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."ELK Elasticsearch is 100% scalable as scalability is built into the design"
"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."
"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."
"The tool's stability and performance are good."
"I value the feature that allows me to share the dashboards to different people with different levels of access."
"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."
"It helps us to analyse the logs based on the location, user, and other log parameters."
"The AI-based attribute tagging is a valuable feature."
"The product has better performance and stability compared to one of its competitors."
"I used Faiss as a basic database."
"Elastic Enterprise Search could improve the report templates."
"It is hard to learn and understand because it is a very big platform. This is the main reason why we still have nothing in production. We have to learn some things before we get there."
"Elasticsearch could improve by honoring Unix environmental variables and not relying only on those provided by Java (e.g. installing plugins over the Unix http proxy)."
"Both the graph feature and the reporting feature are a little bit lacking. The alerting also needs to be improved."
"They should improve its documentation. Their official documentation is not very informative. They can also improve their technical support. They don't help you much with the customized stuff. They also need to add more visuals. Currently, they have line charts, bar charts, and things like that, and they can add more types of visuals. They should also improve the alerts. They are not very simple to use and are a bit complex. They could add more options to the alerting system."
"Ratio aggregation is not supported in this solution."
"I would like to be able to do correlations between multiple indexes."
"Elastic Enterprise Search's tech support is good but it could be improved."
"It would be beneficial if I could set a parameter and see different query mechanisms being run."
"It could be more accessible for handling larger data sets."
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.