Top Free Vector Database Software

How Many Vector Database Software Products Does G2 Track?

Total Products under this Category: 41

Category Stats (Sep 2026)

  • Average Rating: 4.56/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: TiDB (+0.17%) - Among all products in this category, TiDB recorded the largest rating increase compared to last month

Last updated: September 13, 2026

How Does G2 Rank Vector Database Software Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 1,300+ Authentic Reviews
  • 41+ 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

Highlighted products: Elasticsearch, Supabase, Pinecone, Weaviate, Zilliz, 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=pinecone&focus%5B%5D=weaviate&focus%5B%5D=zilliz&focus%5B%5D=pg-vector&focus%5B%5D=tidb&focus%5B%5D=qdrant)

Elasticsearch

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: 289

Who Is the Company Behind Elasticsearch?

  • Seller: Elastic
  • Company Website:
  • Year Founded: 2012
  • HQ Location: San Francisco, CA
  • Twitter: @elastic
    65,200 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    10,457 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 highlight the ease of use of Elasticsearch, making integration and application monitoring seamless and efficient.
  • Users commend the impressive speed of Elasticsearch, allowing efficient handling of large datasets and quick queries.
  • Users love the fast search capabilities of Elasticsearch, allowing for efficient troubleshooting and real-time analytics.
  • Users appreciate the blazing fast performance of Elasticsearch, enhancing their search experiences significantly.
  • Users value Elasticsearch for its powerful search and aggregation capabilities, enhancing performance and workflow efficiency immensely.
Cons
  • Users find Elasticsearch expensive to scale, especially with high data volumes and necessary commercial licenses.
  • Users find Elasticsearch's required expertise challenging, citing complexity and resource-intensive setup as significant hurdles.
  • Users find the learning difficulty of Elasticsearch overwhelming, especially for beginners navigating its complexity and configuration.
  • Users find Elasticsearch's interface to be not user-friendly, complicating search functionality and requiring extensive tuning for performance.
  • Users find difficult learning with Elasticsearch due to its complex configuration and confusing documentation, especially for beginners.

What Are Recent G2 Reviews of Elasticsearch?

Supabase

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: 230

Who Is the Company Behind Supabase?

  • Seller: Supabase
  • Year Founded: 2020
  • HQ Location: Global, US
  • LinkedIn® Page: www.linkedin.com
    436 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Founder, Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 77% Small, 20% Medium

What Do G2 Reviewers Say About Supabase?

AI-generated summary from verified user reviews

Pros
  • Users find Supabase easy to use, enjoying its user-friendly interface and smooth integration with tools for app development.
  • Users appreciate the powerful design and easy integration of Supabase, enhancing their development experience significantly.
  • Users appreciate the user-friendly interface of Supabase, making it easy to set up and integrate smoothly.
  • Users love the easy integrations of Supabase, enhancing their workflow and speeding up project development seamlessly.
  • Users appreciate the seamless integrations of Supabase, enhancing the developer experience across various tools and projects.
Cons
  • Users feel that Supabase has beginner unfriendliness, citing complexity and limited resources for novice coders.
  • Users find the pricing expensive, especially for premium features that should be accessible to all users.
  • Users find Supabase has a high complexity that challenges novice coders and could benefit from better documentation.
  • Users face insufficient guidance in Supabase, making it challenging for novice coders to navigate the platform.
  • Users find that learning difficulties with Supabase arise due to its complexity and limited documentation for novices.

What Are Recent G2 Reviews of Supabase?

Pinecone

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: 4.5/5.0

Total Reviews: 71

Who Is the Company Behind Pinecone?

Who Uses This Product?

  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 73% Small, 23% Medium

What Are Recent G2 Reviews of Pinecone?

Zilliz

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: 52

Who Is the Company Behind Zilliz?

  • Seller: ZILLIZ
  • Year Founded: 2017
  • HQ Location: Redwood City, US
  • Twitter: @milvusio
    5,194 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    150 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 46% Small, 37% Medium

What Do G2 Reviewers Say About Zilliz?

AI-generated summary from verified user reviews

Pros
  • Users find Zilliz to be extremely easy to use, providing intuitive integration and user-friendly documentation.
  • Users praise Zilliz for its extremely fast, low-latency vector search, ensuring impressive query performance even at scale.
  • Users appreciate the exceptional speed and flexibility of Zilliz, enhancing their data retrieval and management capabilities.
  • Users highlight Zilliz's lightning-fast performance, enabling impressive query speeds that enhance overall efficiency.
  • Users commend Zilliz for its comprehensive open-source documentation, enabling quick resolution of development and deployment queries.
Cons
  • Users find the pricing for Zilliz to be quite high, making it less accessible for smaller projects.
  • Users find limited vector column support in Zilliz restrictive, affecting multi-dimensional search capabilities.
  • Users feel the need for insufficient documentation, particularly regarding advanced configurations and performance tuning guidance.
  • Users note the missing features in Zilliz, particularly in sorting and full-text search capabilities.
  • Users find the monthly cost of Zilliz prohibitive, often opting for cheaper alternatives like Hetzner or mini-PCs.

