Vector Database Storage Calculator
RAM/disk for N embeddings at any dimension — float32 vs PQ/binary compression, plus HNSW index overhead.
10M × 768-dim float32 vectors = 30.7 GB before the index — which is why serious deployments quantize. Binary embeddings (1 bit/dim) with float rescoring keep ~95% of retrieval quality at 1/32 the memory.
Formula
About Vector Database Storage Calculator
RAG bills arrive in RAM: vector search engines keep embeddings and their HNSW neighbor graph in memory, and at float32 a modest 10-million-document corpus already wants 30+ GB. This calculator prices any corpus across the compression ladder — float16 (free win), int8 scalar quantization, product quantization's 16× squeeze, and binary embeddings' radical 1-bit-per-dimension — plus the graph overhead everyone forgets. Use it before choosing an embedding model: the difference between 384 and 1536 dimensions is literally a 4× infrastructure bill.
How to use Vector Database Storage Calculator
- 1Enter your values into Vector Database Storage Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Vector Database Storage Calculator?
- ✓Computes Vector Database Storage instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: vectors = N × dim × bytes(encoding).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Which compression should I use for RAG?+
The 2025-era consensus: int8 scalar quantization as the default (negligible quality loss, 4× saving), binary + float-rescoring when memory dominates cost (≈32×, ~3–5% recall cost), PQ when you need a dial between them. Float32 persists mostly by inertia.
How much does the HNSW index add?+
Roughly N × 2M × 4 bytes of neighbor IDs — at the default M=16, ~1.3 GB per 10M vectors, independent of dimension. Higher M improves recall on hard distributions but the links can rival compressed vector data itself at M=48+.
Does dimensionality affect search speed too?+
Linearly — every distance computation touches all dims. Matryoshka-trained embeddings (e.g. OpenAI text-embedding-3) let you truncate to 256–512 dims at small quality cost, compounding with quantization for 10–20× cheaper search.
When should vectors go to disk instead of RAM?+
Past ~100M vectors, memory-mapped or disk-first indexes (DiskANN, pgvector on SSD) become the economic choice: PQ-compressed vectors in RAM for routing, full vectors on NVMe for rescoring. Latency rises from ~1 ms to ~10 ms — fine for most RAG.
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