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: 2026-07-21 4 providers: OpenAI · Together · Fireworks · Anthropic
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

New to this calculator? Start with the ⚡ Playground — a few sliders, instant ballpark. Then switch to the 🧮 Calculator for your exact number.

Two ways to use this: visualize in the Playground, then get your number in the Calculator.

▶Playground A quick, visual way to see which factors move your result the most. Open the playground → ▤Calculator Enter your real workload for a precise result you can apply to your own usage. Go to the calculator →
Fine-Tuning Cost Playground
Drag the sliders to compare the options.
Fine-tune vs frontier API — which is cheaper to run?

A fine-tuned small model is cheap per query but costs upfront to train. It beats calling a frontier model only above enough volume.

Cheaper option
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Change the input sliders below to compare.

Frontier (no FT)
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/ month
Fine-tuned
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/ month

💡Cheaper depends on volume: fine-tuning amortizes training over months, so high, sustained query volume is what tips it past frontier API.

Fine-Tuning Cost Calculator

Enter your exact numbers for a precise result.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges; API fine-tuning as of Jul 2026: open-weight LoRA ~$0.48-0.50/1M training tokens (sub-16B tiers); hosted frontier-vendor tuning ~$0.20-3/1M for current nano-to-flagship tiers, with premium/legacy tiers up to $25/1M. Typical jobs run 3-5 epochs; 500-5,000 curated examples is the common dataset band. Live per-model rates: see the provider picker above.) so you can move forward now — your result gets more accurate as you replace them with your own measured numbers. Values marked * are rough estimates.
🎮 Interactive Guide & Calculator Playground →
Fine-tuning costs upfront but makes inference cheaper. Enter your volumes below to see whether it beats plain prompting for your case.
🎛 CALCULATOR
🎛 Your training setup

Sets dataset size, epochs, tokens, and volume. Pick your provider above; adjust any field after.

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

Hint: Open-weights vendors charge the same rate for fine-tuned and base models; OpenAI charges ~4x base.
Hint: Size of your training data in words. 1-10M is the sweet spot; under 100K rarely helps.
Hint: How many times it studies the data. 3-4 is normal; 10+ usually overfits.
📞 Inference usage (after training)
Hint: Words you send in per request. Fine-tuned models usually need shorter prompts.
Hint: Words it sends back per request.
Hint: Requests per month once it is live.

Results

📈 RESULTS
Year 1 total cost (training + inference)
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🏗
Training (one-time)
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📞
Monthly inference
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📅
Annual inference
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⚖ Break-even vs. base model (no fine-tune)
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💡 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

    Go deeper

    Our playbooks on cutting this number.

    🧮
    Base Model Cost
    Compare against the no-fine-tune path
    💾
    Prompt Cache ROI
    Often cheaper than fine-tuning
    🧬
    Embedding Cost
    RAG instead of fine-tuning?
    📖
    Prompt Caching Playbook
    The other big lever

    Need help using this calculator for your workloads?

    AICost.ai has 50+ calculators and playbooks. Schedule an AvatarVA meeting and we'll work through your real cost scenarios across AI & Cloud: visibility, cost reduction, optimization, forecasting and capacity planning, without sacrificing accuracy or performance.

    📅 Schedule an AvatarVA meeting →
    📖 Data sources & methodology 158 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-21

    Methodology

    • All prices are USD per 1 million tokens, current as of 2026-07-21.
    • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
    • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
    • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
    • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
    • Long-context pricing tiers apply when input exceeds model threshold.
    • Embedding prices are input-only (no output tokens generated).

    Primary sources

    Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

    Anthropic
    2026-07-21
    https://www.anthropic.com/pricing
    Daily snapshot since Sep 2023 · 624 days captured
    Anthropic Docs
    2026-07-21
    https://platform.claude.com/docs/en/about-claude/pricing
    Daily snapshot since Sep 2023 · 624 days captured
    OpenAI
    2026-07-21
    https://openai.com/api/pricing/
    Daily snapshot since Sep 2023 · 625 days captured
    Google AI
    2026-07-21
    https://ai.google.dev/gemini-api/docs/pricing
    Daily snapshot since Dec 2023 · 600 days captured
    Google Vertex
    2026-07-21
    https://cloud.google.com/vertex-ai/generative-ai/pricing
    Daily snapshot since Dec 2023 · 600 days captured
    DeepSeek
    2026-07-21
    https://api-docs.deepseek.com/quick_start/pricing
    Daily snapshot since May 2024 · 539 days captured
    xAI
    2026-07-21
    https://x.ai/api
    Daily snapshot since Nov 2024 · 457 days captured
    Mistral
    2026-07-21
    https://mistral.ai/pricing
    Daily snapshot since Dec 2023 · 598 days captured
    Cohere
    2026-07-21
    https://cohere.com/pricing
    Daily snapshot since Sep 2023 · 624 days captured
    Voyage AI
    2026-07-21
    https://docs.voyageai.com/docs/pricing

    Inferred values (marked with * in calculator tables)

    Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

    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.

    Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →