AI Fine-Tuning Pricing Comparison
Fine-tuning lets you train a pre-existing AI model on your own data to specialize it for your domain, format, or task -- compare training and inference costs across all major providers.
Models Compared
14
Cheapest Training
$0.48/M tokens
Free Training
2 models
| Provider | Model | Training $/M | Inference Input $/M | Inference Output $/M | Min Examples | Hosting |
|---|---|---|---|---|---|---|
| Gemini 2.0 Flash | Free | $0.10 | $0.40 | 100 | Managed | |
| Gemini 1.5 Flash | Free | $0.075 | $0.30 | 100 | Managed | |
| Together AI | Llama 3.1 8B | $0.48 | $0.18 | $0.18 | 1 | Managed |
| Fireworks | Llama 3.1 8B | $0.50 | $0.20 | $0.20 | 1 | Managed + Self-host |
| Mistral | Mistral Small | $2.00 | $0.10 | $0.30 | 1 | Managed |
| Cohere | Command R | $2.00 | $0.15 | $0.60 | 2 | Managed |
| OpenAI | GPT-4o Mini | $3.00 | $0.30 | $1.20 | 10 | Managed |
| Together AI | Llama 3.1 70B | $3.50 | $0.88 | $0.88 | 1 | Managed |
| Fireworks | Llama 3.1 70B | $4.00 | $0.90 | $0.90 | 1 | Managed + Self-host |
| Cohere | Command R+ | $5.00 | $2.50 | $10.00 | 2 | Managed |
| Mistral | Mistral Medium | $6.00 | $2.50 | $7.50 | 1 | Managed |
| OpenAI | GPT-3.5 Turbo | $8.00 | $3.00 | $6.00 | 10 | Managed |
| Together AI | Llama 3.1 405B | $8.00 | $5.00 | $5.00 | 1 | Managed |
| OpenAI | GPT-4o | $25.00 | $3.75 | $15.00 | 10 | Managed |
| Factor | Fine-Tuning | Prompt Engineering |
|---|---|---|
| Setup Time | Hours to days (data prep + training) | Minutes to hours |
| Upfront Cost | Training tokens cost (see table above) | None |
| Per-Request Cost | Lower (shorter prompts needed) | Higher (long system prompts + few-shot examples) |
| Iteration Speed | Slow (retrain on each change) | Fast (edit prompt and test) |
| Best For | Consistent formatting, domain expertise, production workloads | Prototyping, general tasks, low volume |
| Data Requirement | Need curated training examples | No training data needed |
Good Reasons to Fine-Tune
- +Your prompts are consistently long and repetitive, driving up inference costs
- +You need a specific output format that the base model struggles to maintain
- +You have domain-specific terminology or style requirements
- +High volume production use where per-token savings compound
- +You want faster response times by eliminating lengthy few-shot examples
When to Avoid Fine-Tuning
- -You are still experimenting with prompts and do not have a stable use case
- -Prompt engineering or retrieval-augmented generation (RAG) already works well
- -You have fewer than 50 quality training examples
- -Your task changes frequently and you would need to retrain often
- -Low request volume where training cost will not be recouped
Fine-tuning has an upfront training cost but can reduce ongoing inference costs by shortening your prompts. To estimate whether fine-tuning pays off:
- Measure your current prompt length. Count the tokens in your system prompt and few-shot examples. A typical few-shot prompt might use 500-2,000 tokens per request.
- Estimate post-fine-tuning prompt length. Fine-tuned models can typically drop few-shot examples entirely, reducing input to just the user query (50-200 tokens).
- Calculate token savings per request. Multiply the saved input tokens by the per-token input price.
- Compute break-even volume. Divide the total training cost by the per-request savings. For example, if training costs $50 and you save $0.005 per request, you break even after 10,000 requests.
For most production applications with over 10,000 monthly requests, fine-tuning typically pays for itself within the first month.
Fine-tuning is the process of training a pre-trained language model on your own dataset to specialize it for a specific task, domain, or output style. Instead of training from scratch, you adapt an existing model using examples of the inputs and outputs you want.
OpenAI charges $25.00 per million training tokens for GPT-4o fine-tuning. After training, inference costs $3.75 per million input tokens and $15.00 per million output tokens. You need a minimum of 10 training examples, though OpenAI recommends 50-100 for best results.
Yes, Google currently offers free training for Gemini 2.0 Flash and Gemini 1.5 Flash fine-tuning. You only pay for inference after training. However, Google requires a minimum of 100 training examples, which is higher than most other providers.
The minimum varies by provider: Together AI and Fireworks require just 1 example, Cohere requires 2, OpenAI requires 10, and Google requires 100. In practice, most fine-tuning jobs benefit from at least 50-100 high-quality examples, with diminishing returns above 1,000.
For zero training cost, Google Gemini models offer free fine-tuning. For open-source models, Together AI offers Llama 3.1 8B training at $0.48 per million tokens, making it the cheapest paid option. Fireworks also offers competitive pricing with the added benefit of self-hosting your fine-tuned model.
Use prompt engineering first -- it requires no upfront cost and is easy to iterate on. Fine-tuning makes sense when you need consistent formatting, domain-specific behavior, lower per-request latency, or when your prompts are getting too long and expensive. Fine-tuning can actually reduce inference costs by removing the need for lengthy system prompts.
Yes, but options vary by provider. Fireworks explicitly supports both managed hosting and self-hosting of fine-tuned models. Open-source models (Llama, Mistral) fine-tuned through Together AI or Fireworks can typically be exported and self-hosted. OpenAI and Google fine-tuned models can only run on their respective platforms.