RAG bill vs fine-tune setup
Sample
Set embedding model, vector DB base cost, and fine-tune training hours.
What you get
Compares steady-state RAG operating cost against a one-off fine-tune project estimate.
RAG vs Fine-Tuning Cost Comparison Calculator
Should you build a RAG pipeline or fine-tune a model? Enter knowledge-base size, query volume, and LLM $/1M rates. Pull current list prices from the GPT / Claude / Gemini calculators (OpenRouter-backed), then paste rates here so the comparison stays current as models change.
RAG vs Fine-Tuning Cost Comparison Calculator
RAG Pipeline
Embedding$0.26
Vector DB$70.00/mo
LLM inference$315.00/mo
Monthly Total$385.26
Fine-Tuning
Training (one-time)$50.00
Inference$103.50/mo
Monthly (amortized)$107.67
How to Use This Tool
- Enter your knowledge base size (documents × average length).
- Set expected query volume (requests per day).
- Tune embedding, vector DB, and LLM $/1M rates to match your stack.
- Compare monthly RAG vs fine-tuning style costs.
Features
- Full RAG cost stack: embedding, vector DB, retrieval, generation
- Fine-tuning style training + inference estimate
- Editable rates so new models stay comparable
- Break-even style monthly view
Example
Frequently Asked Questions
When is RAG cheaper than fine-tuning?
RAG is typically cheaper when your knowledge base changes often or document counts stay modest. Fine-tuning can win for stable domains with very high query volume — plug in your real rates to verify.
Can I combine RAG and fine-tuning?
Yes. Many teams fine-tune for style/domain and use RAG for fresh facts. Adjust the rate fields to model a hybrid stack.
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