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A vector database is a specialized database that stores, manages, and indexes large-scale data objects in numerical forms in a multi-dimensional space. These objects are known as vector embeddings.
Unlike traditional relational databases that store data in rows and columns, vector databases store information as numbers to fully capture the contextual meaning of the information. This numerical representation allows vector databases to portray different data dimensions, cluster data based on similarities, and execute low-latency queries.
Vector databases process data faster than traditional databases and more accurately identify patterns from large datasets, which makes them ideal for applications involving artificial intelligence (AI), artificial neural networks, natural language processing (NLP), large language models (LLM), computer vision (CV), machine learning (ML), generative AI models, predictive analysis, and deep learning.
How do vector databases work?
Vector databases use different algorithms to index and query vector embeddings. The algorithms use hashing, graph-based search, or quantization to perform approximate nearest neighbor (ANN) searches. A pipeline assembles the algorithms to correctly retrieve a query’s closest vector neighbors.
Despite being comparatively less accurate than known nearest neighbor (KNN) search, ANN search can find high-dimensional vectors efficiently in large datasets. Below is the detailed process of how a vector database works.
Indexing
Indexing in vector databases involves using hashing, graph-based, or quantization techniques for faster record retrieval.
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A hashing algorithm quickly generates approximate results by mapping similar vectors to the same hash bucket. Locality-sensitive hashing (LSH) is a popular technique for mapping nearest neighbors in ANN search. LSH determines similarity by hashing queries into a table and comparing them to a set of vectors.
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The quantization technique divides high-dimensional vector data into smaller chunks for compact representation. After representing those smaller parts using codes, the process combines them. The result represents a vector and its components using an ensemble of codes or a codebook.
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Product quantization (PQ) is a popular quantization method. It finds the most similar code by breaking queries and matching them against the codebook. Unlike other quantization methods, PQ reduces the memory size of indexes.
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Graph-based indexing uses algorithms to create structures that reveal connections and relationships among vectors. For example, the Hierarchical Navigable Small World (HNSW) algorithm produces clusters of similar vectors and draws lines between them. The HNSW algorithm looks at the graph hierarchy to discover nodes containing vectors similar to the query vector. Besides containing a vector index, a vector database also holds a metadata index, which stores the metadata of data objects.
Querying
Vector database querying allows users to extract useful insights by finding vectors with similar characteristics as their data. A vector database uses various mathematical methods or similarity measures to compare indexed vectors with the query vector and find the nearest vector neighbors.
Vector databases use the following similarity measures in image recognition, anomaly detection, and recommendation system applications.
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Cosine similarity uses the cosine angle between two non-zero vectors to plot identical, orthogonal, and diametrically opposed vectors. Identical vectors are denoted by 1, orthogonal vectors by 0, and diametrically opposed vectors by -1. This cosine angle helps a vector database understand if two vectors point in the same direction.
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Euclidean distance calculates distances between vectors in Euclidean space on a range of zero to infinity. While zero represents identical vectors, higher values indicate dissimilarity between vectors.
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Dot product similarity considers the cosine angle, direction, and magnitude between vectors to identify their similarities. It assigns positive values to vectors pointing in the same direction and negative values to those in opposite directions. The dot product remains zero in the case of orthogonal vectors.
Post-processing
Post-processing, or post-filtering, is the final step in a vector database pipeline's process of retrieving the final nearest neighbors. Here, a vector database re-ranks nearest neighbors using a different similarity measure. A database may also filter the nearest neighbors using a query’s metadata.
Key features of vector databases
Vector database software supports horizontal scaling, metadata filtering, as well as the create, read, update, and delete (CRUD) operations with vector storage, vector embeddings, multi-tenancy, and data isolation features.
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Vector storage: A vector database stores, manages, and indexes high-dimensional vector data. It also clusters vectors based on their similarities for efficient low-latency querying and keeps metadata for every vector entry in order to filter queries.
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Complex object representation: Vector databases represent images, videos, words, audio, and paragraphs using an array of numbers or vectors.
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Vector handling: Vector databases use specialized models to efficiently convert raw vector data into vector embeddings or continuous, multi-dimensional vector representations. These embeddings play a role in computing semantic similarity, clustering, and gathering related vectors.
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Rapid scalability: A vector database relies on distributed and parallel processing to handle growing data volumes from machine learning models and AI algorithms. Besides scalability, vector databases also feature fine-tuning capabilities for performance optimization.
