Size and price your vector index
Embedding indexes get expensive quietly: a million 1536-dimension float32 vectors is already several gigabytes before index overhead. Enter your vector count, dimensions, and precision to see raw storage, a realistic indexed-storage estimate, and rough monthly cost across popular vector databases.
How it works
Raw storage is simply vectors × dimensions × bytes_per_value (4 bytes for
float32, 2 for float16, 1 for int8). On top of that, ANN indexes such as HNSW add
graph links and metadata, so the tool applies a typical overhead multiplier to
estimate real on-disk size. The per-database figures convert that size into a
ballpark monthly cost using published storage pricing — useful for comparison,
not for an exact invoice.
Tips
- Quantize when you can. Moving from float32 to int8 cuts storage 4x; test recall on your own queries first.
- Truncate dimensions. Matryoshka-capable models let you store 256–512 dims instead of 1536 with little quality loss — often the biggest single saving.
- Mind query cost too. Storage is only part of the bill; serverless vector DBs also charge per read, so high-QPS workloads can cost more than storage.