# Embedding Storage Calculator

- **URL:** https://codeasystem.com/calculators/developer/embedding-storage-calculator/
- **Category:** developer
- **Description:** Size the memory footprint of a vector collection from count, dimensions and precision, including index overhead and RAM guidance.
- **Primary output:** Storage with index overhead: 8.58 GiB

## Inputs
- Number of vectors (name: `vectorCount`, type: number, example: 1000000)
- Dimensions (name: `dimensions`, type: select)
- Custom dimension count (name: `customDimensions`, type: number, example: 1024)
- Storage precision (name: `precision`, type: select)

## Outputs
- Storage with index overhead: 8.58 GiB
- Raw vectors (1,536 dims × fp32): 5.72 GiB
- Index/memory overhead multiplier: 1.5× (heuristic for HNSW-style indexes)
- Bytes per vector: 6,144 bytes
- RAM recommendation: provision ≈ 9 GiB of memory

## Formula / methodology
```
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.

## How to use
1. **Enter collection size**; Expected steady-state vector count, not today’s count if growth is planned.
2. **Match model dimensions**; Your embedding model’s spec fixes this number; custom covers exotic models.
3. **Consider precision drops**; fp16 halves storage versus fp32 and int8 quarters it; usually worth small recall losses.

## 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.

Result for these inputs:

```
Storage with index overhead: 8.58 GiB
```

## About this calculator
### 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.

## FAQs
### 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.

## Related calculators
- [RAG Chunking Calculator](https://codeasystem.com/calculators/developer/rag-chunking-calculator/)
- [GPU Memory Calculator](https://codeasystem.com/calculators/developer/gpu-memory-calculator/)
- [Storage Growth Calculator](https://codeasystem.com/calculators/developer/storage-growth-calculator/)

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Last updated: 2026-08-23 · Version: 1.0.0 · [HTML version](https://codeasystem.com/calculators/developer/embedding-storage-calculator/)