{"slug": "show-hn-rag-vector-db-cost-calculator", "title": "Show HN: RAG Vector DB Cost Calculator", "summary": "A developer released a cost calculator for RAG vector databases that estimates monthly spend based on chunk count, vector dimension, replication strategy, and query throughput. The tool helps teams identify cost drivers and optimize chunking and retrieval policies to prevent infrastructure cost drift.", "body_md": "### What drives vector database cost the most?\n\nPrimary cost drivers are chunk count, vector dimension, replication strategy, and query throughput. Overly aggressive chunking and retention policies can rapidly inflate monthly spend.\n\nEstimate chunk count, embedding storage, vector index size, and monthly database cost for your RAG knowledge base.\n\nDocument Corpus\n\nChunking Strategy\n\nEmbedding Model\n\n1536 dims · float32 = 6,144 bytes/vector · $0.02/1M tokens\n\nVector Database & Query Load\n\nServerless pay-per-use. Queries billed as read units (vectors scanned × top-k).\n\nConfigure your corpus and click **Calculate**\n\nResults will appear here\n\nProjects storage, index, and query cost for RAG infrastructure as corpus volume and retrieval traffic grow, helping teams prevent silent infrastructure cost drift.\n\nA documentation platform expands from product docs to internal runbooks and ticket history. Vector growth doubles monthly spend. The calculator identifies chunk-policy adjustments and retrieval filtering as the fastest path to cost stabilization.\n\nPrimary cost drivers are chunk count, vector dimension, replication strategy, and query throughput. Overly aggressive chunking and retention policies can rapidly inflate monthly spend.\n\nSmaller chunks and high overlap increase vector count, index size, and write/read load. Chunking strategy should be tuned jointly with retrieval quality goals.\n\nFor many workloads, query path optimization (top-k tuning, filtering, reranking strategy) reduces both cost and latency faster than storage-only optimizations.", "url": "https://wpnews.pro/news/show-hn-rag-vector-db-cost-calculator", "canonical_source": "https://tools.superml.org/calculators/rag-vector-db-cost-calculator", "published_at": "2026-06-26 22:19:14+00:00", "updated_at": "2026-06-26 22:34:54.777759+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-tools", "ai-infrastructure"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/show-hn-rag-vector-db-cost-calculator", "markdown": "https://wpnews.pro/news/show-hn-rag-vector-db-cost-calculator.md", "text": "https://wpnews.pro/news/show-hn-rag-vector-db-cost-calculator.txt", "jsonld": "https://wpnews.pro/news/show-hn-rag-vector-db-cost-calculator.jsonld"}}