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."You have dashboards, it is visual, there are maps, you can create canvases. It's more visual than anything that I've ever used."
"I have found the sort capability of Elastic very useful for allowing us to find the information we need very quickly."
"The special text processing features in this solution are very important for me."
"The most valuable feature for us is the analytics that we can configure and view using Kibana."
"The flexibility and the support for diverse languages that it provides for searching the database are most valuable. We can use different languages to query the database."
"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 analytics with Elastic benefits us due to the huge traffic volume in our organization, which reaches up to 60,000 requests per second. With logs of approximately 25 GB per day, manually analyzing traffic behavior, payloads, headers, user agents, and other details is impractical."
"ELK Elasticsearch is 100% scalable as scalability is built into the design"
"The most valuable features of Elastic Enterprise Search are it's cloud-ready and we do a lot of infrastructure as code. By using ELK, we're able to deploy the solution as part of our ISC deployment."
"Milvus has good accuracy and performance."
"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."
"I like the accuracy and usability."
"The solution must provide AI integrations."
"Something that could be improved is better integrations with Cortex and QRadar, for example."
"Better dashboards or a better configuration system would be very good."
"There are some features lacking in ELK Elasticsearch."
"The one area that can use improvement is the automapping of fields."
"We see the need for some improvements with Elasticsearch. We would like the Elasticsearch package to include training lessons for our staff."
"The metadata gets stored along with indexes and isn't queryable."
"The GUI is the part of the program which has the most room for improvement."
"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' documentation is not very user-friendly and doesn't help me get started quickly."
"Milvus could make it simpler. Simplifying the requirements and making it more accessible. It could be more user-friendly."
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.
See our list of best Vector Databases vendors.
We monitor all Vector Databases reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.