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Fine-Tuning Cost Calculator

Fine-tuning cost and whether it pays back. Tuning usually pays through shorter prompts rather than better output, and where the tuned model prices above the base one that premium can exceed the saving.

Also called: fine tune cost calculator, model training cost.

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₹
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Training cost
₹8,400

₹8,400 to train on 12 million tokens over 3 epochs. If tuning lets you drop 800 tokens of prompt per request, that saves ₹20,000 a month and the training pays back in 0.42 months. The tuned model prices the same as the base model, so the prompt saving is the whole benefit.

Training tokens, millions
12
Monthly saving from a shorter prompt
₹20,000
Monthly premium on tuned inference
₹0
Net monthly saving
₹20,000
Payback
0.42
Cost per epoch
₹2,800
On tuned inference pricing
The tuned model prices the same as the base model, so the prompt saving is the whole benefit.
On prices
Prices are inputs, not constants. Model pricing changes often, so take the current rate from the provider rather than relying on a default here.
How this is calculated
Method and background

How this is calculated

Training cost is examples times tokens times epochs at the training rate. The return usually comes from prompt reduction: a tuned model that no longer needs a long instruction block and few-shot examples saves those tokens on every request, which at volume adds up quickly. The offset is that tuned inference sometimes prices above base inference, and at high request volumes that premium can exceed the prompt saving entirely, making the tuning a net cost.

training cost scales with examples, length and epochs, and inference pricing may differ afterwards
n
Examples
e
Epochs

Worked examples

Each of these is asserted on every build. If a change to the engine ever moved one of these answers, the build would fail before the page could print it.

5,000 examples over three epochs

Training examples
5,000
Tokens per example
800
Epochs
3
Training price per million tokens
₹700
Base model input per million
₹250
Tuned model input per million
₹250
Requests a month after tuning
1,00,000
Prompt tokens saved per request
800

Training cost₹8,400

5000 x 800 x 3 tokens; 80M saved at 250

Open this example

a tuned inference premium erodes the saving

Training examples
5,000
Tokens per example
800
Epochs
3
Training price per million tokens
₹700
Base model input per million
₹250
Tuned model input per million
₹350
Requests a month after tuning
1,00,000
Prompt tokens saved per request
800

Training cost₹8,400

boundary: no tokens remain to carry the premium

Open this example

Method and limits

What it assumes

  • A single tuning run without hyperparameter search.

What it deliberately does not model

  • Multiple runs to find working hyperparameters multiply the training cost.
  • Quality improvements are the usual motivation and are not a cost calculation.
  • Hosting a tuned model sometimes carries a separate charge.

Formula version 1.0.0 · definition 1.0.0 · India · Report a problem with this calculator

Frequently asked questions

Does fine-tuning save money?
Through shorter prompts, often. If the tuned model also prices above the base one, that premium applies to every request and can outweigh the saving at volume.
How many epochs do I need?
Usually two to four. More risks overfitting and multiplies the cost linearly, so it is rarely the right lever.