AI INFRASTRUCTURE
Vector memory sizer
Estimate raw embedding storage and HNSW memory overhead.
Your assumptions
Rates are editable planning assumptions.
Planning estimate · Review methodology below.
Reference scenarios
Calculated using the default assumptions above, varying the first input only.
| Vectors | Raw float32, 1536 dims | Estimated HNSW memory |
|---|---|---|
| 100000 | 0.57 GiB | 0.75 GiB |
| 1000000 | 5.72 GiB | 7.45 GiB |
| 10000000 | 57.22 GiB | 74.51 GiB |
How this estimate works
Raw vector bytes equal vector count times dimensions times bytes per component. The simplified HNSW graph estimate adds M times two times eight bytes per vector, then applies the overhead percentage to vectors and graph together. Outputs use GiB (2^30 bytes). This is a planning approximation, not a database vendor’s sizing guarantee. Metadata, allocator behavior, upper graph layers, deletions, replicas, disk indexes and quantization differ by implementation. Set bytes per component to match your storage format and measure a representative sample.
Start with your measured workload, adjust one assumption at a time, and compare the resulting capacity. Save the scenario link to return to these inputs or share it with your team. Reference scenarios remain fixed so you can compare against a consistent baseline.