What are the cost optimization strategies for RAG systems in production?

RAG cost optimization targets four areas: embedding costs, storage costs, LLM costs, and infrastructure costs. Embedding: batch document processing during off-peak hours, use smaller embedding models for non-critical use cases, cache query embeddings for repeated patterns (can save 30-50% of embedding API calls). Storage: use vector quantization (PQ or scalar quantization) to reduce storage by 4-8x, implement tiered storage (hot/warm/cold) based on document access patterns, and archive unused document embeddings. LLM: implement semantic caching (cache responses for semantically similar queries), use smaller/cheaper models for simple queries with automatic routing to larger models for complex ones, reduce context length by selecting only the most relevant chunks, and use prompt caching for system prompts. Infrastructure: right-size vector database instances based on actual query load, implement auto-scaling for retrieval services, use spot/preemptible instances for batch processing. Cost monitoring: track cost per query across the pipeline, set budgets and alerts, and implement token usage dashboards. Typical production RAG costs: $0.01-0.05 per query for cloud-based systems, with caching reducing this by 40-60%.