LLM Fine-tuning Cost
LLM fine-tuning cost in 2025: dataset curation, LoRA adapters, evals, and production deployment.
What drives Cost
LLM fine-tuning cost is dominated by dataset work, not compute. A clean few-thousand-example LoRA adapter on an open model ships for twelve to thirty thousand. Large-scale supervised fine-tuning with evals, safety tuning, and production hosting runs seventy thousand and up. The tiers below reflect realistic costs including data work, not just GPU hours.
- 01Training data volume and labeling quality
- 02Base model (open weight vs API provider)
- 03Adapter (LoRA, QLoRA) vs full fine-tune
- 04Eval harness and safety testing
- 05Hosting (serverless, dedicated GPU, on-prem)
- 06Ongoing retraining cadence
Typical pricing tiers
- Few-thousand example dataset
- LoRA or QLoRA training
- Baseline evals
- API-compatible inference endpoint
- Tens of thousands of examples
- Multi-epoch training
- Regression eval harness
- Managed hosting
- DPO / RLHF stages
- Safety tuning
- Dedicated inference infra
- Monitoring and drift alerts
No surprise line items
Every engagement is scoped against a written statement of work. Changes are logged weekly and priced transparently. You always know where the number is going before it gets there.
A statement of work with deliverables, acceptance criteria, and a timeline before we start.
Every scope change is logged and priced within a week of being raised. No end-of-quarter surprises.
You own the code, prompts, weights, and infra-as-code. Standard work-for-hire clauses, no lock-in.
Runbooks, architecture diagrams, and a support retainer so your team can take it from here.
100+ companiesquietlyrunonsystemswebuilt.
Pricing questions
Should we fine-tune or stick with prompting?
Prompt first. Fine-tune when tone, format, or latency pressure justifies it and data is clean.
OpenAI fine-tuning or open-weight?
OpenAI for speed and simplicity. Open-weight (Llama, Mistral, Qwen) when cost, data residency, or control matter.
What dataset size do we need?
1-5k examples for LoRA. 20k+ for full SFT. Quality beats quantity - dedupe and rubric-label aggressively.
How long does a fine-tune stay useful?
6-12 months before drift or base model upgrades warrant a retrain. Plan for it.
You may also compare
Telluswhatyouwanttoautomate.We'llreplyinonebusinessday.
Describe the problem, the constraint, the deadline. We'll send back a scoped plan and a senior engineer to kick it off — no sales theater.