# Best Vector Database Software ## How Many Vector Database Software Products Does G2 Track? **Total Products under this Category:** 36 ### Category Stats (Aug 2026) - **Average Rating:** 4.57/5 The average rating of products in this category, based on all submitted ratings _Last updated: August 06, 2026_ ## How Does G2 Rank Vector Database Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 1,100+ Authentic Reviews - 36+ 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. ## G2 Grid® for Vector Database Software ![G2 Grid® for Vector Database Software plotting products by satisfaction and market presence](https://www.g2.com/categories/vector-database/grids.png?focus%5B%5D=8313&focus%5B%5D=135981&focus%5B%5D=1261012&focus%5B%5D=1286545&focus%5B%5D=147279&focus%5B%5D=1286547&focus%5B%5D=120490&focus%5B%5D=1286541) Highlighted products: Elasticsearch, Supabase, Zilliz, Pinecone, Weaviate, PG Vector, TiDB, and Qdrant. Underlying data: [Grid® JSON](https://www.g2.com/categories/vector-database/grids.json?focus%5B%5D=elastic-elasticsearch&focus%5B%5D=supabase-supabase&focus%5B%5D=zilliz&focus%5B%5D=pinecone&focus%5B%5D=weaviate&focus%5B%5D=pg-vector&focus%5B%5D=tidb&focus%5B%5D=qdrant) **Sponsored** ### Amazon DynamoDB Amazon DynamoDB is a pioneering NoSQL, fully managed, serverless database with limitless scalability and single-digit millisecond latency performance enabling customers to develop modern, microservice-based applications through a simple API. Customers enjoy the benefits of DynamoDB’s fully-managed service including broad compliance standards, security integration with AWS Identity and Access Management and numerous disaster recovery services. With DynamoDB Global Tables, customers have a 99.999% highly available, multi-Region, multi-active database supporting local reads and writes for globally distributed users. DynamoDB provides cost management features such as scale-to-zero, Time to Live (TTL) for aging data out, and multiple pricing models including a free tier. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=1005558&secure%5Bchosen_at%5D=2026-08-07T09%3A30%3A39Z&secure%5Bdisplayable_resource_id%5D=1005558&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1005558&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=17357&secure%5Bresource_id%5D=1005558&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fvector-database&secure%5Btoken%5D=6981221c2802e40004b03f6e2f7fc9342a330f84e6505766b18506425a825a52&secure%5Burl%5D=https%3A%2F%2Faws.amazon.com%2Fdynamodb%2F%3Ftrk%3D48b70123-a3f9-4b1c-a18c-f58b526ec750%26sc_channel%3Ddisplay%2Bads&secure%5Burl_type%5D=custom_url) ### [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:** 288 #### Who Is the Company Behind Elasticsearch? - **Seller:** [Elastic](https://www.g2.com/sellers/elastic) - **Company Website:** www.elastic.co - **Year Founded:** 2012 - **HQ Location:** San Francisco, CA - **Twitter:** @elastic 65,200 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=bc8e533876f16af617380aaa3922cb6a39a1d6233f0b32a0fa987a5fdffd799e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F814025%2F&secure%5Burl_type%5D=linkedin_company_website) 5,079 employees on LinkedIn® #### Who Uses This Product? - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 38% Medium, 33% Large #### What Do G2 Reviewers Say About Elasticsearch? _AI-generated summary from verified user reviews_ ##### Pros - Users find Elasticsearch extremely **easy to use** , enhancing their integration and observability capabilities effortlessly. - Users commend Elasticsearch for its **impressive speed** , efficiently handling large datasets and ensuring smooth scalability. - Users value **fast search capabilities** in Elasticsearch, enabling quick access to large datasets and streamlined operations. - Users find the **blazing-fast performance** and thorough documentation of Elasticsearch immensely beneficial for their search needs. - Users admire the **powerful search and aggregation features** of Elasticsearch, appreciating its performance and flexibility at scale. ##### Cons - Users find Elasticsearch **expensive to scale** , especially with high data usage and costly licensing for valuable features. - Users find that **Elasticsearch requires significant expertise** for optimal performance, complicating its setup and management. - Users find the **learning difficulty** of Elasticsearch challenging, especially with complex setups and query management for beginners. - Users feel that Elasticsearch requires **improvement in user-friendliness** and performance tuning, complicating their experience with searches. - Users find the **difficult learning curve** of