Embedding Dimension Calculator (Storage + Memory)

Type the embedding dimension and number of documents — get the disk footprint, RAM cost, and approximate vector DB cost for storing the index. Compare costs across float32 (full precision), float16 (half), int8 (quantized), and binary (1-bit) — common quantization options that trade recall for size.

How to use the Embedding Dimension Calculator (Storage + Memory)

Pick the embedding dimension matching your model (or enter custom). Set the document count. The calculator shows raw float32 storage, then the savings from float16, int8, and binary quantization. Useful when sizing a vector DB cluster or estimating monthly cloud cost for an embedding index.

Sizing an embedding index

An embedding index stores one vector per document, and each vector is a list of floating-point numbers as long as the model's dimension — 384, 768, 1536, 3072. The raw size is simply dimension × documents × bytes per number, and at a million-plus documents that footprint decides how much RAM a vector database needs and what it costs to run.

This calculator takes the dimension and document count and reports disk and RAM cost, then shows how quantization shrinks it: float16 halves it, int8 cuts it to a quarter, and binary packs it dramatically — each trading a little recall for a lot of space. Pair it with the vector DB pricing calculator and the RAG cost estimator to turn that size into a monthly bill.

Common use cases

  • Cluster sizing — work out how much RAM a vector index needs before provisioning.
  • Quantization planning — compare float32, float16, int8, and binary footprints side by side.
  • Model selection — weigh a higher-dimension model's quality against its storage cost.
  • Capacity forecasting — project index growth as the document count rises.
  • Budget estimates — feed the size into a vendor pricing formula for a monthly figure.

Frequently asked questions

How is the raw size calculated?
Dimension × document count × bytes per value. float32 uses 4 bytes per dimension, so a 1536-dim float32 vector is about 6 KB before any index overhead.

What does quantization trade away?
Precision for size. int8 and binary store each value in fewer bits, cutting memory sharply while losing some recall — often a worthwhile trade at large scale, especially with reranking.

Which dimension matches my model?
The presets cover common ones: 384 for MiniLM, 768 for BGE-base, 1536 for text-embedding-3-small and ada-002, and 3072 for text-embedding-3-large. Enter a custom value for anything else.

Does this include the vector DB price?
It reports storage and RAM. For dollar costs across providers, take the size into the vector DB pricing calculator.
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