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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

"The most valuable feature of Elasticsearch is its convenience in handling unstructured data."
"The solution is very good with no issues or glitches."
"The products comes with REST APIs."
"The flexibility and the support for diverse languages that it provides for searching the database are most valuable. We can use different languages to query the database."
"It is highly valuable because of its simplicity in maintenance, where most tasks are handled for you, and it offers a plethora of built-in features."
"The most valuable features of Elastic Enterprise Search are it's cloud-ready and we do a lot of infrastructure as code. By using ELK, we're able to deploy the solution as part of our ISC deployment."
"We can easily collect all the data and view historical trends using the product. We can view the applications and identify the issues effectively."
"The search speed is most valuable and important."
"I used Faiss as a basic database."
"The product has better performance and stability compared to one of its competitors."
 

Cons

"There is another solution I'm testing which has a 500 record limit when you do a search on Elastic Enterprise Search. That's the only area in which I'm not sure whether it's a limitation on our end in terms of knowledge or a technical limitation from Elastic Enterprise Search. There is another solution we are looking at that rides on Elastic Enterprise Search. And the limit is for any sort of records that you're doing or data analysis you're trying to do, you can only extract 500 records at a time. I know the open-source nature has a lot of limitations, Otherwise, Elastic Enterprise Search is a fantastic solution and I'd recommend it to anyone."
"Better dashboards or a better configuration system would be very good."
"This product could be improved with additional security, and the addition of support for machine learning devices."
"There is a maximum of 10,000 entries, so the limitation means that if I wanted to analyze certain IP addresses more than 10,000 times, I wouldn't be able to dump or print that information."
"Dashboards could be more flexible, and it would be nice to provide more drill-down capabilities."
"It was not possible to use authentication three years back. You needed to buy the product's services for authentication."
"The solution's integration and configuration are not easy. Not many people know exactly what to do."
"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 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

"The solution is free."
"The solution is affordable."
"Elastic Search is open-source, but you need to pay for support, which is expensive."
"The solution is less expensive than Stackdriver and Grafana."
"The price of Elastic Enterprise is very, very competitive."
"We are using the free open-sourced version of this solution."
"To access all the features available you require both the open source license and the production license."
"An X-Pack license is more affordable than Splunk."
"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.
842,767 professionals have used our research since 2012.