AI Tools

Embedding Storage Calculator

Calculate storage requirements and generation costs for vector embeddings across popular models from OpenAI, Voyage AI, Google, and open-source providers.

Parameters

Number of Documents*

Total number of documents or chunks to embed.

Embedding Model

Select the embedding model to calculate for.

Results

Raw Vector Storage585.9 MB

100,000 vectors × 1536 dimensions × 4 bytes (float32)

Other results (3)

With Index Overhead (~40%)820.3 MB

Includes HNSW/IVF indexes and metadata storage in vector databases

Embedding Generation Cost$0.5120

~256 tokens/doc × 100,000 docs at $0.02/1M tokens

Model Comparison

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Why Choose Embedding Storage Calculator

Estimate vector storage requirements and embedding generation costs for RAG pipelines. Covers OpenAI text-embedding-3, Voyage AI, Google Gecko, and open-source models like BGE and Nomic.

✓Calculate vector memory and storage requirements for vector databases
✓Estimate API costs for generating embeddings with OpenAI, Google, and Voyage AI
✓Compare vector dimension sizes and storage footprints of major models
✓Plan database scaling and sizing including HNSW index VRAM overhead

How to Use

1Enter the total number of text documents or chunks in your dataset
2Select the target embedding model from the list of providers
3Review the raw binary vector storage size and the estimated size with index overhead
4Analyze the cost estimation for generating those vectors via API

Frequently Asked Questions (FAQ)

What is vector database index overhead?

Vector databases use indexing structures (like HNSW or IVF) to enable fast similarity searches. These indexes require additional memory and storage, typically adding 30% to 50% overhead on top of the raw vector data.

How does vector dimension size affect storage?

Storage is directly proportional to the number of dimensions. A 1536-dimension vector (e.g., OpenAI text-embedding-ada-002) uses twice as much storage as a 768-dimension vector (e.g., Nomic Embed) per document.

What precision is used for storage calculations?

We calculate storage assuming standard 32-bit single-precision floating-point numbers (4 bytes per dimension), which is the default representation for most vector database systems.

Authoritative References & Sources

OpenAI Embeddings

View Source

Voyage AI