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Cost of Embeddings at 100M Documents (ANSWERED)

Scenario BasedRAGMedium15 min read

Medium RAG interview question on cost of embeddings at 100m documents — architecture, trade-offs, eval, and production patterns.

TL;DR — Quick Answer

At 100M docs, embedding cost = tokens × price × reindex frequency. Mitigate with deduplication by content hash, smaller embedding models, batch/off-peak jobs, incremental updates only on change, and caching unchanged chunks.

The Interview Question

Explain cost of embeddings at 100m documents. How would you design, implement, and evaluate this in a production RAG system? Discuss trade-offs and failure modes.

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