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."It provides deep visibility into your cloud and distributed applications, from microservices to serverless architectures. It quickly identifies and resolves the root causes of issues, like gaining visibility into all the cloud-based and on-prem applications."
"Elastic Enterprise Search is scalable. On a scale of one to 10, with one being not scalable and 10 being very scalable, I give Elastic Enterprise Search a 10."
"The most valuable feature of Elastic Enterprise Search is user behavior analysis."
"I appreciate that Elastic Enterprise Search is easy to use and that we have people on our team who are able to manage it effectively."
"The solution is valuable for log analytics."
"Implementing the main requirements regarding my support portal."
"The most valuable features are the detection and correlation features."
"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."
"Something that could be improved is better integrations with Cortex and QRadar, for example."
"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."
"Elastic Enterprise Search can improve by adding some kind of search that can be used out of the box without too much struggle with configuration. With every kind of search engine, there is some kind of special function that you need to do. A simple out-of-the-box search would be useful."
"Improving machine learning capabilities would be beneficial."
"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."
"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)."
"It needs email notification, similar to what Logentries has. Because of the notification issue, we moved to Logentries, as it provides a simple way to receive notification whenever a server encounters an error or unexpected conditions (which we have defined using RegEx)."
"They could improve some of the platform's infrastructure management capabilities."
"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.