Vector Database: Everything You Need to Know

What is a Vector?

Vectors in machine learning (ML) is a generic term for a grouping of numbers, groups of numbers, or numerical values. They represent locations in space.

Vectors are elements of tensors, which themselves function to sort and manage data. There are several of these elements, which differ in size and function:

A scalar is the smallest element: a zero-degree tensor that contains a single number. For example, a system modeling classroom data might represent a grade on one exam (as a percentile) in scalar form as 95.

A vector is a first-order or first-degree tensor that contains multiple scalars of the same type of data. For example, the same classroom model might use the low, mean and high scores for a single exam in vector form: 64, 82, and 97 percent, respectively. Each scalar component of the vector is one of its features or dimensions, representing just one feature of that exam’s results.

Vector numbers can also represent high-dimensional things an ML model generates, such as audio, images, videos, and words. This complex, multi-featured vector data is essential to various AI tasks such as machine learning and natural language processing (NLP).

What are vectors used for? Some example forms and uses of vector data include:

• Text. To understand natural language, chatbots rely on vectors that represent words, phrases, paragraphs, and whole documents.
• Images. Numerical data can describe pixels of an image and combine with them to create a high-dimensional vector.
• Sound. Numerical data can also describe sound waves that in turn can be represented as vectors to enable voice recognition and other AI applications.

What is a Vector Database?

A vector database indexes and stores data collected as mathematical representations for quick retrieval and search. Vector databases allow machine learning models to recall and refer to previous inputs, which in turn enables more powerful, data-based text generation, searches, and recommendations. The vector database can identify data based on metrics that highlight similarities between vectors rather than exact matches, which allows the model to “see” the data in context.

If you shop for new jeans in person, a human salesperson can make suggestions. They may base them on brands and styles that are similar to jeans that you like now, jeans that you have bought in the past, and those that have the most features you like.

When you move your jeans shopping online to an ecommerce store, the platform can make similar suggestions using vector data. For example, you might see suggestions based on “Your last purchase,” or “Customers also bought,” or “Popular styles that also feature a bootleg cut.”

Machine learning models use vector databases to identify objects that are similar, just as a human salesperson can find jeans that are similar and an ecommerce platform can suggest products that are related to previous purchases or have similar features to your ongoing browsing—in fact, most ecommerce stores rely on these kinds of machine learning models.

Vector databases allow computer programs to identify relationships, draw comparisons, and understand context. This is the foundation of advanced artificial intelligence (AI) programs such as large language models (LLMs).

How Do Vector Databases Work?

A vector database is designed so it efficiently indexes, processes, searches, and stores vector embeddings. Most vector databases operate principally on vector embeddings. Vector databases convert data such as text, images, or videos into vectors using machine learning models like transformers for text or convolutional neural networks for images. These numerical representations capture semantic or contextual similarities between data points.

For example, two similar sentences, “How do I make a classic cheesecake?” and “Can I make a traditional cheesecake?” are likely to be represented by vectors that are close in space. Grouping similar data together leads to vector database clustering, and the ability to more efficiently identify similar types of data for queries.

Vectors are stored in a vectorized database that is optimized for high-dimensional data structures. Each data point consists of a vector and its features and metadata such as IDs, labels, or descriptions that assists with both queries and data management.

Queries are generally performed based on similarity, identifying other vectors that are most similar to the queried vector. Similarity is typically computed using distance metrics. Some of these include:

• Cosine similarity, which focuses on the angle between vectors.
• Euclidean distance, which measures straight-line distance in the vector space.
• Dot product, which measures the projection of one vector onto another.

Various machine learning algorithms such as approximate nearest neighbor (ANN) search, locality sensitive hashing (LSH), hierarchical navigable small world (HNSW), and Product Quantization (PQ) can speed up queries and enable fast similarity searches even in massive datasets.

Many vector databases allow combining vector similarity with traditional metadata filtering. For example, you might be able to retrieve vectors close to the query vector and match metadata like “category = clothing.”

