# Best Vector Database Software

  *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*

   Vector databases store data as mathematical vector representations of features, enabling complex similarity search and semantic retrieval across unstructured data, supporting use cases such as recommendation systems, semantic search, fraud detection, and AI-powered applications that require finding contextually related results rather than exact matches.

### Core Capabilities of Vector Database Software

To qualify for inclusion in the Vector Databases category, a product must:

- Provide semantic search capabilities
- Offer metadata filtering to improve the relevance of search results
- Provide data sharding for faster and more scalable results

### Common Use Cases for Vector Database Software

AI engineers and data teams use vector databases to power intelligent search and retrieval capabilities across AI-driven applications. Common use cases include:

- Enabling semantic search that retrieves contextually relevant results beyond keyword matching
- Powering recommendation engines by clustering similar data points through vector embeddings
- Supporting retrieval-augmented generation (RAG) workflows for large language model applications

### How Vector Databases Differ from Other Tools

Vector databases differ fundamentally from [relational databases](https://www.g2.com/categories/relational-databases), which retrieve exact-match results from structured data. Vector databases are designed for similarity-based search across complex, unstructured data, indexing and storing vector embeddings to enable approximate nearest neighbor search at scale. This makes them uniquely suited for AI and machine learning applications that require understanding the meaning and relationships between data points rather than precise matches.

### Insights from G2 on Vector Database Software

Based on category trends on G2, semantic search accuracy and scalability for large embedding datasets stand out as standout capabilities. Faster retrieval performance and improved relevance in AI application outputs stand out as primary benefits of adoption.





## Category Overview

**Total Products under this Category:** 35


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 900+ Authentic Reviews
- 35+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.


## Best Vector Database Software At A Glance

- **Leader:** [Elasticsearch](https://www.g2.com/products/elastic-elasticsearch/reviews)
- **Highest Performer:** [Zilliz](https://www.g2.com/products/zilliz/reviews)
- **Easiest to Use:** [Zilliz](https://www.g2.com/products/zilliz/reviews)
- **Top Trending:** [Supabase](https://www.g2.com/products/supabase-supabase/reviews)
- **Best Free Software:** [Elasticsearch](https://www.g2.com/products/elastic-elasticsearch/reviews)


---

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---

## Top-Rated Products (Ranked by G2 Score)
### 1. [Elasticsearch](https://www.g2.com/products/elastic-elasticsearch/reviews)
  Build next generation search experiences for your customers and employees that support your organization’s technology objectives. Elasticsearch gives developers a flexible toolkit to build AI-powered search applications with an extensible platform that also provides out of the box capabilities Save development cycles and get upgraded search to market faster. Elasticsearch is the world’s most popular search engine, backed by a robust developer community. Elastic’s platform lets you ingest any data source, build modern search experiences that integrate with large language models and generative AI, and visualize analytics for data-driven decision-making and insights. Our consistent investments in machine learning help developers stay ahead of the curve with the fast, highly relevant search, at scale. -- Flexible platform and toolkit to deliver powerful search functionality regardless of development resources and technology objectives. Our open platform delivers consistent functionality for cloud, hybrid, or on-prem deployments with exceptional performance, reliability, and scalability. -- Built-in search analytics and visualization tools give teams access to search data and real-time dashboards for optimizing search results and operations. Non-tech teams can tune search experiences too–no development team needed. -- Next level search relevance using textual search, vector search, hybrid, and semantic search and machine learning model flexibility. Powerful capabilities like a vector database provide the foundation for creating, storing, and searching embeddings to capture the context of your unstructured data. Use machine-learning enabled inference at data ingestion, and bring your own model - open or proprietary - to deliver the best, industry-specific results.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 286


**Seller Details:**

- **Seller:** [Elastic](https://www.g2.com/sellers/elastic)
- **Company Website:** https://www.elastic.co
- **Year Founded:** 2012
- **HQ Location:** San Francisco, CA
- **Twitter:** @elastic (64,544 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/814025/ (4,986 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Senior Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 38% Mid-Market, 33% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (52 reviews)
- Speed (36 reviews)
- Fast Search (35 reviews)
- Results (31 reviews)
- Features (30 reviews)

**Cons:**

- Expensive (28 reviews)
- Required Expertise (26 reviews)
- Learning Difficulty (25 reviews)
- Improvement Needed (24 reviews)
- Difficult Learning (23 reviews)

### 2. [Zilliz](https://www.g2.com/products/zilliz/reviews)
  Zilliz Cloud is a cloud-native vector database platform that stores, indexes, and searches billions of embedding vectors to power enterprise-grade similarity search, recommender systems, retrieval augmented generation, anomaly detection, and more. ​ Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale. Zilliz Cloud features: Superior Al-Powered Search Optimal search strategies and zero manual tuning from AI-powered AutoIndex and Cardinal Search Engine High Performance & Scale A cloud-native database with distributed architecture for on-demand scalability and cost-efficient growth Uncompromising Security & Reliability An enterprise-ready platform delivers reliable performance and enterprise-grade security Build faster with a comprehensive suite of vector database features - High Performance Vector Search - Low Latency with High Recall - Hybrid Search - Various Similarity Metrics - Tunable Consistency - Scale as Needed Reduce TCO with Cloud-Native Vector Search While Milvus offers powerful vector search capabilities, it requires significant investment in skilled engineers, performance tuning, and ongoing maintenance. Zilliz eliminates these costs through our innovative Cardinal search engine and management tools—reducing total cost of ownership by up to 70% and optimized for your use cases: Recommendation Systems, RAG Applications, or Anomaly Detection Systems. Zilliz Cloud is available directly from zilliz.com and also through the AWS, Google Cloud and Azure Marketplaces so you can utilize your pre-contracted cloud credits to simplify your infrastructure stack spending.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 53


