Embedding Dimension Calculator

Created: 8th Feb 26 1 Views

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Estimate storage, RAM, and index size for your vector embeddings.

Embedding Model

Custom Dimensions (if custom selected)

Number of Vectors

Precision

Index Type

Avg Metadata per Vector (bytes)

Common Embedding Models

Here are the most popular embedding models and their dimensions:

OpenAI ada-002: 1536d text-embedding-3-large: 3072d Cohere v3: 1024d all-MiniLM: 384d BGE-large: 1024d E5-large: 1024d

Client-side processing only — no data sent to server.
RAM estimates assume in-memory storage; disk usage is the raw vector bytes.
HNSW indexes typically add 50-100% overhead for the graph structure.
Production systems add further overhead for metadata, IDs, and system processes.
Free to use with no login/signup required.
Report bugs in comments with sample input and expected output.

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