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.


| Product | Mindshare (%) |
|---|---|
| Qdrant | 4.3% |
| PostgreSQL | 13.3% |
| MySQL | 10.5% |
| Other | 71.9% |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| MySQL | 4.1 | 10.5% | 91% | 152 interviewsAdd to research |
| PostgreSQL | 4.2 | 13.3% | 96% | 127 interviewsAdd to research |
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.
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
| Author info | Rating | Review Summary |
|---|---|---|
| Lead Ai Tech And Tech Automation Engineer at a individual & family service with 1-10 employees | 4.5 | I effectively leveraged Qdrant, particularly Qdrant Cloud, for RAG chatbots and no-code automation, significantly reducing development time. Its HNSW search provides high accuracy and scalability, proving superior to other solutions. While inactive clouds terminate quickly, Qdrant offers excellent value and performance. |
| Automation Engineer at a educational organization with 11-50 employees | 4.0 | I used Qdrant for RAG, valuing its speed, filtering, and open-source nature for developer velocity. Operational complexities from dual database management, particularly with Supabase, eventually led me to switch to an integrated vector solution. |
| Co Founder & CEO at SaYukth Private Limited | 4.5 | I value Qdrant for AI data analysis, highlighting its GPU support, light footprint, and fast response times. Despite complex clustering, its open-source nature and strong vector capabilities make it a reliable enterprise solution. |
| AI/ML Intern at a tech vendor with 51-200 employees | 4.5 | I use Qdrant for vector search in my prediction engine, valuing its simplicity, hybrid search, and Python client. It significantly boosted accuracy from 50% to 95% over Faiss. My main concern is a file system lock requiring a Docker service. |
| Chief Ai Scientist at Predictive Systems | 5.0 | I’ve used Qdrant on‑prem for two years in legal and educational work; its sample code, easy setup, and fast hybrid, high‑dimensional vector search improved results. Support is community-based but sufficient. I switched from Faiss and rate it 9/10. |