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

Milvus vs Qdrant comparison

 

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:
 

Categories and Ranking

Milvus
Ranking in Open Source Databases
11th
Ranking in Vector Databases
11th
Average Rating
7.4
Reviews Sentiment
7.5
Number of Reviews
5
Ranking in other categories
No ranking in other categories
Qdrant
Ranking in Open Source Databases
9th
Ranking in Vector Databases
3rd
Average Rating
9.0
Reviews Sentiment
5.7
Number of Reviews
6
Ranking in other categories
AI Data Analysis (12th)
 

Mindshare comparison

As of June 2026, in the Open Source Databases category, the mindshare of Milvus is 4.8%, down from 5.8% compared to the previous year. The mindshare of Qdrant is 4.5%, up from 3.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
Qdrant4.5%
Milvus4.8%
Other90.7%
Open Source Databases
 

Featured Reviews

reviewer2395743 - PeerSpot reviewer
Data Scientist at a tech services company with 1,001-5,000 employees
Helps convert text and other data into a vector space but could provide detailed insights
Milvus is an open-source vector database designed for efficiently handling large-scale, high-dimensional data. It supports various types of data sources and can be deployed on your own premises, which is crucial for maintaining data security. Milvus offers multiple methods for calculating similarities or distances between vectors, such as L2 norm and cosine similarity. These methods help in comparing different vectors based on specific use cases. For instance, in our use case, we find that the L2 distance works best, but you can experiment with different methods to find the most suitable one for your needs. Milvus also includes its own user interface, known as the Milvus Dashboard, which allows you to visualize and manage your data, including embeddings and metadata. You can filter your data based on various criteria, including metadata and file names, which provides flexibility in data management.
Chirag Morajkar - PeerSpot reviewer
Lead Ai Tech And Tech Automation Engineer at a individual & family service with 11-50 employees
Building accurate no-code resume screeners has saved weeks in document search workflows
I see room for improvement in Qdrant based on what another platform called Weaviate offers. Qdrant provides an excellent vector database with a solid searching method. However, it could elevate its offering by integrating embedding features. Currently, for the workflow automation I build, I rely on other platforms for embedding, so incorporating this feature directly in Qdrant Cloud would eliminate the need to depend on external solutions. 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. I think increasing that inactivity window in the free tier would be beneficial, as I have faced limitations due to this seven-day inactivity rule, requiring me to reset up the cloud after its termination.

Quotes from Members

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

Pros

"Milvus has good accuracy and performance."
"Milvus offers multiple methods for calculating similarities or distances between vectors, such as L2 norm and cosine similarity. These methods help in comparing different vectors based on specific use cases. For instance, in our use case, we find that the L2 distance works best, but you can experiment with different methods to find the most suitable one for your needs."
"The solution is well containerized, and since containerization is quick and easy for me, I can scale it up quickly."
"I like the accuracy and usability."
"The best feature of Milvus was finding the closest chunk from a huge amount of data."
"Qdrant is an excellent vector database that anyone would want to use with RAG AI."
"Qdrant has positively impacted my organization by consuming much less time than building systems through coding."
"Qdrant has reduced our response time to less than one second for our 128 KB token sizes, and we are satisfied with that performance."
"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."
"Using Qdrant's hybrid search capability has improved my search results."
 

Cons

"Milvus has higher resource consumption, which introduces complexity in implementation."
"Milvus could make it simpler. Simplifying the requirements and making it more accessible. It could be more user-friendly."
"Milvus' documentation is not very user-friendly and doesn't help me get started quickly."
"I've heard that when we store too much data in Milvus, it becomes slow and does not work properly."
"One of the key limitations is that Qdrant does not have built-in role-based access control, and while being self-hosted is a benefit, it can also be improved."
"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."
"The area for improvement in Qdrant is its clustering capability. 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."
"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."
"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."
 

Pricing and Cost Advice

"Milvus is an open-source solution."
"Milvus is an open-source solution."
Information not available
report
Use our free recommendation engine to learn which Open Source Databases solutions are best for your needs.
900,644 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
13%
Financial Services Firm
9%
Manufacturing Company
8%
University
8%
Comms Service Provider
11%
Financial Services Firm
11%
Manufacturing Company
10%
Computer Software Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business8
 

Questions from the Community

What needs improvement with Milvus?
Milvus could be improved how it could automatically generate insights from the data it holds. Milvus maintains embedding information and knows the relationships between data points. It would be use...
What is your primary use case for Milvus?
Milvus is primarily used in RAG, which involves retrieving relevant documents or data to augment the generation of new content. Milvus helps convert text and other data into a vector space, and the...
What advice do you have for others considering Milvus?
Milvus works well for various use cases and is quite flexible in terms of deployment. For on-premises deployment, you can use the open-source version with Docker. The system requirements are relati...
What is your experience regarding pricing and costs for Qdrant?
Licensing posed no issues, as Qdrant is open-source software with no upfront fees. Initially, the setup cost was low since we utilized a self-hosted model on a small cloud VM. However, as we added ...
What needs improvement with Qdrant?
While Qdrant is an exceptionally fast and efficient search engine within vector bases, our engineering team faced operational challenges during its adoption. Architectural complexity was a key fric...
What is your primary use case for Qdrant?
I have been using Qdrant for almost one and a half years. This was actually one of the first vector databases that we picked up in our organization. We started using the RAG modules to create a per...
 

Comparisons

 

Overview

 

Sample Customers

1. Alibaba Group 2. Tencent 3. Baidu 4. JD.com 5. Meituan 6. Xiaomi 7. Didi Chuxing 8. ByteDance 9. Huawei 10. ZTE 11. Lenovo 12. Haier 13. China Mobile 14. China Telecom 15. China Unicom 16. Ping An Insurance 17. China Life Insurance 18. Industrial and Commercial Bank of China 19. Bank of China 20. Agricultural Bank of China 21. China Construction Bank 22. PetroChina 23. Sinopec 24. China National Offshore Oil Corporation 25. China Southern Airlines 26. Air China 27. China Eastern Airlines 28. China Railway Group 29. China Railway Construction Corporation 30. China Communications Construction Company 31. China Merchants Group 32. China Evergrande Group
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 Milvus vs. Qdrant and other solutions. Updated: June 2026.
900,644 professionals have used our research since 2012.