AI Vector Embedding & Memory Calculator
Calculate vector embedding generation costs, dimensions, and HNSW Vector DB RAM requirements for RAG architectures.
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
2. Vector Index & Production Queries
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How to use the AI Embedding Cost & Vector Memory Calculator
?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
- Select your target embedding model (e.g. OpenAI text-embedding-3-small or large).
- Enter total document chunks and average token length per chunk.
- Choose your vector quantization mode (Float32, Float16, or Int8).
- Review total cost, raw memory size, and recommended vector DB RAM capacity.
Worked Calculation Example
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.
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