How much does fine-tuning an LLM cost across platforms?

It varies by method and platform: full fine-tuning charges for all parameters across training tokens, while LoRA and QLoRA update far fewer parameters and cut GPU-hours dramatically. The calculator compares AWS Bedrock, Google Vertex AI, Azure OpenAI, and self-hosted GPU across training, inference, and hosting costs with 2026 pricing.

What is the difference between full fine-tuning, LoRA, and QLoRA?

Full fine-tuning updates every weight and is the most expensive; LoRA adds small trainable adapter matrices, cutting trainable parameters by orders of magnitude; QLoRA combines LoRA with 4-bit quantization of the base model for the lowest memory and cost. The calculator shows all three methods per platform, so the accuracy-vs-cost trade-off is explicit.

Which fine-tuning pricing traps should I watch for?

Per-token training prices that look small but compound over millions of samples, storage charges for the base model copies, and inference hosting that costs more than the training run. The calculator's platform traps section surfaces these line items so the annual TCO — not the training demo — is the decision number.

LLM Fine-tuning Cost Estimator — Bedrock vs Vertex AI vs Azure vs Self-Hosted GPU

📐 Fine-tuning Workload Profile

Configure model size, dataset, and method. The engine estimates training cost, GPU hours for self-hosted, and monthly inference TCO across all platforms.

Base Model

Dataset Size (tokens)

Epochs

Fine-tuning Method

Monthly Inference (M tokens)Millions of tokens served per month

Self-Hosted GPU Provider

🎯 Fine-Tuning Method Comparison

QLoRA fits 70B models on a single A100 80GB. Full fine-tuning needs 4-8 H100s. Click to compare costs across methods.

QLoRA (4-bit)1×A100 80GB or 1×RTX 4090 · 5–50¢/run · MB-sized adapters

LoRA (16-bit)1-2×A100 80GB · $3-50/run · Multi-tenant adapter serving

Full Fine-Tuning4-8×H100 · $50-500/run · Highest quality · GPU-obsolete risk

📊 Training Cost — Per Platform

Platform	Training Cost	Method / Notes	Model Storage

💰 Monthly Inference Cost + Annual TCO

Platform	Inference Rate	Monthly Inference	Monthly Hosting	Monthly Total	Annual TCO

Fine-Tuning Platform Pricing Traps

AWS Bedrock: Fine-tuned models require Provisioned Throughput (PTU) — a 24/7 hourly commitment with no scale-to-zero. At $7.10/hr for Titan Text Lite PTU (no commitment), the monthly floor is $5,183 before a single inference token. With 1-month commitment: $4,672/month. Even a 6-month commitment leaves you at $3,723/month. Bedrock fine-tuning is not for low-volume inference. It's for production workloads serving millions of tokens daily where the per-token PTU rate beats on-demand pricing.

Azure OpenAI: The training cost is modest ($5-25 for a typical fine-tuning run on GPT-4o-mini). The trap is hosting: $1.70/hour, 24/7, no idle discount. An idle deployment burns $1,224/month serving zero requests. Training cost is irrelevant — hosting is the entire cost decision.

Vertex AI: Gemini fine-tuning uses LoRA under the hood. Inference on tuned models runs at the base model's standard per-token rate with zero tuning surcharge and zero hosting fee. At $0.30/1M tokens input for Gemini Flash: 100M tokens/month inference = $30/month total. Vertex is the only platform where fine-tuning doesn't create a recurring cost obligation.

Self-hosted: QLoRA on an RTX 4090 via RunPod runs ~30 minutes for a 10K-sample 7B dataset at ~$0.50 total. Inference on the same GPU costs ~$0.39/hr. At 100M tokens/month with 8GB VRAM inference: $285/month GPU rental. No hosting fee, no minimum commitment, no surcharge. Self-hosted is cheaper than any managed platform at any inference volume — the trade is operational expertise.

📐 Fine-tuning Workload Profile

🎯 Fine-Tuning Method Comparison

📊 Training Cost — Per Platform

💰 Monthly Inference Cost + Annual TCO

Fine-Tuning Platform Pricing Traps

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