Why GPT-4o-mini and not Claude?

Anthropic doesn't expose fine-tuning publicly in 2026 — Claude models can't be fine-tuned outside enterprise contracts. OpenAI is the default fine-tune target for this calculator's math. If you're committed to Claude, RAG is your only path, and the calculator's RAG side still gives you the cost ceiling: substitute Sonnet's $3/$15 rate for GPT-4o-mini's $0.15/$0.60 to model the Claude-RAG case (pricing snapshot in the source).

What does the math actually compute?

Fine-tune total = (corpus tokens × epochs × $3/1M training rate) + (queries/day × 30 × per-query cost × horizon). RAG total = (corpus tokens × $0.02/1M embedding rate, one-time) + (queries/day × 30 × per-query cost + vector-DB monthly) × horizon. Per-query cost differs because fine-tuned inference uses a 2× surcharge but no retrieved context, while RAG inference uses base rates but pays for the retrieved tokens on every query.

When does fine-tuning win?

When (a) corpus is small enough that training cost amortises quickly, (b) query volume is high enough that the per-query token savings (no retrieved context = ~$0.50-2 saved per 1k queries on GPT-4o-mini) compound, and (c) horizon is long enough to recoup training. The break-even month line in the result tells you exactly when. If break-even lands beyond your horizon, RAG wins for this project.

When (a) corpus is large or changes frequently — RAG can update by re-embedding new documents while fine-tuning requires a full retrain, (b) query volume is low — under ~1k queries/day, RAG's vector DB monthly fee matters less than fine-tune's training cost, or (c) you need source citations — RAG returns the retrieved chunks; fine-tuning hides them inside the model weights.

What's the volume crossover I see in the result?

When RAG wins at your current volume, the calculator estimates the queries-per-day at which fine-tuning would catch up (steady-state). Above that volume, fine-tuning's per-query savings overwhelm its training cost. It's a useful negotiating number — 'we'd switch if we hit X queries/day' — and a planning anchor for capacity work.

Why does RAG charge for retrieved tokens at every query?

Because every RAG query stuffs the retrieved chunks into the LLM input. Even though embedding lookup is cheap (~$0.0002 per query), the LLM input on a 3000-retrieved-token RAG query is ~6× the input of a fine-tuned-model query at the same base prompt size. At scale that token surcharge is the dominant cost.

Can I use fine-tuning AND RAG together?

Yes — and many production teams do. Fine-tune to teach the style and domain vocabulary; RAG to inject the latest facts. The cost adds up: training (one-time) + retrieval inference (every query) + fine-tuned inference surcharge. This calculator doesn't model the hybrid path; for that, run both sides separately and add.

Is the vector DB cost realistic?

$70/month is the Pinecone Standard list price as of Jan 2026 — supports up to 5M vectors, 1 pod, with reasonable burst headroom. Weaviate Cloud Sandbox starts at free, Standard at ~$25, Production at ~$100+. Self-hosting pgvector on a dedicated EC2 t3.large is ~$60/month + ~10% engineer time for ops. Use the input field to plug in your actual quote.

What about embedding refresh costs?

The calculator embeds the corpus once at the start (~$0.02 per 1M tokens with text-embedding-3-small). For corpora that change frequently, add a monthly delta — e.g., 5% corpus turnover at 5M tokens = 250K tokens × $0.02/1M = $0.005/month, basically a rounding error. The bigger cost is the engineer time to maintain the ingest pipeline; the calculator doesn't model that.

Does this account for fine-tune evaluation costs?

Not directly. Fine-tuning typically requires a held-out eval set (~5-10% of your corpus tokens) and you'll spend $50-500 on eval API calls during the training-tuning loop. Add that to the fine-tune total when your corpus is < 500K tokens; for larger corpora the eval cost is < 1% of training and rounds away. The calculator's training-cost line is a lower bound.

What changes when GPT-5 or successor models ship?