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Multi-tenancy: Vector databases grant multiple tenants the means to share a single index while maintaining data isolation for security and privacy. Organizations rely on multi-tenancy to simplify system management and reduce operational overhead.
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Advanced capabilities: Vector databases can perform speedy data processing and advanced search. That’s why they’re appreciated for AI-related tasks, such as pattern recognition, sorting, comparison, and clustering.
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Flexible querying: Vector databases can store multiple information types in a single structure for structured query language (SQL) or NoSQL-based querying. Vector databases take advantage of this flexibility to integrate disparate data sources and create a single, consolidated dataset for AI algorithms to use.
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Built-in data security: Vector databases feature built-in data security and access control measures to protect sensitive data from unauthorized access.
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Suitable for different environments: Organizations can deploy vector databases on traditional, cloud, and hybrid infrastructures, which may consist of local and distributed resources. Deploying AI systems in various environments requires this level of versatility.
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Backup storage: Vector databases store index backups to enable users to easily sort and retrieve data.
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Integration with AI applications: A vector database provides software development kits (SDKs) in different programming languages to process and manage data seamlessly.
Types of vector databases
Different types of vector databases aim for different goals, depending on their architecture, storage models, indexing techniques, and the kind of data they store.
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Text vector databases store and query text data in vector format. They’re ideal for natural language processing tasks.
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Graph vector databases facilitate complex network analysis by storing graphs as vectors. They stand out when it comes to running recommendation systems and social network analysis tasks.
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Image vector databases store and manage images using vectors for retrieval and analysis tasks.
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Multimedia vector databases feature multimedia content management to store video, audio, and images as vectors.
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Quantization-based databases use quantization to index data, enhance retrieval accuracy, and balance memory usage.
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Hashing-based indexing databases rely on key search value mapping to get data from larger datasets.
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Tree-based indexing databases use R-tree or KD-tree structures for indexing and executing tree-based partitioning.
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Disk-based databases can store large datasets because they can store data on disks. However, retrieval slows down with this database.
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In-memory databases offer faster data retrieval than disk-based databases because they keep data in random access memory (RAM). They struggle with limited memory.
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Hybrid databases provide better speed and storage capabilities than in-memory databases because of using both in-memory and disk-based databases.
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Single-node vector databases employ a single computing node for data management. Although they’re easy to set up, the single node limits their hardware capabilities.
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Cloud-based vector databases store, index, and process data using cloud computing environments. Thanks to the underlying cloud infrastructure, these databases efficiently deliver scalability and flexibility.
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Distributed vector databases manage large datasets and query loads by using multiple nodes. This data distribution across machines guarantees improved scalability and fault tolerance.
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GPU-accelerated vector databases speed up computation-intensive tasks like similarity searches with the processing power of graphical processing units (GPU).
Benefits of vector databases
Developers who are considering using vector databases to manage AI-enabled application workloads can expect some of the following benefits.
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High-dimensional data handling: Vector database solutions store, process, manage, query, and retrieve data from high-dimensional spaces. They compute quickly with ANN search, indexing structures, dimensionality reduction, batch processing, and distributed computing.
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Similarity and semantic vector search efficiency: Vector databases can find geometrics properties and distances between vectors in large datasets. This ability to contextualize vectors and understand their similarities makes vector databases ideal for NLP tasks, image recognition, and recommendation engines.
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Advanced analytics and insights: Vector database software features machine learning and real-time analytics capabilities – both crucial for building AI applications with complex algorithms. These algorithms allow organizations to discover market trends and customer behavior insights. As a result, companies no longer need to rely on data mining or manual data analysis processes.
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Personalized user experience development: Vector database systems support the way businesses analyze user behavior insights in order to create personalized experiences, proving vector databases ideal for e-commerce companies, marketing platforms, and content delivery solutions.
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Easy AI and ML integration: Most vector database solutions play nicely with popular AI and ML frameworks. They also feature client libraries and application programming interfaces (APIs) suitable for AI and ML programming.
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Improved speed, accuracy, and scalability: Vector databases use advanced algorithms and modern hardware (GPUs or multi-core processors) to tackle massive datasets. They deliver accurate results and prevent performance degradation. Users can add hardware components to boost data processing capabilities and manage newer AI workloads. This scalability and speedy performance make vector databases suitable for large and complex datasets.
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Ease of use and setup: Anyone with basic coding knowledge and SQL experience can set up and use a vector database. Moreover, vectorized SQL makes it possible to write complex queries quickly.
