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    Qdrant Vector Database

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    Sold by: Qdrant 
    Qdrant is an open-source and fully managed high-performance Vector Database. The vector search engine provides a production-ready service with a convenient API to store, search, and manage vector embeddings.
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    Qdrant Vector Database

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    Sold by: Qdrant 

    Overview

    Qdrant is an open-source and fully managed high-performance Vector Database. The vector search engine provides a production-ready service with a convenient API to store, search, and manage vectors with an additional payload Qdrant is tailored to extended filtering support on additional metadata fields, which can be stored as payload along with vector embeddings. With Qdrant, embeddings, and neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more solutions to make the most of unstructured data. It is easy to use, deploy and scale, blazing fast and accurate simultaneously.

    Highlights

    • Blazing Fast and Accurate
    • Advanced Filtering Support
    • Flexible Storage Options

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    Pricing

    Qdrant Vector Database

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    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.

    Usage costs (1)

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    Dimension
    Cost/unit
    Qdrant cloud usage unit according to the cluster deployment.
    $0.01

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    Usage information

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    Delivery details

    Software as a Service (SaaS)

    SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.

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    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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    11 external reviews
    External reviews are sourced from G2  and are not included in the star rating for this product.
    Rishi K.

    scalability & availability

    Reviewed on Nov 28, 2024
    Review provided by G2
    What do you like best about the product?
    fully manage in all resource ,available on AWS , Google and azure plaform help with vector search technolgy
    What do you dislike about the product?
    non build in visualiztion ,significantly slower searching time in result.
    What problems is the product solving and how is that benefiting you?
    text searching is not enough , Qdrant vector database to find the similar image its detect duplicates ,including picture by text description
    Aarav M.

    Self-hosted Qdrant Vector DB

    Reviewed on Nov 28, 2024
    Review provided by G2
    What do you like best about the product?
    Self-hosting Qdrant on a host is really simple and does not takes a lot of time to setup or troubleshoot issues. The documentation is also up to date. I prefer to install it using Docker to avoid installing dependencies.
    What do you dislike about the product?
    The initial learning curve is high but the documentation and resources makes up for it.
    What problems is the product solving and how is that benefiting you?
    I mainly use Qdrant for searches and building applications where I need to store vectors
    Akhil G.

    depth review of Qdrant.Ai

    Reviewed on Sep 11, 2024
    Review provided by G2
    What do you like best about the product?
    desparate data sources makes easier to consolidate and analyze data from various sources,scaling data,data quality and governance.
    What do you dislike about the product?
    Learning might be quite difficult for who are not familiar with advanved data analytics.
    pricing plans are high.
    What problems is the product solving and how is that benefiting you?
    using this we can unify data from different sources,with its analyzing customer data we can gain clear insight of customer behaviour
    Lexaviere F.

    Open-source platform gives freedom and management capability

    Reviewed on Aug 22, 2024
    Review provided by G2
    What do you like best about the product?
    Qdrant is fast and easily scalable, and I can index and query millions of vectors, essential for my work on image search. This is true because it is an open-source application, thereby allowing me to modify and adapt it to other tools that I use.
    What do you dislike about the product?
    Qdrant does not have integrated visualizations. This makes it difficult to make conclusions and draw visualization of the search results.
    What problems is the product solving and how is that benefiting you?
    Qdrant has been useful as an indexing tool for such high-dimensional vector data as mine. To that extent, it speeds up the search process that enables me to pull similar images for analysis and a search history.
    Randal E.

    Advanced vector database for developer

    Reviewed on Aug 13, 2024
    Review provided by G2
    What do you like best about the product?
    I can quickly scan through huge volumes of vectors – it is relevant for my AI work on image recognition. Since it is an open-source software, it can be used calmly and can be modified and integrated with my existing systems.
    What do you dislike about the product?
    Qdrant also has no incorporated visualization capabilities. Due to its basic functionalities I find it difficult to analyze and interpret the results as there are no additional software installed.
    What problems is the product solving and how is that benefiting you?
    Qdrant enhanced my skills to organise and query great vectors sets. The speed and scalability mean that I can manage a big flow of information and progress in my research faster.
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