Two things. (1) Per-token pricing typically drops with each generation — GPT-4o-mini was 30× cheaper than GPT-3.5-turbo at launch. The calculator updates the rate card quarterly. (2) Larger context windows reduce RAG's retrieval-tokens disadvantage — at 1M-token context, you can stuff more without splitting, so RAG queries get cheaper relative to fine-tuned ones. The cross-over volume shifts accordingly.

Why doesn't this match the OpenAI cost estimator on the docs page?

OpenAI's estimator only models inference cost on a per-prompt basis — it doesn't compare fine-tuned vs base, doesn't include training, doesn't include vector DB. This calculator gives you the full architecture-decision picture across both paths, with the break-even month so you can defend the choice in a budget review.

AI & TechFree · No signup · 85K+/month

Fine-tune vs RAG Calculator — Real Cost of the 2026 ML Architecture Decision

Should you fine-tune GPT-4o-mini on your corpus or run RAG with a vector DB? Plug in your corpus size, query volume, and target horizon. The calculator returns the full economics — training cost, monthly inference, vector DB hosting, break-even month, and the volume at which the other answer takes over.

Instant result
Private — nothing saved
Works on any device
AI insight included

Reviewed by CalcBold EditorialLast verified May 4, 2026Methodology

Corpus size

Total tokens in the knowledge base you want the model to know. 5M ≈ 4,000-page documentation site; 50M ≈ a mid-size enterprise corpus.

Queries per day

Production query volume. 1k = pilot, 10k = early B2B SaaS, 100k+ = consumer scale.

Base input tokens / query

System prompt + user message, EXCLUDING retrieved context. Typical 200-1500.

Output tokens / query

Avg response length. Typical 200-1500 for chat / Q&A workloads.

Retrieved tokens / query (RAG)

Avg context retrieved per query in the RAG path. Typical 1500-6000 for top-k=5 with 500-token chunks.

Fine-tune epochs

How many passes through the corpus during training. OpenAI default is 3; data-rich workloads can stop at 1-2.

Vector DB monthly cost

Pinecone Standard ≈ $70/mo, Weaviate Cloud ≈ $25-100, self-hosted Postgres pgvector ≈ $50-200 once you account for instance + ops time.

Horizon

Amortisation window. 12 months matches typical project budget cycles; 36 months is more honest for stable deployments.

Embed builderDrop the FT vs RAG on your site →Free widget · 3 sizes · custom theme · auto-resizes · no signupGet embed code

What This Calculator Does

The Fine-tune vs RAG Calculator decides the most-asked ML architecture question of 2026: should you fine-tune a base model on your knowledge corpus (one-time training cost + slightly higher per-query rate) or run RAG with a vector database (per-query embedding lookup + larger retrieved-context input)? Plug in corpus size, daily query volume, base prompt size, retrieval size, fine-tune epochs, vector DB cost, and horizon — the calculator returns total cost on each side, the steady-state monthly burn, and the break-even month when fine-tuning catches up with RAG.

The math is OpenAI-anchored — Anthropic doesn't expose public fine-tuning in 2026 — using GPT-4o-mini ($0.15 / $0.60 per 1M base, $0.30 / $1.20 fine-tuned, $3 / 1M training tokens) and text-embedding-3-small ($0.02 / 1M). Pinecone Standard ($70/mo) is the default vector DB; the input field accepts your actual quote.

The Math

Per-query cost differs because fine-tuned inference uses a 2× surcharge but no retrieved context; RAG inference uses base rates but pays for the retrieved tokens on every query. At low retrieval sizes RAG wins; as retrieval grows past ~3K tokens the fine-tune advantage compounds with volume.

