Fine-Tuning Cost

Is fine-tuning cheaper than prompting?

Training cost + ongoing inference cost + break-even volume vs. the base model. Before you kick off that 3-hour job.

Pricing verified: 2026-06-05 4 providers: OpenAI · Together · Fireworks · Anthropic

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What this calculator does

Cost of fine-tuning a model — training time + training tokens + hosting fine-tuned inference.

Why use it

FT has 3 distinct costs (training, storage, inference) — most estimates only cover one
Open-source FT (Llama on your hardware) vs vendor-hosted FT (OpenAI, Anthropic) is a 5-10x cost difference
See whether FT per-query savings ever pay back the training cost at your volume

Enter key values

Training dataset size, base model, expected queries/month post-deployment.

Pick hosting

Vendor-hosted (simplest, most expensive) vs self-hosted on GPU (cheapest at scale).

Read payback

Training cost vs monthly inference savings. Payback period in months.

Act on it

Payback > 6 months + stable workload = FT. Short payback + high volume = definitely FT. Long payback + changing data = stick with RAG.

Enter training details

Training dataset size = number of examples × avg tokens each. Vendor FT APIs charge per training token. Base model determines which FT APIs are available + the multiplier on inference rate (FT inference typically runs 2-5× base list price). Training epochs matter — 3-5 passes is typical. Evaluate data quality carefully; bad data × 3 epochs just bakes bad data in deeper. Note: several vendors are reducing FT support in 2026 — confirm availability before committing.

Choose hosting model

Vendor-hosted FT (OpenAI, Mistral): training is a single API call; inference runs on their infra at an elevated rate vs base. Self-hosted FT (Llama-3 on vLLM): you pay GPU hours for training + GPU hours for serving — much cheaper at scale but you operate the cluster. LoRA/QLoRA adapters are a middle ground: cheap to train, can be swapped per-request.

Interpret the 3-cost breakdown

Training cost (one-time, amortize over expected model lifetime). Storage/maintenance (negligible for vendor; real for self-host). Inference cost (recurring, scales with queries). The payback curve shows cumulative cost for FT vs not-FT (baseline) — intersection is payback point.

Ship the decision

FT makes sense when: task is stable (you won't need to retrain every month), volume is high (amortizes training cost fast), and quality evaluation shows FT matches or beats base+prompting. For variable tasks, skip FT and use a multi-model router instead (Multi-Model Router). Compare RAG alternative at RAG vs Fine-Tune.

📊 Calculator at a glance

🎛 CALCULATOR

🎛 Your training setup

Training is a one-time cost. Inference is ongoing. We break out both.

Fine-tuning provider + base model Open-weights vendors (Together/Fireworks) charge the same rate for FT and base inference. OpenAI charges 4x base for FT inference. Verified 2026-04-25.

Training dataset size (tokens) Typical: 10K-10M tokens. Small datasets (under 100K) often don't help. 1-10M is the sweet spot.

Epochs 3 Default 3-4 epochs. More epochs = more training cost. 10+ is usually overfitting.

📞 Inference usage (after training)

Input tokens / request Fine-tuned models have shorter prompts - less few-shot, no massive system prompt.

Output tokens / request

Requests / month

📈 RESULTS

Year 1 total cost (training + inference)

🏗

Training (one-time)

📞

Monthly inference

📅

Annual inference

⚖ Break-even vs. base model (no fine-tune)

💡 Should you fine-tune?

📊 Fine-tune vs alternatives at your volume

Year 1 total cost across providers + "skip fine-tuning" paths.

Approach	Training cost	Monthly inference	Year 1 total	3-year total

Compare API costs → Try caching instead → ML engineering audit →

🎯 Use this result to

🎯 Decide if fine-tuning pays — Training is one-time. Inference is forever. Numbers show when FT makes sense.
📅 Know your break-even month — Frontier prompt vs FT model. See exactly when the FT investment pays back.
🆚 Compare provider FT pricing — OpenAI, Anthropic, Google, Mistral FT prices vary widely. Pick right host.
🔌 Integrate with your AI agents — MCP available for agentic workflow integration. FT-aware model selection.

📅 Schedule a call to apply this to your workload

Vendor / Model

Field

Why it’s inferred

Anthropic — Claude Sonnet 4.6

cachedInput

Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.

Anthropic — Claude Sonnet 4.5

cachedInput

Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.

Anthropic — Claude Sonnet 4.5

batchInput

Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.

Anthropic — Claude Sonnet 4.5

batchOutput

Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.

Anthropic — Claude Haiku 4.5

cachedInput

Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.

OpenAI — GPT-5.4 Mini

cachedInput

Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.

OpenAI — GPT-5.4 Nano

cachedInput

Derived at 10% of input — OpenAI 90% cache-hit convention.

OpenAI — GPT-5.4 Nano

batchInput

Derived at 50% of input — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Nano

batchOutput

Derived at 50% of output — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Pro

cachedInput

Derived at 10% of input — OpenAI 90% cache-hit convention.

OpenAI — GPT-5.4 Pro

batchInput

Derived at 50% of input — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Pro

batchOutput

Derived at 50% of output — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.2

cachedInput

Derived at 10% of input; no residency uplift.

OpenAI — GPT-5.2

batchInput

Derived at 50% of input.

OpenAI — GPT-5.2

batchOutput

Derived at 50% of output.

OpenAI — GPT-5

cachedInput

Derived at 10% of input.

OpenAI — GPT-5

batchInput

Derived at 50% of input.

OpenAI — GPT-5

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.5 Pro

cachedInput

Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.

OpenAI — GPT-5.5 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5.5 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.2 Pro

cachedInput

Derived at 10% of input — pro-tier convention.

OpenAI — GPT-5.2 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5.2 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.1

batchInput

Derived at 50% of input.

OpenAI — GPT-5.1

batchOutput

Derived at 50% of output.

OpenAI — GPT-5 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5 Nano

cachedInput

Derived at 10% of input.

OpenAI — GPT-5 Nano

batchInput

Derived at 50% of input.

OpenAI — GPT-5 Nano

batchOutput

Derived at 50% of output.

Google — Gemini 3 Flash

cachedInput

Derived at 10% of input — Google caching discount convention ~90%.

Google — Gemini 3.1 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 3.1 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 3.1 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

Google — Gemini 2.5 Pro

cachedInput

Derived at 10% of input.

Google — Gemini 2.5 Flash

cachedInput

Derived at 10% of input.

Google — Gemini 2.5 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 2.5 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 2.5 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

Google — Gemini 2.0 Flash

cachedInput

Derived at 25% of input per Google 2.0 family caching rates.

Google — Gemini 2.0 Flash

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 2.0 Flash

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

Google — Gemini 2.0 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 2.0 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 2.0 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

xAI — Grok 4 (legacy)

cachedInput

Extrapolated at 25% of base.

Is fine-tuning cheaper than prompting?

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Methodology

Primary sources

Inferred values (marked with * in calculator tables)