No more typing reviews! Try our Samantha, our new voice AI agent.
Qdrant Logo

Qdrant Reviews

Vendor: Qdrant
4.5 out of 5
Badge Leader
Leave a review

What is Qdrant?

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. 

Get the Qdrant Buyer's Guide and find out what your peers are saying about Qdrant, MySQL, PostgreSQL and more!
Qdrant is the #3 ranked solution in top Vector Databases solutions, #9 ranked solution in top Open Source Databases, and #12 ranked solution in top AI Data Analysis solutions. PeerSpot users give Qdrant an average rating of 9.0 out of 10. Qdrant is most commonly compared to MySQL: Qdrant vs MySQL. Qdrant is popular among the large enterprise segment, accounting for 49% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a comms service provider, accounting for 11% of all views.
Buyer's Guide
Qdrant
May 2026
Get the report
Helped 896,563 peers since 2012

Featured Qdrant reviews

Read more reviews

Qdrant mindshare

Product category:
Open Source Databases
As of June 2026, the mindshare of Qdrant in the Open Source Databases category stands at 4.5%, up from 3.5% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
Qdrant4.5%
PostgreSQL13.1%
MySQL11.4%
Other71.0%
Open Source Databases

PeerResearch reports based on Qdrant reviews

TypeTitleDate
CategoryOpen Source DatabasesJun 1, 2026Download
ProductReviews, tips, and advice from real usersJun 1, 2026Download
ComparisonQdrant vs MySQLJun 1, 2026Download
ComparisonQdrant vs PostgreSQLJun 1, 2026Download
ComparisonQdrant vs ClickHouseJun 1, 2026Download
Suggested products
TitleRatingMindshareRecommending
MySQL4.111.4%91%152 interviewsAdd to research
PostgreSQL4.213.1%96%127 interviewsAdd to research
 
 
Key learnings from peers
Last updated May 31, 2026

Valuable Features

Qdrant's standout attributes include hybrid search with dense and sparse vectors, payload filtering, quantization for memory efficiency, GPU support, HNSW search method, and Qdrant Cloud. Hybrid search improves semantic and keyword precision. They appreciate Qdrant's ability to handle high-dimensional vectors, speedy processing, reduced search latency, cost savings, and ease of deployment. Its open-source nature, Python client, and built-in UI facilitate experimentation and management in low-code environments.
  • "We saw a clear return on investment from Qdrant, particularly in the engineering time saved and the empowerment of team members to handle self-service tasks instead of reducing headcount."
  • "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 has positively impacted my organization by consuming much less time than building systems through coding."

Room for Improvement

Qdrant users express a need for improvements in real-time updates, intuitive UI for re-indexing, seamless backups, advanced fine-tuning parameters, and embedding features. Clustering and deployment complexities are issues. Documentation requires clarity for those less experienced in AI and vector databases. Challenges include setup complexity, API limitations with file system lock, and keeping dual databases in sync. Expiration of inactive clouds frustrates users, and integration with tools like LangChain could be better.
  • "Architectural complexity was a key friction point, as our primary database was set in Supabase, necessitating synchronization of two separate systems for user data, permissions, and states."
  • "The file system lock in Qdrant prevents the API and scripts from hitting it directly, and to surpass this limitation, I have to run Qdrant client as a service, which incurs additional costs for running it continuously, so if something about that could be done, it would be really amazing."
  • "A pain point I have encountered was the inactive expiration of the cloud created for certain projects; if the cloud is not used for a week, it gets terminated, which is frustrating."

Pricing

Enterprise users noted that Qdrant's free usage allows initial low setup costs due to its open-source nature. While Qdrant itself is economically viable, self-hosting may incur increased developer time costs for backup management and integration with systems like Supabase. As organizations expand, they may explore paid plans with Qdrant Cloud. Users found the upfront pricing and licensing straightforward but acknowledged potential complexities in maintaining efficiency with vector storage and management.

Popular Use Cases

Users primarily employ Qdrant for vector databases in semantic search, legal, educational, and AI data analysis applications. They utilize it for storing high-dimensional embeddings and querying for contextual similarity across documents and multimedia. In practical scenarios, Qdrant facilitates RAG chatbots, multi-tenant knowledge bases, and integrations like Qdrant Cloud for no-code projects, significantly enhancing efficiency and accuracy in document processing, customer support systems, and AI automation in HR and recruiting workflows.

