Pricing Guide

Everything you need to know about LLM fine-tuning costs in 2026—from LoRA adapters to full-parameter training at 70B+ scale.

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LLM Fine-Tuning Pricing — What It Costs in 2026

Cost Overview

LLM fine-tuning costs range from $8,000 for a LoRA adapter to $80,000+ for full-parameter training of 70B+ models.

$8K–$20K

LoRA Adapter

$20K–$45K

Full Fine-Tune 7B

$45K–$80K+

Full Fine-Tune 70B+

Cost Breakdown by Phase

Fine-tuning is not a single line item. Here is how the budget typically breaks down across the project lifecycle.

PhaseCost Range% of BudgetWhat's Included
Data Preparation$2K–$15K~25%Audit, clean, label, and format training data. Includes quality review and train/eval split creation.
GPU Compute$1K–$25K~30%Cloud GPU hours (A100, H100) for training runs. Cost scales with model size and number of epochs.
Training Runs$2K–$15K~20%Hyperparameter tuning, multiple training runs, checkpoint evaluation, and regression testing.
Evaluation$1K–$8K~10%Automated benchmarks, human evaluation, A/B testing against the base model, and bias auditing.
Deployment$2K–$17K~15%Model packaging, inference endpoint setup (vLLM, TensorRT-LLM), monitoring, and CI/CD pipeline.

Fine-Tuning Packages

Choose the tier that matches your model size, data volume, and accuracy requirements.

LoRA Adapter
$8K–$20K
Parameter-efficient fine-tuning that trains a lightweight adapter on top of a frozen base model. Fastest and most cost-effective approach.

Timeline: 3–5 weeks

  • LoRA/QLoRA adapter training
  • Up to 7B parameter base model
  • 500–2,000 training examples
  • Data quality audit & formatting
  • 3 training runs with evaluation
  • Deployment on your cloud or ours
  • 14-day post-launch support
Full Fine-Tune 7B
$20K–$45K
Full-parameter fine-tuning of a 7B model for deep domain specialisation. Ideal when the adapter approach reaches its ceiling.

Timeline: 5–8 weeks

  • Full-parameter weight updates
  • 7B parameter model (Llama, Mistral, etc.)
  • 2,000–10,000 training examples
  • Comprehensive data preparation pipeline
  • 5+ training runs with A/B evaluation
  • Production inference optimisation (vLLM)
  • Human evaluation round
  • 30-day post-launch support
Full Fine-Tune 70B+
$45K–$80K+
Enterprise-grade fine-tuning of 70B+ parameter models for maximum capability. Requires significant compute and data investment.

Timeline: 8–12 weeks

  • Full-parameter or LoRA at 70B+ scale
  • 70B–180B parameter models
  • 5,000–50,000+ training examples
  • Multi-stage data curation pipeline
  • Distributed training across GPU cluster
  • Comprehensive evaluation suite
  • Production deployment with autoscaling
  • Bias & safety auditing
  • Dedicated project team
  • 90-day managed support with SLA
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What Drives LLM Fine-Tuning Costs?

The difference between an $8K LoRA adapter and an $80K+ full fine-tune comes down to four key variables.

Model Size

Larger models require exponentially more GPU memory and compute time. A 7B model can be trained on a single A100 GPU in hours. A 70B model requires a multi-GPU cluster and days of training time, with costs scaling accordingly.

Training Data Volume

More training examples mean longer training runs and more extensive data preparation. The cost of curating 50,000 expert-reviewed examples is fundamentally different from formatting 500 existing documents.

Training Approach

  • LoRA/QLoRA: Trains only adapter weights (0.1–1% of parameters). Fastest, cheapest, often sufficient
  • Full fine-tuning: Updates all model weights. More capable but 5–10x the compute cost
  • Continued pre-training: Extends the base model's knowledge with domain text before fine-tuning. Adds a full training phase

Evaluation Rigour

Production deployments require automated benchmarks, human evaluation, bias auditing, and A/B testing against the base model. A quick proof of concept can skip some of these steps, but enterprise-grade deployments cannot.

Get a Custom Fine-Tuning Quote

Every fine-tuning project is different. The right approach depends on your model choice, data volume, accuracy requirements, and deployment infrastructure. Our LLM Fine-Tuning Services team will review your use case, recommend the optimal training approach, and provide a fixed-price proposal.

See all of our AI development pricing on the pricing overview page, or schedule a free consultation to discuss your project.

FAQs — LLM Fine-Tuning Pricing

Common questions about llm fine-tuning costs and pricing.