Vector DB Pricing Compare
Select your vector count, embedding dimensions, and quantization; get ranked monthly TCO across 12 vector databases — including Serverless (Pinecone, Cloudflare, Astra DB), Managed Dedicated Clusters (Qdrant, Weaviate, Zilliz), PostgreSQL pgvector extensions, and self-hosted VPS baselines.
Workload Presets
Click to load common production scenariosWorkload Parameters
Ranked Monthly Cost (12 eligible)
Sorted lowest to highest total estimated monthly infrastructure costArchitecture & Cost Comparison Insights
Serverless vs Dedicated Clusters
Serverless models (Pinecone, Cloudflare Vectorize, Astra DB) excel for unpredictable traffic or prototype workloads, scaling to zero with no maintenance. However, under high sustained search throughput (10M+ queries/month), dedicated in-memory clusters (Qdrant, Milvus) are substantially cheaper since query RUs are bundled into node RAM.
pgvector vs Specialized Vector DBs
pgvector is unbeatable when vectors live alongside operational SQL data (Supabase, Neon, RDS), eliminating cross-database synchronization. Specialized engines (Qdrant, Pinecone, Weaviate) pull ahead above 10M+ vectors where multi-stage filtering, native quantization, and isolated HNSW indexing memory matter.
Quantization Impact on Infrastructure
Enabling Scalar (INT8) quantization reduces in-memory footprint by 75% while retaining >99% search recall. This turns what would be a $500+/mo dedicated cluster requirement into an affordable $36/mo node or a low-tier self-hosted VPS instance.
Vector Database Pricing & TCO FAQ
How do vector embedding dimensions impact monthly database cost?
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.
What is the cost difference between Serverless and Dedicated Pods/Clusters?
Serverless vector databases (e.g. Pinecone Serverless, Cloudflare Vectorize, Astra DB) 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.
How does INT8 Scalar and Binary Quantization reduce vector search TCO?
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.
When should I choose pgvector over a specialized vector database?
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.
What is the memory overhead of HNSW indexing?
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.
Why is self-hosting on a VPS (e.g., Hetzner) often 10x cheaper?
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 €14.60/mo) provides dedicated raw hardware capable of serving millions of vectors at a fraction of hyperscaler rates.