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About this tool
Estimate total vector index size from vector count, dimensions and data type, including HNSW or IVF overhead and replicas.
The Vector Storage Size Calculator estimates the total storage a vector index needs from vector count, dimensions and data type: raw data is count × dimensions × bytes per dimension, and index overhead follows the Faiss sizing rules (HNSW adds M × 2 × 4 bytes of graph links per vector; IVF adds 8-byte ids plus a 4×√N centroid table, the conservative end of Faiss's recommended 4×√N-16×√N range). It is for engineers capacity-planning a Pinecone, Qdrant, Milvus, pgvector or Faiss deployment who need a defensible RAM and disk figure before provisioning.
Open Vector Storage Size Calculator on AltFTool — it loads instantly in your browser.
Enter Number of vectors (or tap the 100K, 1M, 10M, 100M presets) and Dimensions per vector, then pick a Data type such as float32.
Choose HNSW or IVF as the Index type and set HNSW M (links per node), Metadata per vector (bytes) and Replicas (total copies).
Read the Estimated total index size in IEC units, broken into raw vector data, index overhead as a % of data, metadata and per replica.
HNSW graph links (M × 2 × 4 bytes per vector) and IVF ids plus centroids are added on top of the raw data, not ignored.
float64 down to 1-bit binary quantisation, with metadata bytes and replica count folded into the total.
The arithmetic follows the published Faiss index sizing guidelines rather than a vendor's marketing calculator.
Multiply vector count × dimensions × bytes per dimension for the raw data (4 bytes per dimension for float32), then add index overhead: an HNSW graph adds roughly M × 2 × 4 bytes per vector (128 bytes at the default M=16), and IVF adds an 8-byte id per vector plus about 4×√N float32 centroids (the conservative end of Faiss's recommended 4×√N-16×√N range). Finally multiply by your replica count.
About 5.7 GiB of raw data for 1 million text-embedding-3-small vectors (1,536 dims × 4 bytes × 1M), plus roughly 122 MiB of HNSW graph overhead at M=16 and whatever metadata you store per vector. Most engines also want 20% or more free headroom, so plan around 8 GiB of RAM per replica.
Roughly M × 2 × 4 bytes per vector — 128 bytes at the common default M=16 — following the Faiss guideline that HNSW memory per vector is d × 4 + M × 2 × 4 bytes. For a 1536-dimension float32 vector (6,144 bytes) that is only about 2% extra, but for short or quantised vectors the graph can exceed the data itself.
Yes — int8 scalar quantisation cuts raw vector data to a quarter of float32 and binary quantisation to one thirty-second, but graph overhead and metadata do not shrink with it. That is why a binary-quantised HNSW index carries a much heavier proportional link cost: 128 bytes of graph per vector versus 192 bytes of data for a 1536-dimension binary vector, so the graph is two-thirds as large as the data itself — a far bigger share than at float32 precision, where the same 128 bytes of graph is only about 2% of the 6,144 bytes of data.
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