ai
Client-Side Only
Free Forever

AI Vector Embedding & Memory Calculator

Calculate vector embedding generation costs, dimensions, and HNSW Vector DB RAM requirements for RAG architectures.

🧠 AI Vector Embeddings Cost & Vector DB Memory Calculator

Calculate token generation costs, vector memory footprint, and Pinecone/Qdrant/pgvector RAM requirements for Retrieval-Augmented Generation (RAG).

Total Chunks / Vectors

50,000

1536 dimensions

Corpus Embedding Cost

$0.50

25.00M tokens

Vector RAM (with HNSW)

395.5 MB

FLOAT32 precision

Monthly Query Cost

$0.10/mo

100,000 searches

1. Model & Corpus Sizing

50,000
500 tokens (~375 words)

2. Vector Index & Production Queries

100,000
50 tokens

Advertisement

Share this tool:

How to use the AI Embedding Cost & Vector Memory Calculator

Select your embedding model (OpenAI 3-small/large, Cohere, Voyage, Gemini), enter your document chunk count and average tokens, and configure quantization to estimate initial token spend and vector database storage requirements.

?Frequently Asked Questions

What is the AI Vector Embedding & Memory Calculator?

The AI Vector Embedding Calculator models total token consumption, one-time embedding generation costs across OpenAI, Cohere, Voyage, and Gemini, and calculates the exact RAM and storage footprint required by Vector Databases including HNSW graph overhead and quantization.

Who is the AI Vector Embedding & Memory Calculator for?

Designed for AI engineers, LLM product architects, and data scientists planning Retrieval-Augmented Generation (RAG) and semantic search infrastructure.

How to Use the AI Vector Embedding & Memory Calculator

  1. Select your target embedding model (e.g. OpenAI text-embedding-3-small or large).
  2. Enter total document chunks and average token length per chunk.
  3. Choose your vector quantization mode (Float32, Float16, or Int8).
  4. Review total cost, raw memory size, and recommended vector DB RAM capacity.

Worked Calculation Example

Embedding 100,000 document chunks (500 tokens each) with OpenAI 3-small produces 50M tokens costing $1.00 and requiring ~810 MB of Vector DB RAM.

Frequently Asked Questions

What is the memory size of a 1536-dimension vector?

In standard 32-bit floating point (Float32), each dimension occupies 4 bytes. A 1,536-dimensional vector consumes exactly 6,144 bytes (~6 KB) of raw memory.

How does scalar quantization (Int8) save money?

Int8 quantization reduces 32-bit floats to 8-bit integers, shrinking RAM requirements by 75% while maintaining ~98.5% semantic retrieval accuracy.

Explore Related Utilities & Guides