Question 1

How much does AI fine-tuning cost?

Accepted Answer

OpenAI fine-tuning: training $0.05-0.30 per 1K tokens depending on model, inference 2-3x base model cost. Claude: fine-tuning in development. Gemini: fine-tuning available with custom pricing. Typical training runs cost $50-500 for small datasets, $500-5000 for large datasets.

Question 2

What data do I need for fine-tuning?

Accepted Answer

Minimum 50-100 examples for style/formatting tasks. 500-5000 examples for domain adaptation. 10,000+ for complex reasoning. Format: prompt-completion pairs (JSONL). Quality matters more than quantity—poor examples hurt performance.

Question 3

When should I fine-tune vs use RAG?

Accepted Answer

Fine-tune for: style/formatting consistency, domain-specific vocabulary, reducing prompt size, improving latency by avoiding context injection. Use RAG for: frequently updated data, large document corpus, factual accuracy requirements, cost efficiency on changing data.

Question 4

How does fine-tuning affect inference cost?

Accepted Answer

OpenAI fine-tuned models cost 2-3x base model inference price. GPT-4o fine-tuned: $7.50/1M input vs $2.50/1M base. However, fine-tuning can reduce prompt length, offsetting higher per-token cost. Calculate total cost including training amortization.

Question 5

What are fine-tuning best practices?

Accepted Answer

Best practices: validate data quality before training, use temperature=0 for training examples, monitor validation loss during training, test on held-out examples, start with smaller model (GPT-4o-mini) for experimentation, iterate on data quality not just volume.

AI Fine-tuning Cost Calculator

AI Fine-tuning Cost Calculator

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