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MariaDB vs Qdrant comparison

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Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
6.6
MariaDB offers cost savings, secure AI data handling, SQL integration, and community-driven features, benefiting up to 90% of users.
Sentiment score
5.5
Qdrant boosts ROI by cutting costs, improving productivity, and enhancing efficiency through payload filtering and open-source flexibility.
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.
Developer at a educational organization with 51-200 employees
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.
Automation Engineer at a educational organization with 11-50 employees
This lowers our LLM input token consumption by roughly 30 to 40 percent, translating directly into lower monthly OpenAI API bills.
MLOps Engineer at a tech services company with 501-1,000 employees
The time saved is substantial, with nearly three weeks or more for projects deployed with Qdrant Cloud in no-code platforms.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Customer Service

Sentiment score
6.2
MariaDB offers enterprise support but most users rely on clear documentation and community assistance to resolve issues.
Sentiment score
4.8
Qdrant customer service excels with developer-focused support via Discord and documentation, with reduced need for direct contact.
They came and tuned our queries with one-to-one assistance.
Architect at LTIMindtree
Compared to MongoDB, there are some platform deficiencies, but the support team shouldn't bear that burden.
Co-Founder at Vsigma IT Labs Pvt Ltd
It's open source, so we house it on our server.
Chief Ai Scientist at Predictive Systems
The documentation provided by Qdrant covers most queries effectively.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
Qdrant's customer support is responsive and developer-focused.
MLOps Engineer at a tech services company with 501-1,000 employees
 

Scalability Issues

Sentiment score
6.8
MariaDB is scalable, excelling in small to medium setups but needing extra tools and configuration for larger environments.
Sentiment score
5.7
Qdrant excels in scalability and performance, efficiently managing large datasets, particularly when deployed in Docker for enhanced growth.
A specific challenge I have faced is troubleshooting performance degradation during heavy write transaction tables.
Developer at a educational organization with 51-200 employees
In the recruiting agency project, the reliance on the vector database has expanded from storing hundreds of resumes to thousands.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
When Qdrant is deployed in Docker, it scales really fast, and you can assign multiple CPUs to enhance performance.
Analyst at Synergy Connect
Qdrant is highly scalable, supporting both vertical and horizontal scaling across massive vector data sets.
MLOps Engineer at a tech services company with 501-1,000 employees
 

Stability Issues

Sentiment score
7.7
MariaDB is stable and reliable, though specific configurations like clustering may pose occasional stability issues.
Sentiment score
7.9
Qdrant, built in Rust, is praised for reliability, fast queries, and precision, despite minor cloud termination limitations.
We haven't found issues with the stability of MariaDB.
Co-Founder at Vsigma IT Labs Pvt Ltd
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.
MLOps Engineer at a tech services company with 501-1,000 employees
You need to patch Qdrant as soon as patches are released.
Co Founder & CEO at SaYukth Private Limited
It is easy to use whether on LangChain or on its own.
Product Engineer at a tech vendor with 11-50 employees
 

Room For Improvement

MariaDB enhances integration and UI but needs improved stability, replication, views, BI tool integration, and complex query performance.
Qdrant needs developer experience enhancements, including multi-query fusion, embedding support, schema management, and improved deployment, UI, and documentation.
MariaDB is scalable and easy to scale.
Co-Founder at Vsigma IT Labs Pvt Ltd
Oracle is very advanced compared to MariaDB, and those advanced features are not available in MariaDB.
Architect at LTIMindtree
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.
Developer at a educational organization with 51-200 employees
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.
Product Engineer at a tech vendor with 11-50 employees
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.
Co Founder & CEO at SaYukth Private Limited
Incorporating embedding features directly in Qdrant Cloud would eliminate the need to depend on external solutions.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Setup Cost

MariaDB is a cost-effective, open-source database solution offering a free Community Edition and affordable enterprise alternatives to Oracle.
Qdrant provides cost-effective, predictable billing and zero upfront investment, with open-source access and easy setup options.
MariaDB is in the pricey range, especially for huge databases handling terabytes of data.
Co-Founder at Vsigma IT Labs Pvt Ltd
The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost nothing.
MLOps Engineer at a tech services company with 501-1,000 employees
Using Qdrant is free.
Chief Ai Scientist at Predictive Systems
Regarding pricing, setup costs, and licensing, since I am using only the free tier of Qdrant Cloud, there are no setup costs involved.
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
 

Valuable Features

MariaDB is reliable and scalable, offering open-source benefits, SQL compliance, excellent performance, and broad community support for various needs.
Qdrant provides fast, efficient vector search with hybrid indexing, Python support, and open-source configuration for scalable AI projects.
Encryption is available in MariaDB, so we are secure for transmitting data without concern about moving over networks.
Architect at LTIMindtree
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.
Developer at a educational organization with 51-200 employees
Configuration, setup, and schema design are good features in MariaDB.
Co-Founder at Vsigma IT Labs Pvt Ltd
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.
Chief Ai Scientist at Predictive Systems
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.
Analyst at Synergy Connect
Qdrant supports high-dimension vectors and cosine similarity, which any vector database should have, and it is pretty fast.
CTO at Honeycomb AI
 