Vector database vs. relational database
A vector and a relational database serve different data types and purposes.
Vector databases store high-dimensional data and execute semantic similarity searches for NLP, LLM, recommendation engines, and pattern recognition applications. They store complex unstructured data as vectors for optimal performance in high-dimensional spaces.
A relational database system, on the other hand, stores structured data using rows and columns. These databases rely on indexing methods like hash indexes for query processing. Their systematic information arrangement makes them ideal for business applications that require easy data access.
Who uses vector database software?
Vector databases are used by developers, data scientists, engineers, and businesses looking to build and operationalize vector embeddings with vector databases.
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Healthcare researchers use vector databases to store and retrieve high-dimensional medical imaging data for diagnostic research.
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Web developers rely on vector database solutions to store and process back-end data for high-performance web applications that require speed and scalability.
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Game developers use vector databases to ensure fast processing, minimize lag time, and store player and gaming progress related data.
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Data science professionals rely on vector database systems to analyze large datasets, performance metrics, and market trends—all key to finding improvement areas and making better decisions.
Vector database pricing
Pricing ranges from hundreds to thousands of dollars, depending on features like distributed computing and factors like project complexity, number of machines needed for data processing, and data volume.
Most vector database system companies offer three pricing models:
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Subscription-based pricing covers multiple tiers, each with different features, data storage and retrieval capacity, and a customer support service level agreement (SLA). This pricing model suits organizations planning to scale usage up or down but keep initial investments low.
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Perpetual licenses require buyers to pay a one-time fee to use a vector database system indefinitely. However, some vendors may request an additional annual maintenance fee for product updates and patch releases. No recurring payments are needed, and this option works best for long-term cost savings.
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Usage-based pricing bills customers based on actual usage factors like the number of queries processed, the amount of data stored and retrieved, and the computational resources used. This model is generally cost-efficient as it doesn’t require an up-front investment.
Alternatives to vector databases
Below are vector database alternatives that organizations might find useful.
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Document databases, or document-oriented databases, are non-relational or NoSQL databases that store and query data using JSON, BSON, or XML documents. They suit content management systems, real-time big data applications, and user profile management workloads, which need flexible schemas for speedy development.
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Graph databases are single-purpose platforms that create and manipulate associative and contextual data. They store graph data, which consists of nodes, edges, and properties, using a network of entities and relationships. These databases are ideal for recommendation engines, fraud detection apps, and social networks.
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Time series databases handle time-stamped or time-series data, such as network data, sensor data, application performance monitoring data, and server metrics. They suit organizations looking for top performance from their database infrastructure and enough storage capacity for high-granularity and high-volume datasets from internet of things (IoT) devices.
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Spatial data platforms are relational databases that store and query data related to objects in geometric spaces. Transportation, retail, construction, and public sector companies use them for urban planning, market research, navigation, and resource allocation.
Challenges with vector databases
Organizations that use vector databases should prepare to tackle the following problems.
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Data scale management: Storing and indexing billions of vectors from LLMs causes companies a lot of headaches if they don’t use advanced data structures and algorithms.
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High computational costs: Executing computationally intensive vector similarity searches may increase the cost of using vector databases. Companies can try out alternative algorithms like nearest neighbor search to minimize costs.
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Downtime during updates: This software has to periodically update vector databases to keep data and large language models current, but users may experience downtime during these vector representation updates.
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Storage and maintenance issues: As data size and model complexity increase, organizations must expand data storage and maintain vector databases regularly.
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Concurrency control: Vector database users experience concurrency issues because of high write throughput and complex data structures. These issues result in data inconsistencies, especially during indexing and search engine operations.
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Inaccurate spatial data analysis: Vector database users must validate geospatial coordinates from different sources while working with spatial data. Otherwise, they might encounter data quality issues.
Which companies should buy vector database software?
E-commerce companies, media businesses, technology firms, and supply chain organizations are some of the companies that commonly set up vector databases.
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Technology companies use vector database systems for information storage and retrieval. With semantic search, they discover relevant content, map word embeddings, and fuel content recommendation systems.
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E-commerce businesses rely on vector databases’ recommendation capabilities to interpret consumer behavior and suggest relevant products. They also use vector databases with image-based search functionalities to perform visual similarity searches so guests can find products with photos.
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Social media networks can suggest posts and recommend advertisements based on user engagement pattern analysis, thanks to vector database software solutions. The platforms also moderate and filter harmful content using content embeddings.
