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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
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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.
Qdrant
What do you like best about the product?
Qdrant is an open source database. It allow to perform large queries on a large database.
What do you dislike about the product?
There is nothing to dislike about Qdrant.
What problems is the product solving and how is that benefiting you?
It Creates Qdrant API key for the cloud database to perform multiple actions.
Qdrant Vector DB
What do you like best about the product?
Qdrant is a open source
It is suitable for efficient vector search.
It allows to handle large datasets and high query loads.
It supports High Dimensional Vectors
Best thing of using Qdrant is its speed and reliability.
It is suitable for efficient vector search.
It allows to handle large datasets and high query loads.
It supports High Dimensional Vectors
Best thing of using Qdrant is its speed and reliability.
What do you dislike about the product?
I have deployed qdrant in Azure cloud using AKS, ACI,App service. The setup and integration is very complex.
I have faced timeout issues at initial creation of collection names with client. Due to less documentation it took some time for resolution.
I have faced timeout issues at initial creation of collection names with client. Due to less documentation it took some time for resolution.
What problems is the product solving and how is that benefiting you?
Qdrant allows the embeddings for matching, searching, recommending. It helps to get relevant data from the DB based on similarity search.
We are passing the matched content to LLMs. This helps in resolving model halucinations.
We are passing the matched content to LLMs. This helps in resolving model halucinations.
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