What drives vector database cost the most?
Primary cost drivers are chunk count, vector dimension, replication strategy, and query throughput. Overly aggressive chunking and retention policies can rapidly inflate monthly spend.
Estimate chunk count, embedding storage, vector index size, and monthly database cost for your RAG knowledge base.
Document Corpus
Chunking Strategy
Embedding Model
1536 dims · float32 = 6,144 bytes/vector · $0.02/1M tokens
Vector Database & Query Load
Serverless pay-per-use. Queries billed as read units (vectors scanned × top-k). Configure your corpus and click Calculate
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Projects storage, index, and query cost for RAG infrastructure as corpus volume and retrieval traffic grow, helping teams prevent silent infrastructure cost drift.
A 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.
Primary cost drivers are chunk count, vector dimension, replication strategy, and query throughput. Overly aggressive chunking and retention policies can rapidly inflate monthly spend.
Smaller chunks and high overlap increase vector count, index size, and write/read load. Chunking strategy should be tuned jointly with retrieval quality goals.
For many workloads, query path optimization (top-k tuning, filtering, reranking strategy) reduces both cost and latency faster than storage-only optimizations.