Vector databases also integrate with tools, applications, and frameworks like PyTorch, TensorFlow, or Hugging Face for generating embeddings. They’re used by recommendation systems to suggest items based on user preferences, search engines to retrieve contextually similar documents or images, chatbots to identify the best response to a user query, and anomaly detection to detect outliers in data.

Vector Database Architecture Explained

Key components of vector database architecture include layers for data ingestion, storage, indexing, queries, processing, model management, and analytics.

The data ingestion layer accepts raw data in text, image, audio, and other forms and processes it into vectors using pre-trained or fine-tuned machine learning models to generate vector embeddings.

The vector database persistently stores associated metadata with each data point alongside the vector in the storage layer. The structure is usually optimized for fast retrieval and low memory usage, and compression techniques may be employed to minimize storage size. The system may also use partitioning to split data into manageable chunks for distributed storage.

The indexing layer facilitates efficient retrieval of vectors during queries using the ANN indexes discussed above. The goal is to balance accuracy and query speed.

The query execution layer generates queries from user input processed by the same model used during ingestion. It then performs a similarity search using some distance metric, and refines results using metadata filters before ranking and returning them.

The distributed processing layer ensures scalability and fault tolerance using sharding, replication across nodes, and load balancing.

The model management layer maintains and updates embedding models to improve them over time and synchronize the models used for data ingestion and query vector generation.

The monitoring and analytics layer tracks system performance and provides insights based on vector database benchmarks such as query latency, accuracy of similarity matches, system load, and storage utilization.

Common vector database architectures include:

• Centralized architecture which manages all data and processing on a single server or cluster; this is simpler but less scalable.
• Distributed architecture which distributes data across multiple nodes for scalability and fault tolerance and is common in large-scale deployments.
• Cloud-native architecture built to leverage cloud infrastructure for elasticity and scalability.

Vector Embeddings

A vector embedding is a representation of vector data generated by a neural network that includes semantic information the AI needs to understand relationships and patterns, maintain a long-term memory, and execute complex tasks.

A typical vector database for a deep learning model is made up of vector embeddings. A well-tuned neural network can generate vector embeddings on its own, and these can be used for contextual analysis, generative AI, similarity searches, and other applications.

Vector Indexing

A vector index is a data structure that organizes complex data vectors to ensure they are more efficient and searchable. Some vector indices are standalone platforms, and others are part of vector databases.

Vector Database vs Vector Index  

Vector databases are designed to manage vector embeddings, while standalone vector indices like Facebook AI Similarity Search (FAISS) can significantly improve the search and retrieval of vector embeddings, but lack capabilities that exist in vector databases. There are several advantages to indexing in vector databases versus using standalone vector indices:

• Data management. Vector databases offer data storage features such as deleting, inserting, and updating data that simplify managing and maintaining vector data compared to using a standalone vector index, which demands additional effort to integrate with storage solutions.
• Metadata storage and filtering. Vector database indexing stores metadata associated with each entry, enabling database queries with metadata filters for finer-grained results.
• Scalability. A vector database scales with increasing data volume and user demands, with more support for parallel and distributed processing. A vector index can scale, but may demand custom solutions to achieve that scale, such as deployment and management on Kubernetes clusters. The serverless architectures of vector databases also optimize cost at scale.
• Real-time updates. Vector databases typically support real-time data updates and dynamic changes that ensure new data and fresh results, while a standalone vector index may need a full, computationally expensive and time-consuming re-indexing process to incorporate new data.
• Backups. Vector databases routinely backup all stored data and often allow users to select specific indexes for backup and storage for later use.
• Ecosystem integration. Vector databases can streamline the data management workflow by more easily integrating with other components of a data processing ecosystem such as analytics tools, ETL pipelines, and visualization platforms.
• Data security and access control. Vector databases typically offer built-in access control and data security features that may not be available in standalone vector index solutions.

In summary, using a vector index vs vector database may subject the user to a lack of built-in security measures and real-time updates, scalability challenges, and more difficult integration processes.

