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Milvus 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:
 

Categories and Ranking

Milvus
Ranking in Open Source Databases
14th
Ranking in Vector Databases
14th
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
5th
Ranking in Vector Databases
2nd
Average Rating
9.0
Reviews Sentiment
5.4
Number of Reviews
11
Ranking in other categories
AI Data Analysis (6th)
 

Mindshare comparison

As of October 2026, in the Open Source Databases category, the mindshare of Milvus is 4.6%, down from 5.5% compared to the previous year. The mindshare of Qdrant is 4.3%, up from 4.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
Qdrant4.3%
Milvus4.6%
Other91.1%
Open Source Databases
 

Featured Reviews

Sameer Bhangale - PeerSpot reviewer
Leader, Data Science Practice at a computer software company with 5,001-10,000 employees
Provides quick and easy containerization, but documentation is not very user-friendly
Milvus' documentation is not very user-friendly and doesn't help me get started quickly. Chroma DB provides super user-friendly documentation, enabling new users to get started quickly. Chroma DB's setup doesn't have many dependencies, whereas Milvus usually comes with some dependencies because of the way it needs to be deployed. Unlike Milvus, it's very easy to do POCs with Chroma DB.
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.

Quotes from Members

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

Pros

"The best feature of Milvus was finding the closest chunk from a huge amount of data."
"I like the accuracy and usability."
"The solution is well containerized, and since containerization is quick and easy for me, I can scale it up quickly."
"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 has good accuracy and performance."
"Qdrant has positively impacted my organization mainly by reducing usage costs for AI because in the older system, in order to get the context, we were just dumping everything into the system, which would inflate the cost of the API, whatever AI we were using, such as OpenAI or Claude."
"Qdrant has positively impacted my organization by significantly improving our RAG system's latency, cost-efficiency, and accuracy."
"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."
"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."
"Qdrant is a good and scalable vector database, and it is free."
"The advice I would give to others looking into using Qdrant is that it is very good and they can always rely on it for high performance, especially for local AI development and implementation."
"Qdrant is an excellent vector database that anyone would want to use with RAG AI."
 

Cons

"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."
"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."
"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."
"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."
"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."
"Qdrant is pretty good overall, but it can be improved. For example, the UI could be better, and it has pretty much very generic features that every other vector database provides."
"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."
"A lot of our work is agentic right now, and we have also segmented the content to be logical, so there's not a lot of vector search anymore."
"Qdrant is available through a containerized Docker, but a normal deployment in Qdrant is not there, and that can actually be worked out."
 

Pricing and Cost Advice

"Milvus is an open-source solution."
"Milvus is an open-source solution."
Information not available
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Top Industries

By visitors reading reviews
Computer Software Company
11%
Comms Service Provider
10%
Manufacturing Company
10%
Financial Services Firm
9%
Comms Service Provider
13%
Manufacturing Company
12%
Financial Services Firm
10%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business11
Midsize Enterprise2
 

Questions from the Community

Ask a question
Earn 20 points
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?
I do not have any suggestions for how Qdrant could be improved. So far, I like what has been done and I have used it several times. I have nothing to add to it now, and I do not wish to add more ab...
What is your primary use case for Qdrant?
My main use case for Qdrant is local AI development. A specific example of how I'm using Qdrant in my development work is that I use it for my RAG development using N8n. In my drug development work...
 

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: September 2026.
917,433 professionals have used our research since 2012.