Traditional databases find things by exact match. Query a row by ID, filter by a column, join on a foreign key. But AI applications need a fundamentally different operation: finding items that are similar, not identical. That's the problem a vector database solves.
A vector database stores high-dimensional embeddings — numerical representations of text, images, audio, or code produced by machine learning models — and retrieves the closest matches for a given query. Items with similar meaning land near each other in vector space. The database indexes those vectors and, when asked, returns the nearest neighbors fast. This is the engine behind semantic search, recommendation systems, and RAG pipelines.
How Vector Databases Work
Data comes in, gets converted into vector embeddings using a model (OpenAI's text-embedding, Cohere Embed, open-source sentence-transformers — the choice matters). The database indexes those vectors using structures optimized for high-dimensional space: HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), or product quantization are the most common.
At query time, the same embedding model converts the question into a vector. The index finds the nearest neighbors — the most similar vectors — using cosine similarity, Euclidean distance, or dot product. The tradeoff is precision for speed, and in practice it's a good trade: approximate nearest-neighbor search scales to billions of vectors where brute-force comparison never could.
Key Capabilities
Approximate Nearest-Neighbor (ANN) Search: Similarity search across millions or billions of vectors. A small precision sacrifice buys orders-of-magnitude speed gains.
Metadata Filtering: Combine similarity with traditional filters. Search for similar products but only in a specific category. Similar documents, but only from the last 30 days.
Hybrid Search: Merge dense vector search with sparse keyword search (BM25). Semantic understanding meets exact term matching. Most production deployments end up here.
Real-Time Indexing: New vectors get indexed without rebuilding the entire index. Data stays current.
Scalability: Distributed architectures shard and replicate across clusters, handling billions of vectors with the throughput production workloads demand.
Common Use Cases
- Semantic Search: Move beyond keyword matching. A user searching "how to fix a slow application" finds content about performance optimization — even when those exact words never appear. This is search modernization in practice.
- Retrieval-Augmented Generation (RAG): The retrieval half of RAG. Pull the most semantically relevant documents from a knowledge base, feed them to an LLM, get grounded answers instead of hallucinations.
- Recommendation Systems: Find similar products, content, or users based on behavioral or content embeddings.
- Image and Audio Search: Query by visual or acoustic similarity using embeddings from vision or audio models. Semantic image search is already in production at scale.
- Anomaly Detection: Data points far from their nearest neighbors in vector space are outliers. Simple concept, powerful in practice.
Vector Databases vs. Vector-Capable Databases
The landscape splits two ways. Purpose-built vector databases — Pinecone, Weaviate, Qdrant, Milvus, Chroma — offer specialized indexing algorithms and tight integration with AI workflows. Then there are established databases that have added vector support: Elasticsearch with its dense and sparse vector fields, OpenSearch with its k-NN plugin, PostgreSQL with pgvector, Redis with vector similarity.
Purpose-built options often push the boundaries on indexing performance and AI-native features. General-purpose databases with vector extensions let you manage vectors alongside existing data without adding another system to your stack. The right choice depends on whether vector search is the primary workload or one capability among many — and on how much operational complexity your team is willing to absorb.