A Worked Example

A 5M-token documentation corpus (~4,000 pages), 5,000 queries / day, 500 base input tokens, 400 output tokens, 3,000 retrieved tokens per RAG query, 3 fine-tune epochs, $70/mo Pinecone, 12-month horizon:

Fine-tune training — 5M × 3 × $3 / 1M = $45
Fine-tune per query — (500 × $0.30 + 400 × $1.20) / 1M ≈ $0.000630
Fine-tune monthly — $0.000630 × 5,000 × 30 ≈ $94
Fine-tune total (12mo) — $45 + $94 × 12 ≈ $1,176
RAG embedding (one-time) — 5M × $0.02 / 1M = $0.10
RAG per query — (3,500 × $0.15 + 400 × $0.60) / 1M ≈ $0.000765
RAG monthly — $0.000765 × 5,000 × 30 + $70 ≈ $185
RAG total (12mo) — $0.10 + $185 × 12 ≈ $2,224

Fine-tune wins by ~$1,000 over 12 months. Break-even lands at $45 / ($185 − $94) ≈ 0.5 months — the fine-tune training cost pays back almost immediately because the per-query retrieved-context surcharge dominates RAG's monthly burn. Drop volume to 500 queries/day and the answer flips: RAG wins because the vector DB monthly fee amortises across more queries while fine-tune's per-query advantage shrinks.

When This Is Useful

Use this calculator at the architecture-decision stage, before you've written embedding code or kicked off a fine-tune. It's also useful for retroactively defending a choice in a budget review — “here's our crossover volume; we'll switch when we hit it” is a defendable answer. The calculator's break-even-month line is the single most useful number; if it lands beyond your horizon, RAG wins for this project.

Common Mistakes

Forgetting that RAG charges retrieved tokens on EVERY query.A 3,000-token retrieval × 5,000 queries/day × 30 days = 450M extra input tokens / month. That's the whole story of why RAG gets expensive at high volume. Fine-tuned inference doesn't pay this surcharge because the model has memorised the corpus.
Picking too many epochs.OpenAI's default is 3, but data-rich corpora can converge at 1-2 epochs and over-epoching causes overfitting. Each extra epoch is a full pass through the corpus at $3/1M — drop from 3 to 2 on a 50M corpus and you save $50 in training, which is meaningful at small scale.
Ignoring the corpus-volatility cost. Fine-tune assumes a stable corpus. If your knowledge base changes weekly, you're re-training weekly — and that recurring training cost isn't modelled here. RAG handles updates by re-embedding the delta (~$0.005/month for 5% turnover on 5M tokens), basically free. Pick RAG for fast-moving corpora.
Forgetting that fine-tuning hides citations. RAG returns the retrieved chunks alongside the answer, which is mandatory for many regulated workflows (compliance, legal, healthcare). Fine-tuning bakes the knowledge into the weights — the model gives a fluent answer but can't cite a source. The cost calculation might say fine-tune wins; the citation requirement might force RAG anyway.
Comparing fine-tune training to RAG embedding alone.Embedding is ~$0.02 per 1M tokens — almost free. The real RAG cost is the per-query retrieved-token surcharge (priced at base inference rates) and the vector DB monthly fee. Don't make the apples-to-apples comparison “training vs embedding” — make it on the full per-query and steady-state monthly figures.
Forgetting fine-tune evaluation costs. Fine-tuning typically requires a held-out eval set (~5-10% of corpus tokens) and you'll spend $50-500 on eval API calls during the training-tuning loop. For corpora < 500K tokens this is meaningful; for larger corpora it rounds away. The calculator's training line is a lower bound — bake in eval cost separately on small corpora.

Related Calculators

For raw per-query API cost on the model you'd deploy, run the API Token Cost Calculator. For full project TCO with engineering and ops overhead, the AI Model Cost Calculator adds the integration and observability layers on top. To check whether your prompt + retrieved context fits the model's context window, use the Token Context Window Calculator. And for the self-host alternative entirely, the Self-host vs API Calculator sits next door.

Frequently Asked Questions

The most common questions we get about this calculator — each answer is kept under 60 words so you can scan.