Elasticsearch challenging, requiring considerable effort and time to master. #### What Are Recent G2 Reviews of Elasticsearch? **["Simple UI, Seamless Integrations, and Strong Elasticsearch Performance"](https://www.g2.com/survey_responses/elasticsearch-review-12835645)** **Rating:** 4.5/5.0 stars _— Antonia F._ [Read full review](https://www.g2.com/survey_responses/elasticsearch-review-12835645) **["Impressive Speed and Powerful Near Real-Time Search with Elasticsearch"](https://www.g2.com/survey_responses/elasticsearch-review-12579166)** **Rating:** 5.0/5.0 stars _— Ertuğrul D._ [Read full review](https://www.g2.com/survey_responses/elasticsearch-review-12579166) ### [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:** 4.6/5.0 **Total Reviews:** 148 #### Who Is the Company Behind Supabase? - **Seller:** [Supabase](https://www.g2.com/sellers/supabase) - **Year Founded:** 2020 - **HQ Location:** Global, US - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=90292f271b5b4feb535f6c4c2b32bb22ad2b1d3a612cb21ca0afc68b74ac3772&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsupabase%2F&secure%5Burl_type%5D=linkedin_company_website) 357 employees on LinkedIn® #### Who Uses This Product? - **Who Uses This:** Founder, Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 71% Small, 23% Medium #### What Do G2 Reviewers Say About Supabase? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **ease of use** of Supabase, highlighting its simple setup and user-friendly backend experience. - Users appreciate the **powerful design and intuitive API** of Supabase, making development both easy and enjoyable. - Users find Supabase to be **user-friendly** , appreciating its simplicity, great UI, and seamless integration with tools. - Users find **easy integrations** with Supabase enhance their development experience and streamline project setups effortlessly. - Users appreciate the **seamless integrations** of Supabase, making it easy to connect with various development tools smoothly. ##### Cons - Users find Supabase has **beginner unfriendliness** , as its complexity and limited support hinder novice coders' experience. - Users find the **pricing high** , particularly for premium services that should be accessible to all users. - Users find the **high complexity** of Supabase challenging, particularly for novice coders seeking user-friendly tools. - Users feel Supabase lacks **insufficient guidance** , particularly for novice coders needing better documentation and user-friendliness. - Users find **learning difficulty** with Supabase, especially novices who struggle with complex tools and limited community support. #### What Are Recent G2 Reviews of Supabase? **["Supabase gave us BaaS speed without locking us into a proprietary database"](https://www.g2.com/survey_responses/supabase-review-13211696)** **Rating:** 4.5/5.0 stars _— Yurii L._ [Read full review](https://www.g2.com/survey_responses/supabase-review-13211696) **["Supabase: Complete, Developer-Friendly Backend That Speeds Up Development"](https://www.g2.com/survey_responses/supabase-review-13209007)** **Rating:** 4.5/5.0 stars _— Atharva S._ [Read full review](https://www.g2.com/survey_responses/supabase-review-13209007) ### [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 #### Who Is the Company Behind Zilliz? - **Seller:** [ZILLIZ](https://www.g2.com/sellers/zilliz) - **Year Founded:** 2017 - **HQ Location:** Redwood City, US - **Twitter:** @milvusio 5,194 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=1a445ac2021a5ec7051b353eae3c3b8c66730e01ab41e44e6af2a839d01bf844&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fzilliz&secure%5Burl_type%5D=linkedin_company_website) 147 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 45% Small, 36% Medium #### What Do G2 Reviewers Say About Zilliz? _AI-generated summary from verified user reviews_ ##### Pros - Users find Zilliz to be highly **user-friendly** , with intuitive platforms and excellent support for seamless integration. - Users commend the **extremely fast, low-latency vector search** of Zilliz, enhancing efficiency at massive scales. - Users highlight the **exceptionally fast retrieval speed** and flexible indexing features of Zilliz for efficient data management. - Users highlight the **lightning-fast query performance** of Zilliz, making it highly efficient for processing large datasets. - Users appreciate the **comprehensive open-source documentation** of Zilliz, facilitating efficient development and deployment. ##### Cons - Users find the **pricing expensive** , particularly for smaller projects, making it less accessible for individual developers. - Users find