To control RAG costs for a semantic search app, batch document indexing and estimate token spend before rollout. Only send the top retrieved chunks to the chat model for answer generation. A useful token cost estimate should separate embedding input during indexing, retrieval-time operations, and answer generation input and output. Estimating goes beyond simple token counts; it involves evaluating chunk size, overlap, and the top-k setting, as these directly impact prompt length and cost. Practical estimation begins with representative documents and real user questions to calculate token totals for various chunking strategies. Recall is crucial; a lower chunk count is only beneficial if the most relevant passage is still retrieved. Reranking can improve context ordering, allowing fewer chunks to be sent to the chat model.Indexing should be treated as a separate batch job from the user-facing request path. This prevents high ingestion volumes from leading to unexpected prompt bills. Retries during document indexing require idempotency keys or client-supplied identifiers to avoid duplicate data. Before optimizing prompts or models, it is essential to make the document distribution visible and identify oversized chunks. Using provider-specific token counting calls with appropriate backoff and retry strategies is key for accurate estimates.Batch indexing offers benefits for large backfills by allowing for job monitoring and audit trails, keeping uploads separate from embedding creation. It is important not to blur retry policies between polling batch jobs and idempotent write operations. While batch indexing isn't ideal for immediate searchability, a small synchronous path can handle that need. The choice of RAG stack components, like OpenAI, Anthropic, Google Gemini, Pinecone, Weaviate, or Infrai, depends on existing workflows and team priorities. Migration should only occur if it genuinely improves the system, not just for marginal cost savings. Ultimately, the code must deliver grounded answers, and a clean cost estimate is meaningless with weak retrieval.