Question 1

When does fine-tuning save more money than prompt engineering?

Accepted Answer

Fine-tuning typically saves money when you have high request volume (50K+/month) and a large training dataset (1K+ examples). The upfront training cost is amortized over time via lower per-token inference costs.

Question 2

What is RAG and how does it compare to fine-tuning for costs?

Accepted Answer

RAG (Retrieval-Augmented Generation) injects relevant documents into prompts at inference time. It has lower one-time setup cost than fine-tuning but higher ongoing monthly infrastructure costs due to vector DB hosting and embedding updates.

Question 3

How do I calculate the break-even point for fine-tuning?

Accepted Answer

Break-even = Training Cost ÷ (Monthly Prompt Cost − Monthly Fine-tune Cost). Our calculator does this automatically based on your model, training data size, and request volume.

Question 4

How much does it cost to fine-tune GPT-4o?

Accepted Answer

OpenAI charges $0.025 per 1K training tokens for GPT-4o fine-tuning. With 5,000 training examples averaging 500 tokens each, training costs approximately $62.50. Fine-tuned inference is billed at $3.00/1M input tokens.

Question 5

Is fine-tuning worth it for small datasets?

Accepted Answer

Generally no. Fine-tuning with fewer than 500 examples often leads to overfitting. RAG or few-shot prompting typically produces better results at smaller data scales.

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