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Elastic Search vs OpenSearch comparison

 

Comparison Buyer's Guide

Executive Summary

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
6.5
Number of Reviews
92
Ranking in other categories
Indexing and Search (1st), Cloud Data Integration (5th), Search as a Service (1st)
OpenSearch
Ranking in Vector Databases
10th
Average Rating
0.0
Number of Reviews
0
Ranking in other categories
Open Source Databases (15th)
 

Mindshare comparison

As of April 2026, in the Vector Databases category, the mindshare of Elastic Search is 4.0%, down from 6.2% compared to the previous year. The mindshare of OpenSearch is 6.1%, down from 9.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases Mindshare Distribution
ProductMindshare (%)
Elastic Search4.0%
OpenSearch6.1%
Other89.9%
Vector Databases
 

Featured Reviews

Anurag Pal - PeerSpot reviewer
Technical Lead at a consultancy with 10,001+ employees
Search and aggregations have transformed how I manage and visualize complex real estate data
Elastic Search consumes lots of memory. You have to provide the heap size a lot if you want the best out of it. The major problem is when a company wants to use Elastic Search but it is at a startup stage. At a startup stage, there is a lot of funds to consider. However, their use case is that they have to use a pretty significant amount of data. For that, it is very expensive. For example, if you take OLTP-based databases in the current scenario, such as ClickHouse or Iceberg, you can do it on 4GB RAM also. Elastic Search is for analytical records. You have to do the analytics on it. According to me, as far as I have seen, people will start moving from Elastic Search sooner or later. Why? Because it is expensive. Another thing is that there is an open source available for that, such as ClickHouse. Around 2014 and 2012, there was only one competitor at that time, which was Solr. But now, not only is Solr there, but you can take ClickHouse and you have Iceberg also. How are we going to compete with them? There is also a fork of Elastic Search that is OpenSearch. As far as I have seen in lots of articles I am reading, users are using it as the ELK stack for logs and analyzing logs. That is not the exact use case. It can do more than that if used correctly. But as it involves lots of cost, people are shifting from Elastic Search to other sources. When I am talking about pricing, it is not only the server pricing. It is the amount of memory it is using. The pricing is basically the heap Java, which is taking memory. That is the major problem happening here. If we have to run an MVP, a client comes to me and says, "Anurag, we need to do a proof of concept. Can we do it if I can pay a 4GB or 16GB expense?" How can I suggest to them that a minimum of 16GB is needed for Elastic Search so that your proof of concept will be proved? In that case, what I have to suggest from the beginning is to go with Cassandra or at the initial stage, go with PostgreSQL. The problem is the memory it is taking. That is the only thing.
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Top Industries

By visitors reading reviews
Financial Services Firm
11%
Computer Software Company
10%
Manufacturing Company
9%
Retailer
7%
Financial Services Firm
17%
Computer Software Company
11%
Manufacturing Company
9%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business38
Midsize Enterprise10
Large Enterprise46
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?
On the subject of pricing, Elastic Search is very cost-efficient. You can host it on-premises, which would incur zero cost, or take it as a SaaS-based service, where the expenses remain minimal.
What needs improvement with ELK Elasticsearch?
From the UI point of view, we are using most probably Kibana, and I think they can do much better than that. That is something they can fine-tune a little bit, and then it will definitely be a good...
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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. Amazon 2. Netflix 3. Yelp 4. Adobe 5. IBM 6. Microsoft 7. Cisco 8. Oracle 9. Salesforce 10. eBay 11. Spotify 12. Airbnb 13. Twitter 14. LinkedIn 15. Pinterest 16. Slack 17. Dropbox 18. Expedia 19. Uber 20. Lyft 21. Square 22. Zillow 23. Reddit 24. Hulu 25. Twitch 26. Booking.com 27. Etsy 28. Groupon 29. StubHub 30. TripAdvisor 31. Wayfair 32. Zappos
Find out what your peers are saying about Microsoft, Elastic, Redis and others in Vector Databases. Updated: March 2026.
885,728 professionals have used our research since 2012.