We performed a comparison between Elastic Search and Faiss 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."Data indexing of historical data is the most beneficial feature of the product."
"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 Elastic Enterprise Search is user behavior analysis."
"The solution has good security features. I have been happy with the dashboards and interface."
"It gives us the possibility to store and query this data and also do this efficiently and securely and without delays."
"The AI-based attribute tagging is a valuable feature."
"The tool's stability and performance are good."
"The initial setup is very easy for small environments."
"I used Faiss as a basic database."
"The product has better performance and stability compared to one of its competitors."
"Elastic Enterprise Search could improve its SSL integration easier. We should not need to go to the back-end servers to do configuration, we should be able to do it on the GUI."
"It was not possible to use authentication three years back. You needed to buy the product's services for authentication."
"There is an index issue in which the data starts to crash as it increases."
"There are some features lacking in ELK Elasticsearch."
"Something that could be improved is better integrations with Cortex and QRadar, for example."
"I would like to see more integration for the solution with different platforms."
"There are potential improvements based on our client feedback, like unifying the licensing cost structure."
"Elastic Search needs to improve its technical support. It should be customer-friendly and have good support."
"It could be more accessible for handling larger data sets."
"It would be beneficial if I could set a parameter and see different query mechanisms being run."
Elastic Search is ranked 1st in Vector Databases with 59 reviews while Faiss is ranked 2nd in Vector Databases with 2 reviews. Elastic Search is rated 8.2, while Faiss is rated 8.0. The top reviewer of Elastic Search writes "Played a crucial role in enhancing our cybersecurity efforts ". On the other hand, the top reviewer of Faiss writes "Provides quick query search and has a big database". Elastic Search is most compared with Milvus, Pinecone, Azure Search, Amazon Kendra and Qdrant, whereas Faiss is most compared with Chroma, Qdrant, Pinecone, Milvus and OpenSearch. See our Elastic Search vs. Faiss 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.