**Seller Details:**

- **Seller:** [ZILLIZ](https://www.g2.com/sellers/zilliz)
- **Year Founded:** 2017
- **HQ Location:** Redwood City, US
- **Twitter:** @milvusio (5,174 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/zilliz (139 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 45% Small-Business, 36% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (9 reviews)
- Speed (8 reviews)
- Features (6 reviews)
- Performance (6 reviews)
- Documentation (5 reviews)

**Cons:**

- Expensive (4 reviews)
- Limitations (3 reviews)
- Insufficient Documentation (2 reviews)
- Missing Features (2 reviews)
- Cost Concerns (1 reviews)

### 3. [Supabase](https://www.g2.com/products/supabase-supabase/reviews)
  Supabase is an open-source backend-as-a-service (BaaS) platform that enables developers to build and scale applications efficiently without managing server infrastructure. Launched in 2020 as an alternative to Firebase, Supabase offers a suite of tools including a PostgreSQL database, authentication, real-time subscriptions, and storage capabilities. By leveraging the robustness of PostgreSQL, Supabase provides a scalable and secure foundation for modern web and mobile applications. Key Features and Functionality: - PostgreSQL Database: Each Supabase project includes a dedicated PostgreSQL database, offering full SQL support and advanced features such as JSON handling, full-text search, and vector support. - Instant APIs: Supabase automatically generates RESTful and GraphQL APIs based on your database schema, eliminating the need for manual coding and accelerating development. - Authentication and Authorization: The platform provides built-in user authentication with support for various sign-in methods, including email/password, magic links, and social logins. It also integrates seamlessly with PostgreSQL's Row Level Security for fine-grained access control. - Real-time Capabilities: Supabase enables real-time data synchronization via WebSockets, allowing applications to respond instantly to database changes. - Edge Functions: Developers can deploy serverless functions close to users for low-latency execution, facilitating scalable and efficient backend logic. - File Storage: Supabase offers scalable storage solutions for managing and serving files, complete with configurable access policies to ensure data security. Primary Value and User Solutions: Supabase addresses the challenges developers face in building and scaling applications by providing a comprehensive, open-source backend platform. It eliminates the complexities of managing server infrastructure, allowing developers to focus on creating feature-rich applications. With its real-time capabilities, robust authentication, and seamless integration with PostgreSQL, Supabase empowers developers to build secure, scalable, and responsive applications efficiently.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 40


**Seller Details:**

- **Seller:** [Supabase](https://www.g2.com/sellers/supabase)
- **Year Founded:** 2020
- **HQ Location:** Global, US
- **LinkedIn® Page:** https://www.linkedin.com/company/supabase/ (270 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 78% Small-Business, 15% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (9 reviews)
- Features (7 reviews)
- Easy Integrations (5 reviews)
- Setup Ease (5 reviews)
- User-Friendly (5 reviews)

**Cons:**

- Beginner Unfriendliness (2 reviews)
- Expensive (2 reviews)
- High Complexity (2 reviews)
- Insufficient Guidance (2 reviews)
- Learning Difficulty (2 reviews)

### 4. [Weaviate](https://www.g2.com/products/weaviate/reviews)
  Weaviate is an AI-native vector database designed to simplify the process of building and scaling search and generative AI applications for developers of all levels. Open source and built with modern AI workloads in mind, Weaviate powers use cases like semantic and hybrid search, chatbots, and AI-driven agents. Weaviate integrates seamlessly with the AI ecosystem across the stack, offering pre-built modules for popular Large Language Models (LLMs) and machine learning frameworks. Its unique multi-tenant architecture, purpose-built for both vectors and objects, enables efficient large-scale deployments while maintaining enterprise-grade performance and reliability. With flexible deployment options—including on-premises, cloud, hybrid environments, and Bring Your Own Cloud (BYOC)—Weaviate meets the needs of diverse organizations, from startups to large enterprises. These options empower teams to choose the deployment model that aligns with their operational and regulatory requirements. Weaviate also provides robust data privacy, compliance, and access control features, ensuring security and trustworthiness for production environments. Key Features and Benefits: • AI-Native Architecture: Built specifically for vector-based and generative AI workloads. • Use Cases: Supports semantic and hybrid search, chatbots, agents, and other AI-driven applications. • Hybrid Search Capabilities: Combines vector and keyword-based search for superior accuracy and relevance. • Multi-Tenant Efficiency: Scales to millions of tenants with full data isolation and flexible storage tiers for cost optimization. • Flexible Deployment: Deploy on-premises, in the cloud, as part of a hybrid environment, or using BYOC for maximum control and adaptability. • Enterprise Security: Features SOC 2 certification, regular penetration testing, and role-based access control (RBAC) for comprehensive data protection. Weaviate empowers organizations to innovate faster, streamline data operations, and launch AI applications that are secure, scalable, and state-of-the-art.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 29


**Seller Details:**

- **Seller:** [Weaviate](https://www.g2.com/sellers/weaviate)
- **Year Founded:** 2019
- **HQ Location:** Amsterdam, NL
- **Twitter:** @weaviate_io (19,160 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/weaviate-io (90 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 76% Small-Business, 14% Mid-Market


### 5. [Pinecone](https://www.g2.com/products/pinecone/reviews)
  Pinecone is the developer-favorite and most trusted vector database for building accurate and performant AI applications at scale in production. Fully managed, easy to use, with the best cost/performance at scale.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 39


**Seller Details:**

- **Seller:** [Pinecone Systems](https://www.g2.com/sellers/pinecone-systems)
- **Year Founded:** 2019
- **HQ Location:** New York, NY
- **LinkedIn® Page:** https://www.linkedin.com/company/pinecone-io/ (127 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 85% Small-Business, 13% Mid-Market


