

Find out in this report how the two Vector Databases solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Thanks to Qdrant's open-source nature, our initial licensing and setup costs were nearly zero, allowing for swift testing and launch of our RAG prototype.
The time saved is substantial, with nearly three weeks or more for projects deployed with Qdrant Cloud in no-code platforms.
I have seen a significant return on investment from using Qdrant because it is very easy to integrate and highly efficient, saving a lot of time in my day-to-day operations, which ultimately saves money as well.
The efficiency has increased significantly, and our workflows are consolidated into a single platform, thus reducing the operational overhead we previously faced.
The objective was to scale as data loads with high-performing query model responses.
I have seen a return on investment in terms of time saved.
It's open source, so we house it on our server.
The documentation provided by Qdrant covers most queries effectively.
I rate the technical support of Qdrant as a nine because I think we have never reached out to them directly, but Qdrant has good support available online, and I can get answers from forums.
The customer support is very proactive and responsive twenty-four hours per day, seven days per week.
Their team is capable of resolving issues efficiently, allowing users to create tickets and receive support.
We have our own account manager who keeps us informed of the latest solutions and improvements in the system.
In the recruiting agency project, the reliance on the vector database has expanded from storing hundreds of resumes to thousands.
When Qdrant is deployed in Docker, it scales really fast, and you can assign multiple CPUs to enhance performance.
Qdrant handles growing workloads and data volumes well for me, which was a significant reason for my shift from other popular alternatives to Qdrant.
As our data volumes grow, we can expand resources without significant performance degradation.
SingleStore's scalability is really nice, as the process model includes master aggregator and slave aggregators or child aggregators, making it very well scalable both vertically and horizontally.
I have added both storage and additional instances many times, and it worked perfectly.
You need to patch Qdrant as soon as patches are released.
It is easy to use whether on LangChain or on its own.
Qdrant is stable, except for the limitation concerning the termination of inactive clouds after a week.
It handles large workloads and maintains consistent performance very well overall.
The performance and usability of SingleStore as a main database engine are significantly superior to other both paid and open-source solutions.
I have not seen any downtime.
Fast large-scale filtering operations could be implemented, such as automatic index suggestions, adaptive query planning, and smart indexing of metadata fields, which would make Qdrant even more efficient.
While it has clustering functionality, it is not easy to set up, and not everyone can configure the clustering, so there is room for improvement in the clustering configuration.
Incorporating embedding features directly in Qdrant Cloud would eliminate the need to depend on external solutions.
Error handling needs attention. When it fails due to memory, it only indicates that but not exactly in which process it failed.
The data which is sent to DataDog sometimes does not match with the SingleStore dashboard.
Better documentation for advanced use cases, especially where data volumes are very high and queries are frequent, would help us manage the feature more efficiently.
Using Qdrant is free.
Regarding pricing, setup costs, and licensing, since I am using only the free tier of Qdrant Cloud, there are no setup costs involved.
Licensing posed no issues, as Qdrant is open-source software with no upfront fees.
My experience with pricing, setup cost, and licensing is that it can be a bit expensive for startups.
The setup cost was surprisingly good, and our transfer to Helios was almost seamless, as far as databases go, and it was not expensive.
The ability of Qdrant to handle high-dimensional vectors for my AI projects is pretty fast, and I think it's the best we have used so far.
An accuracy boost was definitely observed from 45 to 50% using Faiss to around 85 to 95% using Qdrant, and the users are really happy as they are getting suggested really good schemes that would take a lot of time to find.
Qdrant supports high-dimension vectors and cosine similarity, which any vector database should have, and it is pretty fast.
SingleStore has impacted my organization positively by enabling us to run low-latency analytics and model-driven use cases at scale, which is quite difficult for OLAP and OLTP databases alone.
A very nice and useful feature is its compatibility with MySQL API, letting any system that can connect to MySQL also connect to SingleStore, thus solving many interoperability issues with different systems.
SingleStore is a distributed database that has the advantage that at any moment when resources are insufficient, there is a possibility to add more nodes, and everything is added online.
| Product | Mindshare (%) |
|---|---|
| Qdrant | 6.4% |
| SingleStore | 3.4% |
| Other | 90.2% |


| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 1 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Large Enterprise | 5 |
Qdrant is a powerful tool for efficiently organizing and searching large volumes of data. It is particularly useful for tasks such as data indexing, similarity search, and recommendation systems.
With fast and accurate results, it is suitable for various applications including e-commerce, content management, and data analysis. Users appreciate Qdrant's efficient search capabilities, high performance, and ease of use.
Its quick and accurate retrieval of relevant information allows for easy navigation and analysis of large datasets.
The intuitive interface and straightforward setup process make it accessible to users with varying levels of technical expertise.
SingleStore delivers the performance you need for enterprise AI, providing the most performant data platform for apps and analytics at scale. SingleStore enables organizations to scale from one to one million customers in one unified platform. SingleStore offers transparent pricing as shown here https://www.singlestore.com/pricing/
SingleStore caters to over 400 customers globally, including major banks and tech companies in 50+ countries and 40+ verticals. It offers seamless scaling for both transactional and analytical workloads, simplifying data management with its MySQL compatibility and real-time processing capabilities. SingleStore's distributed architecture ensures speed and reliability, efficiently handling large data volumes.
What are the key features of SingleStore?
What benefits can users find in SingleStore reviews?
Top banks and fintech companies leverage SingleStore for efficient management of financial data, while media and telecom industries use it for scalable metadata management and improved data processing. Retail and eCommerce sectors benefit from enhanced transactional capabilities, reducing the need for separate databases and optimizing reporting processes. SingleStore's capacity to unite diverse workloads makes it a strategic choice across many sectors.
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