

BigID Next and Qdrant serve different roles in data management, with the former focusing on data discovery and intelligence, while the latter specializes in vector similarity search and database capabilities. Despite Qdrant's strengths in specialized search functions, BigID Next seems more favorable due to its comprehensive feature set.
Features: BigID Next offers data discovery, privacy management, and comprehensive data insight, providing a unified platform for data management. Qdrant provides real-time vector similarity search, high scalability, and efficient database integrations, positioning itself as a strong contender for applications requiring advanced search capabilities.
Ease of Deployment and Customer Service: BigID Next offers robust deployment options with strong customer support, making it accessible for a wide range of enterprise needs. Qdrant provides flexibility through containerized deployment options like Docker, appealing to tech-savvy teams that can leverage its open-source nature.
Pricing and ROI: BigID Next requires a significant investment with comprehensive ROI potential through its wide scope of features, aimed at enterprises seeking thorough data governance. In contrast, Qdrant allows for cost-effective entry due to its open-source model, although ROI is linked closely to specific use cases and needs substantial implementation effort to achieve full value.
It is one of the best tools in the market.
We have seen returns across all three aspects: fewer employees needed, money saved, and time saved with BigID.
I have seen a return on investment from using BigID, particularly as it is a regulatory and compliance tool that helps avoid potential penalties for non-compliance.
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.
BigID has one of the best technical support teams.
I would rate the customer support a six because you cannot directly reach out to L3 or L2 support if there's a major issue.
developing the custom connectors was relatively easy because of the courses I attended at BigID University and the support given by the BigID engineering team.
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.
I have added very large data sources to the BigID environment, and it remains stable.
BigID is scalable, allowing you to purchase as many scanners as you want.
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.
BigID is generally stable, however, there is a noted issue with bulk tagging that can affect scan results.
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.
There is also an issue with incident tagging, where all objects get tagged without an option to untag specific ones, and reverting changes is only possible through MongoDB Central, which can lead to data loss for certain periods.
I want them to focus on data mapping, assessment, automation workflow, and privacy incident management.
BigID deserves recognition for the data discovery part, which has been wonderful and quite accurate, along with the confidence level process that allows us to fine-tune results for better accuracy from the database.
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.
BigID might be expensive as it involves various paid services, like data retention and graphic rights management.
The pricing is competitive in the market, however, I need to ask for the right price.
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.
One of the best features of BigID is its strength in data discovery and governance.
BigID simplifies things by integrating both data protection and data privacy in one environment, making it easier for end users.
The most valuable feature of BigID is its large number of classifiers, which allow us to scan for specific data such as SSN numbers.
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 (%) |
|---|---|
| BigID Next | 0.8% |
| Qdrant | 0.4% |
| Other | 98.8% |


| Company Size | Count |
|---|---|
| Small Business | 5 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
BigID Next offers advanced data discovery, classification, and governance tools, streamlining compliance with privacy laws while integrating seamlessly with Microsoft platforms.
BigID Next provides comprehensive data management through machine learning-enhanced capabilities, supporting data discovery and classification for both structured and unstructured data. By simplifying processes for GDPR and CCPA compliance, and facilitating data scanning and mapping across databases, it optimizes data management. Automation is central to its design, with solutions for DSAR requests, organizing data with security labels, and ensuring a holistic organizational data view. Improvements in navigation, bug fixes, and scan reliability remain essential, along with enhancing classifiers for broader region coverage.
What features does BigID Next offer?BigID Next is commonly implemented in industries needing robust data governance, such as finance and healthcare, where data privacy and compliance with regulations are critical. It aids in scanning and classifying extensive data volumes, helping businesses maintain regulatory compliance while managing data risks effectively.
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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