### 6. [TiDB](https://www.g2.com/products/tidb/reviews)
  TiDB is an advanced open-source, distributed SQL database solution designed to help data-intensive businesses manage and scale their data operations seamlessly. Developed by PingCAP, TiDB combines the scalability of NoSQL databases with the full functionality of traditional relational database management systems (RDBMS). This unique architecture allows organizations to build petabyte-scale clusters while efficiently handling millions of tables, numerous concurrent connections, and frequent schema changes without experiencing downtime. The target audience for TiDB includes large enterprises, software-as-a-service (SaaS) providers, and digital-native companies that require robust data management capabilities. These organizations often face challenges related to data scalability, operational complexity, and the need for high availability. TiDB addresses these challenges by offering a solution that supports a wide range of workloads, including transactional, analytical, operational, and artificial intelligence (AI) tasks. Its multi-tenant architecture further enhances operational agility, allowing businesses to adapt to changing demands quickly. Key features of TiDB include seamless scalability, which enables organizations to expand their database infrastructure effortlessly as their data needs grow. The platform's MySQL compatibility ensures that developers can easily integrate TiDB into existing workflows and leverage familiar tools and platforms. Additionally, TiDB supports online DDL (Data Definition Language) operations, allowing for worry-free schema changes that do not disrupt ongoing processes. This operational flexibility is critical for businesses that require constant uptime and reliability. TiDB also prioritizes data security and availability, boasting built-in ACID (Atomicity, Consistency, Isolation, Durability) compliance and a remarkable 99.99% availability rate. The database adheres to various regulatory standards, including GDPR, SOC, HIPAA, and PCI, ensuring that organizations can trust their data management practices. Notable companies such as Databricks, Pinterest, and Plaid have adopted TiDB, allowing them to concentrate on growth and innovation rather than the complexities of data infrastructure management. With its AI-driven innovations and multi-cloud capabilities, TiDB stands out as a powerful solution for businesses looking to enhance their data management strategies. By providing unmatched agility, resilience, and security, TiDB empowers organizations to unlock their full potential in an increasingly data-driven world. For more information, please visit TiDB.io.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 67


**Seller Details:**

- **Seller:** [PingCAP](https://www.g2.com/sellers/pingcap)
- **Company Website:** https://tidb.io
- **Year Founded:** 2015
- **HQ Location:** Sunnyvale 
- **Twitter:** @PingCAP (7,215 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/pingcap (495 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Senior Software Engineer, DBA
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 31% Enterprise, 27% Small-Business


#### Pros & Cons

**Pros:**

- Scalability (52 reviews)
- Ease of Use (32 reviews)
- Database Management (29 reviews)
- Compatibility (26 reviews)
- Performance (25 reviews)

**Cons:**

- Learning Curve (22 reviews)
- Performance Issues (15 reviews)
- Slow Performance (13 reviews)
- Difficult Learning (11 reviews)
- Poor Documentation (11 reviews)

### 7. [PG Vector](https://www.g2.com/products/pg-vector/reviews)
  PGVector is an open-source extension for PostgreSQL that enables efficient vector similarity searches directly within the database. It allows users to store and query vector data alongside traditional relational data, facilitating tasks such as machine learning model integration, recommendation systems, and natural language processing applications. Key Features and Functionality: - Vector Storage: Supports single-precision, half-precision, binary, and sparse vectors, accommodating diverse data types. - Similarity Search: Offers both exact and approximate nearest neighbor search capabilities, utilizing distance metrics like L2 (Euclidean, inner product, cosine, L1, Hamming, and Jaccard distances. - Indexing: Provides indexing methods such as HNSW (Hierarchical Navigable Small World and IVFFlat (Inverted File with Flat quantization to optimize search performance. - Integration: Compatible with any language that has a PostgreSQL client, enabling seamless incorporation into existing applications. - PostgreSQL Features: Maintains full support for PostgreSQL's ACID compliance, point-in-time recovery, and JOIN operations, ensuring data integrity and reliability. Primary Value and User Solutions: PGVector addresses the challenge of integrating vector similarity search within relational databases by embedding this functionality directly into PostgreSQL. This integration eliminates the need for external systems or complex data pipelines, simplifying architecture and reducing latency. Users can perform efficient similarity searches on vector data stored alongside their relational data, streamlining workflows in applications like recommendation engines, image and text retrieval, and other AI-driven solutions.


  **Average Rating:** 3.8/5.0
  **Total Reviews:** 12


**Seller Details:**

- **Seller:** [pgvector](https://www.g2.com/sellers/pgvector)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Mid-Market, 42% Small-Business


### 8. [CrateDB](https://www.g2.com/products/cratedb/reviews)
  The real-time database for analytics, search, and AI. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source, multi-model, distributed and containerized database that runs queries in milliseconds, regardless of data complexity, volume and velocity.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 82


**Seller Details:**

- **Seller:** [CrateDB](https://www.g2.com/sellers/cratedb)
- **Company Website:** https://cratedb.com/
- **Year Founded:** 2013
- **HQ Location:** Redwood City, CA
- **Twitter:** @cratedb (4,179 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/crateio/ (44 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 54% Small-Business, 31% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (12 reviews)
- SQL Usage (11 reviews)
- Easy Integrations (10 reviews)
- Flexibility (10 reviews)
- Features (9 reviews)

**Cons:**

- Lack of Features (5 reviews)
- Software Limitations (4 reviews)
- Limited Features (3 reviews)
- Poor Documentation (3 reviews)
- Complex Configuration (2 reviews)

