Modern AI applications – from large language models (LLMs) and semantic search to RAG (retrieval-augmented generation) – rely on vector representations of data. Unlike keyword-based search, vectors capture the meaning of text, images, or audio, letting AI systems find related content even when exact words differ.
In fact, experts note that traditional databases “fall short in handling complex, high-dimensional data,” whereas vector databases “store data as multi-dimensional vectors” for smarter, faster search.
For example, an image search engine will convert your query (“sunset over mountains”) and candidate images into vectors, then use cosine similarity to retrieve the most relevant results.
In this way, vectors have become the foundation of modern AI systems – the “silent engines driving the efficiency and accuracy of AI applications”.
What Is a Vector?
A vector in AI and machine learning is simply a fixed-size array of numbers that encodes the meaning or features of a data object.
Embedding models transform raw data into numeric vectors.
Crucially, vector similarity – not exact text matching – drives search: a query about “pasta recipe” will match a document on “how to cook pasta” because their vectors are similar, even though the words differ.
In short, vectors are numerical representations of data that capture semantics and context.
What Is a Vector Database?
Traditional relational databases store structured data in tables (rows and columns) and excel at exact matching queries, but they struggle with unstructured or high-dimensional data like text and images.
A vector database is specifically built to handle embeddings (vectors) of unstructured data. Instead of indexes on keywords, it maintains indexes over vector space, allowing semantic similarity search.
For example, when you ask a question, the system converts the query into a vector (via an embedding model) and then finds the nearest vectors in the database. The database might be populated with vectors from many documents; even if query words don’t match exactly, their vectors will be close if they have similar meaning. In practice, a vector database stores each item’s embedding (often alongside metadata) and uses Approximate Nearest Neighbour (ANN) algorithms to quickly retrieve the top-k similar vectors.
Major vector database examples include Chroma, Weaviate, Qdrant, and others.
How Vector Databases Work
Vector databases follow a workflow of embedding, indexing, filtering, and query retrieval. Key steps include:
- Embeddings: Raw data (text, images, audio, etc.) is passed through an AI model to produce fixed-length numeric embeddings. Each vector captures the semantic essence of the input. For example, text embeddings map words and sentences to vectors so that similar meanings are close together; image embeddings do the same for visual content.
- Indexing: Once embeddings are created, the database builds a specialised index to enable fast nearest-neighbour search. Techniques like Hierarchical Navigable Small World graphs (HNSW), product quantisation (PQ), or locality-sensitive hashing (LSH) organise the high-dimensional vectors. These indexes allow the system to quickly find vectors that are closest to a query vector without scanning every item.
- Metadata & Filtering: In many vector DBs, each vector can carry metadata (tags, categories, numeric fields, etc.). During a search, one can combine semantic similarity with metadata filters.
- Query & Retrieval: When a user query arrives, it is similarly embedded into a vector. The vector database then performs an ANN search to find the top-K closest vectors in its index. These candidates represent the most semantically relevant items. The system can then re-rank or post-filter these results as needed.
In summary, the query flow is: input → embedding model → query vector → ANN search → nearest vectors (plus any metadata filtering) → return results. This process is what powers fast, semantic “closest match” searches in AI applications.
Popular Vector Databases Used in Production
Modern AI systems often use specialised vector databases. Below are some well-known options:
- Weaviate Vector Database
Weaviate is an open-source vector database that emphasises flexibility and semantic search. It uses a schema/ontology model (with a GraphQL API) so you can define classes and properties for your data. It supports hybrid search: you can combine exact keyword and vector queries in one request. Weaviate is designed for enterprise use, offering features like RBAC, multi-tenancy, and deployment on cloud or on-premise. According to industry sources, Weaviate can scale to billions of objects and return nearest neighbours in milliseconds. Because of this, it’s popular for applications that need schema-driven data and advanced filtering alongside vector similarity (e.g. semantic search on business data, AI-driven knowledge graphs).
- Qdrant Vector Database
Qdrant is an open-source, self-hosted vector search engine written in Rust. It’s built for performance and rich filtering. Qdrant provides a simple API service for storing vectors and payloads, and uses a custom HNSW index to deliver fast, accurate similarity search. Unique to Qdrant is its advanced filtering: you can attach arbitrary data to each vector (called “payload”) and filter search results by those fields. Qdrant also supports geo-search and hybrid queries. Its cloud-native design and horizontal scaling make it attractive for production deployments where teams need full control over infrastructure. Common use cases include recommendation engines and search with complex filtering requirements. In short, Qdrant excels when you need open-source flexibility, high performance, and metadata-based filtering.
- ChromaDB Vector Database
Chroma (ChromaDB) is a lightweight, open-source vector store designed primarily for experimentation and small deployments. It’s easy to install locally and integrates with Python ML stacks (LangChain, LlamaIndex, etc.). Chroma allows you to quickly ingest text documents, convert them to embeddings, and perform similarity queries. Its same APIs work from a Jupyter notebook up to a production cluster. Because of its simplicity, Chroma is often used for local development, proof-of-concepts, or academic projects. While it can scale, it lacks some of the enterprise features of others. Still, it’s an excellent way to prototype LLM-based apps before moving to a full-scale vector database.
Choosing the Right Vector Database
There is no single “best” vector database; the right choice depends on your needs. Key factors include scale, deployment model, and feature needs. If you need open-source control or on-premise install, consider Weaviate or Qdrant.
You should also think about data types and queries: Do you need hybrid keyword+vector search? Do you require complex metadata filtering or ACID transactions?
In practice, teams often prototype with a lightweight store (like Chroma) and plan for a production-grade system early. Early design decisions matter: indexing strategy, embedding model choice, and metadata schema all affect future performance. Match the database to your use case.
Vectors as AI Infrastructure
Vectors are not just a transient data format; they are becoming a core part of AI infrastructure.
As AI pioneer blogs note, vector databases have become central to the “digital experience” of modern systems. Once your application relies on vector search, your database choice and data pipeline will shape its performance and capabilities.
Understanding vectors early means building systems that scale. For instance, investing in good embedding models and proper indexing upfront makes downstream similarity search fast and accurate.
As vector databases become core infrastructure, specialised vector migration tools have begun to emerge. These tools automate the movement of embeddings and associated metadata between vector databases, reducing downtime and operational risk.
To address this growing need, we have launched Vector Migration, a platform designed to help teams transition vector data across systems more smoothly. It enables organisations to move existing embeddings into a new vector database without reworking their retrieval pipelines, making it easier to evolve AI architectures as requirements change.
