RAG vs Fine-Tune Calculator
Side-by-side monthly cost: RAG (embeddings + vector DB + retrieval) vs fine-tuning.
Quick Answer
RAG wins below ~1M monthly calls when context is dynamic. Fine-tuning wins when the same compressed knowledge serves millions of calls and you can drop a large system prompt. The two aren't mutually exclusive — most production systems combine them.
RAG inputs
Fine-tune inputs
Shared
RAG (Sonnet 4.6 + Weaviate + 3-small)
Fine-tune (GPT-4o-mini)
About This Tool
The RAG vs Fine-Tune Calculator stacks the full monthly cost of both architectures so you can pick the right one for your workload. RAG cost includes embedding generation, vector database storage and queries, and LLM inference with retrieved context inflating the prompt. Fine-tuning cost includes amortized training plus inference at the bumped rate.
RAG cost structure
Four components: embeddings (one-time at $0.02/M for 3-small, plus 5% monthly churn), vector DB storage and queries (Weaviate-style at ~$25/M vectors + $0.095/M queries), LLM input cost inflated by retrieved context (typically 2-5K extra tokens per call), and LLM output. The LLM input portion dominates at high request volume.
Fine-tune cost structure
Three components: one-time training amortized over 12 months, inference input at 2x base (and shorter — fine-tunes don't need long system prompts), and inference output at 2x base. The inference markup is the killer — it lasts forever and compounds with every call.
The break-even logic
RAG wins when retrieved context is dynamic (knowledge bases, customer data, news), when corpus volume is large (10K+ docs), or when call volume is moderate (under 1M/month). Fine-tuning wins when the same compressed pattern repeats across very high call volumes and you can replace a 5K+ token system prompt with model weights. Above ~5M calls/month with stable instructions, fine-tuning often wins.
The hybrid pattern
Most production AI systems use both. Fine-tune for tone, structure, and domain language. RAG for facts, recent events, and customer-specific data. Cost is additive, but the quality combination is unmatched. Companies like Harvey, Klarna, and GitHub Copilot all run hybrid stacks.
Drill into specific costs with the embeddings cost calculator, vector DB cost calculator, fine-tuning cost calculator, and Claude cost calculator. For total stack budgeting, see AI monthly budget calculator.