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A quick and easy to setup vector database for RAG needs
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.
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scalability & availability
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
Self-hosted Qdrant Vector DB
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
depth review of Qdrant.Ai
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
Open-source platform gives freedom and management capability
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.
Advanced vector database for developer
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.
Qdrant is vector search engine promising the scale
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.
Offers tremendous opportunities for customization
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.
Excellent vector database with advanced features
What do you like best about the product?
What I like best about Qdrant is its efficiency in indexing and searching high-dimensional vectors. The ease of integration with AI-based applications and the ability to perform semantic search queries are major advantages. Additionally, the support for multiple programming languages makes Qdrant versatile and accessible for different development teams
What do you dislike about the product?
One of the few downsides of Qdrant is that the initial learning curve can be steep for those unfamiliar with vector-based databases. While the documentation is well-done, more practical examples or video tutorials would be helpful to ease the onboarding process for new users. Furthermore, some advanced features require manual configuration, which might not be straightforward for everyone.
What problems is the product solving and how is that benefiting you?
Qdrant has been invaluable in our data analytics pipeline, where we needed an efficient way to manage and search through large sets of vector embeddings. This was particularly beneficial in our recommendation system for a diverse product catalog. Qdrant’s ability to quickly process and retrieve similar items based on vector similarity allowed us to enhance the relevance and personalization of recommendations.
High Performance & Scalability
What do you like best about the product?
it is optimized for speed and scalability, capable of handling large datasets with high throughput. The engine uses state-of-the-art algorithms to ensure fast query responses.
What do you dislike about the product?
High performance comes with high resource usage, which might be a consideration for smaller deployments.
What problems is the product solving and how is that benefiting you?
The straightforward API and comprehensive documentation make it easy to set up and use, even for those new to vector search engines.Highly customizable to fit specific needs, including various distance metrics and index configurations.Provides high-precision results for nearest neighbor searches, crucial for applications needing exact matches.
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