Vector DB Pricing Compare

Vector dimensions directly dictate memory and disk consumption. A 1,536-dimensional vector (OpenAI text-embedding-3-small) uses 6,144 bytes in standard 32-bit float (FP32), whereas a 768-dimensional vector (Nomic / BGE) uses 3,072 bytes. When stored in-memory with HNSW graph indexes, every doubling of dimension doubles RAM footprint, which can push dedicated clusters into significantly higher pricing tiers.

Serverless vector databases (e.g. Pinecone Serverless, Cloudflare Vectorize) charge strictly per-gigabyte of storage and per-million Read Units (RUs). They scale to zero at minimal idle cost. Dedicated clusters (e.g. Qdrant Cloud, Pinecone Pods, Weaviate Dedicated) have fixed hourly floor costs ($9 to $70+/mo minimum) but provide unlimited queries within node RAM capacity, making them substantially cheaper under sustained high-QPS production traffic.

Scalar Quantization (INT8) compresses 32-bit floats into 8-bit integers, reducing memory requirements by 75% with negligible accuracy drop (<1% recall loss). 1-Bit Binary Quantization compresses vectors up to 32x, allowing millions of embeddings to reside in minimal RAM on affordable VPS instances (e.g. Hetzner Cloud) rather than expensive multi-gigabyte managed clusters.

pgvector (on PostgreSQL, Supabase, Neon, or self-hosted) is ideal when your vector count is under 10-20 million and vectors reside alongside existing relational business data (users, auth, metadata). It eliminates dual-database sync lag and allows single ACID transactions. Dedicated vector engines (Qdrant, Milvus/Zilliz, Pinecone) become advantageous above 20M+ vectors where specialized distributed indexing (HNSW, DiskANN) outpaces Postgres vacuuming and shared memory.

Hierarchical Navigable Small World (HNSW) builds multi-layer proximity graphs to achieve sub-10ms nearest neighbor search. The graph edges and adjacency lists typically add 1.4x to 2.0x storage overhead on top of the raw vector arrays. For 1M 1536-dim vectors (6.14GB raw), an HNSW index typically requires ~9.2GB to 12GB of combined RAM and high-speed NVMe.

Managed cloud providers bundle multi-tenant compute markup, automated failover, and high gross margins into their storage and RU rates. Running open-source Qdrant, Weaviate, or pgvector on a modern NVMe VPS (e.g. Hetzner CPX31 with 4 vCPU, 8GB RAM, 160GB NVMe for $17.02/mo) provides dedicated raw hardware capable of serving millions of vectors at a fraction of hyperscaler rates.