Unpopular opinion: RAG is overrated, fine-tuning small models is the future
Everyone's building RAG pipelines but getting mediocre results. I switched to fine-tuning domain-specific 3B models and the quality difference is staggering. Here's my cost analysis.
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# RAG vs Fine-Tuning: A Cost Analysis
## RAG Pipeline
- Embedding cost: $0.02/1k docs
- Vector DB: $50/mo (Pinecone)
- API calls: $0.03/query (GPT-4)
- Latency: 2-5 seconds
- Accuracy on domain questions: 74%
## Fine-tuned 3B Model
- Training: $200 one-time (4x A100, 6hrs)
- Inference: $0.001/query (self-hosted)
- Latency: 200ms
- Accuracy on domain questions: 91%
## The Math
At 10k queries/day:
- RAG: $300/month + $50 DB = $350/mo
- Fine-tuned: $30/month (GPU hosting)
Break-even: 2 weeks. After that, you save $320/month WHILE getting better results.
The catch? You need good training data.#RAG#Fine-tuning#Hot Take
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