What is a vector database?

In a vector database, data points are stored as arrays of numbers called “vectors,” which can be compared and clustered based on similarity. This design enables low-latency queries, making it ideal for artificial intelligence (AI) applications.

Vector databases are growing in popularity because they deliver the speed and performance needed to drive generative AI use cases. In fact, according to 2025 research, vector database adoption grew 377% year over year—the fastest growth reported across any large language model (LLM)-related technology.

The nature of data has shifted dramatically in recent years. It is no longer confined to structured information stored neatly in the rows and columns of traditional databases. Unstructured data—including social media posts, images, videos and audio—is growing in both volume and value, reshaping enterprise AI strategies while putting new demands on data infrastructure.

Traditional relational databases excel at managing structured and semi-structured datasets within defined schemas. However, loading and preparing unstructured data in a relational database for AI workloads is labor-intensive.

Traditional search compounds this limitation: it relies on discrete tokens such as keywords, tags or metadata and returns results based on exact matches. A search for “smartphone,” for example, retrieves only content containing that specific term.

Vector databases take a fundamentally different approach. Instead of rows and columns, data points are represented as dense vectors where each dimension represents a learned characteristic of the data. These high-dimensional vector embeddings exist in vector space, where relationships between items can be measured geometrically.

Because each dimension represents a latent feature—an inferred characteristic learned through mathematical models and algorithms—vector representations capture hidden patterns. A vector search query for “smartphone” can also return semantically related results such as “cellphone” or “mobile device,” even if those exact words do not appear.

By modeling data in high-dimensional space and applying specialized indexing techniques, vector databases make it possible to perform low-latency similarity search across large datasets—something relational databases were not designed to support.

The rapid rise of LLMs, generative AI systems and advanced natural language processing (NLP) workflows has changed how organizations handle and store data. Today’s AI workloads depend on fast, real-time interaction with vector data as well as seamless integration with retrieval-augmented generation (RAG) pipelines.

Vector databases provide the infrastructure to support these demands. They enable low-latency similarity search across large volumes of unstructured data, powering AI applications such as chatbots and recommendation systems.

To understand how vector databases operate, it helps to establish two core concepts: vectors, which describe data in numerical form, and vector embeddings, which translate unstructured content into high-dimensional representations that capture meaning and context.

Vectors are a subset of tensors. In machine learning (ML), tensor is a generic term for a group of numbers—or a grouping of groups of numbers—in n-dimensional space. Tensors function as a mathematical bookkeeping device for data. Working up from the smallest element:

• A scalar is a zero-dimensional tensor, containing a single number. For example, a system modeling weather data might represent a single day’s high temperature (in Fahrenheit) in scalar form as 85.

• A vector is a one-dimensional (or first-degree or first-order) tensor, containing multiple scalars of the same type of data. Building on our example, a weather model might use the low, mean and high temperatures for a single day in vector form: 62, 77, 85. Each scalar component is a feature—that is, a dimension—of the vector, representing a feature of that day’s weather.

In other words, vectors are a way of organizing numbers into a structured form. But for AI systems to process that unstructured information, the data must be translated into numerical arrays. This translation is achieved through vector embeddings.

Vector embeddings are numerical representations of data points that convert various types of data—including text and images—into arrays of numbers that ML models can process.

To achieve this, embedding models learn how to map input data into a high-dimensional vector space. That vector space reflects patterns learned through a task-specific loss function, which quantifies prediction errors. Vector embeddings can then be used by downstream AI models, like neural networks used in deep learning, to perform tasks like classification, retrieval or clustering.

Consider a small corpus of words, where the word embeddings are represented as 3-dimensional vectors:

• cat [0.2, -0.4, 0.7]
• dog [0.6, 0.1, 0.5]

In this example, each word (“cat”) is associated with a unique vector ([0.2, -0.4, 0.7]). The values in the vector represent the word’s position in a 3-dimensional vector space. Words with similar meanings or contexts are expected to have similar vector representations. The vectors for “cat” and “dog” would be close together, reflecting their semantic relationship.

Similarly, the words “car” and “vehicle” share the same meaning but are spelled differently. For an AI application to perform semantic search, the vector representations of “car” and “vehicle” must capture their shared meaning. Vector embeddings encode this meaning numerically, making them the backbone of recommendation engines, chatbots and generative applications like OpenAI’s ChatGPT.