Why GPT-4o-mini and not Claude?
Anthropic doesn't expose fine-tuning publicly in 2026 — Claude models can't be fine-tuned outside enterprise contracts. OpenAI is the default fine-tune target for this calculator's math. If you're committed to Claude, RAG is your only path, and the calculator's RAG side still gives you the cost ceiling: substitute Sonnet's $3/$15 rate for GPT-4o-mini's $0.15/$0.60 to model the Claude-RAG case (pricing snapshot in the source).
What does the math actually compute?
Fine-tune total = (corpus tokens × epochs × $3/1M training rate) + (queries/day × 30 × per-query cost × horizon). RAG total = (corpus tokens × $0.02/1M embedding rate, one-time) + (queries/day × 30 × per-query cost + vector-DB monthly) × horizon. Per-query cost differs because fine-tuned inference uses a 2× surcharge but no retrieved context, while RAG inference uses base rates but pays for the retrieved tokens on every query.
When does fine-tuning win?
When (a) corpus is small enough that training cost amortises quickly, (b) query volume is high enough that the per-query token savings (no retrieved context = ~$0.50-2 saved per 1k queries on GPT-4o-mini) compound, and (c) horizon is long enough to recoup training. The break-even month line in the result tells you exactly when. If break-even lands beyond your horizon, RAG wins for this project.
When does RAG win?
When (a) corpus is large or changes frequently — RAG can update by re-embedding new documents while fine-tuning requires a full retrain, (b) query volume is low — under ~1k queries/day, RAG's vector DB monthly fee matters less than fine-tune's training cost, or (c) you need source citations — RAG returns the retrieved chunks; fine-tuning hides them inside the model weights.
What's the volume crossover I see in the result?
When RAG wins at your current volume, the calculator estimates the queries-per-day at which fine-tuning would catch up (steady-state). Above that volume, fine-tuning's per-query savings overwhelm its training cost. It's a useful negotiating number — 'we'd switch if we hit X queries/day' — and a planning anchor for capacity work.
Why does RAG charge for retrieved tokens at every query?
Because every RAG query stuffs the retrieved chunks into the LLM input. Even though embedding lookup is cheap (~$0.0002 per query), the LLM input on a 3000-retrieved-token RAG query is ~6× the input of a fine-tuned-model query at the same base prompt size. At scale that token surcharge is the dominant cost.
Can I use fine-tuning AND RAG together?
Yes — and many production teams do. Fine-tune to teach the style and domain vocabulary; RAG to inject the latest facts. The cost adds up: training (one-time) + retrieval inference (every query) + fine-tuned inference surcharge. This calculator doesn't model the hybrid path; for that, run both sides separately and add.
Is the vector DB cost realistic?
$70/month is the Pinecone Standard list price as of Jan 2026 — supports up to 5M vectors, 1 pod, with reasonable burst headroom. Weaviate Cloud Sandbox starts at free, Standard at ~$25, Production at ~$100+. Self-hosting pgvector on a dedicated EC2 t3.large is ~$60/month + ~10% engineer time for ops. Use the input field to plug in your actual quote.
What about embedding refresh costs?
The calculator embeds the corpus once at the start (~$0.02 per 1M tokens with text-embedding-3-small). For corpora that change frequently, add a monthly delta — e.g., 5% corpus turnover at 5M tokens = 250K tokens × $0.02/1M = $0.005/month, basically a rounding error. The bigger cost is the engineer time to maintain the ingest pipeline; the calculator doesn't model that.
Does this account for fine-tune evaluation costs?
Not directly. Fine-tuning typically requires a held-out eval set (~5-10% of your corpus tokens) and you'll spend $50-500 on eval API calls during the training-tuning loop. Add that to the fine-tune total when your corpus is < 500K tokens; for larger corpora the eval cost is < 1% of training and rounds away. The calculator's training-cost line is a lower bound.
What changes when GPT-5 or successor models ship?
Two things. (1) Per-token pricing typically drops with each generation — GPT-4o-mini was 30× cheaper than GPT-3.5-turbo at launch. The calculator updates the rate card quarterly. (2) Larger context windows reduce RAG's retrieval-tokens disadvantage — at 1M-token context, you can stuff more without splitting, so RAG queries get cheaper relative to fine-tuned ones. The cross-over volume shifts accordingly.
Why doesn't this match the OpenAI cost estimator on the docs page?
OpenAI's estimator only models inference cost on a per-prompt basis — it doesn't compare fine-tuned vs base, doesn't include training, doesn't include vector DB. This calculator gives you the full architecture-decision picture across both paths, with the break-even month so you can defend the choice in a budget review.