How to Estimate and Control Costs in RAG-Based Semantic Search Apps

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Retrieval-Augmented Generation (RAG) systems incur costs across three distinct stages: document embedding during indexing, retrieval-time processing, and answer generation using chat models. Developers can keep expenses predictable by splitting documents into chunks, generating embeddings in batch, and passing only the top retrieved chunks into the answer-generation prompt. Chunk size, overlap, and the number of retrieved results (top-k) directly affect prompt length and cost, so these settings should be estimated against real documents and user queries before production deployment. Reranking retrieved chunks can improve context quality, allowing fewer chunks to be sent to the language model without sacrificing answer accuracy. Idempotency safeguards are also critical during document indexing, as naive retries can create duplicate chunk records that distort retrieval results.

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