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
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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
Kawalpreet J.
A quick and easy to setup vector database for RAG needs
Reviewed on Dec 05, 2024
Review provided by G2
What do you like best about the product?
In our organization, we developed an RAG application and needed a way to store embeddings. I looked after many open-source tools like Pinecone and Superduperdb. Qdrant worked the best. The setup on our server was super easy, and their documentation is very elaborate. I also think the embedding search is more accurate than the other platforms I piloted with. We are still currently using Qdrant for our RAG application and are happy with it.
What do you dislike about the product?
Inability to perform rich operations from UI without writing code/query. For example, if I want to delete all collections or collections matching a name pattern, or even if I want to select multiple collections and delete, that is not possible through UI.
What problems is the product solving and how is that benefiting you?
Enable storing and searching of embeddings for AI applications.
Leave a comment0 comments
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.
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.