### 9. [Qdrant](https://www.g2.com/products/qdrant/reviews)
  Qdrant is the leading, high-performance, scalable, open-source vector database and search engine, essential for building the next generation of AI/ML applications. Qdrant is able to handle billions of vectors, supports the matching of semantically complex objects, and is implemented in Rust for performance, memory safety, and scale.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 12


**Seller Details:**

- **Seller:** [Qdrant](https://www.g2.com/sellers/qdrant)
- **Year Founded:** 2021
- **HQ Location:** Berlin, Berlin
- **Twitter:** @qdrant_engine (13,174 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/qdrant/ (116 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 58% Small-Business, 33% Mid-Market


### 10. [Milvus](https://www.g2.com/products/milvus/reviews)
  Milvus is a highly flexible, reliable, and blazing-fast cloud-native, open-source vector database. It powers embedding similarity search and AI applications and strives to make vector databases accessible to every organization. Milvus can store, index, and manage a billion+ embedding vectors generated by deep neural networks and other machine learning (ML) models. This level of scale is vital to handling the volumes of unstructured data generated to help organizations to analyze and act on it to provide better service, reduce fraud, avoid downtime, and make decisions faster. Milvus is a graduated-stage project of the LF AI & Data Foundation.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 11


**Seller Details:**

- **Seller:** [ZILLIZ](https://www.g2.com/sellers/zilliz)
- **Year Founded:** 2017
- **HQ Location:** Redwood City, US
- **Twitter:** @milvusio (5,174 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/zilliz (139 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 45% Small-Business, 36% Mid-Market


### 11. [Relevance AI](https://www.g2.com/products/relevance-ai/reviews)
  Relevance AI is the home of the AI workforce: where anyone can build and recruit teams of AI agents to complete tasks on autopilot. Our no-code platform is built for ops teams, no technical background required. Subject-matter experts can use Relevance to design powerful AI agents and AI teams without relying on developer resources. Scale excellence across every area or team with your intelligent, purpose-built AI workforce.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 21


**Seller Details:**

- **Seller:** [Relevance AI](https://www.g2.com/sellers/relevance-ai)
- **Year Founded:** 2020
- **HQ Location:** Sydney, Australia 
- **Twitter:** @RelevanceAI_ (3,775 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/relevanceai (124 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 81% Small-Business, 10% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (10 reviews)
- Features (8 reviews)
- AI Integration (7 reviews)
- Customization (7 reviews)
- Efficiency (7 reviews)

**Cons:**

- Cost (5 reviews)
- Expensive (4 reviews)
- Interface Complexity (3 reviews)
- Limited Features (3 reviews)
- Learning Curve (2 reviews)

### 12. [KX](https://www.g2.com/products/kx-kx/reviews)
  We power the time-aware data-driven decisions that enable fast-moving organizations to realize the full potential of their AI investments and outpace competitors. Our technology delivers transformational value by addressing data challenges around completeness, timeliness, and efficiency. We enable organizations to understand change over time and generate faster, more accurate insights — at any scale, and with cost efficiency. Our technology is essential to the operations of the world's top investment banks, aerospace and defense, high-tech manufacturing, healthcare and life sciences, automotive, and fleet telematics organizations. The primary audience for KX encompasses line-of-business leaders, developers, data scientists, and data engineers who require sophisticated analytics capabilities to create high-performance, data-driven applications. With its unmatched speed and scalability, KX allows users to efficiently process large volumes of data, whether in cloud environments, on-premises, or at the edge. This flexibility ensures that organizations can integrate KX technology into their existing workflows seamlessly, enhancing their analytical capabilities without causing disruptions to ongoing operations. KX distinguishes itself in the analytics landscape through its independently benchmarked performance, recognized as the fastest available on the market. This speed is vital for businesses that depend on real-time data insights to inform their decision-making processes. By enabling users to uncover richer, actionable insights quickly, KX facilitates faster and more informed choices, driving competitive advantage and transformative growth. Its ability to manage complex data sets and deliver insights promptly is particularly advantageous for industries that operate in fast-paced environments, where timely information is critical. Key features of KX include advanced time series and vector data analytics capabilities, which enable efficient management and analysis of extensive data volumes. Furthermore, KX integrates seamlessly with popular analytics tools, enhancing their performance and allowing users to maximize their existing investments. The platform's architecture is designed for high performance, ensuring that organizations can scale their analytics operations as needed without sacrificing speed or efficiency. With a global presence across North America, Europe, and Asia Pacific, KX is trusted by leading organizations to spearhead their data and AI initiatives. By providing a powerful analytics solution, KX not only enhances operational efficiency but also fosters a culture of innovation, empowering businesses to remain competitive in an increasingly data-driven world.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 50


**Seller Details:**

- **Seller:** [KX](https://www.g2.com/sellers/kx-a145756d-91d3-463e-a51d-9e13b1ac577c)
- **Year Founded:** 1996
- **HQ Location:** NY, USA
- **Twitter:** @kxsystems (4,168 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/kx-systems (527 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Banking
  - **Company Size:** 57% Enterprise, 25% Small-Business


#### Pros & Cons

**Pros:**

- Speed (11 reviews)
- Performance (9 reviews)
- Tool Power (7 reviews)
- Efficiency (6 reviews)
- Fast Processing (6 reviews)

**Cons:**

- Learning Curve (12 reviews)
- Difficult Learning (7 reviews)
- Steep Learning Curve (7 reviews)
- Complexity (2 reviews)
- Expensive (2 reviews)

### 13. [Vespa](https://www.g2.com/products/vespa/reviews)
  Vespa unifies vector, text, structured data, and ML ranking into one high-performance engine, powering fast, trustworthy, and massively scalable AI applications. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Users can easily build recommendation applications on Vespa. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 8