the **limited vector column support** and lacking features affect their overall experience with Zilliz. - Users feel that the **insufficient documentation** hinders understanding of advanced configurations and performance optimization for Zilliz. - Users experience **missing features** in Zilliz, such as unsupported sorting on scalar fields and incomplete full-text search functionality. - Users find the **monthly costs too high** for small projects compared to alternatives like hosting on Hetzner. #### What Are Recent G2 Reviews of Zilliz? **["Efficient Zilliz for Entity Alignment, Disambiguation, and RAG Recall in Knowledge Graphs"](https://www.g2.com/survey_responses/zilliz-review-11913845)** **Rating:** 5.0/5.0 stars _— andrew c._ [Read full review](https://www.g2.com/survey_responses/zilliz-review-11913845) **["An Excellent Vector Database for Developers"](https://www.g2.com/survey_responses/zilliz-review-11889008)** **Rating:** 5.0/5.0 stars _— a n._ [Read full review](https://www.g2.com/survey_responses/zilliz-review-11889008) ### [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:** 51 #### Who Is the Company Behind Pinecone? - **Seller:** [Pinecone Systems](https://www.g2.com/sellers/pinecone-systems) - **Year Founded:** 2019 - **HQ Location:** New York, NY - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=1009fb55e5b7a2a8c4018edaa3d28bcf9e2151ca5594d0f56607aca2797b652f&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fpinecone-io%2F&secure%5Burl_type%5D=linkedin_company_website) 135 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 74% Small, 25% Medium #### What Are Recent G2 Reviews of Pinecone? **["Pinecone Makes Vector Search and RAG Fast, Scalable, and Easy to Ship"](https://www.g2.com/survey_responses/pinecone-review-13205872)** **Rating:** 4.5/5.0 stars _— Atharva S._ [Read full review](https://www.g2.com/survey_responses/pinecone-review-13205872) **["Fast, Hands-Off Serverless Vector Search That Scales Effortlessly"](https://www.g2.com/survey_responses/pinecone-review-13195075)** **Rating:** 4.5/5.0 stars _— Muhammed A._ [Read full review](https://www.g2.com/survey_responses/pinecone-review-13195075) ### [Weaviate](https://www.g2.com/products/weaviate/reviews) Weaviate is an open-source vector database that helps developers build and scale AI applications such as semantic search, retrieval-augmented generation (RAG), recommendation, and AI agents. It stores data objects together with their vector embeddings, allowing applications to combine vector similarity with keyword and structured filters (hybrid search) to retrieve relevant results across billions of objects. Weaviate is built for developers and teams working with modern AI workloads, from individual projects to enterprise production systems. It integrates across the AI stack with pre-built modules for common embedding and large language model (LLM) providers, including OpenAI, Anthropic, Cohere, Google, AWS Bedrock, and Hugging Face, so teams can bring their own models or use Weaviate Embeddings. Key capabilities include: - Vector and hybrid search that combines semantic similarity with keyword matching and metadata filters - Built-in vectorization and pluggable embedding and LLM providers, so models can be changed without re-architecting - Native multi-tenancy with data isolation, and role-based access control (RBAC) - Query Agent, which converts natural-language questions into database queries and returns answers with source citations - Engram, a managed memory service that gives AI agents persistent, personalized memory Weaviate can be self-hosted under the open-source BSD-3-Clause license or run as a fully managed service through Weaviate Cloud on AWS, Google Cloud, and Azure, with a Bring Your Own Cloud (BYOC) option that runs Weaviate inside a customer's own cloud environment. Weaviate Cloud includes a free tier with no credit card required and no expiration, along with paid tiers for teams moving into production. For organizations with compliance requirements, Weaviate is SOC 2 Type II certified, with HIPAA compliance available for regulated workloads. **Average Rating:** 4.5/5.0 **Total Reviews:** 40 #### Who Is the Company Behind Weaviate? - **Seller:** [Weaviate](https://www.g2.com/sellers/weaviate) - **Year Founded:** 2019 - **HQ Location:** Amsterdam, NL - **Twitter:** @weaviate\_io 19,218 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7ec56b00b8ecd368de973b60f0815875f947e65f5b8609f44b655836f9925eab&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fweaviate-io&secure%5Burl_type%5D=linkedin_company_website) 73 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 57% Small, 30% Medium #### What Are Recent G2 Reviews of Weaviate? **["Weaviate’s Powerful Vector Search with a Developer-Friendly, Scalable API"](https://www.g2.com/survey_responses/weaviate-review-13210140)** **Rating:** 4.5/5.0 stars _— Atharva S._ [Read full review](https://www.g2.com/survey_responses/weaviate-review-13210140) **["Weaviate Review"](https://www.g2.com/survey_responses/weaviate-review-13213620)** **Rating:** 4.0/5.0 stars _— Vibhor J._ [Read full review](https://www.g2.com/survey_responses/weaviate-review-13213620) ### [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 #### Who Is the Company Behind PG Vector? - **Seller:** [pgvector](https://www.g2.com/sellers/pgvector) - **HQ Location:** N/A - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 50% Medium, 42% Small #### What Are Recent G2 Reviews of PG Vector? **["PG Vector: Game-Changing Embeddings for PostgreSQL"](https://www.g2.com/survey_responses/pg-vector-review-9035667)** **Rating:** 4.0/5.0 stars _— Sangeetha k._ [Read full review](https://www.g2.com/survey_responses/pg-vector-review-9035667) **["SQL- PG vector"](https://www.g2.com/survey_responses/pg-vector-review-9116298)** **Rating:** 5.0/5.0 stars _— Nishant M._ [Read full review](https://www.g2.com/survey_responses/pg-vector-review-9116298) ### [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 #### Who Is the Company Behind TiDB? - **Seller:** [PingCAP](https://www.g2.com/sellers/pingcap) - **Company Website:** tidb.io - **Year Founded:** 2015 - **HQ Location:** Sunnyvale - **Twitter:** @PingCAP 7,268 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=97feeff0f3bb4906e69c91330a88caf7e60daa531a2894ebf5640640b4467879&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fpingcap&secure%5Burl_type%5D=linkedin_company_website) 501 employees on LinkedIn® #### Who Uses This Product? - **Who Uses This:** Senior Software Engineer, DBA - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 31% Large, 27% Small #### What Do G2 Reviewers Say About TiDB? _AI-generated summary from verified user reviews_ ##### Pros - Users praise TiDB's **seamless scalability** , allowing efficient data handling and growth without manual intervention. - Users appreciate the **ease of use** of TiDB, enjoying seamless integration and simple deployment across various platforms. - Users appreciate the **horizontal scalability and strong consistency** of TiDB, enhancing their database management experience. - Users value the **MySQL compatibility** of TiDB, facilitating smooth adoption and seamless scaling as data grows. - Users commend TiDB for its **exceptional performance** , handling large data efficiently and supporting real-time analytics seamlessly. ##### Cons - Users find the **steep learning curve** of TiDB particularly challenging when adapting to its distributed architecture and performance tuning. - Users report **performance issues** with TiDB, particularly in TiFlash and overall resource requirements, affecting their development experience. - Users experience **slow performance** with TiDB, especially in smaller environments, impacting overall efficiency and resource utilization. - Users face a **difficult learning** curve with TiDB's distributed architecture, especially regarding performance tuning and compatibility. - Users find TiDB's **poor documentation** complicates understanding complex setups and optimizing features, particularly for beginners. #### What Are Recent G2 Reviews of TiDB? **["TiDB Delivers NoSQL-Scale with SQL Simplicity and MySQL Compatibility"](https://www.g2.com/survey_responses/tidb-review-12342698)** **Rating:** 5.0/5.0 stars _— yugesh K._ [Read full review](https://www.g2.com/survey_responses/tidb-review-12342698) **["Seamless Integration, Reliable and Scalable"](https://www.g2.com/survey_responses/tidb-review-12354194)** **Rating:** 4.5/5.0 stars _— Abhishek ._ [Read full review](https://www.g2.com/survey_responses/tidb-review-12354194) ### [Qdrant](https://www.g2.com/products/qdrant/reviews) Qdrant is a high-performance, composable vector search engine built in Rust for production-grade semantic, hybrid, and agentic workloads. **Average Rating:** 4.5/5.0 **Total Reviews:** 12 #### Who Is the Company Behind Qdrant? - **Seller:** [Qdrant](https://www.g2.com/sellers/qdrant) - **Year Founded:** 2021 - **HQ Location:** Berlin, Berlin - **Twitter:** @qdrant\_engine 13,291 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=1982907dc6870db7621af02c433d6f6898f1e30ee0d5ac23f3dcc73e50b74514&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fqdrant%2F&secure%5Burl_type%5D=linkedin_company_website) 145 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 58% Small, 