Estimating the infrastructure costs for a production Retrieval-Augmented Generation (RAG) pipeline reveals that LLM generation accounts for the majority of expenses, typically 78-84% of the total bill. While embedding a corpus is relatively inexpensive, even for large datasets, the ongoing costs are driven by query processing, which includes embedding the user's question, vector search, reranking, and finally, sending retrieved information to the LLM for answer generation. Savings are best achieved by optimizing the retrieval process to send fewer, more relevant chunks to the LLM, rather than focusing solely on cheaper vector database solutions. AI
IMPACT Understanding RAG infrastructure costs is crucial for optimizing LLM application development and deployment.
RANK_REASON Article provides a cost estimation methodology for a specific AI application (RAG pipeline), detailing its components and cost drivers.
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