AI Vector Embedding & Memory Calculator
Calculate vector embedding generation costs, dimensions, and HNSW Vector DB RAM requirements for RAG architectures.
Calculate token indexing costs, raw vector bytes, and HNSW graph RAM requirements for Pinecone, Qdrant, Milvus, and pgvector.
Raw Bytes = Vectors × Dimensions × BytesPerDim · Total RAM = Raw Vector MB × (1 + 29% HNSW Index Overhead)Raw Vector Payload
293.0 MB
50,000 × 1536d
Corpus Embedding Cost
$0.50
25.00M tokens
Total RAM (+29% HNSW)
377.9 MB
FLOAT32 (4B/dim)
Monthly Query Cost
$0.10/mo
100,000 searches
1. Model & Corpus Sizing
2. Vector Index & Production Queries
Embedding prices checked on 2026-10-16 from OpenAI official pricing.
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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 models (checked 2026-10-16), and calculates the exact RAM and storage footprint required by Vector Databases including HNSW graph index overhead and scalar 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, text-embedding-3-large, Cohere, or Gemini).
- 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, HNSW graph index overhead, 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 vector memory.
What is HNSW index overhead in vector databases?
Hierarchical Navigable Small World (HNSW) graph indexing creates link structures between vectors to allow millisecond approximate nearest neighbor searches. This graph structure adds roughly 20% to 50% additional RAM overhead above raw vector storage.
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 in benchmark tests.
How much RAM do vector embeddings consume?
A 1,536-dimensional vector stored as 32-bit float requires ~6KB per vector. With HNSW graph indexing overhead (+30%), 1 million vectors consume approximately 8.1 GB of RAM.
What is vector quantization?
Quantization compresses full 32-bit floating-point numbers into 16-bit or 8-bit integers, reducing memory footprint by 50% to 75% with minimal recall degradation.
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