33% Medium #### What Are Recent G2 Reviews of Qdrant? **["Self-hosted Qdrant Vector DB"](https://www.g2.com/survey_responses/qdrant-review-10578214)** **Rating:** 4.5/5.0 stars _— Aarav M._ [Read full review](https://www.g2.com/survey_responses/qdrant-review-10578214) **["A quick and easy to setup vector database for RAG needs"](https://www.g2.com/survey_responses/qdrant-review-10606645)** **Rating:** 4.5/5.0 stars _— Kawalpreet J._ [Read full review](https://www.g2.com/survey_responses/qdrant-review-10606645) ### [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 #### Who Is the Company Behind CrateDB? - **Seller:** [CrateDB](https://www.g2.com/sellers/cratedb) - **Company Website:** cratedb.com - **Year Founded:** 2013 - **HQ Location:** Redwood City, CA - **Twitter:** @cratedb 4,176 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=121f4072f17568c8e57d89de0363476840b4fc79b39ceb272ef7594a38994b21&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcrateio%2F&secure%5Burl_type%5D=linkedin_company_website) 44 employees on LinkedIn® #### Who Uses This Product? - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 54% Small, 31% Medium #### What Do G2 Reviewers Say About CrateDB? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **ease of setup and SQL querying** , which makes CrateDB accessible and efficient for data analysis. - Users value the **fast SQL queries** in CrateDB, enabling seamless streaming and processing of complex data efficiently. - Users appreciate the **easy integrations** of CrateDB, facilitating seamless connections with tools for efficient data handling. - Users value the **flexibility** of CrateDB, appreciating its adaptability to various data types and deployment setups. - Users praise the **multi-model data support and advanced search capabilities** , finding it versatile and easy to use. ##### Cons - Users note a **lack of features** in CrateDB, particularly in comparative insights and advanced SQL functions. - Users find **software limitations** in CrateDB, including a lack of advanced features and support for complex operations. - Users express concerns about the **limited features** of CrateDB, particularly regarding connectors and analytical capabilities. - Users find the **poor documentation** of CrateDB lacking, especially regarding custom configurations and integrations. - Users find **complex configuration** challenges in CrateDB, especially regarding Kubernetes and sharding management. #### What Are Recent G2 Reviews of CrateDB? **["Easy Setup, High Performance SQL"](https://www.g2.com/survey_responses/cratedb-review-12043586)** **Rating:** 4.0/5.0 stars _— Marcin G._ [Read full review](https://www.g2.com/survey_responses/cratedb-review-12043586) **["Scalable Real-Time Time-Series SQL That’s Built for Observability"](https://www.g2.com/survey_responses/cratedb-review-12452794)** **Rating:** 5.0/5.0 stars _— Cristian M._ [Read full review](https://www.g2.com/survey_responses/cratedb-review-12452794) ### [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 #### Who Is the Company Behind Milvus? - **Seller:** [ZILLIZ](https://www.g2.com/sellers/zilliz) - **Year Founded:** 2017 - **HQ Location:** Redwood City, US - **Twitter:** @milvusio 5,194 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=1a445ac2021a5ec7051b353eae3c3b8c66730e01ab41e44e6af2a839d01bf844&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fzilliz&secure%5Burl_type%5D=linkedin_company_website) 147 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 42% Small, 33% Medium #### What Are Recent G2 Reviews of Milvus? **["Native and Efficient Vector Storage for Modern AI"](https://www.g2.com/survey_responses/milvus-review-12113376)** **Rating:** 5.0/5.0 stars _— Pablo H._ [Read full review](https://www.g2.com/survey_responses/milvus-review-12113376) **["Improving AI Testing Efficiency with Scalable Vector Search Using Milvus"](https://www.g2.com/survey_responses/milvus-review-12719468)** **Rating:** 5.0/5.0 stars _— Bharat V._ [Read full review](https://www.g2.com/survey_responses/milvus-review-12719468) ### [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 #### Who Is the Company Behind KX? - **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:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=eef4e5083e8584217de5e815e0456b4102092203ee10366e8b6e38767635c2ca&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fkx-systems&secure%5Burl_type%5D=linkedin_company_website) 492 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Financial Services, Banking - **Company Size:** 57% Large, 25% Small #### What Do G2 Reviewers Say About KX? _AI-generated summary from verified