Developer & Tech

Embedding Storage Calculator

What this does

Size the memory footprint of a vector collection from count, dimensions and precision, including index overhead and RAM guidance.

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Calculator inputs

How to use it

Using the embedding storage calculator

  1. 01

    Enter collection size

    Expected steady-state vector count, not today’s count if growth is planned.

  2. 02

    Match model dimensions

    Your embedding model’s spec fixes this number; custom covers exotic models.

  3. 03

    Consider precision drops

    fp16 halves storage versus fp32 and int8 quarters it; usually worth small recall losses.

Good to know

Where the multiplier goes

Graph indexes store neighbor lists alongside vectors; IVF stores centroid assignments; every engine keeps deletion bitmaps and ID maps. The 1.5× figure covers modest HNSW settings; aggressive M values or huge metadata can double it, which is why we call it a heuristic.

Quantization economics

Product or scalar quantization shrinks footprints 4–32× with modest recall loss, often making the difference between one beefy node and a cluster. Disk-backed indexes extend the trade to storage, swapping cheap disk for higher query latency.

How it's calculated

The math behind this calculator

raw = vectors × dims × bytes/dim withIndex = raw × 1.5 (HNSW-style heuristic)

Each vector occupies dimensions × bytes-per-dimension; multiply by count for the raw payload. Approximate nearest-neighbor structures add links, metadata and load-factor slack, so a stated 1.5× multiplier estimates realistic memory; labeled a heuristic because HNSW parameters and engines vary widely.

Assumptions & limitations

  • 1.5× index/memory overhead is a named heuristic, not a guarantee.
  • Metadata payloads and IDs not included.
  • Quantization trades recall for footprint; benchmark before committing.

Worked example

One million 1536-dimension fp32 vectors occupy about 5.72 GiB raw, roughly 8.58 GiB once typical index overhead lands; provision ~9 GiB of RAM.

FAQ

Frequently asked questions

Does this include document text storage?
No; only vector payloads plus index structure. Source documents usually live in cheaper object storage beside the index.
Why GiB instead of GB?
Memory provisioning uses binary GiB; mixing decimal GB into RAM planning under-provisions nodes by ~7%.
How accurate is 1.5× really?
Directionally right for common setups. Measure your engine after load tests; Qdrant/pgvector/Milvus publish their own overhead guidance.

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