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LLM Fine-Tuning Cost Calculator

Estimate training and hosting cost to fine-tune an LLM across managed APIs and local/cloud GPU routes.

Scenario presets

Training & usage workload

Training examples Tokens per example Epochs Monthly usage requests Usage input tokens / request Usage output tokens / request Managed provider Local GPU route Electricity rate ($/kWh) Local GPU utilization (%) Projection (months)

Estimates are directional. Last updated: 2026-07-08. See notes.

Total training tokens

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Managed training cost

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Monthly managed usage

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First-period managed total

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Local training cost

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Local GPU period total

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Cheapest managed provider

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Training hours on local GPU

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Cost per 1k usage requests

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Break-even vs cheapest managed

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Managed provider comparison

Provider	Training cost	Monthly usage	Period total	Note

Verdict

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Frequently asked questions

What does this calculator estimate?

It estimates the one-time training cost and ongoing usage/hosting cost to fine-tune a large language model on a managed API or a local/cloud GPU.

Which is cheaper: managed API or local GPU?

Managed APIs win for small, infrequent jobs and low monthly usage. Local GPU becomes cheaper when you train repeatedly or serve many requests, but only if utilization is high enough to amortize hardware or rental cost.

Does training output cost apply?

Most providers only charge for training input tokens. We model usage input/output separately after the model is hosted.

How is local GPU cost calculated?

We divide total training tokens by the GPU's estimated training tokens per hour to get rental hours, then multiply by the hourly rental rate. We add electricity only if you select owned hardware; otherwise the cloud hourly rate already covers power.

What about data preparation and labeling?

Those costs are excluded. Use the AI Agent Data Labeling Cost Calculator and Synthetic Data Generator Cost Calculator to budget training data creation.

How do I reduce fine-tuning cost?

Shorten examples, reduce epochs, use a smaller base model, cache repeated context, batch requests, and prefer lower-cost providers like GPT-4o mini or Gemini Flash for narrow tasks.

API Pricing Comparator → Prompt Cache Savings Calc → Local LLM ROI Calc → Synthetic Data Cost Calc →

Rates are approximate public list prices and exclude volume discounts, data preparation, and egress. Local GPU estimates assume rented cloud GPU instances; self-owned hardware TCO is shown separately.