How many vectors will my corpus produce?

Vectors ≈ total tokens ÷ chunk size. Each chunk of text becomes one embedding vector, so a larger corpus or smaller chunks means more vectors to store and search.

How big is one embedding vector?

Raw storage ≈ dimension × 4 bytes (float32). A 1536-dimension vector is about 6 KB before metadata and index overhead. Indexes typically add 1.5-3× on top of the raw float storage.

Are the vector-DB prices exact?

No — they are simplified per-GB or per-vector estimates clearly labelled as such. Real pricing depends on pods, replicas, tiers and query volume. Use this to size the order of magnitude, then confirm with the vendor.

No. The entire calculation runs in your browser. Nothing you enter is stored or transmitted.

Embedding Storage Cost Calculator

Embedding storage cost calculator

Before building a RAG pipeline it pays to size the vector store. This tool turns a document corpus into a vector count, estimates raw storage, and projects monthly cost on Pinecone, Weaviate or Qdrant — so you know whether you are looking at a few dollars or a few thousand.

How it works

The math starts from your corpus and chunking:

total_tokens = documents × avg_doc_tokens
vectors      = total_tokens ÷ chunk_size
raw_bytes    = vectors × dimension × 4   (float32)
stored_bytes ≈ raw_bytes × index_overhead   (~2×)
monthly_cost ≈ stored_GB × provider_rate

Embedding dimension is the biggest lever on storage: a 3072-dim vector is twice the size of a 1536-dim one. Index structures (HNSW, IVF) add overhead on top of the raw floats, which the calculator includes with a realistic multiplier.

Tips

Use a smaller-dimension model where quality allows — text-embedding-3-small (1536) is half the storage of large (3072) and often good enough.
Many vector DBs support quantization (int8 / binary) that can cut storage 4-32× with modest recall loss — huge at scale.
Larger chunks mean fewer vectors and less storage, but worse retrieval precision. Tune chunk size against retrieval quality, not just cost.
Storage is often the smaller cost — query volume and compute frequently dominate, so pair this with a per-query cost estimate.