We performed a comparison between Elastic Search and Milvus based on real PeerSpot user reviews.
Find out in this report how the two Vector Databases solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."I have found the sort capability of Elastic very useful for allowing us to find the information we need very quickly."
"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 feature of the solution is its utility and usefulness."
"It's a stable solution and we have not had any issues."
"We had many reasons to implement Elasticsearch for search term solutions. Elasticsearch products provide enterprise landscape support for different areas of the company."
"It gives us the possibility to store and query this data and also do this efficiently and securely and without delays."
"A good use case is saving metadata of your systems for data cataloging. Various systems, like those opened in metadata and similar applications, use Elasticsearch to store their text data."
"Search is really powerful."
"I like the accuracy and usability."
"The best feature of Milvus was finding the closest chunk from a huge amount of data."
"The solution is well containerized, and since containerization is quick and easy for me, I can scale it up quickly."
"Milvus has good accuracy and performance."
"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."
"There are some features lacking in ELK Elasticsearch."
"The UI point of view is not very powerful because it is dependent on Kibana."
"Elastic Search needs to improve authentication. It also needs to work on the Kibana visualization dashboard."
"This product could be improved with additional security, and the addition of support for machine learning devices."
"It should be easier to use. It has been getting better because many functions are pre-defined, but it still needs improvement."
"The solution has quite a steep learning curve. The usability and general user-friendliness could be improved. However, that is kind of typical with products that have a lot of flexibility, or a lot of capabilities. Sometimes having more choices makes things more complex. It makes it difficult to configure it, though. It's kind of a bitter pill that you have to swallow in the beginning and you really have to get through it."
"They could improve some of the platform's infrastructure management capabilities."
"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."
Elastic Search is ranked 1st in Vector Databases with 59 reviews while Milvus is ranked 7th in Vector Databases with 4 reviews. Elastic Search is rated 8.2, while Milvus is rated 7.6. The top reviewer of Elastic Search writes "Played a crucial role in enhancing our cybersecurity efforts ". On the other hand, the top reviewer of Milvus writes "Provides quick and easy containerization, but documentation is not very user-friendly". Elastic Search is most compared with Faiss, Pinecone, Azure Search, Amazon Kendra and Qdrant, whereas Milvus is most compared with Faiss, LanceDB, Chroma, OpenSearch and Redis. See our Elastic Search vs. Milvus report.
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