

Find out in this report how the two AI Data Analysis solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Using Cohesity DataProtect is easier to manage, and it simplifies various components into one architecture, reducing the need for extensive human resources to manage backups.
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 support can depend on the region, and for larger customers, I advise having a Technical Account Manager for better assistance.
For the support, I can provide a rating of four only because they initially provide some steps, but later say they are not sure, which is a problem in a production environment.
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.
Cohesity DataProtect is built on a scale-out architecture, which means it can effectively scale to meet various needs.
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.
On the whole, any problems were more related to hardware limitations rather than issues with Cohesity DataProtect itself.
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.
The container functionality is very limited at the moment, not covering the whole container.
While there are improvements to be made, such as providing support for older systems like IBM iSeries and tandem systems from HP, the solution overall shifts from older methods to modern practices.
There is room to improve the user interface of Cohesity DataProtect for more intuitive navigation.
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.
I find Cohesity DataProtect to be expensive.
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.
The platform is based on a scale-out architecture with each node having compute, RAM, SSD, and HDD.
Global deduplication ensures that only unique data blocks are stored, significantly reducing storage consumption.
The option to maintain evidence in Europe for regulatory compliance, the ability to maintain the backup with the same technology and same control plane, along with the same solutions to use backup solutions such as S3 or similar services in AWS, is what we are working with.
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.
| Product | Mindshare (%) |
|---|---|
| Qdrant | 0.4% |
| Cohesity DataProtect | 0.5% |
| Other | 99.1% |


| Company Size | Count |
|---|---|
| Small Business | 21 |
| Midsize Enterprise | 22 |
| Large Enterprise | 43 |
| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 1 |
Cohesity DataProtect integrates with VMware and cloud services like AWS and Azure, offering rapid VM restores and mass recovery, ransomware protection with immutable snapshots, intuitive UI, and scalability. It also consolidates data management, reducing data fragmentation.
Cohesity DataProtect provides comprehensive data protection and management through a user-friendly platform. It offers seamless integration with existing infrastructure, minimizing downtime and maximizing data security. Intuitive features like automated processes, centralized management, and robust search capabilities enhance operational efficiency. Despite areas needing improvement in reporting, interface usability, and legacy support, the platform remains a reliable choice for data backup, recovery, and ransomware protection. Users benefit from its compatibility with VMware, SQL, and Exchange and its ability to replace outdated tape systems while supporting cloud replication and test environments.
What key features does Cohesity DataProtect offer?Cohesity DataProtect is successfully implemented across industries such as finance, healthcare, and education, optimizing data protection and compliance needs. Organizations leverage its robust backup and recovery capabilities, ensuring data integrity and security while facilitating efficient resource use and operation management.
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.
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