

Find out in this report how the two Open Source Databases solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Since it handles vector storage and similarity searches natively alongside your relational data, I can seamlessly combine precise SQL filters with vector queries, which guarantees that the data retrieved for my AI application is always up-to-date and consistent.
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.
They came and tuned our queries with one-to-one assistance.
Compared to MongoDB, there are some platform deficiencies, but the support team shouldn't bear that burden.
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.
A specific challenge I have faced is troubleshooting performance degradation during heavy write transaction tables.
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.
We haven't found issues with the stability of MariaDB.
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.
MariaDB is scalable and easy to scale.
Oracle is very advanced compared to MariaDB, and those advanced features are not available in MariaDB.
The key area where MariaDB could be improved is its native GUI tooling; while the command-line interface works perfectly fine, the built-in visual tools for administration, management, and query design feel outdated compared to some competitors.
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.
MariaDB is in the pricey range, especially for huge databases handling terabytes of data.
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.
Encryption is available in MariaDB, so we are secure for transmitting data without concern about moving over networks.
Being able to store unstructured JSON directly into a column while still using standard SQL functions such as JSON_EXTRACT to query specific keys has saved me from having to constantly alter our database schemas.
Configuration, setup, and schema design are good features in MariaDB.
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 | 4.4% |
| MariaDB | 5.6% |
| Other | 90.0% |

| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 12 |
| Large Enterprise | 26 |
| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 1 |
MariaDB is a robust database solution known for its scalability, speed, and user-friendliness. It supports seamless integration and provides reliable performance in handling large datasets, offering strong community backing alongside its open-source nature.
MariaDB is renowned for efficiently managing large databases and complex relationships while being stable and easy to integrate. With advanced features like replication, encryption, and SQL compatibility, it offers fast query processing. Its straightforward installation and management processes facilitate seamless enterprise integrations and ensure high performance in real-time data scenarios. However, enhancements in enterprise integration, clustering, and scalability are necessary. Addressing challenges with complex queries, security, and user experience would be advantageous, alongside offering robust technical support and a competitive pricing model.
What are the key features of MariaDB?MariaDB is extensively utilized in backend support for cloud telephony platforms, ERP systems, and financial software, being crucial for web application development and data storage. Entities favor its compatibility with MySQL for complex join queries and its support for structured data management in SaaS applications.
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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