user reviews_ ##### Pros - Users highlight the **lightning-fast performance** of KX's kx-dashboard, making report creation quick and efficient. - Users praise the **exceptional performance** of KX, highlighting its speed and efficiency in dashboard creation. - Users highlight the **powerful and versatile** capabilities of KX products for efficient data processing and problem-solving. - Users admire the **powerful and versatile tools** of KX, enhancing productivity and problem-solving efficiency. - Users highlight the **unparalleled real-time data processing** of KX, enabling swift and informed decision-making in dynamic markets. ##### Cons - Users find the **learning curve steep** , requiring significant time and dedication to master KX effectively. - Users find the **difficult learning** curve of KX challenging, especially due to a lack of external resources. - Users find the **learning curve steep** for KX, making initial understanding and coding efficiency challenging. - Users find KX licenses **expensive** , especially for hobbyists and small organizations, impacting long-term adoption decisions. - Users face **initial difficulties** with KX due to limited resources and challenges in integration with other applications. #### What Are Recent G2 Reviews of KX? **["The best ecosystem for quants and data engineers"](https://www.g2.com/survey_responses/kx-review-10651130)** **Rating:** 5.0/5.0 stars _— Alex T._ [Read full review](https://www.g2.com/survey_responses/kx-review-10651130) **["KX is an incredible company with the best time-series database in the world."](https://www.g2.com/survey_responses/kx-review-11467454)** **Rating:** 5.0/5.0 stars _— Kenan D._ [Read full review](https://www.g2.com/survey_responses/kx-review-11467454) ### [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:** 20 #### Who Is the Company Behind Relevance AI? - **Seller:** [Relevance AI](https://www.g2.com/sellers/relevance-ai) - **Year Founded:** 2020 - **HQ Location:** Sydney, Australia - **Twitter:** @RelevanceAI\_ 3,784 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=c46ebf1f09b1b4a9374faeb2ccce8757d4a5c5f77e7bae03658df4b90b54ae5c&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Frelevanceai&secure%5Burl_type%5D=linkedin_company_website) 121 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Computer Software - **Company Size:** 85% Small, 10% Medium #### What Do G2 Reviewers Say About Relevance AI? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **ease of use** of Relevance AI, enjoying its intuitive setup and seamless management of agents. - Users value the **incredible versatility** of Relevance AI, enabling custom agents for various functions effortlessly. - Users appreciate the **advanced AI integration** of Relevance AI, which enhances efficiency and reduces manual workload. - Users value the **extensive customization options** in Relevance AI, enabling tailored solutions for diverse needs and functions. - Users commend **efficiency** in Relevance AI, benefiting from automated tasks and reliable, secured results across various applications. ##### Cons - Users find the **high cost** of Relevance AI prohibitive and frustrating, especially when unable to utilize the service. - Users express frustration over the **high costs** of Relevance AI, making it less accessible for general use. - Users find the **interface complex** , citing a confusing onboarding experience and busy design that hinders usability. - Users find the **limited features** of Relevance AI frustrating, as they lack important tools for optimal performance. - Users find the **learning curve challenging** , with onboarding being confusing and no in-person training available. #### What Are Recent G2 Reviews of Relevance AI? **["The Best Platform for Creating Custom Agents"](https://www.g2.com/survey_responses/relevance-ai-review-12424146)** **Rating:** 5.0/5.0 stars _— Leopoldo E._ [Read full review](https://www.g2.com/survey_responses/relevance-ai-review-12424146) **["A powerful AI agent builder that builds them for you"](https://www.g2.com/survey_responses/relevance-ai-review-10916575)** **Rating:** 4.5/5.0 stars _— Will M._ [Read full review](https://www.g2.com/survey_responses/relevance-ai-review-10916575) ### [Vespa.ai](https://www.g2.com/products/vespa-ai/reviews) Developers building customer-facing, large-scale search, Retrieval-Augmented Generation (RAG), and recommendation systems face a core challenge: retrieving and operationalizing data in real time. Data is fragmented across formats, including PDFs, free text, and semi-structured sources. This makes it difficult to unify, index, and serve data efficiently to