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Elastic Search vs Faiss comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 5, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Elastic Search
Ranking in Vector Databases
2nd
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
67
Ranking in other categories
Indexing and Search (1st), Cloud Data Integration (9th), Search as a Service (1st)
Faiss
Ranking in Vector Databases
5th
Average Rating
8.0
Reviews Sentiment
8.8
Number of Reviews
2
Ranking in other categories
Open Source Databases (13th)
 

Mindshare comparison

As of April 2025, in the Vector Databases category, the mindshare of Elastic Search is 5.8%, down from 7.1% compared to the previous year. The mindshare of Faiss is 9.2%, down from 17.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases
 

Featured Reviews

Anand_Kumar - PeerSpot reviewer
Captures data from all other sources and becomes a MOM aka monitoring of monitors
Scalability and ROI are the areas they have to improve. Their license terms are based on the number of cores. If you increase the number of cores, it becomes very difficult to manage at a large scale. For example, if I have a $3 million project, I won't sell it because if we're dealing with a 10 TB or 50 TB system, there are a lot of systems and applications to monitor, and I have to make an MOM (Mean of Max) for everything. This is because of the cost impact. Also, when you have horizontal scaling, it's like a multi-story building with only one elevator. You have to run around, and it's not efficient. Even the smallest task becomes difficult. That's the problem with horizontal scaling. They need to improve this because if they increase the cores and adjust the licensing accordingly, it would make more sense.
Vasu Bansal - PeerSpot reviewer
Provides quick query search and has a big database
I did not face any issues integrating Faiss with other tools. I would recommend the solution to other users. Faiss has facilitated my AI-driven project very well. I recommend that other users use it for their AI projects because it provides quick query search and has a big database. Overall, I rate the solution nine and a half out of ten.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"All the quality features are there. There are about 60 to 70 reports available."
"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 products comes with REST APIs."
"I have found the sort capability of Elastic very useful for allowing us to find the information we need very quickly."
"Elastic Search makes handling large data volumes efficient and supports complex search operations."
"It is stable."
"The most valuable feature is the out of the box Kibana."
"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."
"The product has better performance and stability compared to one of its competitors."
"I used Faiss as a basic database."
 

Cons

"Its licensing needs to be improved. They don't offer a perpetual license. They want to know how many nodes you will be using, and they ask for an annual subscription. Otherwise, they don't give you permission to use it. Our customers are generally military or police departments or customers without connection to the internet. Therefore, this model is not suitable for us. This subscription-based model is not the best for OEM vendors. Another annoying thing about Elasticsearch is its roadmap. We are developing something, and then they say, "Okay. We have removed that feature in this release," and when we are adapting to that release, they say, "Okay. We have removed that one as well." We don't know what they will remove in the next version. They are not looking for backward compatibility from the customers' perspective. They just remove a feature and say, "Okay. We've removed this one." In terms of new features, it should have an ODBC driver so that you can search and integrate this product with existing BI tools and reporting tools. Currently, you need to go for third parties, such as CData, in order to achieve this. ODBC driver is the most important feature required. Its Community Edition does not have security features. For example, you cannot authenticate with a username and password. It should have security features. They might have put it in the latest release."
"Scalability and ROI are the areas they have to improve."
"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."
"Both the graph feature and the reporting feature are a little bit lacking. The alerting also needs to be improved."
"We have an issue with the volume of data that we can handle."
"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."
"The real-time search functionality is not operational due to its impact on system resources."
"An improvement would be to have an interface that allows easier navigation and tracing of logs."
"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."
 

Pricing and Cost Advice

"ELK has been considered as an alternative to Splunk to reduce licensing costs."
"To access all the features available you require both the open source license and the production license."
"The pricing structure depends on the scalability steps."
"We are using the free version and intend to upgrade."
"The version of Elastic Enterprise Search I am using is open source which is free. The pricing model should improve for the enterprise version because it is very expensive."
"The tool is an open-source product."
"We are using the Community Edition because Elasticsearch's licensing model is not flexible or suitable for us. They ask for an annual subscription. We also got the development consultancy from Elasticsearch for 60 days or something like that, but they were just trying to do the same trick. That's why we didn't purchase it. We are just using the Community Edition."
"We use the free version for some logs, but not extensive use."
"It is an open-source tool."
"Faiss is an open-source solution."
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Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
15%
Government
9%
Manufacturing Company
8%
Computer Software Company
19%
Financial Services Firm
13%
Manufacturing Company
9%
Educational Organization
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about ELK Elasticsearch?
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 anal...
What is your experience regarding pricing and costs for ELK Elasticsearch?
I don't know about pricing. That is dealt with by the sales team and our account team. I was not involved with that.
What needs improvement with ELK Elasticsearch?
I found an issue with Elasticsearch in terms of aggregation. They are good, yet the rules written for this are not really good. There is a maximum of 10,000 entries, so the limitation means that if...
What do you like most about Faiss?
I used Faiss as a basic database.
What needs improvement with Faiss?
I didn't know what algorithm was being learned to fetch my query. It would be beneficial if I could set a parameter and see different query mechanisms being run. I can then compare the results to s...
 

Comparisons

 

Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud
No data available
 

Overview

 

Sample Customers

T-Mobile, Adobe, Booking.com, BMW, Telegraph Media Group, Cisco, Karbon, Deezer, NORBr, Labelbox, Fingerprint, Relativity, NHS Hospital, Met Office, Proximus, Go1, Mentat, Bluestone Analytics, Humanz, Hutch, Auchan, Sitecore, Linklaters, Socren, Infotrack, Pfizer, Engadget, Airbus, Grab, Vimeo, Ticketmaster, Asana, Twilio, Blizzard, Comcast, RWE and many others.
1. Facebook 2. Airbnb 3. Pinterest 4. Twitter 5. Microsoft 6. Uber 7. LinkedIn 8. Netflix 9. Spotify 10. Adobe 11. eBay 12. Dropbox 13. Yelp 14. Salesforce 15. IBM 16. Intel 17. Nvidia 18. Qualcomm 19. Samsung 20. Sony 21. Tencent 22. Alibaba 23. Baidu 24. JD.com 25. Rakuten 26. Zillow 27. Booking.com 28. Expedia 29. TripAdvisor 30. Rakuten 31. Rakuten Viber 32. Rakuten Ichiba
Find out what your peers are saying about Elastic Search vs. Faiss and other solutions. Updated: March 2025.
845,040 professionals have used our research since 2012.