RAG vs Fine-Tuning · strategic decision tool
Should you fine-tune or build RAG?
The question every AI team asks. Head-to-head cost comparison with realistic workload assumptions.
Pricing verified: 2026-09-11
CFO + CTO decision tool
What this calculator does
At what query volume does fine-tuning a smaller model beat running RAG on a big one?
Why use it
- RAG has linear cost (cost scales with queries + context size)
- Fine-tuning has a fixed training cost + lower per-query cost — break-even point depends on volume
- Quality tradeoff is workload-specific — the calc models the cost side so you can reason about quality separately
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.
RAG vs Fine-tuning — which is cheaper, and which fits?
Queries scale both costs. Doc-change rate and domain decide which approach actually fits — the cheaper one isn’t always the right one.
Cheaper option
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Change the input sliders below to compare.
✓ Cheaper
RAG
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/ month
✓ Cheaper
Fine-tuning
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/ month
💡Cheaper now is pure cost. Best fit weighs your workload: frequently-changing docs favor RAG (always fresh); a narrow, stable domain favors fine-tuning (amortizes over time).
Fine-tuning and RAG solve different problems at different costs. Enter your volumes below to see which is cheaper for your case.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges) 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.
1
Enter key values
Queries/month, context size, base model (big, for RAG), FT target (smaller, to fine-tune).
2
Set FT training cost
Rough estimate: $2000-$20000 depending on data volume. Default is typical.
3
Read break-even
Shows the queries/month at which FT becomes cheaper than RAG. Also shows cost at current volume.
4
Act on it
Below break-even: stick with RAG. Above: FT is cheaper, but evaluate quality carefully before switching.
1
Enter RAG and FT sides
RAG side: queries/month, retrieved context tokens (top-K × chunk size), base model (the big one you're currently using). FT side: target model (smaller — Haiku 4.5, GPT-5-mini, or vendor-hosted FT-capable model), training dataset size, training cost estimate. Fine-tuning has narrowed in 2026 as several providers wind down their FT platforms — verify the FT API is still available for your target model before committing to a multi-week training cycle.
2
Model the FT economics
Fine-tuning has a one-time training cost (amortized) + lower per-query cost (no retrieval, smaller model). RAG has no training cost but higher per-query cost (big model + context tokens). Break-even = training cost / (RAG per-query - FT per-query). At 10K queries/mo with $5K training cost, break-even is ~6 months. At 1M queries/mo, break-even is days.
3
Interpret the cost curves
Plot shows RAG cost (flat slope) vs FT cost (y-intercept = training cost, lower slope). Intersection = break-even. Beyond intersection, FT is cheaper per query cumulatively. Before intersection, RAG is cheaper. The quality question is separate — FT can be equal, better, or worse quality depending on task. Always evaluate on held-out data before switching.
4
Make the decision
If you're above break-even AND your task has stable patterns (repeated questions, structured outputs), FT is the play. If your corpus changes often, stick with RAG — FT goes stale. Hybrid: use RAG for fresh content, FT for reliable workflows. For the cost side of RAG specifically, see RAG Pipeline Cost. For FT cost details, see Fine-Tuning Cost.
👇 Now try the calculator below with your own AI workloads
🎛 Your use case
We'll compute both paths with realistic assumptions for each.
Hint: Requests per day once it's live.
Hint: Words in a typical question.
Hint: Words in the answer.
📚 Your knowledge corpus
Hint: How many documents you'd search over (RAG side).
Hint: Length of a typical doc, in words.
Hint: The biggest factor. Often-changing content favors RAG; stable content favors fine-tuning.
⚙ Approach settings
Hint: The model that writes answers in the RAG setup.
Hint: The model that turns docs into searchable form (RAG side).
Hint: The provider and base model you'd fine-tune.
Hint: How much labeled training data you'd need to gather, in words.
Analyzing...
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RAG (yr 1): —
Fine-tune (yr 1): —
📚 RAG approach
Embedding + retrieval + base LLM
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year 1 total
🎓 Fine-tuning approach
Custom-trained smaller model
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year 1 total
💡 Recommendations
⚖ Side-by-side tradeoffs
Cost is only one dimension. These factor into the real decision.
| Dimension | 📚 RAG | 🎓 Fine-Tuning |
|---|---|---|
| Update cycle | Real-time (re-embed new docs) | Hours-to-days (re-train) |
| Data freshness | ✅ Always current | ⚠️ Frozen at training time |
| Citation / provenance | ✅ Native (returns source docs) | ❌ Black box |
| Style / tone learning | ⚠️ Limited (prompt engineering) | ✅ Strong (the main FT strength) |
| Domain vocabulary | ⚠️ Base LLM's knowledge | ✅ Learned from examples |
| Prompt length | Long (query + retrieved context) | Short (model knows context) |
| Latency | Retrieval + generation (200-500ms extra) | Faster (no retrieval) |
| Build time | Days (pipeline setup) | Weeks (label data + train + eval) |
| Hallucinations | ✅ Grounded in retrieved docs | ⚠️ Can hallucinate learned patterns |
| Multi-tenant safe | ✅ Per-user index isolation | ❌ Single model for all |