**Seller Details:**

- **Seller:** [Vespa](https://www.g2.com/sellers/vespa)
- **Company Website:** https://vespa.ai/
- **Year Founded:** 2023
- **HQ Location:** Trondheim, NO
- **LinkedIn® Page:** https://www.linkedin.com/company/vespa-ai/ (51 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 63% Small-Business, 25% Enterprise


### 14. [Chroma Vector Database](https://www.g2.com/products/chroma-vector-database/reviews)
  the AI-native open-source embedding database


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 6


**Seller Details:**

- **Seller:** [Chroma](https://www.g2.com/sellers/chroma)
- **Year Founded:** 1991
- **HQ Location:** Bellows Falls, Vermont, United States
- **LinkedIn® Page:** https://www.linkedin.com/company/chroma-technology-corp (106 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 67% Small-Business, 17% Enterprise


### 15. [Faiss](https://www.g2.com/products/faiss/reviews)
  Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 4


**Seller Details:**

- **Seller:** [Meta Platforms, Inc](https://www.g2.com/sellers/meta-platforms-inc)
- **Year Founded:** 2008
- **HQ Location:** Menlo Park, CA
- **Twitter:** @Meta (9,930,056 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/meta/ (150,070 employees on LinkedIn®)
- **Ownership:** NASDAQ: META

**Reviewer Demographics:**
  - **Company Size:** 100% Small-Business


### 16. [SingleStore](https://www.g2.com/products/singlestore/reviews)
  SingleStore enables organizations to scale from one to one million customers, handling SQL, JSON, full text and vector workloads — all in one unified platform.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 114


**Seller Details:**

- **Seller:** [SingleStore](https://www.g2.com/sellers/singlestore)
- **Year Founded:** 2011
- **HQ Location:** San Francisco, CA
- **Twitter:** @SingleStoreDB (15,470 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/singlestore/ (546 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Software Developer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 39% Enterprise, 37% Small-Business


### 17. [Tembo](https://www.g2.com/products/tembo/reviews)
  Tembo is a multi-workload Postgres managed service that enables organizations to harness the full power of Postgres for transactional, analytical, and AI workloads. With robust SaaS and self hosted deployment options, Tembo enables everyone – from the smallest startups to the Fortune 500 – to go “all in” on Postgres, achieving unprecedented stability and efficiency across a variety of applications and use cases. With Tembo, customers get all the stability, reliability, and extensibility of Postgres open source with enhanced observability, compliance, and developer experience.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 26


**Seller Details:**

- **Seller:** [Tembo](https://www.g2.com/sellers/tembo)
- **Year Founded:** 2022
- **HQ Location:** Cincinnati, US
- **Twitter:** @tembo_io (3 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/tembo-inc/ (31 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 85% Small-Business, 15% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (16 reviews)
- Features (12 reviews)
- Integrations (10 reviews)
- Ease of Setup (8 reviews)
- Easy Integrations (8 reviews)

**Cons:**

- Limited Flexibility (5 reviews)
- AWS Dependency (4 reviews)
- Cloud Limitations (4 reviews)
- Expensive (4 reviews)
- Limited Customization (4 reviews)

### 18. [Meilisearch](https://www.g2.com/products/meilisearch/reviews)
  Meilisearch empowers developers and business teams to create the most intuitive search experience that increases search-based conversions


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 4


**Seller Details:**

- **Seller:** [Meilisearch](https://www.g2.com/sellers/meilisearch)
- **Year Founded:** 2018
- **HQ Location:** Paris, FR
- **Twitter:** @meilisearch (5,092 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/meilisearch/ (30 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 60% Small-Business, 40% Mid-Market


#### Pros & Cons

**Pros:**

- Customer Support (3 reviews)
- Ease of Use (3 reviews)
- Easy Integrations (2 reviews)
- Features (2 reviews)
- Helpful (2 reviews)

**Cons:**

- Limited Features (2 reviews)
- Search Functionality (2 reviews)
- Cost Increase (1 reviews)
- Cost Issues (1 reviews)
- Expensive (1 reviews)

### 19. [MyScale](https://www.g2.com/products/myscale/reviews)
  MyScale is a powerful SQL vector database that offers minimal learning curve, maximum value, and a cost-effective solution for organizations seeking optimal performance and efficiency in their data management strategies. It enables every developer to build production-grade GenAI applications with powerful and familiar SQL.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 2


**Seller Details:**

- **Seller:** [MyScale](https://www.g2.com/sellers/myscale)
- **Year Founded:** 2022
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/myscale/ (2 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Small-Business


### 20. [SvectorDB](https://www.g2.com/products/svectordb/reviews)
  Vector database built from the ground up for serverless. The only vector database with built-in native CloudFormation / CDK support


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 2


**Seller Details:**

- **Seller:** [SvectorDB](https://www.g2.com/sellers/svectordb)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Mid-Market, 50% Enterprise


### 21. [Typesense](https://www.g2.com/products/typesense/reviews)
  Typesense is a modern, privacy-friendly, open source search engine (with a hosted SaaS option) meticulously engineered for performance & ease-of-use. It uses cutting-edge search algorithms that take advantage of the latest advances in Hardware Capabilities & AI / Machine Learning. We serve 1.6+ Billion Searches per month, across 1K+ customers around the world, just on Typesense Cloud, and several Billions more in self-hosted clusters every month. Typesense reduces the time-to-market for developers to build a blazing-fast search experience that provides relevant results out-of-the-box, all without breaking the bank and without operational overhead.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 5


**Seller Details:**

- **Seller:** [Typesense](https://www.g2.com/sellers/typesense)
- **Year Founded:** 2016
- **HQ Location:** Houston, US
- **Twitter:** @TypeSense (15,862 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/typesense/ (12 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 80% Small-Business, 40% Mid-Market


