Tiered Vector Storage Can Cut AI Infrastructure Costs by Up to 98%

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A startup CTO saw monthly vector database costs jump from $2,000 to $20,000 after scaling from 50 million to 500 million embeddings following product-market fit. Analysis revealed that over 80% of those vectors were queried less than once a week, yet all were stored at the same high-cost, high-performance tier. Vector search involves a three-way tradeoff between cost, recall, and latency, and most teams apply a single uniform storage strategy regardless of actual access patterns. Tiering vectors by query frequency — using in-memory storage for hot data and SSD or object storage for cold data — can reduce per-vector costs from roughly $0.12/month in RAM to as low as $0.0002/month in object storage. Tools like Amazon OpenSearch Service support multiple storage tiers, including on-disk modes that keep only a compressed navigation graph in memory while storing full-precision vectors on SSD.

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