Vector Storage Explained

Various mechanisms and techniques are used to store high dimensional vectors to allow for efficient retrieval, updates, and scalability. They are all critical to vector databases.

Vectors are stored based on use case and scale. In-memory storage (RAM) is used for real-time applications requiring low latency. Disk-based storage on SSDs or distributed storage systems is best for larger datasets. And cloud storage offers scalability and durability for massive datasets.

Vectors can be stored as raw arrays or organized using specialized data structures. Dense vectors represent all dimensions explicitly, even if some values are zero. In contrast, sparse vectors represent only non-zero dimensions, saving memory for high-dimensional data with many zeros.

All vector storage is indexed to enable fast queries, and integrates various techniques. Approximate nearest neighbor search (often just called ANN indexing) optimizes similarity search with structures like HNSW or FAISS partitions. KD- or Ball tree-based structures can be used for smaller datasets or lower dimensions. And hashing techniques such as LSH map vectors into buckets with high similarity.

Vectors often have associated metadata stored alongside them to enable hybrid queries that combine vector similarity with traditional filtering. For example: “Find vectors similar to X where category = “clothing.”

Large-scale applications partition and distribute vectors across multiple nodes. Techniques like sharding (dividing data into chunks) and replication (copying data across nodes) ensure scalability and fault tolerance.

What is the difference between a vector store vs vector database?

Vector stores are tools for storage and retrieval that are optimized for the technical specifications of embedded vector data, while vector databases are more comprehensive systems with additional features.

Different Types of Vector Databases

Any vector database comparison includes a few alternatives to consider.

Vector databases come in different types, optimized for specific use cases or deployment environments. Proprietary, stand-alone, fully vectorized databases are designed to manage, index, and retrieve high-dimensional vector embeddings, enabling advanced applications like semantic search, recommendation systems, and AI-driven tasks.

Other options such as open-source solutions with built-in RESTful APIs and support for various programming languages or data lakes with integrated vector database similarity search capabilities are also available.

Here’s a detailed look at the types of vector databases and related concepts:

Cloud vector databases are managed services hosted on cloud platforms such as Amazon Web Services (AWS), Azure, or Google Cloud Platform (GCP). They offer fully managed scalability, high availability and fault tolerance, and can integrate with other cloud services. Milvus on cloud, Pinecone, and Weaviate Cloud Service are examples of cloud vector databases.

Typical use cases for cloud vector databases include large-scale applications where infrastructure management is outsourced, and services that require dynamic scaling and global reach.

Retrieval-Augmented Generation (RAG) vector databases are used in systems that combine information retrieval with generative AI models. Vector databases for RAG are optimized for fast retrieval of knowledge-base embeddings and support hybrid search based on vector similarity and metadata filtering.

Typical use cases for RAG vector databases include applications with OpenAI GPT or similar LLMs, such as dynamic context injection or response enhancement.

AI vector databases are designed specifically for AI workloads, integrating tightly with machine learning pipelines. They offer GPU acceleration for faster vector computations and seamlessly integrate with AI frameworks like TensorFlow and PyTorch. Examples of AI vector databases include Zilliz, a GPU-accelerated version of Milvus.

Use cases for AI vector databases include real-time AI applications like image or video analysis, AI model serving pipelines, or GPU acceleration.

Vector databases for LLMs are tailored to manage embeddings generated by LLMs. They offer high-dimensional vector support for embeddings from models like GPT, metadata filtering, and hybrid query capabilities.

Some typical use cases for vector databases for LLMs include chatbots and conversational AI systems as well as semantic search in text-heavy applications.

Semantic search vector databases are specialized for semantic search, where similarity is based on meaning rather than exact matches. They offer advanced indexing techniques for fast similarity search and support for contextual, sentence-level embeddings.

Use cases for semantic search vector databases include search engines for text, images, or multimodal data and e-commerce platforms—really any application with meaning-based search as the primary goal.

