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Building RAG Is Easy. Paying for Production RAG Is Hard.
Retrieval Augmented Generation (RAG) is the standard way to give LLMs access to private knowledge. Tutorial-level RAG looks cheap. Production RAG at scale routinely costs $5,000-$50,000+/month.
Here s the real cost breakdown of production RAG pipelines in 2026, where the money goes, and how to cut your bill 60% through AI Credits.
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The 4 Cost Components of RAG
1. Embedding Generation
Converting documents and queries to vectors.
Pricing examples:
- OpenAI text-embedding-3-small: $0.02 per 1M tokens
- OpenAI text-embedding-3-large: $0.13 per 1M tokens
- Voyage AI: $0.05-$0.15 per 1M tokens
- Cohere: $0.10 per 1M tokens
For 100M tokens of documents: $2-$15
2. Vector Database
Storing and searching vectors at scale.
Pricing examples:
- Pinecone Serverless: $0.33-$0.66 per 1M vectors stored
- Weaviate Cloud: $25-$295/month
- Qdrant Cloud: $25-$300/month
- pgvector (Supabase): Included in Postgres pricing
For 10M document chunks: $30-$300/month
3. LLM Generation Calls
The expensive part. Each query sends retrieved context + question to an LLM.
Pricing examples:
- GPT-5: $1.25/$10 per MTok
- Claude Sonnet 4.6: $3/$15 per MTok
- Gemini 2.5 Flash: $0.30/$2.50 per MTok
For 1M queries with 5K tokens each: $1,500-$15,000
4. Reranking (Optional)
Improving retrieval quality with a reranker.
Pricing examples:
- Cohere Rerank: $1 per 1K queries
- Voyage Rerank: $0.05 per 1K queries
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Real Cost Examples by Use Case
Internal Knowledge Base (100K docs, 1K queries/day)
| Component | Monthly Cost |
|---|---|
| Embeddings (one-time) | $2 |
| Vector DB | $50 |
| LLM calls (Claude Sonnet) | $450 |
| Reranking | $30 |
| Total | $532/month |
With AI Credits at 50% off LLM: $307/month Annual savings: $2,700
Customer Support Bot (1M docs, 10K queries/day)
| Component | Monthly Cost |
|---|---|
| Embeddings | $20 |
| Vector DB | $200 |
| LLM calls (Claude Sonnet) | $4,500 |
| Reranking | $300 |
| Total | $5,020/month |
With AI Credits at 50% off LLM: $2,770/month Annual savings: $27,000
Enterprise Search (10M docs, 100K queries/day)
| Component | Monthly Cost |
|---|---|
| Embeddings | $200 |
| Vector DB | $1,500 |
| LLM calls (Claude Sonnet) | $45,000 |
| Reranking | $3,000 |
| Total | $49,700/month |
With AI Credits at 50% off LLM: $27,200/month Annual savings: $270,000
Where the Money Actually Goes
In production RAG, LLM generation calls are typically 80-90% of total cost. The embeddings, vector DB, and reranking are minor costs compared to LLM consumption.
This means: the biggest lever to reduce RAG costs is reducing LLM call costs. And the easiest way to do that is buying discounted credits via AI Credits.
How to Cut RAG Costs 60%
1. Buy Discounted LLM Credits
Since LLM calls are 80-90% of cost, AI Credits at 50-60% off LLM credits delivers 40-54% total savings.
2. Use Cheaper Models for Retrieval Tasks
Don t use Claude Opus to format retrieved chunks. Use Haiku or GPT-4.1 Nano for the simple steps and reserve Sonnet/Opus for the actual answer generation.
3. Implement Aggressive Caching
Cache common queries and their answers. A good cache hit rate (30-50%) cuts LLM calls dramatically.
4. Limit Context Size
Don t retrieve and send 20 chunks when 5 would do. Tighter retrieval means fewer input tokens.
5. Use Cheaper Embeddings for Common Cases
text-embedding-3-small ($0.02/MTok) often works as well as text-embedding-3-large ($0.13/MTok) for many use cases. 6.5x savings on embedding costs.
Frequently Asked Questions
How much does a RAG pipeline cost in production?
Internal knowledge bases run $500-$1,000/month. Customer support bots run $5K-$15K/month. Enterprise search can exceed $50K/month. LLM calls dominate costs.
What s the biggest cost in a RAG pipeline?
LLM generation calls - typically 80-90% of total cost. Vector DB and embeddings are minor in comparison. Cut LLM costs with AI Credits.
Should I use Claude or GPT for RAG?
Claude Sonnet 4.6 generally produces better RAG answers than GPT-5. But GPT-5 is cheaper. Test both and route accordingly. Buy both at discount via AI Credits.
Can I save on RAG by using cheaper embeddings?
Yes. text-embedding-3-small at $0.02/MTok works well for most cases vs text-embedding-3-large at $0.13/MTok. 6.5x savings on embedding costs.
What s the cheapest vector database?
pgvector on Supabase or Postgres is the cheapest for most use cases. Pinecone Serverless is competitive at smaller scale.
How do I optimize my RAG pipeline for cost?
Reduce LLM call costs (biggest lever), implement caching, use smaller embeddings, tighter retrieval, and buy discounted credits via AI Credits.
Production RAG Doesn t Have to Be Expensive
Build RAG for what it actually costs - then cut that in half with discounted credits.
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Production RAG at 60% less cost. Save at aicredits.co.