### 22. [Vald](https://www.g2.com/products/vald-vald/reviews)
  Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 2


**Seller Details:**

- **Seller:** [Vald](https://www.g2.com/sellers/vald)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Small-Business


### 23. [ApertureDB](https://www.g2.com/products/aperturedb/reviews)
  ApertureDB is a vector + graph database purpose-built to streamline the development and scaling of multimodal AI and analytics applications. Designed for modern AI and analytics workflows, it combines multimodal data management, vector search capabilities and knowledge graph into a single integrated solution. With ApertureDB developers and organizations get 2-10X faster vector search performance than the competition, save 6 to 9 months on average in infrastructure setup time and improve machine learning teams productivity by 10X. It powers use cases like semantic search, RAG chatbots, Generative AI applications and AI-driven agents. ApertureDB seamlessly integrates across your AI stack including popular large scale Language Models (LLMS), AI and machine learning frameworks and workflows. Its robust multi-tenant architecture, designed to handle complex multimodal data text, images, videos, embeddings, metadata and easily scales for large-scale deployments while maintaining enterprise-grade performance and reliability. ApertureDB offers flexible deployment options and optimized pricing performance. Available in the cloud, on-premises or hybrid, ApertureDB meets the needs of diverse organizations, from startups to large enterprises. Our optimized pricing empowers teams to choose a deployment model that aligns with their budget and can scale effortlessly without breaking the bank.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1


**Seller Details:**

- **Seller:** [ApertureData](https://www.g2.com/sellers/aperturedata)
- **Year Founded:** 2018
- **HQ Location:** Mountain View, US
- **LinkedIn® Page:** https://www.linkedin.com/company/aperturedata (12 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Small-Business


### 24. [CockroachDB](https://www.g2.com/products/cockroachdb/reviews)
  Overview Cockroach Labs is the creator of CockroachDB, the cloud-native, resilient, distributed SQL database enterprises worldwide trust to run mission-critical AI and other applications that scale fast, avert and survive disaster, and thrive everywhere. It runs on the Big 3 clouds, on prem, and in hybrid configurations powering Fortune 500, Forbes Global 2000, and Inc. 5000 brands, and game-changing innovators, including OpenAI, CoreWeave, Adobe, NETFLIX, Booking.com, DoorDash, FANDUEL, Cisco Systems, P&G, UiPath, FORTINET, Roblox, EA, BestBuy, SpaceX, NVIDIA, The VA, Squarespace, The Home Depot, and Hewlett Packard Enterprise. Cockroach Labs has customers in 40+ countries across all world regions, 25+ verticals, and 50+ Use Cases. Cockroach Labs operates its own ISV Partner Ecosystem powering Payments, Identity Management (IDM/IAM), Banking & Wallet, Trading, and other high-demand use cases. Cockroach Labs is an AWS Partner of the Year finalist and has achieved AWS Competency Partner certifications in Data & Analytics and Financial Services (FSI). CockroachDB pricing is available at https://www.cockroachlabs.com/pricing/ Vector, RAG, and GenAI Workloads CockroachDB includes native support for the VECTOR data type and pgvector API compatibility, enabling storage and retrieval of high-dimensional embeddings. These vector capabilities are critical for Retrieval-Augmented Generation (RAG) pipelines and GenAI workloads that rely on similarity search and contextual embeddings. By supporting distributed vector indexing within the database itself, CockroachDB removes the need for external vector stores and allows AI applications to operate against a single, consistent data layer. C-SPANN Distributed Indexing At the core of CockroachDB’s vector search capabilities is the C-SPANN indexing engine. C-SPANN provides scalable approximate nearest neighbor (ANN) search across billions of vectors while supporting incremental updates, real-time writes, and partitioned indexing. This ensures low-latency retrieval in the tens of milliseconds, even under high query throughput. The algorithm eliminates central coordinators, avoids large in-memory structures, and leverages CockroachDB’s sharding and replication to deliver scale, resilience, and global consistency. Machine Learning and Apache Spark Integration CockroachDB integrates with modern ML workflows by supporting embeddings generated through frameworks such as AWS Bedrock and Google Vertex AI. Its compatibility with the PostgreSQL JDBC driver allows seamless integration with Apache Spark, enabling distributed processing and advanced analytics on CockroachDB data. PostgreSQL Compatibility and JSON Support CockroachDB speaks the PostgreSQL wire protocol, so applications, drivers, and tools designed to work with Postgres can connect to CockroachDB without modification, enabling seamless use of familiar SQL features and integration with the wider Postgres ecosystem. This includes support for advanced data types such as JSON and JSONB, which allow developers to store and query semi-structured data natively. Geospatial and Graph Capabilities CockroachDB also provides first-class geospatial data support, allowing developers to store, query, and analyze spatial data directly in SQL. For graph workloads, CockroachDB employs JSON flexibility to represent relationships and delivers query capabilities for graph-like traversals. This combination enables hybrid applications that merge relational, geospatial, document, and graph data within a single platform. Analytics, BI, and Integration To support high-performance analytics and BI, CockroachDB supports core analytical use cases and functions including Enterprise Data Warehouse, Lakehouse, and Event Analytics, and offers materialized views for precomputing complex joins and aggregations. Its PostgreSQL wire compatibility ensures direct connectivity with all relevant BI and analytics apps and tools including Amazon Redshift, Snowflake, Kafka, Google BigQuery, Salesforce Tableau, Databricks, Cognos, Looker, Grafana, Power BI, Qlik Sense, SAP, SAS, Sisense, and TIBCO Spotfire. Data scientists can interact with CockroachDB through Jupyter Notebooks, querying structured and semi-structured data and loading results for analysis. Change data capture (CDC) streams provide real-time updates to analytics pipelines and feature stores, keeping downstream systems fresh and reliable. Columnar vectorized execution accelerates query processing, optimizes transactional throughput, and minimizes latency for demanding distributed workloads. MOLT AI-Powered Migration Organizations often know their data infrastructure is not supporting the business, but find it too painful to change. CockroachDB’s MOLT (Migrate Off Legacy Technology) is designed to enable safe, minimal-downtime database migrations from legacy systems to CockroachDB. MOLT Fetch supports data migration from PostgreSQL, MySQL, SQL Server, and Oracle, with SQL Server and DB2 coming soon. CockroachDB also has a portfolio of data replication platform integrations including Precisely, Striim, Qlik, Confluent, IBM, etc. Together, these capabilities ensure that CockroachDB supports both operational and analytical workloads, bridging traditional SQL applications with emerging Gen AI and ML use cases.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 27