To facilitate fast and scalable semantic retrieval, vector databases rely on three core functions:

• Vector storage
• Vector indexing
• Vector search

At a foundational level, vector databases store embeddings. Each has a fixed number of dimensions and is typically stored alongside metadata such as title, source, timestamp or category, which can be queried using metadata filters.

Because embeddings are generated in advance and stored, vector databases can retrieve similar vector embeddings without recomputing representations at query time. This separation of generation and retrieval supports low-latency similarity search at scale.

Many systems also support hybrid search that combines vector similarity with metadata constraints—for instance, retrieving semantically similar documents created within a specific date range or category.

To accelerate similarity search in high-dimensional space, vector databases create indexes on stored vector embeddings. Indexing maps the vectors to new data structures, enabling faster similarity or distance searches between vectors.

These indexes support approximate nearest-neighbor (ANN) search, which retrieves similar vectors without scanning the entire dataset. Common ANN indexing algorithms include hierarchical navigable small world (HNSW) and locality-sensitive hashing (LSH):

• HNSW creates a hierarchical, multi-layer graph that uses long-range links in upper layers and dense local links in the bottom layer.¹

• LSH groups vectors into buckets using a hash function so that similar vectors fall into the same bucket.

In addition to ANN indexes, vector databases often use product quantization (PQ) to reduce memory usage. PQ converts each dataset into a short code that preserves relative distance (rather than storing every vector), allowing systems to store larger collections while maintaining efficient search performance.

Vector search is the retrieval layer of a vector database used to discover and compare similar data points. Rather than matching exact keywords or values, it captures the semantic relationships between elements. This context-aware retrieval capability underpins RAG systems, which in turn supply relevant context to AI systems and retrieval-based machine learning models.

When a user prompts an AI model, the model generates an embedding of that query, known as a query vector. The database then compares the query vector against indexed vectors and calculates similarity scores to identify the nearest neighbors.

Vector search applies multiple algorithms to conduct an ANN search. These algorithms are gathered in a pipeline to quickly and accurately retrieve data neighboring the vector that is queried (for example, products that are visually similar in an e-commerce catalog). Since embeddings are precomputed and stored in indexed form, results are returned within milliseconds.

Once the relevant vectors are identified, they’re compared either by calculating their similarity or with a distance metric. Common methods include:

• Cosine similarity: Measures the angular distance between vectors to determine how aligned they are in direction.

• Jaccard similarity: Compares the overlap between two sets relative to their total elements.

• Dot product: Evaluates similarity based on the magnitude and direction of vectors.

• Euclidean distance: Calculates the straight-line distance between vectors in high-dimensional space.

• Hamming distance: Counts differing positions between binary vectors.

The database returns the highest-ranking vectors according to these similarity calculations, supporting machine learning tasks such as semantic search and other natural language processing workflows.

Vector databases are increasingly central to enterprise AI strategies because they deliver a range of benefits:

• Speed and performance: Vector databases use various indexing techniques to enable faster searching. Vector indexing and distance-calculating algorithms can help optimize performance when searching for relevant results across datasets with millions, if not billions, of data points.

• Scalability: Vector databases can store and manage massive amounts of unstructured data by scaling horizontally with additional nodes, maintaining performance as query demands and data volumes increase.

• Lower cost of ownership: Because they enable faster data retrieval, vector databases speed the training of foundation models.

• Data management: Vector databases typically provide built-in data management features to easily update and insert new unstructured data.

• Flexibility: Vector databases are built to handle the added complexity of using images, videos or other multidimensional data.

Vector databases can be customized to meet specific business and AI use cases. Often, organizations start with a general-purpose embedding model such as IBM® Granite™, Meta’s Llama-2 or Google’s Flan. Models are then enhanced using enterprise data stored in a vector database. This combination improves the relevance and accuracy of downstream AI applications.

The applications for vector databases are vast and expanding. Key use cases include:

• Retrieval-augmented generation
• Conversational AI
• Recommendation engines
• Anomaly detection

RAG enables LLMs to retrieve facts from an external knowledge base. Enterprises increasingly favor RAG for its faster time-to-market, efficient inference and reliable output, particularly in areas such as customer care, HR and talent management.

By grounding the model in trusted enterprise data, RAG reduces hallucinations and gives users access to the underlying sources for verification. Because the inference stage performs the highest-volume retrieval operations, it requires fast, precise and scalable access to high-dimensional vector embeddings.

