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Elastic Search vs Exalead 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 Indexing and Search
1st
Average Rating
8.2
Reviews Sentiment
6.5
Number of Reviews
90
Ranking in other categories
Cloud Data Integration (5th), Search as a Service (1st), Vector Databases (2nd)
Exalead
Ranking in Indexing and Search
12th
Average Rating
6.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2026, in the Indexing and Search category, the mindshare of Elastic Search is 12.0%, down from 26.3% compared to the previous year. The mindshare of Exalead is 2.4%, down from 4.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Indexing and Search Mindshare Distribution
ProductMindshare (%)
Elastic Search12.0%
Exalead2.4%
Other85.6%
Indexing and Search
 

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.
it_user916404 - PeerSpot reviewer
Senior Manager, PLM Competency Center at a transportation company with 5,001-10,000 employees
The indexing feature is handy but the user interface and support can be better
I am not sure if I will recommend this solution to others. There should definitely be an improvement in the user interface. It should be more friendly and customize until it's perfect. On a scale from 1 to 10, I rate this solution a six, because there is room for improvement.

Quotes from Members

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

Pros

"I value the feature that allows me to share the dashboards to different people with different levels of access."
"Dashboard is very customizable."
"It is stable."
"It's a stable solution and we have not had any issues."
"From the customer side, Elastic Search is super fast and very efficient, delivering results quickly."
"The forced merge and forced resonate features reduce the data size increasing reliability."
"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 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."
"I find the indexing feature the most valuable."
 

Cons

"The documentation regarding customization could be better."
"Machine learning on search needs improvement."
"The setup is somewhat complicated due to multiple dependencies and relations with different systems."
"According to me, as far as I have seen, people will start moving from Elastic Search sooner or later. Why? Because it is expensive."
"New Relic could be more flexible, similar to Elasticsearch."
"I have not explored Elastic Search at the most. Searching from vector DB is available in Elastic Search, and there is one more concept of graph searching or graph database searching. I have not explored it, but if it is not there, that would be an improvement area where Elastic Search can improve."
"We'd like to see more integration in the future, especially around service desks or other ITSM tools."
"Technical support should be faster."
"The scalability of the solution is a problem."
 

Pricing and Cost Advice

"The tool is not expensive. Its licensing costs are yearly."
"we are using a licensed version of the product."
"The solution is affordable."
"There is a free version, and there is also a hosted version for which you have to pay. We're currently using the free version. If things go well, we might go for the paid version."
"This product is open-source and can be used free of charge."
"We are paying $1,500 a month to use the solution. If you want to have endpoint protection you need to pay more."
"It can move from $10,000 US Dollars per year to any price based on how powerful you need the searches to be and the capacity in terms of storage and process."
"The premium license is expensive."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
11%
Manufacturing Company
10%
Retailer
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business38
Midsize Enterprise10
Large Enterprise45
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.
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Find out what your peers are saying about Elastic, Luigi's Box, OpenText and others in Indexing and Search. Updated: February 2026.
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