

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.
I have seen a return on investment with MySQL, as it allows us to manage with fewer employees, focusing on business logic rather than database management.
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.
This lowers our LLM input token consumption by roughly 30 to 40 percent, translating directly into lower monthly OpenAI API bills.
The time saved is substantial, with nearly three weeks or more for projects deployed with Qdrant Cloud in no-code platforms.
I would rate the documentation and online support a 10 out of 10.
We have no issues and usually receive timely responses.
It's open source, so we house it on our server.
The documentation provided by Qdrant covers most queries effectively.
Qdrant's customer support is responsive and developer-focused.
Meeting scalability requirements through cloud computing is an expensive affair.
MySQL's scalability is currently adequate, as we have increased operations from ten thousand to twelve thousand devices, and it is working fine for us.
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 is highly scalable, supporting both vertical and horizontal scaling across massive vector data sets.
We face certain integration issues, especially when we integrate the database with security solutions like IBM QRadar.
From my experience, MySQL was pretty stable.
Built in Rust, it delivers sub-15 millisecond response times and rock-solid update and write-ahead logging to guarantee that newly indexed data is immediately searchable without dropping queries or producing inconsistent context for LLMs.
You need to patch Qdrant as soon as patches are released.
It is easy to use whether on LangChain or on its own.
It could be more beneficial if MySQL can enhance its data masking functionality in the same way it has improved data encryption.
Oracle could improve on scalability.
The load balancer, MySQL LB, which is used to connect to the application, lacks clear documentation.
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.
Oracle has different components, so if you need security, you have to procure a different license, but here everything is inbuilt and it's not costly.
The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost nothing.
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.
With Oracle, we have to buy another solution for encryption and masking, but MySQL supports native encryption, which enhances our return on investment.
The main feature we utilize in MySQL is the view, and I can say that it is the most valuable feature for our needs.
MySQL contributes to improved system reliability, faster application performance, and higher developer productivity.
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 (%) |
|---|---|
| MySQL | 12.5% |
| Qdrant | 4.3% |
| Other | 83.2% |


| Company Size | Count |
|---|---|
| Small Business | 75 |
| Midsize Enterprise | 34 |
| Large Enterprise | 64 |
| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 2 |
MySQL is an open-source database known for its ease of use and high performance. It offers features like replication and clustering, making it ideal for diverse applications. Its cost-effectiveness and LAMP integration are key advantages for businesses.
MySQL supports a variety of languages and platforms, providing reliable, scalable data management. Its graphical interface and LAMP architecture integration enhance its usability, while community support further strengthens its appeal. Challenges include scalability issues with large databases, lack of advanced clustering, and limited high-availability features. Complex queries may affect performance, and integration can pose difficulties. The outdated interface and insufficient documentation are also concerns, along with replication and backup reliability issues.
What are MySQL's key features?MySQL is widely implemented in industries such as web development, e-commerce, and finance. It's used for managing dynamic websites, powering e-commerce platforms, and supporting financial applications. Its compatibility with PHP and cost-effectiveness make it suitable for CMS platforms like WordPress. With cloud services integration, MySQL is a backend choice for scalable applications in various sectors.
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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