Why Vector Databases Matter for AI
Vector databases store and query high-dimensional vector embeddings — the numerical representations that AI models use to understand text, images, audio, and more. If you are building any AI application that involves semantic search, retrieval-augmented generation (RAG), recommendation systems, or anomaly detection, you need a vector database.
The market has exploded. According to MarketsandMarkets, the vector database market is projected to reach $4.3 billion by 2028, growing at 23.7% CAGR.
How Vector Search Works
An embedding model converts text into vectors — arrays of 768 to 4,096 floating-point numbers. Similar concepts produce similar vectors. Vector databases use approximate nearest neighbour (ANN) algorithms to find the most similar vectors efficiently.
The key ANN algorithms:
- HNSW (Hierarchical Navigable Small World) — The most common algorithm. Builds a graph structure for fast traversal. Excellent recall (typically 95-99%) with sub-millisecond query latency at million-scale.
- IVF (Inverted File Index) — Partitions vectors into clusters. Lower memory footprint than HNSW but slightly lower recall.
- Product Quantisation (PQ) — Compresses vectors to reduce memory usage by 4-8x. Trades recall accuracy for memory efficiency.
The Contenders
Pinecone — Fully Managed Simplicity
- Best for — Teams that want zero operational overhead. Startups shipping fast.
- Performance — Sub-10ms p99 latency at million-scale.
- Pricing — Serverless starts free (up to 100K vectors). Pod-based: $70-$300/month per pod.
- EU data residency — Available on AWS eu-west-1 and GCP europe-west1.
Weaviate — Feature-Rich Open Source
- Best for — Teams needing hybrid search (vector + keyword), built-in vectorisation, multi-modal support.
- Performance — Sub-15ms p99 latency at million-scale with HNSW.
- Unique features — Built-in vectorisation modules, generative search, multi-tenancy support.
Qdrant — Rust-Powered Performance
- Best for — Best performance-to-resource ratio. Latency and memory efficiency critical.
- Performance — Sub-5ms p99 latency at million-scale. Excellent memory efficiency.
- Unique features — Advanced filtering, quantization options, recommendation API, multi-vector support.
pgvector — PostgreSQL Extension
- Best for — Teams already running PostgreSQL. Vectors are a feature, not the core workload.
- Performance — Good up to ~5 million vectors with HNSW indexing.
- Unique features — Full SQL compatibility. Join vector search with relational data.
Decision Framework
| Scenario | Recommendation |
|---|---|
| Already on PostgreSQL, < 5M vectors | pgvector |
| Startup, ship fast, < 10M vectors | Pinecone or Qdrant Cloud |
| Need hybrid search | Weaviate |
| Billion-scale, enterprise | Milvus or Qdrant |
| Prototyping RAG system | Chroma |
For production RAG systems in European enterprises, we most frequently recommend Qdrant or pgvector. If you are building an AI application, our LLM integration service includes vector database selection and deployment. Read our complete RAG system guide for the broader picture, or contact us for a consultation.