Free AI Tool · Fine-Tuning · LoRA · Training Cost · GPT · Gemini · Llama · Inference Markup
AI Fine-Tuning Cost Calculator
Calculate the cost of fine-tuning AI models across 6 providers. Enter your dataset size, epochs and expected inference volume. Compare training costs, inference markups and break-even points versus prompt engineering. Covers OpenAI GPT-4.1, Google Gemini, Together AI LoRA and Mistral. June 2026 verified prices.
How to Use the AI Fine-Tuning Cost Calculator
Enter your training dataset size in tokens, number of epochs, expected monthly inference volume and input/output ratio. Furthermore, click Calculate to see training cost, monthly inference cost and year-1 total across 8 models from 6 providers. The tool ranks options by total first-year cost. Additionally, the recommendation highlights the cheapest option for your specific usage pattern.
- Enter dataset sizeTotal tokens in your training data. Furthermore, 1,000 examples of 500 tokens each equals 500,000 tokens.
- Set epochsTraining passes over the dataset. Furthermore, 3 epochs is the typical starting point.
- Estimate inferenceMonthly tokens through your fine-tuned model. Furthermore, set the input/output split.
- Compare providersView ranked comparison of training, inference and year-1 costs.
- Copy resultsCopy the full comparison for procurement discussions.
Competitor Gap Analysis
No free tool calculates fine-tuning costs across multiple providers with inference markup comparison. Furthermore, most pricing pages show training cost only, ignoring the ongoing inference premium that often exceeds training cost within months.
| Feature | Provider pricing pages | LazyTools |
|---|---|---|
| Multi-provider comparison | No (single provider) | 8 models, 6 providers |
| Training + inference combined | Rare | Year-1 total cost |
| Epoch multiplication | No | Auto-calculates |
| Input/output ratio | No | Adjustable split |
| Copy comparison | No | Full text report |
Fine-Tuning Pricing by Provider (June 2026)
Training costs range from $0.48 per million tokens (Together AI LoRA on Llama 8B) to $4.00 per million (Mistral Large). Furthermore, the critical hidden cost is inference markup. OpenAI charges 1.5x to 2x base model rates for fine-tuned model inference. Google Gemini charges no inference markup. Additionally, Together AI and Fireworks charge standard hosted rates for LoRA-adapted models.
| Provider | Model | Training $/M | Inference in $/M | Inference out $/M |
|---|---|---|---|---|
| OpenAI | GPT-4.1 | $3.00 | $3.00 | $12.00 |
| OpenAI | GPT-4.1 Mini | $0.80 | $0.80 | $3.20 |
| Gemini 2.0 Flash | $3.00 | $0.30 | $2.50 | |
| Together AI | Llama 8B LoRA | $0.48 | $0.18 | $0.59 |
| Together AI | Llama 70B LoRA | $2.40 | $0.88 | $0.88 |
| Mistral | Small | $1.00 | $0.20 | $0.60 |
When Fine-Tuning Is Worth It
Fine-tuning makes financial sense when it replaces long system prompts on high-volume workloads. Furthermore, a 2,000-token system prompt costs $6 per million requests on Sonnet 4.6 input pricing. If fine-tuning eliminates that prompt and you make 1 million requests per month, you save $6,000 monthly in input costs alone. Additionally, fine-tuned models often produce shorter, more precise output, further reducing output token costs.
Fine-tuning is not worth it for low-volume applications under 100,000 requests per month. Furthermore, the training cost plus inference markup often exceeds the savings from shorter prompts at low volumes. The break-even point depends on your dataset size, prompt reduction and inference volume. Moreover, prompt engineering and few-shot examples often achieve 80 to 90 percent of fine-tuning quality at zero training cost.
LoRA vs Full Fine-Tuning
Low-Rank Adaptation (LoRA) trains only a small number of additional parameters rather than modifying the entire model. Furthermore, LoRA reduces training cost by 70 to 90 percent and training time by a similar margin. Quality reaches 80 to 95 percent of full fine-tuning for most tasks. Additionally, LoRA adapters can be exported and deployed on your own infrastructure.
Together AI and Fireworks specialise in LoRA fine-tuning on open-source models. Furthermore, training Llama 3.1 8B via LoRA costs just $0.48 per million tokens. The resulting adapter can run on a single GPU. Additionally, you can create multiple LoRA adapters for different tasks and swap them at inference time without reloading the base model.
OpenAI Fine-Tuning API Wind-Down
In May 2026, OpenAI announced it is winding down its self-serve fine-tuning API and platform. Furthermore, this means new fine-tuning jobs may become enterprise-only. Teams currently relying on OpenAI fine-tuning should evaluate alternatives: Google Gemini (zero inference markup), Together AI (cheapest LoRA), or self-hosted solutions. Additionally, existing fine-tuned models continue to work but migration planning is prudent.
References
1. OpenAI API Pricing, June 2026.
2. Google AI / Gemini Pricing.
3. Together AI: LoRA Fine-Tuning Pricing.
4. AI Cost Check: Fine-Tuning Costs 2026.
5. Price Per Token: Fine-Tuning Comparison.
6. Awesome Agents: Fine-Tuning Costs.
Frequently Asked Questions
Related AI Tools
AI Credit & Cost Calculator
Compare API costs for 20+ AI models from 7 providers. Furthermore, includes use-case presets and recommendations.
→AI Token Counter
Count tokens with cost estimates for GPT, Claude and Gemini. Furthermore, shows context window fit for 9 models.
→AI ROI Calculator
Calculate AI automation return on investment with payback period. Furthermore, includes 3-year cumulative projections.
→AI Model Benchmark Comparator
Compare MMLU, HumanEval and GSM8K scores for 12 models. Furthermore, highlights category leaders.
→AI Context Window Planner
Plan token budgets for system prompts and RAG chunks. Furthermore, shows model fit for 8 models.
→AI Prompt Template Builder
Generate production-ready system prompts for 8 categories. Furthermore, includes token count and cost estimate.
→