Vector Databases — Concepts, Terminologies, and Industry Players

What Are Vector Databases?

Traditional databases store structured data in rows and columns. Vector databases, by contrast, are designed to store and query embeddings — high-dimensional vectors produced by models that capture semantic meaning. For example, the sentence “The cat sat on the mat” can be transformed into a 1,536-dimensional vector (using OpenAI embeddings). Storing such vectors allows for similarity search, enabling systems to retrieve semantically relevant information instead of relying on keyword matching.

Why Do We Need Them?

As LLMs and multimodal systems become context-hungry, agents must recall knowledge, past interactions, and reference documents. A vector database provides:

Semantic search: retrieving relevant results by meaning, not just keywords.
Context injection: supplying the most relevant chunks to LLM prompts.
Scalability: storing millions or billions of embeddings while maintaining fast query speed.
Low latency: powering real-time applications like RAG (Retrieval-Augmented Generation), recommendation engines, and chatbots.

Without vector stores, AI systems would remain memory-less and struggle to scale in production.

Key Concepts and Terminologies

Embeddings: numerical representations of text, images, audio, or code in high-dimensional space.
Dimensionality: the length of embedding vectors (e.g., 384, 768, 1536 dimensions).
Similarity Metrics: mathematical functions to compare vectors, such as cosine similarity, Euclidean distance, or dot product.
Approximate Nearest Neighbor (ANN) Search: algorithms that speed up similarity search by trading exactness for efficiency (e.g., HNSW, IVF, PQ).
Indexing: data structures that organize vectors for fast search, like inverted files or hierarchical graphs.
Recall vs. Latency Trade-off: higher recall ensures better accuracy but increases query time; ANN balances both.
Hybrid Search: combining semantic vector search with keyword/metadata filters.
Persistence & Scaling: distributed storage, replication, and cloud-native scaling for production workloads.

Vector Database Providers in the Market

Several players provide vector databases or integrate vector search into broader data platforms:

Pinecone – A fully managed cloud-native vector database, widely used in RAG pipelines.
Weaviate – Open-source and cloud-hosted options, strong support for hybrid search and modular extensions.
Milvus – An open-source, high-performance vector database backed by Zilliz, optimized for billion-scale datasets.
FAISS (Facebook AI Similarity Search) – A library (not a full database) for efficient similarity search; often embedded in custom solutions.
Chroma – Lightweight, developer-friendly vector database popular in LangChain/LLM projects.
Qdrant – Open-source with strong focus on high-performance ANN search and production readiness.
Vespa – Large-scale serving engine for search and recommendation, integrates dense and sparse retrieval.
Elasticsearch / OpenSearch – Traditional search engines that now support dense vector search alongside keyword indexing.
Redis Vector – Redis modules with vector similarity search, blending cache + vector store.
Postgres Extensions (pgvector) – Add-on for PostgreSQL to handle embeddings directly in relational DB workflows.

Final Thoughts

Vector databases are the memory layer of modern AI systems. They transform embeddings into actionable context, powering RAG, semantic search, and Agentic AI workflows. With diverse players — from fully managed SaaS (Pinecone) to open-source frameworks (Milvus, Weaviate, FAISS) — the space is evolving rapidly. As models scale and applications demand low-latency semantic recall, vector databases will remain a foundational pillar of AI infrastructure.