Embedding Storage Calculator
Size your vector database before you build it. Enter document counts, chunking, embedding dimensions, and data type to see raw storage, realistic index overhead, and an approximate monthly hosting cost.
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Total vectors
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Raw vectors + metadata
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With HNSW index (~1.5-2x)
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Bytes per vector
Approximate monthly storage cost (managed vector DB)
| Example rate tier | Price / GB-month | Monthly cost (raw) | Monthly cost (with index) |
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How the math works: total vectors = documents x chunks per document. Each vector takes dimensions x bytes-per-dimension (float32 = 4, float16 = 2, int8 = 1) plus your metadata bytes (IDs, source text pointers, filter fields). Why the index overhead? HNSW, the graph index most vector databases use for fast approximate search, stores neighbor links for every vector on top of the raw data. In practice deployed indexes commonly land around 1.5x to 2x the raw vector size, so this tool shows the raw figure and a 1.5-2x range. Flat (brute-force) indexes add almost nothing but search slower at scale; IVF variants sit in between. The dollar figures use example managed vector database storage rates and are approximate, as of July 2026 - real pricing varies widely by provider and plan (serverless vs pod-based, read/write unit charges, replicas), so check provider pricing pages before budgeting. Tip: float16 or int8 quantization cuts storage 2-4x with usually small recall loss.