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Financial institutions, like banks, financial service providers, and brokerage trading platforms, analyze market data and detect fraudulent transactions using data processing and pattern analysis functionalities.
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Supply chain management companies discover product similarity patterns for inventory optimization and demand forecasting. With vector databases, these businesses also analyze location vectors to detect supply chain anomalies and improve delivery routes.
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Music and video streaming platforms let visitors perform content-based multimedia searches and share personalized content recommendations based on user preference analysis, all with the help of vector database software.
How to choose the best vector database?
Choosing the right vector database can be tricky. Before deciding, evaluate business needs, technology requirements, enterprise readiness, and developer experience.
Identify business needs and priorities
Enterprises on the hunt for generative AI must be able to articulate why they want to use vector databases in sales, marketing, or customer operations. Depending on their objectives, they can choose from self-hosted, open-source, or managed vector database solutions.
Self-hosted and open-source vector database solutions are ideal for companies with engineering teams.
Serverless, managed solutions are for businesses looking to establish production-ready environments.
Organizations with engineering teams benefit from a cost-efficient machine learning operations (MLOps) setup for training ML models and gathering feedback. Making vector databases part of the MLOps pipeline is slightly easier for these companies.
Evaluate technological features
At this stage, buyers should consider vector database solutions' technology features, enterprise readiness, and developer friendliness. The best vector databases typically feature the following functionalities.
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Data freshness: How long does it take for new data querying?
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Query latency: How long does executing a query take? What about receiving results?
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Query per second (QPS): How many queries can it handle in a second?
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Namespace: Does the vector database search index by namespace?
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Accuracy: How fast can a solution return accurate results during an ANN search?
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Hybrid search: Does the vector database support semantic and keyword searches?
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Metadata filtering: Can users use metadata to filter vectors when querying?
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Monitoring: Does the system monitor metrics and detect problems?
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Security and compliance: Does the platform encrypt data at rest and in transit? Does it comply with the General Data Protection Regulation (GDPR); the Health Insurance Portability and Accountability Act (HIPAA); and System and Organization Controls (SOC)?
Review vendor viability and support
Study potential vendors’ onboarding materials, tutorials, customer support SLAs, and technical support. These factors help buyers determine whether they’ll receive timely troubleshooting assistance when issues arise. Buyers should also assess whether the vendor has helpful support documentation or community events.
Evaluate deployment and total cost of ownership
Buyers must consider factors like ease of use and the availability of integrations when considering a vector database solution. Ideally, the solution features APIs and SDKs for different kinds of clients and integrates with preferred cloud providers, LLMs, and existing systems.
Moreover, buyers should choose solutions that scale horizontally and vertically when the workload demands it. Don’t forget to look at licensing, infrastructure, and maintenance costs.
Make an informed decision
Test a proof of concept with real-life data and workloads. These tests let you measure a vector database solution’s performance against performance benchmarks of other solutions under similar conditions. Before finalizing a solution, remember to assess pricing, support, and feature-related pros and cons.
How to implement vector databases
For maximum efficiency, follow the best practices below as you set up your vector database.
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Data complexity and requirements: Besides understanding the kind of data your organization uses, ensure you’re confident about its complexity, size, and update frequency. These factors help buyers select the right vector database.
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Important features: Consider important factors for success, such as scalability, storage options, integration availability, indexing capabilities, and performance.
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Software and hardware optimization: When deploying vector databases on-premises or in the cloud, choose software and hardware options suitable for vector processing. Evaluate the cloud-native configuration and availability of specialized hardware accelerators during cloud deployment.
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Data security: Organizations must check whether vector database vendors have sufficient security measures, such as activity monitoring, data encryption, and access control.
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Scalability: Designing a database architecture during deployment that scales with data volumes saves time and effort in the future.
Vector database trends
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Geospatial big data applications: Disaster management, environmental monitoring, defense, and urban planning organizations are steadily using vector databases more to analyze geospatial big data. Efficient satellite imagery data querying and location data retrieval allow these companies to deliver location-based services, recognize patterns, and create predictive models for forecasting future outcomes.
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Edge computing for spatial applications: Autonomous vehicles, public safety organizations, and agriculture companies rely on vector database systems for spatial data storage and processing at the edge. Using vector databases also helps them distribute data across nodes and save data transfer bandwidth.
Researched and written by Shalaka Joshi
Reviewed and edited by Aisha West
