Monthly RAG Cost — 1,000 queries/day (Quality Setup)
Embedding: text-embedding-3-large | Generation: Premium model | Vector search: $25/mo
Scaling RAG Costs
How costs grow with query volume:
| Queries/Day | Flash (Budget) | GPT-4o (Premium) | Sonnet 4 (Premium) |
|---|---|---|---|
| 100 | $0.11/mo | $4.05/mo | $6.75/mo |
| 1,000 | $1.08/mo | $40.50/mo | $67.50/mo |
| 10,000 | $10.80/mo | $405/mo | $675/mo |
| 100,000 | $108/mo | $4,050/mo | $6,750/mo |
At 100K queries/day, the difference between Flash ($108/mo) and Sonnet 4 ($6,750/mo) is $6,642/month — enough to hire a developer.
How to Reduce RAG Costs
- Use budget models for generation: Gemini Flash or GPT-4o mini handle most RAG queries well — the retrieved context does the heavy lifting
- Optimize chunk size: Smaller chunks = fewer tokens per query. Aim for 200-400 tokens per chunk
- Limit retrieved chunks: 3-5 chunks is usually enough. More chunks = more input tokens
- Cache common queries: If the same question gets asked repeatedly, cache the response
- Compress context: Summarize retrieved chunks before sending to the LLM
- Use hybrid search: Combine vector search with keyword search to improve relevance and reduce the number of chunks needed
Recommended RAG Stack by Budget
Startup (< $10/month)
- Embedding: OpenAI text-embedding-3-small ($0.02/1M)
- Vector DB: Pinecone free tier or pgvector
- Generation: Gemini 2.5 Flash-Lite ($0.10/$0.40)
Growth ($50-200/month)
- Embedding: OpenAI text-embedding-3-large ($0.13/1M)
- Vector DB: Pinecone or Qdrant ($25-70/mo)
- Generation: GPT-4o ($2.50/$10.00) for quality, Flash for volume
Enterprise ($500+/month)
- Embedding: Custom fine-tuned embeddings
- Vector DB: Dedicated cluster with replication
- Generation: Claude Sonnet 4.6 for complex queries, Flash for simple ones
- Caching: Redis cache for common queries
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