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 | 4.2% |
| PostgreSQL | 14.4% |
| Firebird SQL | 11.9% |
| Other | 69.5% |
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 |
|---|---|---|
| AI Developer at Hecta.ai | 4.0 | I primarily use Qdrant for semantic search and RAG pipelines, valuing its hybrid search, payload filtering, and quantization for cost and memory efficiency. It significantly improved answer relevance and saved my organization millions, though I wish for better documentation and re-indexing tools. |
| 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. |
