AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Specify Vector Count
Enter the number of indexed vectors (e.g. 100,000, 1M, 10M vectors).
2. Set Dimensions & Precision
Choose embedding dimensions (384, 768, 1024, 1536, 3072) and precision format (Float32, Float16, or Int8 Quantized).
3. Choose Index Algorithm
Select HNSW (fastest retrieval, +50-80% RAM index overhead) or Flat / IVFFlat (lower memory, higher search latency).
4. Review Infrastructure Sizing
Review required RAM, disk storage, and recommended cloud instance specs across Pinecone, pgvector, and Qdrant.
Formula or logic used
Vector Sizing & Quantization Mathematics
Raw vector storage is deterministic based on floating-point precision: Float32 requires 4 bytes per dimension, Float16 requires 2 bytes, and Int8 Scalar Quantization requires only 1 byte.
Bytes Per Vector = Dimensions × Precision Bytes (Float32 = 4, Float16 = 2, Int8 = 1)Raw Vector Memory (MB) = (Vector Count × Bytes Per Vector) / (1024 × 1024)HNSW Graph Index Overhead = Raw Vector Memory × 0.65 (for M=16, efConstruction=100)Metadata Payload = Vector Count × Average Metadata Bytes (~500 bytes/chunk)Total Recommended RAM = (Raw Vector Memory + HNSW Index Overhead + Metadata) × 1.25 (Safety Buffer)
Examples
Example 1: 1 Million OpenAI text-embedding-3-small Vectors
Requires a 16 GB or 32 GB RAM cloud instance (or 1x Pinecone pod / Qdrant cluster node).
Example 2: 1 Million Int8 Quantized Vectors
Quantization allows running 1M vectors comfortably on an affordable 8GB RAM PostgreSQL/pgvector instance.
Common use cases
Cloud Vector Database Cost Forecasting
Determine exact pod counts and instance sizing for Pinecone, Qdrant Cloud, and Zilliz.
Self-Hosted pgvector Hardware Provisioning
Calculate PostgreSQL shared_buffers and maintenance_work_mem sizes for building HNSW indexes.
Quantization Tradeoff Evaluation
Compare RAM requirements between uncompressed Float32 and compressed Int8 embeddings.
Multi-Tenant Vector Isolation Sizing
Calculate total database growth across thousands of client enterprise knowledge bases.
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Frequently asked questions
Why does an HNSW index require so much extra RAM?
Hierarchical Navigable Small World (HNSW) graphs maintain multi-layer node connections and edge lists for each vector to enable sub-millisecond approximate nearest neighbor (ANN) searches, adding 50% to 80% overhead on top of raw vector bytes.
What is scalar quantization (SQ) in vector databases?
Scalar quantization compresses 32-bit floating point numbers (Float32) into 8-bit integers (Int8). This reduces memory consumption by 75% with minimal accuracy degradation (typically less than 1-2% drop in recall).
Can vector indexes run from disk instead of RAM?
Certain engines (like Qdrant and Milvus) support memory-mapped files (mmap) and on-disk payload storage, keeping only the HNSW graph in RAM while streaming vectors from fast NVMe SSDs to reduce server RAM costs.
How much RAM does pgvector need for 100k vectors?
For 100,000 OpenAI 1,536-dimensional vectors using pgvector HNSW, you need approximately 1.2 to 1.5 GB of dedicated RAM for the index to fit entirely in PostgreSQL memory buffers.
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