AITOT

Kalkulator

Kalkulator Biaya Fine-tuning LLM

Hitung biaya fine-tuning — token training × tarif per juta, plus uplift per token untuk inference model custom.

Harga diperbarui:

AITOT LLM Fine-tuning Cost calculator memperkirakan biaya training + inference uplift untuk model fine-tuned di OpenAI (GPT-4o, GPT-4o-mini, o3), Anthropic Claude (invite-only), Google Vertex (Gemini), Together AI (LoRA untuk Llama 4, Qwen, Mistral).

Training cost = training tokens × epochs × per-million rate. OpenAI GPT-4o-mini: $3/M training tokens. Together Llama 4 70B LoRA: $1.20/M. Mayoritas fine-tunes produksi $50-$500 one-time.

Toggle epochs (default 3) dan volume inference. Di bawah 10M token/bulan, fine-tuning jarang kalahkan prompts. Di atas 100M, fine-tuned smaller model kalahkan larger model dengan prompts 3-10×.

Total tahun 1 · termurah

Fireworks · Llama 4 8B

$248

ProviderModel dasarBiaya trainingInference bulananTotal tahun 1
FireworksLlama 4 8B

≤16B LoRA SFT tier

$8$20$248
CohereCommand R$30$48$606
OpenAIGPT-4o mini

Stale — OpenAI moved to per-hour training 2026-05; verify pending

$45$48$621
MistralMistral Small 3

$2/mo hosting per deployed adapter

$45$58$741
FireworksLlama 4 70B

16-80B LoRA SFT tier

$45$90$1,125
TogetherLlama 3.3 70B

Legacy v3 line; verify pending 2026-05-18 — no longer top-listed on Together pricing

$75$88$1,131
OpenAIGPT-5 mini

Stale — OpenAI moved to per-hour training 2026-05; verify pending

$60$96$1,212
TogetherLlama 4 Maverick (LoRA SFT)

$16 minimum charge; Maverick = ~70B-class

$120$120$1,560
OpenAIo3-mini

Stale — OpenAI moved to per-hour training 2026-05; verify pending

$75$136$1,707
TogetherLlama 4 Maverick (LoRA DPO)$300$120$1,740
AWS BedrockClaude Haiku 4.5 (custom)

Provisioned throughput required

$120$303$3,756
MistralMistral Large 2$135$564$6,903
OpenAIGPT-4o

Stale — OpenAI moved to per-hour training 2026-05; verify pending

$375$600$7,575

Biaya training = token × epoch × tarif per juta. Inference memakai tarif fine-tuned yang selalu lebih tinggi dari model dasar. Total tahun 1 = training sekali + 12 bulan inference.

Yang dilakukan kalkulator ini

Multi-provider

OpenAI fine-tuning, Together LoRA, Vertex tuning, self-host estimates.

Training + inference split

Training cost one-time dipisah dari uplift bulanan.

Slider epochs

Default 3 epochs.

Modeling inference uplift

Fine-tuned biaya 1.5-3× base.

Total tahun-1

Training one-time + 12 bulan inference = satu angka budget.

LoRA vs full fine-tuning

LoRA Together 10× lebih murah dari full FT OpenAI.

Perbandingan cepat

Fine-tuning 5M training tokens, 50M inference/bulan, 3 epochs

ProviderTraining CostInference UpliftTotal Tahun-1
Together Llama 4 70B (LoRA)$18+$50/bulan$618
OpenAI GPT-4o-mini$45+$120/bulan$1,485
Google Gemini 2.5 Flash tune$75+$150/bulan$1,875
OpenAI GPT-4o$375+$1,200/bulan$14,775
OpenAI o3$2,250+$3,500/bulan$44,250

Tahun-1 = training + 12 × uplift bulanan.

Cara menggunakan kalkulator

Hitung training + inference uplift cost untuk LLMs fine-tuned.

  1. 1

    Masukkan training tokens

    Total token di dataset training. 100 examples × 500 tokens = 50k.

  2. 2

    Set epochs

    Default 3. Lebih dari 4 biasanya overfit.

  3. 3

    Perkirakan inference bulanan

    Berapa token fine-tuned model akan serve/bulan.

  4. 4

    Bandingkan provider

    LoRA Together termurah; OpenAI full FT termahal.

Kenapa pakai kalkulator ini

  • 5 provider diperbarui bulanan
  • Training + inference split
  • Perbandingan LoRA vs full FT
  • Angka budget tahun-1
  • Modeling epoch + token
  • Tanpa login

Pertanyaan yang sering diajukan

Berapa biaya fine-tune LLM 2026?+
Biaya training: 1M token × rate per juta. OpenAI GPT-4o-mini fine-tuning: $3/M token training. Anthropic Claude Haiku fine-tuning (terbatas): $5/M. Together AI Llama 4 70B LoRA: $1.20/M. Mayoritas fine-tune produksi $50–$500.
Berapa inference uplift model fine-tuned?+
Fine-tuned biaya 1.5–3× per token vs base di inference. OpenAI GPT-4o-mini base: $0.15/M input. Fine-tuned: $0.30/M input. Rencanakan — fine-tune volume tinggi hemat hanya jika juga pindah ke class model lebih kecil.
Kapan fine-tuning hemat vs prompt engineering?+
Break-even sekitar 10M token bulan. Di bawah, fine-tuning jarang kalahkan prompt few-shot well-crafted. Di atas 100M dengan task definisi stabil, fine-tune model kecil sering kalahkan model besar dengan prompt 3–10× total cost.
Berapa epoch sebaiknya fine-tune?+
Default 3 epoch untuk data instruction-style dan 1–2 untuk data completion. Lebih dari 4 epoch biasanya overfit. Kalkulator kali token training × epoch untuk total billable — bump kecil di epoch tambah biaya signifikan.
Bisakah fine-tune Claude atau hanya OpenAI?+
OpenAI: fine-tuning GPT-4o, GPT-4o-mini, dan o3 GA. Anthropic Claude invite-only 2026. Google Vertex tawarkan Gemini tuning. Together AI tawarkan LoRA fine-tuning untuk semua open-weight major. Self-host Axolotl + Modal jalur termurah open weight.
Berapa banyak data training untuk fine-tune efektif?+
50–500 contoh kualitas tinggi untuk adaptasi style/format. 1,000–10,000 untuk domain knowledge. Di atas 10,000 contoh, gain plateau. Kualitas kalahkan kuantitas — 100 contoh curated tangan sering melewati 5,000 noisy. Token count untuk billing, bukan kualitas.