What Are Recent G2 Reviews of Zilliz?

TiDB

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: 66

Who Is the Company Behind TiDB?

  • Seller: PingCAP
  • Company Website:
  • Year Founded: 2015
  • HQ Location: Sunnyvale
  • Twitter: @PingCAP
    7,268 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    507 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: DBA, Senior Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 32% Large, 27% Small

What Do G2 Reviewers Say About TiDB?

AI-generated summary from verified user reviews

Pros
  • Users value TiDB's seamless scalability, effortlessly managing increasing data and users while maintaining strong performance and consistency.
  • Users appreciate the ease of use of TiDB, benefiting from its simple implementation and smooth deployment.
  • Users highlight TiDB's horizontal scalability and strong consistency, making database management straightforward and efficient.
  • Users value the MySQL compatibility of TiDB, facilitating smooth adoption and seamless scaling as data grows.
  • Users value TiDB's high performance in handling vast data and executing analytical queries efficiently in real-time.
Cons
  • Users face a steep learning curve with TiDB's distributed architecture, complicating onboarding and performance tuning for newcomers.
  • Users experience performance issues with TiDB, particularly in vector search and resource requirements for testing and scaling.
  • Users notice slow performance in TiDB, especially in smaller deployments requiring excessive system resources for optimal use.
  • Users find the difficult learning curve of TiDB challenging, especially for teams transitioning from simpler database systems.
  • Users find TiDB's poor documentation challenging, complicating setup and usage for beginners and real-world applications.

What Are Recent G2 Reviews of TiDB?

Milvus

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
  • Year Founded: 2017
  • HQ Location: Redwood City, US
  • Twitter: @milvusio
    5,194 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    150 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 42% Small, 33% Medium

What Are Recent G2 Reviews of Milvus?

CrateDB

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
  • Company Website:
  • Year Founded: 2013
  • HQ Location: Redwood City, CA
  • Twitter: @cratedb
    4,176 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    48 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Software Engineer, Data 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 value the ease of setup and SQL querying in CrateDB, enhancing their productivity and efficiency.
  • Users appreciate the scalability of CrateDB, enabling effective handling of massive datasets with speed and flexibility.
  • Users value the versatile data support in CrateDB, enjoying features like multi-model data and advanced search capabilities.
  • Users value the flexibility of CrateDB, enabling diverse deployment setups and adaptable solutions for various needs.
  • Users praise CrateDB for its scalability with large datasets, ensuring efficient handling of diverse data types.
Cons
  • Users find insufficient training makes CrateDB challenging to implement and utilize effectively without technical support.
  • Users note a lack of features in CrateDB, particularly missing comparative insights and advanced analytical SQL functionalities.
  • Users find poor customer support and limited documentation, making the learning curve challenging for beginners.
  • Users find the documentation lacking, especially regarding custom configurations and integrations in CrateDB.

What Are Recent G2 Reviews of CrateDB?

KX

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
  • Year Founded: 1996
  • HQ Location: NY, USA
  • Twitter: @kxsystems
    4,168 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    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 admire the lightning-fast speed of KX's kx-dashboard, enhancing their experience in creating user reports and dashboards.
  • Users find KX's performance exceptional, appreciating its speed and advanced functionalities for creating dashboards and reports.
  • Users highlight the extreme power and versatility of KX products, appreciating their efficiency in data processing.
  • Users praise the powerful and versatile tools of KX, enhancing problem-solving efficiency with advanced support and development.
  • Users commend KX for its unparalleled real-time data processing, enabling quick and efficient, data-driven decisions.
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 limited external resources and cryptic programming language.
  • Users note a steep learning curve with KX due to limited external resources, but find training materials helpful.
  • Users find KX licenses to be expensive, especially for hobby users or small organizations with limited needs.
  • Users find the initial difficulties with KX challenging due to steep learning curves and limited online resources.

What Are Recent G2 Reviews of KX?

Relevance AI

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
  • Year Founded: 2020
  • HQ Location: Sydney, Australia
  • Twitter: @RelevanceAI_
    3,784 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    115 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, enabling quick setup and intuitive management of agents.
  • Users value the versatile features of Relevance AI, enabling custom AI agents for diverse functions effortlessly.
  • Users value the advanced AI integration in Relevance AI, enhancing efficiency across various tasks and minimizing manual efforts.
  • Users highlight the versatile customization capabilities of Relevance AI, making it ideal for diverse automation tasks.
  • Users appreciate the efficiency of Relevance AI, automating tasks seamlessly and enhancing productivity across various applications.
Cons
  • Users find the cost of Relevance AI too high, making it less affordable for regular use and frustrating.
  • Users find Relevance AI too expensive, feeling the high costs make it unreasonable for general use.
  • Users find the interface complex, often feeling lost during onboarding and dealing with busy UX/UI challenges.
  • Users find the limited features of Relevance AI restrict their ability to achieve high-quality graphical outputs.
  • Users find the learning curve challenging, but the interface becomes intuitive once familiarized with the system.

What Are Recent G2 Reviews of Relevance AI?

