RAG vs fine-tuning: which gives better ROI?
Is it worth it? To compare RAG and fine-tuning on ROI, you have to weigh what the AI costs against what it saves or earns. This page helps teams adapting models to their data put both sides on the same page and see the payback, not just the invoice.
Start here: RAG Cost Calculator
Results update automatically as you type.
- Total chunks
- 2,000
- Ingestion (one-time)
- $0.0205
- Vector storage
- 0.01 GB
- Storage / month
- $0.00343
- Per query
- $0.0113
- Queries / month(1,000/day)
- $337.52
- Total / month
- $337.53
Then: Fine-Tuning Cost Calculator
Results update automatically as you type.
- • Fine-tuned models also bill higher per-token inference rates than the base model.
- Training tokens
- 2,000,000
- Epochs
- 3
- Training price / 1M
- $8.00
- Total training cost
- $48.00
Why this isn't trivial
The part people underestimate: RAG adds per-query retrieval cost but stays current, while fine-tuning has upfront cost and goes stale as data changes. In practice the biggest savings come from using RAG for changing knowledge and fine-tuning for stable style/format, so it is worth modelling before you commit.
How it's calculated
We estimate this by comparing RAG's ongoing per-query cost with fine-tuning's upfront plus leaner per-request cost. Every figure uses the current provider prices baked into the site (reviewed daily), and you can override any input to match your own assumptions.
Frequently asked questions
When is RAG the better ROI?+
When your knowledge changes often — no re-training needed.
When does fine-tuning win?+
For stable behavior at high volume, where shorter prompts repay training cost.
Are these prices up to date?+
Yes. The model prices behind this calculator are refreshed and reviewed daily, so your estimate reflects current provider rates rather than a stale snapshot.
Related
Estimates for planning. Pricing data last reviewed 28 July 2026.