Vector Database

The missing piece that makes AI actually useful with your company's own data.

Vector databases have emerged as the critical infrastructure layer enabling enterprise AI applications, with the market projected to grow from $1.5 billion in 2024 to $8.4 billion by 2028. Every time an AI assistant accurately answers a question about your company's policies, a recommendation engine suggests a relevant product, or a fraud detection system identifies suspicious patterns, vector databases are working behind the scenes. Organisations deploying vector databases for Retrieval-Augmented Generation (RAG) applications report 40-60% improvements in AI response accuracy compared to using large language models alone. For any enterprise building AI-powered applications with proprietary data, the vector database has become as fundamental as the relational database was to the web era.

What It Is

A vector database is a specialised database designed to store, index, and search high-dimensional vectors — mathematical representations of data that capture semantic meaning. When you convert a document, image, product description, or audio clip into a vector using an AI model, you create a numerical fingerprint that encodes its meaning. A vector database stores millions or billions of these fingerprints and can instantly find the ones most similar to any query, even when there are no exact keyword matches.

Think of it this way: a traditional database is like a filing cabinet that finds documents by exact labels — it can find all documents tagged "quarterly report" but cannot find a document about "financial performance" unless those exact words appear. A vector database understands meaning: it knows that "quarterly report" and "financial performance" are semantically related and can find relevant documents based on conceptual similarity rather than exact keyword matches. This capability is what powers modern AI applications that seem to genuinely understand context and intent.

The technical process involves converting raw data (text, images, audio) into vector embeddings using AI models, storing these vectors in the database with specialised indexing structures, and performing similarity searches using mathematical distance calculations. When a user asks a question, the query is converted into a vector, the database finds the closest matching vectors, and the associated original data is retrieved and used to inform the AI's response. This process, called Retrieval-Augmented Generation, has become the standard architecture for enterprise AI applications that need to leverage proprietary knowledge.

Why It Matters for Business

The primary business value of vector databases lies in making AI applications genuinely useful with enterprise data. Large language models, despite their capabilities, have a fundamental limitation: they only know what was in their training data. They do not know your company's policies, products, customer history, or internal knowledge. Vector databases bridge this gap by providing a mechanism to inject relevant, proprietary information into AI responses in real time. This transforms AI from a general-purpose tool into a knowledgeable business-specific assistant.

The ROI is substantial and measurable. Customer service organisations implementing RAG with vector databases report 35-50% improvements in first-contact resolution rates and 25-40% reductions in average handling time, as AI assistants can accurately access product documentation, troubleshooting guides, and customer history. Legal teams using AI with vector databases for contract review and research report 60-70% reductions in document search time. Knowledge management applications see 40-50% improvements in employee productivity when information retrieval is powered by semantic search rather than traditional keyword-based systems.

Vector databases also address a critical enterprise AI concern: data privacy and sovereignty. Unlike sending proprietary data to be incorporated into a large language model's training data, RAG with vector databases keeps your data within your infrastructure. The AI model never stores your data — it simply receives relevant excerpts at query time. This architecture is far more compatible with data governance requirements and has become the preferred approach for organisations in regulated industries.

Enterprise Use Cases

Pinecone, one of the leading dedicated vector database providers, counts companies like Microsoft, Samsung, and Shopify among its customers. Shopify uses vector search to power its product discovery features, enabling merchants' customers to find products using natural language descriptions rather than exact keywords. A customer searching for "comfortable shoes for standing all day" receives relevant results even if no product description contains those exact words. This semantic search capability has been shown to increase product discovery by 20-30% compared to traditional search.

Healthcare organisations are deploying vector databases for clinical decision support. Mayo Clinic stores medical literature, clinical guidelines, and anonymised patient case vectors, enabling clinicians to query the system with natural language and receive semantically relevant clinical evidence. When a doctor asks about "treatment options for refractory hypertension in elderly patients," the vector database retrieves the most relevant clinical studies and guidelines, even if those documents use different terminology. This approach has reduced clinical literature search time by over 70% and improved evidence-based decision-making.

Financial services firms use vector databases for regulatory compliance and risk management. JPMorgan Chase implemented a vector database containing millions of regulatory documents, internal policies, and historical compliance cases. Compliance officers can query the system with natural language questions and receive semantically relevant regulatory guidance in seconds, rather than manually searching through thousands of documents. The bank reports that this capability has reduced compliance research time by 60% and improved the consistency of regulatory interpretations across global offices.

Vendor Landscape

The vector database market has exploded with options. Pinecone leads the dedicated vector database category with managed cloud service pricing from $70-1,400+ per month depending on index size and query volume. Milvus (Zilliz) is the leading open-source option, available as a managed service (Zilliz Cloud) or self-hosted. Weaviate offers an open-source vector database with strong enterprise adoption. Qdrant has gained traction for its Rust-based performance efficiency. Chroma, a newer entrant, targets developer simplicity for AI application development.

Traditional database vendors have also added vector capabilities: PostgreSQL with pgvector extension, MongoDB Atlas Vector Search, Elasticsearch with vector search, and SingleStoreDB all now support vector operations. For enterprises with existing database investments, these integrated options may be preferable to adding a dedicated vector database. Pricing for integrated vector search is typically included in existing database licensing, making it the most cost-effective option for moderate-scale use cases.

Strategic Considerations

Choose your vector database strategy based on scale, expertise, and integration needs. For organisations just beginning with AI applications, integrated vector search in existing databases (PostgreSQL pgvector, MongoDB Atlas) offers the lowest barrier to entry and simplest operations. For dedicated, high-scale AI applications processing millions of documents with demanding latency requirements, purpose-built vector databases (Pinecone, Milvus, Weaviate) provide superior performance and specialised features. Consider hybrid approaches: use integrated vector search for departmental projects and prototypes, and dedicated vector databases for production, customer-facing AI applications. Plan for embedding model strategy as well — the quality of your vectors depends on the quality of your embedding models, and different models produce different vector representations that may not be compatible. Standardise on a small number of embedding models to ensure interoperability across your vector database deployments.