A disaggregated storage vector system is one in which vector storage and compute are decoupled, allowing for independent scaling. This in turn offers scalable storage for massive datasets and is more cost efficient thanks to the separation between compute and storage resources.

Typical use cases for disaggregated vector storage include large-scale AI systems needing independent scaling of data and query workloads and cloud-based deployments where storage costs are optimized separately.

Multimodal vector database retrieval systems support vectors from different modalities, such as text, image, and audio. They do this by combining embeddings from multiple data types into a unified search space to enable cross-modal queries, for example: “Find images similar to this text description.”

Use cases for multimodal vector database retrieval systems include content recommendation platforms such as Pinterest or YouTube and advanced search engines that handle diverse media types.

Serverless Vector Database

A serverless vector database is a subtype of cloud vector database that does not require the user to manage any servers. Instead, the vector database is housed in the cloud and scales up or down automatically without manual intervention based on demand.

Vector Database vs Graph Database

Vector databases vs graph databases are both powerful data management tools, but they have different strengths and use cases. Vector databases are best suited for similarity searches, high-dimensional data management, and machine learning tasks such as content-based image retrieval and word embeddings.

In contrast, graph databases are better for analyzing relationships and complex networks that require real-time visibility and insights such as social networking platforms and knowledge graphs.

Vector Database vs Relational Database

Traditional database systems, and relational databases in particular, are best for conducting precise search operations and managing structured data with formats that are predefined. In contrast, vector databases specialize in indexing, storing, and retrieving unstructured types of data, such as audio, video, image, and text content. Unlike traditional relational databases which store data in rows and columns, vector database clustering stores data points based on similarity.

Vector Database Use Cases

Top use cases for vector databases include: anomaly detection, biometric identification applications, fraud detection, image and video search, natural language processing (NLP), personalized advertising, recommendation systems for e-commerce platforms, and analysis of complex data like genomic sequences or medical images. Essentially, the use of vector databases is appropriate for any situation in high-dimensional space where you need to find similar items based on complex data representations, often generated by machine learning models.

When to Use a Vector Database

Here are some more specific vector database examples:

• Semantic document retrieval. Searches for research papers, legal documents, or articles based on meaning rather than exact matches help researchers find more detailed results.
• Generative RAG for developers. Code assistants fetch code snippets or documentation for developers.
• Video scene search. Users search for images or videos using visual or textual queries and can locate specific images or scenes in a video repository by simply describing target content.
• Fraud and anomaly detection in e-commerce. These vector database applications identify patterns or behaviors that deviate from the norm such as fake reviews or fraudulent purchase patterns and ensure product listings adhere to platform guidelines.
• Personalized learning and training. Tailoring content or recommendations to individual employees for training and suggesting resources for upskilling based on job role and performance data.
• Content moderation for social media platforms. Identifying and filtering inappropriate or harmful content in text, images, or videos and detecting similar harmful posts or comments based on embeddings.

About the Vector Database Market <H2>

The vector database market is expected to grow from $1.98 billion in 2023 to $2.46 billion in 2024, at a compound annual growth rate (CAGR) of 24.3%. Several factors should contribute to this growth, including: the rise of location-based services (LBS), growth of applications in smart cities, the 5G network, and demand for real-time spatial analytics.

Some of the top vector databases for 2024 include Chroma, Pinecone, and pgvector. Some of the top open-source vector databases for 2024 include Faiss, Milvus, Qdrant, and Weaviate.

Advantages of Vector Databases

Some of the benefits of vector databases include:

Semantic understanding and efficient similarity search. Unlike traditional databases, vector databases store and query high-dimensional data and capture semantic meaning. Your query for “black pants” will also pull up black leggings, black jeans, and black slacks, for example.

Scalability for large datasets. Vector databases can handle billions of vector embeddings while maintaining query performance, and their distributed architectures allow horizontal scaling across nodes.

Multimodal support. Vector databases enable the integration and retrieval of vectors from different modalities, such as text, image, and audio. They also allow for hybrid query capabilities that combine traditional structured filters with vector similarity search.

