Growth Scale: 1,000 Queries/Day (30,000/month)
Monthly RAG Cost — 1,000 queries/day
Enterprise Scale: 10,000 Queries/Day (300,000/month)
Monthly RAG Cost — 10,000 queries/day
At enterprise scale, the difference between Llama 4 Scout ($21.60/mo) and Claude Sonnet 4.6 ($3,420/mo) is $3,398/month — enough to fund an entire engineering sprint.
Recommended RAG Stack by Budget Tier
Budget Tier: Under $10/month
- Embedding: OpenAI text-embedding-3 ($0.02/1M tokens)
- Vector DB: ChromaDB (free, self-hosted) or Pinecone free tier (1GB)
- Generation: Llama 4 Scout ($0.08/$0.08) or DeepSeek V4 Pro ($0.55/$2.19)
This stack handles up to ~500 queries/day within $10/month. Llama 4 Scout is the most cost-effective option by far — at $0.08 per 1M tokens for both input and output, it costs virtually nothing per query.
Mid Tier: $50/month
- Embedding: OpenAI text-embedding-3 ($0.02/1M tokens)
- Vector DB: Weaviate Cloud free tier (1M objects) or Pinecone starter
- Generation: DeepSeek V4 Pro for volume, GPT-4o for quality-critical queries
With $50/month you can serve ~2,000 queries/day on DeepSeek V4 Pro alone, or use a hybrid approach routing high-stakes queries to GPT-4o.
Premium Tier: $200/month
- Embedding: OpenAI text-embedding-3 or Cohere embed-v4 for multilingual ($0.10/1M)
- Vector DB: Pinecone paid tier ($70/mo) or Weaviate dedicated
- Generation: GPT-4o ($2.50/$10) for most queries, Claude Sonnet 4.6 ($3/$15) for complex reasoning
At $200/month you can serve ~1,500 queries/day on GPT-4o with a quality-boosted hybrid stack using Claude Sonnet 4.6 for the most important queries.
Cost Optimization Tips for RAG
- Use budget models for generation: The retrieved context does the heavy lifting — most RAG queries work well with cheaper models like Llama 4 Scout or DeepSeek V4 Pro
- Optimize chunk size: Smaller chunks mean 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 = higher cost
- Cache common queries: If the same question gets asked repeatedly, cache the response and skip the generation step entirely
- Compress context: Summarize retrieved chunks before sending to the LLM to reduce input token count
- Use hybrid search: Combine vector search with keyword search to improve relevance and reduce the number of chunks needed
- Rerank results: Use a lightweight reranker to filter the top chunks before generation — fewer, higher-quality chunks mean lower generation costs
- Monitor token usage: Track input/output tokens per query to identify waste and optimize prompt templates
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