Fine-tuning looks attractive until you account for data prep, evals, and drift. RAG is cheaper to start but demands solid retrieval quality. Here’s how to decide.
Cost model for fine-tuning
Include labeling, cleaning, training runs, eval cycles, and ongoing re-training as data drifts. Hosting costs can dominate if you need low latency.
Cost model for RAG
You pay for embedding, vector storage, and retrieval. Quality hinges on chunking, metadata, and ranking. RAG stays flexible as your corpus changes.
Decision rule of thumb
Choose RAG when your data changes weekly or you can’t afford expensive eval pipelines. Fine-tune when tasks are narrow, stable, and high-volume.
Key takeaways
- Budget for ongoing evals if you fine-tune.
- RAG is cheaper to start; success depends on retrieval quality.
- Pick based on data drift and volume, not hype.