifts with large datasets.
How many epochs should I train?
Two to four epochs is typical for instruction data. More epochs on a small dataset overfits; better data beats more passes.
Is the fine-tuned model mine?
On open-weight bases, yes, when your platform lets you keep the weights. On Allocate, fine-tuned weights stay inside your isolation boundary and belong to you, and they stay yours if you leave.
What does the same run cost on Allocate?
Training runs on Allocate are quoted flat before you commit: fees and usage in writing, no surprises on the invoice. Book a build session and we scope it on the call.
More free tools
These numbers assume you rent the GPUs yourself. On Allocate the same run is quoted flat before you commit, the weights are yours, and every task your agents complete keeps training the model.