Service and Support

Qdrant's customer service is community-driven, with effective online and forum support. Users find their documentation comprehensive and rarely need direct assistance. The community aspect ensures support availability without additional costs since it's open source. Information and guidance provided on the internet are considered satisfactory, with explanations that meet users' needs. Users confidently rate Qdrant's technical support as quite good and reliable, underscoring a positive experience with available resources and community platforms.

Deployment

Qdrant's initial setup is straightforward, especially for developers. Users appreciate its open-source Apache 2.0 license, allowing self-hosting with zero licensing cost via Docker, making it accessible for startups. Pricing is unclear, but the managed cloud offering is competitive. Large-scale applications may see increased costs, though still favorable compared to alternatives like Pinecone. Experience with HNSW searching shows notable accuracy, with significant time saved on projects deployed in no-code platforms.

Stability

Qdrant requires consistent maintenance, with users applying minor updates and considering migration time for major ones. It is mostly stable, except for issues like inactive clouds being terminated after a week. Despite these, users find Qdrant operates reliably, showing resilience even with file lock challenges. It consistently demonstrates stability in practical applications.
These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.
Download our Qdrant Buyer's Guide for additional reliable information.

Top industries

By visitors reading reviews
Comms Service Provider
11%
Financial Services Firm
11%
Computer Software Company
10%
Manufacturing Company
8%
Government
6%
Construction Company
6%
Outsourcing Company
5%
Educational Organization
5%
Retailer
5%
University
5%
Media Company
4%
Healthcare Company
4%
Energy/Utilities Company
3%
Real Estate/Law Firm
2%
Pharma/Biotech Company
2%
Wholesaler/Distributor
2%
Hospitality Company
2%
Legal Firm
2%
Non Profit
1%
Transportation Company
1%
Recreational Facilities/Services Company
1%
Performing Arts
1%
Agriculture
1%
Insurance Company
1%
Renewables & Environment Company
1%
Leisure / Travel Company
1%

Compare Qdrant with alternative products

Go!

Learn more about Qdrant

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.

Qdrant customers

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

Related questions

  • 175
When evaluating Open Source Databases, what aspect do you think is the most important to look for?
  • 169
Did you switch from a different solution to MySQL? Can you list a few pros and cons for making the move?
  • 255
Which database is the best for session cashing?
  • 156
Why is Open Source Databases important for companies?
  • 305
Which low-code (no-code) database solution do you prefer?

Product Categories

Product Categories
Open Source Databases
Vector Databases
AI Data Analysis

Popular Comparisons

Popular Comparisons
MySQL vs Qdrant Logo
MySQL vs Qdrant
PostgreSQL vs Qdrant Logo
PostgreSQL vs Qdrant
Redis vs Qdrant Logo
Redis vs Qdrant
Elastic Search vs Qdrant Logo
Elastic Search vs Qdrant
Microsoft Azure Cosmos DB vs Qdrant Logo
Microsoft Azure Cosmos DB vs Qdrant
MongoDB Enterprise Advanced vs Qdrant Logo
MongoDB Enterprise Advanced vs Qdrant
ClickHouse vs Qdrant Logo
ClickHouse vs Qdrant
Milvus vs Qdrant Logo
Milvus vs Qdrant
Pinecone vs Qdrant Logo
Pinecone vs Qdrant
Chroma vs Qdrant Logo
Chroma vs Qdrant
LanceDB vs Qdrant Logo
LanceDB vs Qdrant
OpenSearch vs Qdrant Logo
OpenSearch vs Qdrant
Cassandra vs Qdrant Logo
Cassandra vs Qdrant
SQLite vs Qdrant Logo
SQLite vs Qdrant
Supabase Vector vs Qdrant Logo
Supabase Vector vs Qdrant
See all alternatives
 
Qdrant Reviews Summary
Author infoRatingReview Summary
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 1-10 employees4.5I 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 employees4.0I 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 Limited4.5I 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.
Analyst at Synergy connect4.5I 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 Systems5.0I’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.