When does fine-tuning an LLM make financial sense?

Fine-tuning pays off when you make 100K+ API calls per month with consistent prompt structure, AND the fine-tuned model reduces output tokens by 30%+. The break-even point depends on training cost ($100-$5,000+) and per-call savings. Below 10K calls/month, fine-tuning almost never saves money.

How much does fine-tuning cost compared to regular API calls?

Fine-tuning a GPT-4o mini costs $100-500 for training. GPT-5 mini costs $300-1,500. GPT-5 costs $1,000-5,000. Open-source models on Together.ai cost $50-500. But the real cost comparison depends on how many API calls you make and how much output tokens shrink after fine-tuning.

Can you fine-tune Claude or Gemini models?

No. As of 2026, Anthropic (Claude) and Google (Gemini) do not offer fine-tuning. Fine-tuning is available for OpenAI models, open-source models via Together.ai/Fireworks, and DeepSeek models. For Claude and Gemini, use RAG or prompt engineering instead.

Fine-Tuning vs API Calls: When Does Fine-Tuning Actually Save Money?

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