applications and end users. Without the right infrastructure, applications become slow, brittle, and costly to scale. Vespa addresses this by unifying structured, unstructured, vector, and tensor data in a single system, enabling efficient, real-time retrieval and ranking at scale. The Vespa AI search platform is built for real-time retrieval, ranking, and inference on AWS, powering customer-facing applications including search, RAG, recommendations, and personalization. It unifies structured, unstructured, vector, and tensor data to deliver fast, accurate, and highly relevant results at millisecond latency. Vespa is purpose-built for customer-facing experiences where latency, relevance, and scale directly impact engagement, conversion, and revenue. By combining full-text search, vector search, and machine-learned ranking within a single query pipeline, Vespa delivers consistent, high-quality results across every user interaction. Its tensor-based ranking architecture enables applications to evaluate multiple signals simultaneously, including semantic meaning, behavioral data, and real-time context, enabling results to continuously adapt to user intent and business priorities. Ranking and inference run directly within the engine, eliminating external pipelines and enabling real-time updates to content, models, and business signals. Running on AWS, Vespa delivers elastic scalability, high availability, and fully managed infrastructure through Vespa Cloud. Automated provisioning, scaling, monitoring, and upgrades reduce operational overhead while supporting high-throughput, low-latency workloads. Vespa is trusted in production by organizations including Perplexity, Spotify, and Yahoo to power large-scale, real-time search, recommendation, and AI applications. Developers use Vespa to build responsive, intelligent applications that enhance the customer experience, improve conversion rates, and drive measurable business outcomes. **Average Rating:** 4.6/5.0 **Total Reviews:** 8 #### Who Is the Company Behind Vespa.ai? - **Seller:** [Vespa.ai](https://www.g2.com/sellers/vespa-ai) - **Company Website:** vespa.ai - **Year Founded:** 2023 - **HQ Location:** Trondheim, NO - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=aea3cb3ffb650aa774cfc2690f272f714dfaeb4e79adfd4b928dc229ad6e56cd&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fvespa-ai%2F&secure%5Burl_type%5D=linkedin_company_website) 51 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 63% Small, 25% Large #### What Are Recent G2 Reviews of Vespa.ai? **["Best Gen AI software to build your own infrastructure"](https://www.g2.com/survey_responses/vespa-ai-review-9938329)** **Rating:** 4.5/5.0 stars _— Vignesh H._ [Read full review](https://www.g2.com/survey_responses/vespa-ai-review-9938329) **["Vepsa decreased costs, latency, and management for billions of searches per month"](https://www.g2.com/survey_responses/vespa-ai-review-9781061)** **Rating:** 5.0/5.0 stars _— Verified User in Marketing and Advertising_ [Read full review](https://www.g2.com/survey_responses/vespa-ai-review-9781061) ### [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 #### Who Is the Company Behind Chroma Vector Database? - **Seller:** [Chroma](https://www.g2.com/sellers/chroma) - **Year Founded:** 1991 - **HQ Location:** Bellows Falls, Vermont, United States - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=bf4210b7c57c659a56f0f599771dc36b5180928624c3c3c791beac49859cdeee&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fchroma-technology-corp&secure%5Burl_type%5D=linkedin_company_website) 106 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 67% Small, 17% Medium #### What Are Recent G2 Reviews of Chroma Vector Database? **["ChromaDB review"](https://www.g2.com/survey_responses/chroma-vector-database-review-10735345)** **Rating:** 4.5/5.0 stars _— Dwaipayan B._ [Read full review](https://www.g2.com/survey_responses/chroma-vector-database-review-10735345) **["chroma db has high performance db which helps alot for implement these applications like nlp and ml"](https://www.g2.com/survey_responses/chroma-vector-database-review-10240111)** **Rating:** 4.0/5.0 stars _— Amit K._ [Read full review](https://www.g2.com/survey_responses/chroma-vector-database-review-10240111) ### [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 #### Who Is the Company Behind Faiss? - **Seller:** [Meta Platforms, Inc](https://www.g2.com/sellers/meta-platforms-inc) - **Year Founded:** 2008 - **HQ Location:** Menlo Park, CA - **Twitter:** @Meta 9,891,711 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8ddd5ae494d43c8871a4a073a634fa2f05131a85b5f7f4df13a9fc1cffa0ec84&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmeta%2F&secure%5Burl_type%5D=linkedin_company_website) 