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BA Insight vs Elastic Search 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

BA Insight
Ranking in Indexing and Search
18th
Average Rating
8.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
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)
 

Mindshare comparison

As of March 2026, in the Indexing and Search category, the mindshare of BA Insight is 2.7%, up from 1.0% compared to the previous year. The mindshare of Elastic Search is 12.0%, down from 26.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Indexing and Search Mindshare Distribution
ProductMindshare (%)
Elastic Search12.0%
BA Insight2.7%
Other85.3%
Indexing and Search
 

Featured Reviews

it_user265773 - PeerSpot reviewer
GDS Search Services Leader, Knowledge Services at a financial services firm with 10,001+ employees
Refiners help to easily narrow down results based on metadata applied to content. Previews allow users to take action from search results without opening the document.
The preview feature made the results page take too long to load. It also took a long time to generate document previews. Randomly, it would show “cannot load preview”. I do not know the reason, but that happened a lot and we had to turn this feature off. Preview load time could be reduced. We saw that it takes forever to load a document preview, and at times, after waiting, it just gave a ‘cannot load preview error’.
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.

Quotes from Members

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

Pros

"Refiners help to easily narrow down results based on metadata applied to content, and the preview feature helps users to take action from the search results page without opening the documents."
"The most valuable feature of Elastic Enterprise Search is the opportunity to search behind and between different logs."
"From a technical point of view, there are no significant issues recalled as Elastic Search has been absolutely awesome for this use case and covers 100% of the needs."
"We had many reasons to implement Elasticsearch for search term solutions. Elasticsearch products provide enterprise landscape support for different areas of the company."
"I appreciate the indexing capabilities and the speed of indexing in their product, which demonstrates how quickly logs are collected and stored."
"Elasticsearch includes a graphical user interface (GUI) called Kibana. The GUI features are extremely beneficial to us."
"The machine learning features of Elastic Search are very interesting, including the possibility to include models such as ELSER and different multilingual models that let us fine-tune our searches and use them in our search projects."
"The best feature of Elastic Search that I appreciate is its monitoring capability."
"The forced merge and forced resonate features reduce the data size increasing reliability."
 

Cons

"The preview feature made the results page take too long to load."
"There were also some difficult times with parallel and point-in-time interfaces, so better documentation could help, particularly more example-driven content."
"According to me, as far as I have seen, people will start moving from Elastic Search sooner or later. Why? Because it is expensive."
"There is an index issue in which the data starts to crash as it increases."
"Both the graph feature and the reporting feature are a little bit lacking. The alerting also needs to be improved."
"In Elastic Search, the improvements I would like to see require many resources."
"This product could be improved with additional security, and the addition of support for machine learning devices."
"It would be useful to include an assistant into Kibana for recommendations, advice, tutorials, or things that can help improve my daily work with Elastic Search."
"Could have more open source tools and testing."
 

Pricing and Cost Advice

Information not available
"This product is open-source and can be used free of charge."
"The premium license is expensive."
"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."
"The solution is free."
"We are paying $1,500 a month to use the solution. If you want to have endpoint protection you need to pay more."
"The cost varies based on factors like usage volume, network load, data storage size, and service utilization. If your usage isn't too extensive, the cost will be lower."
"The solution is less expensive than Stackdriver and Grafana."
"To access all the features available you require both the open source license and the production license."
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Top Industries

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

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business38
Midsize Enterprise10
Large Enterprise45
 

Questions from the Community

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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...
 

Comparisons

 

Also Known As

BA Insight Enterprise Search Essentials, BA Insight Enterprise Search
Elastic Enterprise Search, Swiftype, Elastic Cloud
 

Overview

 

Sample Customers

AARP, Amgen, Blank Rome, Chevron, Australian Government, EY, Hogan Lovells, Keurig Green Mountain, OFWAT, Pfizer, Stanford University, US Army, White & Case
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.
Find out what your peers are saying about Elastic, Luigi's Box, OpenText and others in Indexing and Search. Updated: February 2026.
884,873 professionals have used our research since 2012.