Vector databases excel at indexing, storing and retrieving these embeddings, providing the speed, precision and scale needed for applications such as fraud detection systems and predictive maintenance platforms.

Vector databases, particularly when used to implement RAG frameworks, can help improve virtual agent interactions by enhancing the agent’s ability to parse relevant knowledge bases efficiently and accurately. Agents can provide real-time contextual answers to user queries, along with the source documents and page numbers for reference.

E-commerce sites can use vectors to represent customer preferences and product attributes. This allows them to improve customer experience and retention by suggesting items similar to past purchases. Streaming platforms and social media applications apply the same approach, recommending videos, music or posts based on similarity to content a user has previously viewed or shared.

By representing normal behavior as vectors in high-dimensional space, organizations can detect outliers based on vector distance. Data points that fall far from established clusters can signal fraud, system faults or unusual activity patterns. Because similarity is calculated mathematically, anomalies can be detected in real time across massive datasets—from network traffic to sensor readings in industrial systems. This allows teams to intervene before small deviations escalate into costly incidents.

While vector databases are well suited for fact-based retrieval across many AI applications, they are not ideal for every type of query.

Workloads such as topic summarization or broad thematic analysis require an LLM to read through all relevant context rather than rely solely on nearest-neighbor matches. In these scenarios, a list index or another non-vector structure may provide faster, more efficient results, since they can quickly surface the first relevant elements without navigating vector space.

Vector databases support a wide range of AI workloads, but the value they deliver varies by role. In most enterprises, users fall into two broad groups: builders, who design and implement AI-driven experiences, and operators, who scale and maintain those systems in production.

Builders create the applications, pipelines and models that rely on vector search, using vector databases to store embeddings and power AI applications.

Operators ensure vector workloads remain scalable and reliable. They manage how vector databases run in production and how they fit into broader data and AI ecosystems.

Organizations have a breadth of options when choosing a vector database capability. To find one that meets their data and AI needs, many organizations consider:

• Types of vector databases
• Integration with a data ecosystem
• Tools for creating and deploying vector databases

There are a few options organizations can choose from, including:

• Stand-alone vector databases: Proprietary, fully vectorized databases such as Pinecone.

• Open source vector databases: Open source solutions such as Weaviate or Milvus, which provide built-in RESTful APIs and support for Python and Java programming languages.

• Data lakehouses with integrated vector capabilities: Data lakehouses with vector database capabilities integrated, such as IBM watsonx.data™.

• Vector extensions for existing databases: Vector database and database search extensions—such as PostgreSQL’s open-source pgvector extension—which provides vector similarity search capabilities. An SQL vector database can combine the advantages of a traditional SQL database with the power of a vector database.

• Search engines with vector support: Platforms such as OpenSearch, which provide built-in vector search features along with RESTful APIs for ingesting and querying embeddings.

An emerging option for running vector workloads is a serverless vector database. Serverless designs remove the need to manage or provision infrastructure, allowing teams to focus on embedding generation and application development rather than cluster operations. Capacity can scale automatically based on query volume and data size, helping teams handle unpredictable workloads without performance tuning.

Serverless vector databases are especially useful for rapid prototyping, event-driven AI applications and development environments where cost control and operational simplicity are priorities.

Vector databases should not be considered as stand-alone capabilities, but rather a part of a broader data and AI ecosystem.

Many offer APIs, native extensions or can be integrated with databases. Because vector databases are built to use enterprise data to enhance models, organizations must also have proper data governance and security in place to help ensure that the data used to train LLMs can be trusted.

Beyond APIs, many vector databases use programming-language-specific SDKs that can wrap around the APIs. Using the SDKs, developers often find it easier to work with the data in their apps.

To optimize vector database development, LangChain is an open-source orchestration framework for developing applications that use LLMs.

Available in both Python-based and JavaScript-based libraries, LangChain’s tools and APIs simplify the process of building LLM-driven apps such as virtual agents using local and cloud-based vector stores. In fact, LangChain provides access to a broad ecosystem with 1,000+ total integrations across LLMs, embeddings, vector stores, document loaders, tools and more. 

A data lakehouse can be paired with an integrated vector database to help organizations unify, curate and prepare vectorized embeddings for their generative AI applications. This enhances the relevance and precision of their AI workloads and, ultimately, delivers better business outcomes.