Categories and Ranking

MariaDB
Ranking in Open Source Databases
9th
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
62
Ranking in other categories
Relational Databases Tools (9th)
Qdrant
Ranking in Open Source Databases
5th
Average Rating
8.8
Reviews Sentiment
5.8
Number of Reviews
10
Ranking in other categories
Vector Databases (2nd), AI Data Analysis (6th)
 

Mindshare comparison

As of September 2026, in the Open Source Databases category, the mindshare of MariaDB is 5.6%, down from 6.8% compared to the previous year. The mindshare of Qdrant is 4.3%, up from 4.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
Qdrant4.3%
MariaDB5.6%
Other90.1%
Open Source Databases
 

Featured Reviews

AB
Co-Founder at Vsigma IT Labs Pvt Ltd
Has supported web application data needs but requires design adjustments to manage complex queries efficiently
Complex queries in MariaDB where the query needs to parse thousands of lines or data values face some performance issues. For small and medium-size volume, it is pretty good. If it goes beyond certain data and complex queries, we see performance issues. We tried the advanced replication feature between different regions, replicating data specifically residing on MariaDB to two different regions of MariaDB data, and there were some technical snags in terms of slowness and longer processing time. Point-in-time recovery in MariaDB is good for small databases. When data volume increases beyond 5 GB or 10 GB per day or runs into double-digit GBs, we found some performance issues. For data below 10 GB, it works fine. Performance is the primary focus area for MariaDB, particularly during transactions or complex query jobs where slow performance is observed. MariaDB is scalable and easy to scale.
Pawel Cislo - PeerSpot reviewer
MLOps Engineer at a tech services company with 501-1,000 employees
Adaptive assistant has delivered faster grounded answers and has reduced token costs significantly
The main limitations I notice come down to developer experience and native features rather than performance. Building hybrid retrieval and fusion pipelines still requires considerable manual orchestration and code. Having more built-in multi-query fusion strategies natively inside Qdrant would be a significant time-saver. Additionally, managing dynamic metadata schemas and tracking index build progress during bulk ingest could be more transparent in the web UI. To make things easier for developers, Qdrant could provide more native tools for managing payload schema evolution over time. As metadata needs change in production RAG systems, updating existing payloads across large collections currently requires custom migration scripts. Built-in schema versioning and simpler automated index testing during CI/CD would make running Qdrant in rapidly evolving production environments even smoother. I rate Qdrant 9 out of 10 because its speed, sub-15 millisecond retrieval, and single-stage payload filtering make it top-tier for production RAG pipelines. I deduct one point mainly for developer experience. Setting up multi-query fusion still requires extra boilerplate code, payload metadata schema updates require custom migration scripts, and real-time visibility into HNSW graph indexing progress during bulk ingest could be improved in the web UI.
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912,930 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Comms Service Provider
10%
Computer Software Company
10%
Financial Services Firm
9%
University
8%
Comms Service Provider
12%
Manufacturing Company
11%
Financial Services Firm
10%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise12
Large Enterprise26
By reviewers
Company SizeCount
Small Business10
Midsize Enterprise2
 

Questions from the Community

What is your experience regarding pricing and costs for MariaDB?
MariaDB is in the pricey range, especially for huge databases handling terabytes of data. The cost depends on the volume of data and different features enabled during configuration, such as backup ...
What needs improvement with 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 de...
What is your primary use case for MariaDB?
My main use case for MariaDB was web applications, and I have been using MariaDB for just about a year now as I was in the development team. A specific web application where I used MariaDB is a hea...
What is your experience regarding pricing and costs for Qdrant?
I find Qdrant's pricing and licensing extremely straightforward and cost-effective. The core product is open source under Apache 2.0, so initial experimentation and local integration testing cost n...
What needs improvement with Qdrant?
Qdrant is available through a containerized Docker, but a normal deployment in Qdrant is not there, and that can actually be worked out. That was one aspect I thought about, because I need to have ...
What is your primary use case for Qdrant?
We have a full-fledged RAG system using Qdrant Vector Database, and that is how it has benefited us. For example, we have implemented a techno-commercial evaluator using that, and it is in producti...
 

Comparisons

 

Overview

 

Sample Customers

Google, Wikipedia, Tencent, Verizon, DBS Bank, Deutsche Bank, Telefónica, Huatai Securities
1. Airbnb 2. Amazon 3. Apple 4. BMW 5.Cisco 6. CocaCola 7. Dell 8. Disney 9. Google 10. HP 11. IBM 12. Intel 13. JPMorgan Chase 14. Kraft Heinz 15. L'Oreal 16. McDonalds 17. Merck 18. Microsoft 19. Nike20. Oracle 21. PG 22. PepsiCo 23. Procter and Gamble 24. Samsung 25. Shell 26. Sony 27. Toyota 28. Visa 29. Walmart 30. WeWork
Find out what your peers are saying about MariaDB vs. Qdrant and other solutions. Updated: September 2026.
912,930 professionals have used our research since 2012.