# Vector Database Cost Calculator

> Estimate vector database size and monthly cost from vector count, dimensions, precision, metadata, index overhead, replicas, storage and query prices.

Category: Software, cloud & AI · Applies to: Worldwide · Last updated: 2026-10-29
Interactive calculator: https://calculators.geekmidst.com/calculators/vector-database-cost-calculator

Each vector takes dimensions × bytes per dimension, plus metadata. Add index overhead and replicas to get stored size, then price storage, queries and writes.

Also called: Embedding storage calculator, Vector index size calculator.

## Inputs
- Number of vectors (default 10000000)
- Dimensions (default 768)
- Precision (options: float32 (4 bytes), float16 (2 bytes), int8 (1 byte), binary (1 bit); default float32)
- Metadata per vector (unit bytes; default 200; optional)
- Index overhead (unit %; default 50; optional)
- Copies stored (default 1)
- Price per GB-month (default 0.25)
- Queries per month (unit million; default 5; optional)
- Price per million queries (default 2.00; optional)
- Writes per month (unit million; default 1; optional)
- Price per million writes (default 1.00; optional)
- Currency

## Results
- Monthly cost
- Raw vectors and metadata
- Stored size
- Stored size in binary units
- Storage
- Queries
- Writes

## Formula

```
Raw bytes = Vectors × (Dimensions × Bytes per dimension + Metadata);  Stored = Raw × (1 + Overhead%) × Copies;  Cost = Stored GB × Price + Queries × Query price + Writes × Write price
```

Where:
- Bytes per dimension: 4 (float32), 2 (float16), 1 (int8), 0.125 (binary)
- Copies: primary plus replicas

## Worked example
1. Per vector: 768 × 4 + 200 = 3,272 bytes; × 10 million = 32.72 GB.
2. With 50% index overhead: 49.08 GB × 0.25 = 12.27.
3. Queries 5 × 2.00 = 10; writes 1 × 1.00 = 1; total 23.27.

**Result:** Ten million 768-dimension float32 vectors need about 49 GB with index overhead, about 23.27 a month at these example prices.

## What the result means

Dimensions and precision drive size. Quantising float32 to int8 cuts raw vector storage by 75%, usually at a small cost in recall; check the trade-off on your own queries.

## Assumptions and limits
- All vectors have the same dimensions and average metadata size.
- Index overhead applies to the whole stored record.
- Many managed services price by pods, nodes or read and write units instead of GB; convert your plan to these terms or enter an equivalent rate.
- Memory-resident indexes may need RAM sized to the index, which is priced differently from disk.

## Questions

### How big is one embedding?

Dimensions × bytes per value. A 768-dimension float32 vector is 3,072 bytes; a 1,536-dimension one is 6,144 bytes.

### Does embedding generation cost go here?

Not directly. Creating embeddings is a one-off token cost per document; use the LLM Token Cost Calculator with your embedding model’s price.

## Related calculators
- [LLM Token Cost Calculator](https://calculators.geekmidst.com/calculators/llm-token-cost-calculator)
- [AI Chatbot Cost Forecast Calculator](https://calculators.geekmidst.com/calculators/ai-chatbot-cost-forecast-calculator)
- [Cloud Storage Cost Calculator](https://calculators.geekmidst.com/calculators/cloud-storage-monthly-cost-calculator)
- [Database Storage Growth Calculator](https://calculators.geekmidst.com/calculators/database-storage-growth-calculator)

Source: GM¹X CalcVerse — https://calculators.geekmidst.com/calculators/vector-database-cost-calculator