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."The forced merge and forced resonate features reduce the data size increasing reliability."
"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."
"Dashboard is very customizable."
"The product is scalable with good performance."
"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 initial installation and setup were straightforward."
"I am impressed with the product's Logstash. The tool is fast and customizable. You can build beautiful dashboards with it. It is useful and reliable."
"I like how it allows us to connect to Kafka and get this data in a document format very easily. Elasticsearch is very fast when you do text-based searches of documents. That area is very good, and the search is very good."
"The product has better performance and stability compared to one of its competitors."
"I used Faiss as a basic database."
"It should be easier to use. It has been getting better because many functions are pre-defined, but it still needs improvement."
"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)."
"Elastic Search could benefit from a more user-friendly onboarding process for beginners."
"There is a lack of technical people to develop, implement and optimize equipment operation and web queries."
"I would like to see more integration for the solution with different platforms."
"The documentation regarding customization could be better."
"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."
"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."
"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.