Total RAG Cost: Embeddings + Vector DB + Generation
Embeddings are just one part of your RAG pipeline cost. Here's a full breakdown for a system processing 10,000 queries/day:
| Component | Budget Stack | Mid-Tier Stack | Premium Stack |
|---|---|---|---|
| Embedding (query + doc) | $0.60/mo | $3.00/mo | $3.90/mo |
| Vector DB (Pinecone/Weaviate) | $0/mo (free tier) | $70/mo | $200/mo |
| LLM generation | $15/mo (Flash) | $150/mo (Sonnet) | $450/mo (GPT-5.5) |
| Total | ~$16/mo | ~$223/mo | ~$654/mo |
Key takeaway: Embedding costs are a small fraction (1-5%) of total RAG costs. Don't over-optimize embeddings at the expense of retrieval quality — the LLM generation cost dwarfs embedding costs.
Dimension Reduction: A Cost Trick
OpenAI's embedding-3 models support dimension reduction without retraining. You can reduce from 3072 to 256 dimensions with minimal quality loss:
Dimension Reduction Impact
| Dimensions | Storage (1M docs) | Quality Impact |
|---|---|---|
| 3072 (full) | ~12 GB | Baseline |
| 1536 | ~6 GB | Negligible |
| 512 | ~2 GB | ~2-3% accuracy drop |
| 256 | ~1 GB | ~5-8% accuracy drop |
If you're on a tight budget, use 512 dimensions from text-embedding-3-large. You get 97% of the quality at 1/6 the storage cost.
5-Step Decision Framework
- Start with OpenAI text-embedding-3-small ($0.02/1M tokens) — it's the default for a reason: great quality, low cost, 1536 dimensions
- Test retrieval quality — measure recall@10 on your actual data. If it's below 90%, upgrade to text-embedding-3-large
- Check document length — if documents exceed 8191 tokens, split them or use Cohere (512 token limit but search-optimized)
- Consider multilingual needs — Cohere embed-multilingual-v3.0 handles 100+ languages; OpenAI is primarily English
- Optimize dimensions — use dimension reduction to cut storage costs without meaningful quality loss
Calculate your RAG pipeline cost: Use our free calculator to estimate embedding + generation costs for your specific workload.
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