RAG vs fine-tuning is rarely either/or. We measured both across 35 production builds — here's the actual cost reality.
Methodology
35 production AI deployments — 18 RAG-only, 9 fine-tune-only, 8 hybrid. Measured build cost (engineering hours × rate), monthly run cost (LLM + vector DB + retrieval infra), and 24-month TCO assuming median traffic growth.
Build cost
- RAG: median $14K (range $4K-$60K)
- Fine-tuning: median $22K (range $8K-$95K)
- Hybrid: median $28K
Monthly run cost (per 100K queries)
- RAG: $480 (most cost = retrieval + LLM tokens)
- Fine-tuned model API: $290 (lower per-token but full prompts every call)
- Self-hosted fine-tuned: $190 amortized (requires real MLOps)
- Hybrid: $410
24-month TCO
- RAG: $25K (low traffic) → $180K (high traffic)
- Fine-tuning: $30K → $130K
- Hybrid: $42K → $175K
When to use each
- RAG when: knowledge changes frequently, sources need citation, query patterns are unpredictable
- Fine-tuning when: knowledge is stable, format/style is the goal, query volume is high enough to amortize the build cost
- Hybrid when: you need both stable behavior + fresh knowledge (which is most production systems)
Want help deciding for your use case? Book a 30-minute call or read our RAG vs fine-tuning guide.
Cite as: Creative Genius (2026). RAG vs Fine-Tuning Cost Comparison 2026. Retrieved from creativegenius.ai/research/rag-vs-fine-tuning-cost-comparison-2026