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Elastic Search vs OpenText Knowledge Discovery (IDOL) 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
99
Ranking in other categories
Cloud Data Integration (5th), Search as a Service (1st), Vector Databases (5th)
OpenText Knowledge Discover...
Ranking in Indexing and Search
6th
Average Rating
8.4
Reviews Sentiment
6.3
Number of Reviews
5
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2026, in the Indexing and Search category, the mindshare of Elastic Search is 10.1%, down from 24.1% compared to the previous year. The mindshare of OpenText Knowledge Discovery (IDOL) is 6.2%, down from 6.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Indexing and Search Mindshare Distribution
ProductMindshare (%)
Elastic Search10.1%
OpenText Knowledge Discovery (IDOL)6.2%
Other83.7%
Indexing and Search
 

Featured Reviews

reviewer2817942 - PeerSpot reviewer
Senior Software Engineer at a consultancy with 11-50 employees
Logging and vector search have transformed observability and empowered reliable ai agents
Elastic Search is not specifically being used for certain purposes. I deploy Elastic Search database on the cloud and use cloud services so that nobody can attack. However, I do not use Elastic Search to resolve attack issues. The basic main purpose of Elastic Search, as of now, I feel it can do more in the AI area. Sometime I saw that when I am developing RAG and have to generate the embeddings, which I call metadata, sometimes it tries to fail. That durability or issue handling should be improved, but apart from that, I did not find anything as of now. As per my use case, whatever I am using seems pretty good. Apart from that, some definitely improvement will be there. One improvement is that it should be faster. Whenever I am searching any logs, it takes much time. For example, if I open my log in Notepad or a similar tool, I can search the text within a second. With Elastic Search, it takes a little bit of time, ten to fifteen seconds. That can be improved. Sometimes, engineers take time to assign when I create a ticket.
ERICK RAMIREZ - PeerSpot reviewer
Team Lead Solutions Architect at IMEXPERTS DO BRASIL
Scales linearly and vertically; primarily used in AI
If I am not wrong, IDOL is working to release improvements in new capabilities in the next six months. There is room for improvement in some very important capabilities in visual analytics. They have been focusing on improving the face recognition algorithm. The accuracy of object detection could be improved as well and I know they are working on that at the moment. I would like to see some machine learning capabilities added to the next release.

Quotes from Members

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

Pros

"It's a stable solution and we have not had any issues."
"The solution has improved our organization by allowing us to quickly search data from multiple systems saving valuable time."
"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."
"The most valuable features are the detection and correlation features."
"The initial installation and setup were straightforward."
"The solution is stable and reliable."
"Data indexing of historical data is the most beneficial feature of the product."
"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."
"Satisfaction and ability to find relevant content has increased over 50% based on our before and after survey results."
"IDOL is a scalable solution: If a client needs to process more unstructured media content, they can scale IDOL both linearly and vertically, and you can scale this solution very easily."
"Enterprise search success (finding what documents you're looking for) has gone up over 30% with users finding their hit on the first page of results as opposed to the 2,3,4th or giving up entirely."
"Speed improvements over older Fetch architecture."
"Capability of processing and analysing unstructured data, like audio and video analysis."
"IDOL has several important visual analytics, like face recognition and object detection and recognition."
 

Cons

"Regarding what I dislike about Elastic Search, there is one issue that occurs because Elastic Search is not my primary database; it serves as a substitute database for the searching part."
"The price could be better. Kibana has some limitations in terms of the tablet to view event logs. I also have a high volume of data. On the initialization part, if you chose Kibana, you'll have some limitations. Kibana was primarily proposed as a log data reviewer to build applications to the viewer log data using Kibana. Then it became a virtualization tool, but it still has limitations from a developer's point of view."
"In terms of product improvement, ratio aggregation is not supported in this solution."
"I would rate the stability a seven out of ten. We faced a few issues."
"Elastic Enterprise Search's tech support is good but it could be improved."
"They should improve its documentation. Their official documentation is not very informative."
"This is not exactly a stable solution, which is why we are considering another compatible tool, and whether we go on with Elasticsearch or change it."
"Elastic Enterprise Search could improve the report templates."
"There is room for improvement in some very important capabilities in visual analytics. They have been focusing on improving the face recognition algorithm. The accuracy of object detection could be improved as well and I know they are working on that at the moment."
"Technical support could improve a lot."
"Understanding how to optimize Lua Scripting configuration to improve performance. Lua Scripts added to a CFS configuration can cause the CFS processing to slow down, if the scripts are not scoped to only run against specific indexing jobs or database content."
"IDOL's tech support needs improvement."
"The interface needs to be mobile friendly, which I understand is in the backlog of future improvements."
"On-premise implementation and installation is very complicated."
 

Pricing and Cost Advice

"We use the free version for some logs, but not extensive use."
"ELK has been considered as an alternative to Splunk to reduce licensing costs."
"The premium license is expensive."
"I rate Elastic Search's pricing an eight out of ten."
"The solution is not expensive because users have the option of choosing the managed or the subscription model."
"We are using the free open-sourced version of this solution."
"The tool is an open-source product."
"Elastic Search is open-source, but you need to pay for support, which is expensive."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Manufacturing Company
9%
Computer Software Company
8%
Retailer
6%
Transportation Company
16%
Construction Company
11%
Government
9%
Financial Services Firm
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business40
Midsize Enterprise12
Large Enterprise49
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for ELK Elasticsearch?
Elastic Search is easy to use in Azure cloud. Mostly, my full company uses Azure cloud, so it is easy to use. Cost-wise, my company found Elastic Search is good. Cost matters. Based on cost and use...
What needs improvement with ELK Elasticsearch?
The initial configuration could be easier; at first, the learning curve is a little high, and over time, it becomes easier. For me, the initial configuration might be improved.
What is your primary use case for ELK Elasticsearch?
We use Elastic Search for a research application based on paper study, and the primary usage is for indexing the data and then functioning in a similar way to an e-commerce search bar.
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Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud
Micro Focus IDOL, HPE Autonomy IDOL, HPE IDOL
 

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.
RTVE, Krungthai Bank, Kainos, Capax Discovery
Find out what your peers are saying about Elastic Search vs. OpenText Knowledge Discovery (IDOL) and other solutions. Updated: June 2026.
900,747 professionals have used our research since 2012.