Real-time and low-latency queries. Vector databases are optimized for applications requiring real-time responses, such as recommendation engines, voice assistants, or fraud detection.

Integration with AI workflows. A vector database works seamlessly with machine learning and deep learning pipelines, including embeddings from models like BERT, GPT, or ResNet. Many vector databases offer SDKs, APIs, and integrations with popular ML frameworks, and managed cloud services reduce operational complexity.

Challenges of Vector Databases

There are many advantages to these systems, but there are a number of challenges of vector databases as well:

High dimensionality. As the number of dimensions increases, the effectiveness of distance metrics like Euclidean or cosine similarity diminishes. Mitigating this issue requires specialized indexing techniques.

Scalability vs latency trade-off. Balancing the need for scalability with low-latency performance can be challenging, especially in distributed systems. Indexing large datasets can consume significant memory and computational resources.

Indexing overhead. Building and maintaining efficient indexes for billions of vectors can be resource-intensive. Index updates may require significant time or re-indexing.

Cost. High-dimensional embeddings consume substantial storage, especially for dense vectors. Running ANN queries at scale often requires high-performance hardware.

Complexity. Integrating vector databases into existing systems may require redesigning data pipelines or query patterns. Hybrid systems combining traditional databases with vector databases can add architectural complexity.

Cold storage and archive. Vectors stored in memory for low latency may not be cost-effective for infrequently accessed data. Cold storage solutions introduce latency but are more economical.

Accuracy vs speed. Approximate methods used for fast ANN retrieval may sacrifice accuracy. Applications requiring exact nearest neighbors may experience slower performance.

Lack of standardization. Many vector databases have unique APIs and query languages, making interoperability challenging. Migrating from one vector database to another can be labor-intensive.

Security and privacy. Sensitive embeddings—for example those derived from personally identifiable information (PII) such as user behavior or medical data—raise privacy concerns and require robust security measures.

Use-case specific challenges. Effective use of vector databases often requires knowledge of embeddings, ANN algorithms, and tuning hyperparameters such as the number of neighbors or recall thresholds. Working with multimodal data requires managing embeddings from diverse data types and ensuring efficient retrieval across modalities. Running real-time applications demands achieving millisecond-level latency. And working with dynamic data requires frequent updates to dynamic datasets.

WEKA and Vector Databases

The WEKA® Data Platform is an ideal storage solution for vector databases in Retrieval-Augmented Generation (RAG) pipelines because it delivers the high-performance, low-latency, and scalability required for AI-driven workloads. Vector databases, such as FAISS, Milvus, or Weaviate, rely on rapid similarity searches across massive high-dimensional datasets. WEKA’s ability to deliver ultra-low-latency reads and writes—combined with its parallel file system architecture—ensures that vector embeddings can be retrieved and processed quickly, reducing bottlenecks in RAG applications that rely on fast recall for generating accurate responses.

Another key advantage of WEKA is its ability to handle both a variety of IO types and sizes  within a single configuration. Many RAG implementations require the integration of vector embeddings with large-scale unstructured data sources, such as PDFs, images, and video. WEKA’s data platform eliminates the need for separately tuned storage silos, enabling high-speed access to both vector indexes and the raw data that underpins generative AI models. Additionally, its native support for GPUs and multi-node AI infrastructure accelerates complex AI workloads, ensuring that both training and inference benefit from WEKA’s high-throughput performance.

Finally, WEKA offers seamless scalability and cloud-native flexibility, making it well-suited for dynamic AI deployments. As vector databases grow alongside expanding AI models, WEKA’s ability to scale performance linearly across on-premises and cloud environments ensures that RAG applications can handle increasing data volumes without sacrificing performance. With enterprise-grade resilience, snapshots, and multi-tenancy support, WEKA also enhances the reliability and security of AI-driven pipelines, making it a compelling choice for businesses looking to operationalize RAG at scale.

Contact WEKA today to learn more about how WEKA can improve your cloud workloads.