Compare the Top Vector Databases in the USA as of November 2025

What are Vector Databases in the USA?

Vector databases are a type of database that use vector-based data structures, rather than the traditional relational models, to store information. They are used in artificial intelligence (AI) applications such as machine learning, natural language processing and image recognition. Vector databases support fast and efficient data storage and retrieval processes, making them an ideal choice for AI use cases. They also enable the integration of structured and unstructured datasets into a single system, offering enhanced scalability for complex projects. Compare and read user reviews of the best Vector Databases in the USA currently available using the table below. This list is updated regularly.

  • 1
    TigerData

    TigerData

    TigerData

    TigerData is a high-performance, cloud-native PostgreSQL platform built for real-time analytics, time-series processing, vector workloads and intelligent agent-driven applications. It retains full SQL compatibility while delivering exceptional speed and scale: the platform powers millions of databases, supports streaming data across devices and applications, and enables organizations to consolidate transactional and analytical workloads in a single engine. TigerData enhances PostgreSQL with extensions and execution optimizations that provide low-latency queries, high-concurrency insert rates, hybrid operational/analytical use cases and vector embedding support for AI-driven workloads. Developers gain simplicity and reliability by staying within PostgreSQL’s ecosystem, familiar tools, connectors and syntax, while unlocking performance on par with purpose-built time-series or vector engines.
    Starting Price: $30 per month
  • 2
    CrateDB

    CrateDB

    CrateDB

    The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data.
  • 3
    ZeusDB

    ZeusDB

    ZeusDB

    ZeusDB is a next-generation, high-performance data platform designed to handle the demands of modern analytics, machine learning, real-time insights, and hybrid data workloads. It supports vector, structured, and time-series data in one unified engine, allowing recommendation systems, semantic search, retrieval-augmented generation pipelines, live dashboards, and ML model serving to operate from a single store. The platform delivers ultra-low latency querying and real-time analytics, eliminating the need for separate databases or caching layers. Developers and data engineers can extend functionality with Rust or Python logic, deploy on-premises, hybrid, or cloud, and operate under GitOps/CI-CD patterns with observability built in. With built-in vector indexing (e.g., HNSW), metadata filtering, and powerful query semantics, ZeusDB enables similarity search, hybrid retrieval, filtering, and rapid application iteration.
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