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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
100
Ranking in other categories
Cloud Data Integration (5th), Search as a Service (1st), Vector Databases (6th)
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 August 2026, in the Indexing and Search category, the mindshare of Elastic Search is 9.8%, down from 22.7% compared to the previous year. The mindshare of OpenText Knowledge Discovery (IDOL) is 6.4%, up from 6.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Indexing and Search Mindshare Distribution
ProductMindshare (%)
Elastic Search9.8%
OpenText Knowledge Discovery (IDOL)6.4%
Other83.8%
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

"The solution has improved our organization by allowing us to quickly search data from multiple systems saving valuable time."
"We chose Elasticsearch because we could build a model in a short amount of time, allowing us to build a whole setup in one month and get 93% accuracy, with complex AI-based features built in a shorter span and with high accuracy that wasn't possible with other search enterprise vendors we used."
"The most valuable feature of Elastic Enterprise Search is the Discovery option for the visualization of logs on a GPU instead of on the server."
"The solution offers good stability."
"I like how it allows us to connect to Kafka and get this data in a document format very easily. Elasticsearch is very fast when you do text-based searches of documents. That area is very good, and the search is very good."
"The positive impact I've seen from using Elastic Search includes replacing conventional databases and being able to store much more unstructured data."
"Elastic Search is very quick when handling a large volume of data."
"The observability is the best available because it provides granular insights that identify reasons for defects."
"Satisfaction and ability to find relevant content has increased over 50% based on our before and after survey results."
"IDOL has several important visual analytics, like face recognition and object detection and recognition."
"Speed improvements over older Fetch architecture."
"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."
"Capability of processing and analysing unstructured data, like audio and video analysis."
 

Cons

"I think the pricing of Elastic Search is really, really expensive."
"Elastic Enterprise Search could improve the report templates."
"From the UI point of view, we are using most probably Kibana, and I think they can do much better than that."
"The solution's integration and configuration are not easy. Not many people know exactly what to do."
"Logstash has been a challenge and needs improvements in data ingestion reconciliation."
"There are some features lacking in ELK Elasticsearch."
"Could have more open source tools and testing."
"There are a few things that did not work for us. When doing a search in a bigger setup, with a huge amount of data where there are several things coming in, it has to be on top of the index that we search."
"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."
"Technical support could improve a lot."
"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."
"On-premise implementation and installation is very complicated."
 

Pricing and Cost Advice

"we are using a licensed version of the product."
"It can be expensive."
"The solution is not expensive because users have the option of choosing the managed or the subscription model."
"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."
"We are paying $1,500 a month to use the solution. If you want to have endpoint protection you need to pay more."
"The tool is an open-source product."
"This is a free, open source software (FOSS) tool, which means no cost on the front-end. There are no free lunches in this world though. Technical skill to implement and support are costly on the back-end with ELK, whether you train/hire internally or go for premium services from Elastic."
"The solution is less expensive than Stackdriver and Grafana."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
11%
Manufacturing Company
9%
Computer Software Company
7%
Outsourcing Company
7%
Construction Company
15%
Comms Service Provider
12%
Manufacturing Company
9%
Government
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business40
Midsize Enterprise12
Large Enterprise50
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for ELK Elasticsearch?
The pricing for Elastic Search is mainly budgeted according to the organization budget, so we take it as a yearly subscription, and that is acceptable since we do get a fair discount when we are ta...
What needs improvement with ELK Elasticsearch?
When we get the logs, it is mostly about how we edit the configurations and how we make changes according to the requirements of our organization. In these cases, the logs sometimes can be a bit in...
What is your primary use case for ELK Elasticsearch?
I am the Elastic Search admin for my organization, and we are using Elastic Search to handle the traffic to GCP. The monitoring of all the clusters and all the deployments are quite good, and compa...
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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: August 2026.
908,858 professionals have used our research since 2012.