

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.
If I find myself stuck in a cyber recovery situation, this tool can help me avoid spending my money on ransom payments.
The level of confidence that Cohesity Data Cloud delivers to the clients is worth that cost.
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.
issues with Cohesity Data Cloud have not been encountered, suggesting a robust service.
They need to work faster to meet client requirements, especially when business is affected.
They probably upstaffed and made sure their knowledge was more up-to-date.
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.
Scaling depends on subscription levels - when customers exceed their subscribed storage capacity, they can pay Cohesity to scale the resources.
There are no issues with scalability on the cloud end.
It's easy to add additional nodes to a current existing cluster, making it quite easy to expand.
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.
Compared to other tools, it is very efficient and simple to learn.
I couldn't find anything negative about Cohesity Data Cloud specifically.
Cohesity Data Cloud is quite reliable.
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.
Issues such as ransomware protection and fixing vulnerabilities should be prioritized.
Cohesity Data Cloud scans backups by default for ransomware and malware, sending notifications if there are any security concerns or compromised systems.
The primary drawback is the need to transfer large amounts of data to the cloud via an internet connection, requiring significant bandwidth.
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.
Cohesity Data Cloud is more costly in the long term compared to physical tapes.
Comparatively, compared to IBM and Commvault, Cohesity Data Cloud offers the best deal for my environment.
All organizations are very interested in as-a-service model where they do not pay upfront cost, but they only get the services and pay for what they use as they use it.
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.
It replicates data to the cloud in a tamper-proof manner, offering protection against ransomware attacks since it is not under administrative control.
They have a feature called DataSock, which enhances data protection.
The initial deployment of Cohesity Data Cloud, from my experience, is easy.
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.
The best features of Qdrant are GPU support, which enables very fast processing, and a very light footprint as it uses fewer resources.
| Product | Mindshare (%) |
|---|---|
| Qdrant | 0.4% |
| Cohesity Data Cloud | 0.5% |
| Other | 99.1% |

| Company Size | Count |
|---|---|
| Small Business | 5 |
| Midsize Enterprise | 1 |
| Large Enterprise | 7 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
Cohesity Data Cloud offers scalable and secure data management, ensuring fast deployment and robust protection against threats like ransomware.
Cohesity Data Cloud integrates seamlessly with major infrastructure, provides comprehensive data management, and enhances data continuity with effective security against ransomware. With features like global deduplication, virtualization, and simplified cloud management through Helios, it addresses the needs of users. Though some users report challenges with setup and costs, it still offers performance optimization and supports critical services like NFS and S3.
What are the key features of Cohesity Data Cloud?In industries like finance, healthcare, and technology, Cohesity Data Cloud plays a crucial role in data protection, recovery, and consolidation. Organizations utilize it for secure backup and disaster recovery, accommodating diverse environments like physical servers and cloud platforms such as Azure and AWS. Its integration with SaaS services ensures data continuity while minimizing risks.
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.
We monitor all AI Data Analysis reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.