Vespa.ai

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
  • Company Website:
  • Year Founded: 2023
  • HQ Location: Trondheim, NO
  • LinkedIn® Page: www.linkedin.com
    51 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 63% Small, 25% Large

What Are Recent G2 Reviews of Vespa.ai?

SingleStore

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

Who Is the Company Behind SingleStore?

  • Seller: SingleStore
  • Year Founded: 2011
  • HQ Location: San Francisco, CA
  • Twitter: @SingleStoreDB
    15,447 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    551 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Developer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 39% Large, 37% Small

What Are Recent G2 Reviews of SingleStore?

What Are G2 Users Discussing About SingleStore?

Tembo

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

Who Is the Company Behind Tembo?

  • Seller: Tembo
  • Year Founded: 2022
  • HQ Location: Cincinnati, US
  • Twitter: @tembo_io
    3 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    32 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 85% Small, 15% Medium

What Do G2 Reviewers Say About Tembo?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Tembo, finding it simple to set up and manage tasks effectively.
  • Users love the user-friendly dashboard of Tembo, which simplifies task management and enhances team collaboration.
  • Users value the easy integrations with various data sources, streamlining their workflow and enhancing data management.
  • Users appreciate the ease of setup with Tembo, enabling quick deployment and user-friendly navigation.
  • Users value the easy integrations of Tembo, enhancing efficiency and simplifying complex data management tasks effortlessly.
Cons
  • Users find the limited flexibility of Tembo frustrating, as it restricts integration and customization options.
  • Users find the limited integration to only AWS a significant drawback, wishing for broader cloud service support.
  • Users highlight the restrictive cloud limitations of Tembo, particularly its exclusive support for AWS.
  • Users find Tembo to be expensive, especially for small businesses due to variable pricing and additional service costs.
  • Users find the limited customization of Tembo restrictive, impacting flexibility and functionality for their specific needs.

What Are Recent G2 Reviews of Tembo?

Meilisearch

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

Who Is the Company Behind Meilisearch?

  • Seller: Meilisearch
  • Year Founded: 2018
  • HQ Location: Paris, FR
  • Twitter: @meilisearch
    5,076 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    30 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 60% Small, 40% Medium

What Do G2 Reviewers Say About Meilisearch?

AI-generated summary from verified user reviews

Pros
  • Users praise the responsive customer support of Meilisearch, enhancing their experience and confidence in the product.
  • Users appreciate the ease of use of Meilisearch, making it simple to manage and integrate into their systems.
  • Users appreciate the seamless integrations of Meilisearch, enhancing efficiency in platform and environment setups.
  • Users value the seamless integration of Meilisearch, enhancing developer experience and efficiency across platforms.
  • Users rave about Meilisearch's lightning-fast and relevant search results, greatly enhancing their asset library experience.
Cons
  • Users note the search functionality lacks clarity and insights, hindering overall user experience and efficiency.
  • Users express concern over the cost increase, finding the new pricing model less fair for specific project needs.
  • Users are concerned about cost issues due to recent pricing changes, which may negatively impact project viability.
  • Users find the cloud pricing per search expensive when dealing with high traffic, despite self-hosting options.
  • Users find limited automation in Meilisearch, particularly with deployment, lack of insights, and missing suggestions index.

What Are Recent G2 Reviews of Meilisearch?

Typesense

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

Who Is the Company Behind Typesense?

  • Seller: Typesense
  • Year Founded: 2016
  • HQ Location: Houston, US
  • Twitter: @TypeSense
    15,830 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    12 employees on LinkedIn®

Who Uses This Product?

  • Company Size: 80% Small, 40% Medium

What Are Recent G2 Reviews of Typesense?

CockroachDB

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

Who Is the Company Behind CockroachDB?

  • Seller: Cockroach Labs
  • Year Founded: 2015
  • HQ Location: New York, NY
  • Twitter: @CockroachDB
    13,571 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    724 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 55% Small, 34% Medium

What Do G2 Reviewers Say About CockroachDB?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of management and reliability of CockroachDB, enabling them to focus on development without disruptions.
  • Users value the ease of use of CockroachDB, enjoying its straightforward SQL implementation and seamless scalability.
  • Users highlight CockroachDB's seamless performance and automatic scalability, ensuring reliable data management without operational headaches.
  • Users appreciate the scalability of CockroachDB, enabling seamless management of distributed databases across multiple locations.
  • Users value the automatic scaling and reliability of CockroachDB, making big data management effortless and efficient.
Cons
  • Users find the learning curve steep for CockroachDB, especially regarding configuration and understanding its unique operational aspects.
  • Users find CockroachDB's setup and configuration to have a steep complexity for beginners, impacting overall user experience.
  • Users find the difficult learning curve of CockroachDB challenging, especially for beginners trying to optimize configuration.
  • Users face feature limitations in CockroachDB due to a steep learning curve and less mature advanced functionalities.
  • Users note the complexity and steep learning curve of CockroachDB, which may hinder optimal configuration and understanding.

What Are Recent G2 Reviews of CockroachDB?

What Are G2 Users Discussing About CockroachDB?