Compare the Top Vector Databases as of December 2025

What are Vector Databases?

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 currently available using the table below. This list is updated regularly.

  • 1
    Cloudflare

    Cloudflare

    Cloudflare

    Cloudflare Vectorize is a high-speed, globally distributed vector database built to power modern AI applications like search, recommendation, and Retrieval Augmented Generation (RAG). It enables developers to store and query embeddings — representations of text, images, and other data — with lightning-fast performance at the edge. Vectorize integrates natively with Cloudflare’s AI developer stack, including Workers AI and AI Gateway, providing a unified environment for AI inference, monitoring, and scalability. Designed for low latency and cost efficiency, Vectorize automatically optimizes and scales vector storage as data and traffic grow. Its global deployment ensures proximity to users and ML runtimes, enhancing both performance and reliability. With Vectorize, developers can build and deploy full-stack AI solutions faster and more affordably than ever.
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    Starting Price: $20 per website
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  • 2
    Amazon S3 Vectors
    Amazon S3 Vectors is the first cloud object store with native support for storing and querying vector embeddings at scale, delivering purpose-built, cost-optimized vector storage for semantic search, AI agents, retrieval-augmented generation, and similarity-search applications. It introduces a new “vector bucket” type in S3, where users can organize vectors into “vector indexes,” store high-dimensional embeddings (representing text, images, audio, or other unstructured data), and run similarity queries via dedicated APIs, all without provisioning infrastructure. Each vector may carry metadata (e.g., tags, timestamps, categories), enabling filtered queries by attributes. S3 Vectors offers massive scale; now generally available, it supports up to 2 billion vectors per index and up to 10,000 vector indexes per bucket, with elastic, durable storage and server-side encryption (SSE-S3 or optionally KMS).
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