**Seller Details:**

- **Seller:** [Cockroach Labs](https://www.g2.com/sellers/cockroach-labs)
- **Year Founded:** 2015
- **HQ Location:** New York, NY
- **Twitter:** @CockroachDB (13,532 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/cockroach-labs/ (720 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 55% Small-Business, 34% Mid-Market


#### Pros & Cons

**Pros:**

- Database Management (4 reviews)
- Ease of Use (4 reviews)
- Performance (4 reviews)
- Scalability (4 reviews)
- Big Data Handling (3 reviews)

**Cons:**

- Learning Curve (4 reviews)
- Complexity (2 reviews)
- Difficult Learning (2 reviews)
- Feature Limitations (2 reviews)
- Limitations (2 reviews)

### 25. [Featureform Embedding Hub](https://www.g2.com/products/featureform-embedding-hub/reviews)
  Experience a comprehensive database designed to provide embedding functionality that, until now, required multiple platforms. Elevate your machine learning quickly and painlessly through Embeddinghub.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 1


**Seller Details:**

- **Seller:** [Featureform](https://www.g2.com/sellers/featureform)
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** https://www.linkedin.com/company/featureform-ml/ (12 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 100% Enterprise




## Parent Category

[Database Software](https://www.g2.com/categories/database-software)



## Related Categories

- [Relational Databases](https://www.g2.com/categories/relational-databases)
- [Database as a Service (DBaaS) Providers](https://www.g2.com/categories/database-as-a-service-dbaas)
- [Time Series Databases](https://www.g2.com/categories/time-series-databases)



---

## Buyer Guide

### Learn More About Vector Database Software

A vector database is a specialized [database](https://www.g2.com/articles/what-is-a-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](https://www.g2.com/categories/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)](https://www.g2.com/articles/what-is-artificial-intelligence), [artificial neural networks](https://www.g2.com/glossary/artificial-neural-network-definition), [natural language processing (NLP)](https://www.g2.com/articles/natural-language-processing), [large language models (LLM)](https://www.g2.com/articles/large-language-models), [computer vision (CV)](https://learn.g2.com/computer-vision), [machine learning (ML)](https://www.g2.com/articles/machine-learning), 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)](https://learn.g2.com/k-nearest-neighbor) 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.

- 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. 
- 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. 
- **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. 
- **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](https://www.g2.com/glossary/metadata-definition) 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](https://www.g2.com/glossary/anomaly-detection-definition), and recommendation system applications. 

- **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. 
- **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. 
- **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. 

- **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. 
- **Complex object representation:** Vector databases represent images, videos, words, audio, and paragraphs using an array of numbers or vectors. 
- **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. 
- **Rapid scalability:** A vector database relies on distributed and [parallel processing](https://www.g2.com/glossary/parallel-processing-definition) to handle growing data volumes from machine learning models and AI algorithms. Besides [scalability](https://www.g2.com/glossary/scalability), vector databases also feature fine-tuning capabilities for performance optimization. 
- **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.
- **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. 
- **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. 
- **Built-in data security:** Vector databases feature built-in [data security](https://www.g2.com/glossary/data-security-definition) and [access control](https://www.g2.com/glossary/access-control-definition) measures to protect sensitive data from unauthorized access. 
- **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.
- [**Backup**](https://www.g2.com/articles/what-is-backup) **storage:** Vector databases store index backups to enable users to easily sort and retrieve data. 
- **Integration with AI applications:** A vector database provides [software development kits (SDKs)](https://www.g2.com/articles/sdk) 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. 

- **Text vector databases** store and query text data in vector format. They’re ideal for [natural language processing](https://www.g2.com/glossary/natural-language-processing-definition) tasks. 
- **Graph vector databases** facilitate complex [network](https://www.g2.com/articles/what-is-a-network) analysis by storing graphs as vectors. They stand out when it comes to running recommendation systems and social network analysis tasks. 
- **Image vector databases** store and manage images using vectors for retrieval and analysis tasks.
- **Multimedia vector databases** feature multimedia content management to store video, audio, and images as vectors.
- **Quantization-based databases** use quantization to index data, enhance retrieval accuracy, and balance memory usage.
- **Hashing-based indexing databases** rely on key search value mapping to get data from larger datasets.
- **Tree-based indexing databases** use R-tree or KD-tree structures for indexing and executing tree-based partitioning.
- **Disk-based databases** can store large datasets because they can store data on disks. However, retrieval slows down with this database.
- **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. 
- **Hybrid databases** provide better speed and storage capabilities than in-memory databases because of using both in-memory and disk-based databases.
- **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. 
- **Cloud-based vector databases** store, index, and process data using [cloud computing](https://www.g2.com/articles/cloud-computing) environments. Thanks to the underlying cloud infrastructure, these databases efficiently deliver scalability and flexibility. 
- **Distributed vector databases** manage large datasets and query loads by using multiple nodes. This data distribution across machines guarantees improved scalability and fault tolerance. 
- **GPU-accelerated vector databases** speed up computation-intensive tasks like similarity searches with the processing power of [graphical processing units (GPU)](https://www.g2.com/glossary/gpu-vs-cpu#:~:text=GPUs%20accelerate%203D%20and%20graphics%20rendering%20tasks%20related%20to%20gaming%20and%20animation.%20This%20is%20done%20by%20breaking%20down%20complex%20tasks%20into%20smaller%20components%20and%20parallelly%20running%20multiple%20mathematical%20calculations.). 

