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Vector Database Storage Sizer

Estimate storage and memory for a vector database from vector count, dimensions, precision, metadata, index links and replicas.

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The tool

Enter your numbers below. Results update as you type, and nothing leaves your browser.

Everything you enter stays on your own machine. Nothing is sent to us or to anyone else, which you can confirm in your browser network tab.

What people use it for

  • Sizing infrastructure for retrieval-augmented generation
  • Choosing between embedding models
  • Estimating savings from quantisation
  • Budgeting a managed vector database plan

About this tool

What the Vector Database Storage Sizer does

Vector search stores one embedding per item, so size grows with the number of vectors, their dimensions and the bytes per value. Graph indexes such as HNSW add links per vector. This calculator totals raw vectors, index overhead, metadata and replicas.

Formula

  • Raw vectors = vectors × dimensions × bytes per value
  • HNSW links ≈ vectors × M × 2 × 4 bytes
  • Total = (raw + links + metadata) × copies

Worked example

Using the starting values in the calculator above:

Input Value
Number of vectors 10,000,000
Dimensions 1,536
Precision float32 (4 bytes)
HNSW links per vector (M) 16
Metadata per vector 200 bytes
Copies (including primary) 2
Result Value
Total with replicas 120.6
One copy 60.3
Raw vectors 57.2
Graph index links 1.2
Metadata 1.9
Bytes per vector 6,472

How to read the result

Quantising from float32 to int8 cuts raw vector size by three quarters, usually with a small loss of recall. Many systems keep full precision on disk and quantised vectors in memory.

Reducing dimensions, where your embedding model supports it, shrinks both storage and query cost in proportion.

Limits to keep in mind

  • Real systems add write-ahead logs, segment overhead and free space for compaction; leave headroom.

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Questions

Questions about this tool

The things clients actually want to know — costs, timelines, ownership and what happens after launch.

Dimensions × bytes per value. A 1,536-dimension float32 vector needs 6,144 bytes before index and metadata.

The number of neighbour links each node keeps in the graph. Higher M improves recall but uses more memory.

Usually only slightly, and it can be offset by re-ranking the top results with full-precision vectors.

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Tell us where you want the business to be in twelve months. We will come back with a costed plan, a realistic timeline, and the numbers we expect to move — no obligation, no jargon.

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  • Fixed-price proposal in 48 hours
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Talk to a developer

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Response time
Within 1 business hour
Where we work
UK, USA & the Gulf
Contracts
No lock-in, monthly rolling
Working since
2023
infobestwebdeveloper.org

Free strategy session

Let’s build something worth finding.

Tell us where you want the business to be in twelve months. We will come back with a costed plan, a realistic timeline, and the numbers we expect to move — no obligation, no jargon.

  • 30-minute call, no pitch deck
  • Fixed-price proposal in 48 hours
  • You own everything we build

Talk to a developer

Not a sales team

Response time
Within 1 business hour
Where we work
UK, USA & the Gulf
Contracts
No lock-in, monthly rolling
Working since
2023
infobestwebdeveloper.org
Free consultation

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