158,764 employees on LinkedIn® - **Ownership:** NASDAQ: META #### Who Uses This Product? - **Company Size:** 100% Small #### What Are Recent G2 Reviews of Faiss? **["diagnosis of FAISS"](https://www.g2.com/survey_responses/faiss-review-10240072)** **Rating:** 4.0/5.0 stars _— Akhil G._ [Read full review](https://www.g2.com/survey_responses/faiss-review-10240072) **["Powerful and Scalable Vector Search with High Performance"](https://www.g2.com/survey_responses/faiss-review-10234187)** **Rating:** 5.0/5.0 stars _— Revanth C._ [Read full review](https://www.g2.com/survey_responses/faiss-review-10234187) - ‹ Prev‹ Prev - 1 - [2](/categories/vector-database?order=g2_score&page=2#product-list) - [3](/categories/vector-database?order=g2_score&page=3#product-list) - [Next ›Next ›](/categories/vector-database?order=g2_score&page=2#product-list) Spotlight Categories [Identity Verification Software](https://www.g2.com/categories/identity-verification) [Electronic Data Interchange (EDI) Software](https://www.g2.com/categories/electronic-data-interchange-edi) [Operational Risk Management Software](https://www.g2.com/categories/operational-risk-management) [Compensation Management Software](https://www.g2.com/categories/compensation-management) [Travel Management Solutions](https://www.g2.com/categories/travel-management) Similar Categories - [Relational Databases](/categories/relational-databases) - [NoSQL Databases](/categories/nosql-databases) - [Database Management Systems (DBMS)](/categories/database-management-systems-dbms) - [Big Data](/categories/big-data) - [Database as a Service (DBaaS)](/categories/database-as-a-service-dbaas) - [Database Comparison](/categories/database-comparison) - [Desktop Database](/categories/desktop-database) - [Real-time Analytic Database](/categories/real-time-analytic-database) - [Structured Data Archiving and Application Retirement](/categories/structured-data-archiving-and-application-retirement) - [Time Series Databases](/categories/time-series-databases) [Browse Vector Database Themes](/categories/vector-database/themes) ![Shalaka Joshi](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Shalaka Joshi") SJ Researched and written by [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi) Updated April 9, 2026 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. Show More ### Vector Database Topics - [How do vector databases work?](#how-do-vector-databases-work) - [Key features of vector databases](#key-features-of-vector-databases) - [Types of vector databases](#types-of-vector-databases) - [Benefits of vector databases](#benefits-of-vector-databases) - [Vector database vs. relational database](#vector-database-vs-relational-database) - [Who uses vector database software?](#who-uses-vector-database-software) - [Vector database pricing](#vector-database-pricing) - [Alternatives to vector databases](#alternatives-to-vector-databases) - [Software and services related to vector databases](#software-and-services-related-to-vector-databases) - [Challenges with vector databases](#challenges-with-vector-databases) - [Which companies should buy vector database software?](#which-companies-should-buy-vector-database-software) - [How to choose the best vector database?](#how-to-choose-the-best-vector-database) - [How to implement vector databases](#how-to-implement-vector-databases) - [Vector database trends](#vector-database-trends) [ ### Vector Database Topics Expand/Collapse ](#) - [How do vector databases work?](#how-do-vector-databases-work) - [Key features of vector databases](#key-features-of-vector-databases) - [Types of vector databases](#types-of-vector-databases) - [Benefits of vector databases](#benefits-of-vector-databases) - [Vector database vs. relational database](#vector-database-vs-relational-database) - [Who uses vector database software?](#who-uses-vector-database-software) - [Vector database pricing](#vector-database-pricing) - [Alternatives to vector databases](#alternatives-to-vector-databases) - [Software and services related to vector databases](#software-and-services-related-to-vector-databases) - [Challenges with vector databases](#challenges-with-vector-databases) - [Which companies should buy vector database software?](#which-companies-should-buy-vector-database-software) - [How to choose the best vector database?](#how-to-choose-the-best-vector-database) - [How to implement vector databases](#how-to-implement-vector-databases) - [Vector database trends](#vector-database-trends) ## 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)