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.
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
Details
Pricing
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.
Dimension | Cost/unit |
|---|---|
Qdrant cloud usage unit according to the cluster deployment. | $0.01 |
Vendor refund policy
Contact support@qdrant.com
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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.
Resources
Vendor resources
Support
Vendor support
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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Customer reviews
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.
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
Leave a comment0 comments
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.
Andrey L.
Qdrant is vector search engine promising the scale
Reviewed on Aug 09, 2024
Review provided by G2
What do you like best about the product?
A tool for creating vector collections and performing vector operations. It excels at vector distance searches, offers convenient auto-completion features, and includes a free tier for evaluation.
What do you dislike about the product?
Although the interface is quite simple, it still has limited capabilities.
What problems is the product solving and how is that benefiting you?
Qdrant is a straightforward vector database, but its scalability remains an open question.
Jefferson A.
Offers tremendous opportunities for customization
Reviewed on Aug 09, 2024
Review provided by G2
What do you like best about the product?
In the pursuit of my AI research, Qdrant can expedite the process of searching high-dimensional vector data. The options and setting let me work on terabytes of data and perform similarity search in real time.
What do you dislike about the product?
Qdrant does not come with graphical utilities that can provide data visualization. This poses a problem when it comes to interpreting the retrieved results particularly for higher-orders of dimensions.
What problems is the product solving and how is that benefiting you?
They solve the problems in AI development - how to efficiently search large vector datasets. This in turn enables me to interact with the data in terms of relationships much faster hence information generation and model building.