Try our new research platform with insights from 80,000+ expert users

Elastic Search vs Milvus comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 5, 2025

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

Elastic Search
Ranking in Vector Databases
2nd
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
67
Ranking in other categories
Indexing and Search (1st), Cloud Data Integration (9th), Search as a Service (1st)
Milvus
Ranking in Vector Databases
6th
Average Rating
7.4
Reviews Sentiment
7.5
Number of Reviews
5
Ranking in other categories
Open Source Databases (9th)
 

Mindshare comparison

As of April 2025, in the Vector Databases category, the mindshare of Elastic Search is 5.8%, down from 7.1% compared to the previous year. The mindshare of Milvus is 8.8%, up from 8.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases
 

Featured Reviews

Anand_Kumar - PeerSpot reviewer
Captures data from all other sources and becomes a MOM aka monitoring of monitors
Scalability and ROI are the areas they have to improve. Their license terms are based on the number of cores. If you increase the number of cores, it becomes very difficult to manage at a large scale. For example, if I have a $3 million project, I won't sell it because if we're dealing with a 10 TB or 50 TB system, there are a lot of systems and applications to monitor, and I have to make an MOM (Mean of Max) for everything. This is because of the cost impact. Also, when you have horizontal scaling, it's like a multi-story building with only one elevator. You have to run around, and it's not efficient. Even the smallest task becomes difficult. That's the problem with horizontal scaling. They need to improve this because if they increase the cores and adjust the licensing accordingly, it would make more sense.
Sameer Bhangale - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"I find the solution to be fast."
"The most valuable feature of Elasticsearch is its convenience in handling unstructured data."
"The most valuable feature of Elastic Enterprise Search is the Discovery option for the visualization of logs on a GPU instead of on the server."
"The ability to aggregate log and machine data into a searchable index reduces time to identify and isolate issues for an application. Saves time in triage and incident response by eliminating manual steps to access and parse logs on separate systems, within large infrastructure footprints."
"There's lots of processing power. You can actually just add machines to get more performance if you need to. It's pretty flexible and very easy to add another log. It's not like 'oh, no, it's going to be so much extra data'. That's not a problem for the machine. It can handle it."
"The most valuable features are its user-friendly interface and seamless navigation."
"The products comes with REST APIs."
"The most valuable features are the data store and the X-pack extension."
"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."
"Milvus has good accuracy and performance."
"The best feature of Milvus was finding the closest chunk from a huge amount of data."
"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."
 

Cons

"I have not been using the solution for many years to know exactly the improvements needed. However, they could simplify how the YML files have to be structured properly."
"They're making changes in their architecture too frequently."
"There are potential improvements based on our client feedback, like unifying the licensing cost structure."
"Both the graph feature and the reporting feature are a little bit lacking. The alerting also needs to be improved."
"I would rate the stability a seven out of ten. We faced a few issues."
"We see the need for some improvements with Elasticsearch. We would like the Elasticsearch package to include training lessons for our staff."
"Technical support should be faster."
"I don't see improvements at the moment. The current setup is working well for me, and I'm satisfied with it. Integrating with different platforms is also fine, and I'm not recommending any changes or enhancements right now."
"I've heard that when we store too much data in Milvus, it becomes slow and does not work properly."
"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."
"Milvus has higher resource consumption, which introduces complexity in implementation."
 

Pricing and Cost Advice

"The solution is not expensive because users have the option of choosing the managed or the subscription model."
"The premium license is expensive."
"ELK has been considered as an alternative to Splunk to reduce licensing costs."
"The version of Elastic Enterprise Search I am using is open source which is free. The pricing model should improve for the enterprise version because it is very expensive."
"The solution is free."
"We are using the open-sourced version."
"The solution is less expensive than Stackdriver and Grafana."
"The price of Elastic Enterprise is very, very competitive."
"Milvus is an open-source solution."
"Milvus is an open-source solution."
report
Use our free recommendation engine to learn which Vector Databases solutions are best for your needs.
842,767 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
15%
Government
9%
Manufacturing Company
8%
Computer Software Company
21%
Manufacturing Company
9%
Financial Services Firm
9%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about ELK Elasticsearch?
Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time anal...
What is your experience regarding pricing and costs for ELK Elasticsearch?
I don't know about pricing. That is dealt with by the sales team and our account team. I was not involved with that.
What needs improvement with ELK Elasticsearch?
I found an issue with Elasticsearch in terms of aggregation. They are good, yet the rules written for this are not really good. There is a maximum of 10,000 entries, so the limitation means that if...
What do you like most about Milvus?
I like the accuracy and usability.
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...
 

Comparisons

 

Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud
No data available
 

Overview

 

Sample Customers

T-Mobile, Adobe, Booking.com, BMW, Telegraph Media Group, Cisco, Karbon, Deezer, NORBr, Labelbox, Fingerprint, Relativity, NHS Hospital, Met Office, Proximus, Go1, Mentat, Bluestone Analytics, Humanz, Hutch, Auchan, Sitecore, Linklaters, Socren, Infotrack, Pfizer, Engadget, Airbus, Grab, Vimeo, Ticketmaster, Asana, Twilio, Blizzard, Comcast, RWE and many others.
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
Find out what your peers are saying about Elastic Search vs. Milvus and other solutions. Updated: March 2025.
842,767 professionals have used our research since 2012.