### Benefits of vector databases

Developers who are considering using vector databases to manage AI-enabled application workloads can expect some of the following benefits.

- **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.
- **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](https://www.g2.com/articles/image-recognition), and recommendation engines.
- **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](https://www.g2.com/articles/data-mining) or manual [data analysis](https://www.g2.com/articles/data-analysis-process) processes. 
- **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](https://www.g2.com/categories/content-delivery-network-cdn). 
- **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)](https://www.g2.com/glossary/api-definition) suitable for AI and ML programming.
- **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. 
- **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](https://www.g2.com/articles/relational-databases), 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.

- **Healthcare researchers** use vector databases to store and retrieve high-dimensional medical imaging data for diagnostic research. 
- **Web developers** rely on vector database solutions to store and process back-end data for high-performance web applications that require speed and scalability. 
- **Game developers** use vector databases to ensure fast processing, minimize lag time, and store player and gaming progress related data. 
- **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:

- **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. 
- **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. 
- **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.

- [**Document databases**](https://www.g2.com/categories/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.
- [**Graph databases**](https://www.g2.com/categories/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](https://www.g2.com/glossary/fraud-detection-definition) apps, and [social networks](https://www.g2.com/categories/social-networks).
- [**Time series databases**](https://www.g2.com/categories/time-series-databases) handle time-stamped or time-series data, such as network data, sensor data, application performance monitoring data, and [server](https://www.g2.com/glossary/server-definition) 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](https://www.g2.com/glossary/internet-of-things-definition) (IoT) devices.
- **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. 

### Software and services related to vector databases

Organizations may also use the following software and services alongside vector databases.

- [**Geographic information systems**](https://www.g2.com/categories/gis) **(GIS)** capture, store, analyze, and manage location data based on the positions of the Earth’s surface. Organizations turn to GISs when they need help understanding patterns and relationships among geographic data.
- **Spatial data analysis tools** give organizations the power to visualize and analyze location-specific features and boundaries on the Earth. Organizations use these tools to process the physical location data of objects on the Earth. 
- **Web mapping software,** or web GIS, facilitates access to internet-based geospatial maps using web browser interfaces. 

### Challenges with vector databases

Organizations that use vector databases should prepare to tackle the following problems.

- **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. 
- **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. 
- **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.
- **Storage and maintenance issues:** As data size and model complexity increase, organizations must expand data storage and maintain vector databases regularly. 
- **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. 
- **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](https://www.g2.com/glossary/data-quality-definition) 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. 

- **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. 
- **E-commerce businesses** rely on vector databases’ recommendation capabilities to interpret [consumer behavior](https://learn.g2.com/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. 
- [**Social media networks**](https://www.g2.com/categories/social-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. 
- **Financial institutions,** like banks, [financial service providers](https://www.g2.com/categories/business-finance), and [brokerage trading platforms](https://www.g2.com/categories/brokerage-trading-platforms), analyze market data and detect fraudulent transactions using data processing and pattern analysis functionalities.
- [**Supply chain management companies**](https://www.g2.com/glossary/supply-chain-management-definition) 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.
- **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](https://www.g2.com/articles/best-vector-databases) typically feature the following functionalities.

- **Data freshness:** How long does it take for new data querying?
- **Query latency:** How long does executing a query take? What about receiving results?
- **Query per second (QPS):** How many queries can it handle in a second?
- **Namespace:** Does the vector database search index by namespace?
- **Accuracy:** How fast can a solution return accurate results during an ANN search?
- **Hybrid search:** Does the vector database support semantic and keyword searches? 
- **Metadata filtering:** Can users use metadata to filter vectors when querying? 
- **Monitoring:** Does the system monitor metrics and detect problems?
- **Security and compliance:** Does the platform encrypt data at rest and in transit? Does it comply with the General Data Protection Regulation ([GDPR](https://www.g2.com/glossary/gdpr-definition)); the Health Insurance Portability and Accountability Act ([HIPAA](https://www.g2.com/glossary/hipaa-definition)); 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.

- **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. 
- **Important features:** Consider important factors for success, such as scalability, storage options, integration availability, indexing capabilities, and performance. 
- **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. 
- [**Data security**](https://www.g2.com/glossary/data-security-definition) **:** Organizations must check whether vector database vendors have sufficient security measures, such as activity monitoring, data [encryption](https://www.g2.com/articles/what-is-encryption), and [access control](https://www.g2.com/glossary/access-control-definition). 
- **Scalability:** Designing a database architecture during deployment that scales with data volumes saves time and effort in the future.

### Vector database trends

- **Geospatial big data applications:** Disaster management, environmental monitoring, defense, and urban planning organizations are steadily using vector databases more to analyze geospatial [big data](https://www.g2.com/articles/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](https://www.g2.com/articles/predictive-analytics#predictive-analytics-vs-predictive-modeling:~:text=Similarly%2C-,predictive%20modeling,-is%20the%20process) for forecasting future outcomes.
- [Edge computing](https://learn.g2.com/trends/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_](https://research.g2.com/insights/author/shalaka-joshi)

_Reviewed and edited by_ [_Aisha West_](https://learn.g2.com/author/aisha-west)