What is the cheapest AI API for fine-tuning?

The cheapest fine-tuning is with open-weight models on platforms like Together.ai or Lambda. Llama 3.1 8B costs ~$0.20 per million training tokens. For hosted fine-tuning, OpenAI charges $25/M training tokens for GPT-4o mini. After fine-tuning, inference costs depend on the base model — a fine-tuned GPT-4o mini costs $0.30/$1.20 per 1M tokens (3x the base price).

How much does it cost to fine-tune an LLM?

Fine-tuning costs vary by dataset size and model. A typical 10K-example dataset (~5M tokens) costs: Llama 3.1 8B on Together.ai ~$1, GPT-4o mini on OpenAI ~$125, GPT-4o on OpenAI ~$750. Open-weight models on self-hosted GPUs are cheapest (~$0.20/M tokens). The ongoing inference cost after fine-tuning is often more important than the one-time training cost.

Is fine-tuning cheaper than using a larger model?

Often yes. Fine-tuning a small model (8B-70B) to match a large model's performance on a specific task can reduce inference costs by 50-90%. For example, a fine-tuned Llama 3.1 8B ($0.10/$0.10) can match GPT-4o ($2.50/$10.00) on classification tasks — a 25x cost reduction. The break-even point is usually 1-10M inference tokens/month, depending on the fine-tuning investment.

Cheapest AI API for Fine-Tuning

Find the cheapest AI API for fine-tuning and custom models. Compare 42 models for training and inference costs.

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Training tokens (millions)

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Monthly inference tokens (millions)

Input / Output ratio (%)

Fine-Tuning Inference Cost Ranking

Every model ranked by monthly inference cost after fine-tuning. Training is a one-time cost; inference is ongoing.

Top Picks by Budget

Budget Fine-Tuning (under $50/month)

Llama 3.1 8B (Together.ai)$5.00/mo inference

Training: ~$2 one-timeTotal Y1: $62

GPT-oss 20B (OpenAI)$13.50/mo inference

Production Fine-Tuning ($50-200/month)

GPT-4o mini (OpenAI)$27.00/mo inference

Training: ~$125 one-timeTotal Y1: $449

Mistral Small 4$12.00/mo inference

Enterprise Fine-Tuning ($200+/month)

GPT-4o (OpenAI)$187.50/mo inference

Training: ~$750 one-timeTotal Y1: $3,000

Claude Haiku 4.5$90.00/mo inference

Strategy: Fine-Tune Small, Infer Cheap

The biggest mistake in fine-tuning is using expensive models. Fine-tune a small model on your specific task — it often matches large model performance at a fraction of the inference cost.

Fine-Tuning vs Large Model (Classification, 50M tokens/month)

Option A: GPT-4o (no fine-tune)$187.50/mo

Option B: Fine-tuned GPT-4o mini$27.00/mo + $125 training

Option C: Fine-tuned Llama 3.1 8B$5.00/mo + $2 training

Savings (Option C vs A)97% cheaper ($62 vs $2,250 Y1)

A fine-tuned 8B model can match GPT-4o on narrow tasks (classification, extraction, formatting) while costing 97% less in inference. The training cost is negligible — $2-125 one-time vs hundreds per month in inference savings.

Fine-Tuning Considerations

Inference cost dominates: Training is a one-time cost. At 50M tokens/month, inference costs $60-1,800/month. Training costs $2-750 once. Always optimize for inference cost, not training cost.
Open-weight models are cheapest: Llama, Mistral, and DeepSeek models on Together.ai or Lambda are 5-10x cheaper to fine-tune than OpenAI or Anthropic hosted models.
Fine-tuning multiplier: Most providers charge 3-4x the base inference price for fine-tuned model inference. A $0.10/$0.30 base model costs $0.30/$1.20 when fine-tuned.
Dataset quality > size: A high-quality 5K-example dataset often outperforms a noisy 50K dataset. Invest in data quality, not quantity — it also reduces training costs.
Consider prompt engineering first: Before fine-tuning, try few-shot prompting with a larger model. If that works, it's zero training cost. Fine-tune only when prompts hit their limits.

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