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Elastic Search vs IBM Watson Discovery 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
96
Ranking in other categories
Cloud Data Integration (5th), Search as a Service (1st), Vector Databases (2nd)
IBM Watson Discovery
Ranking in Indexing and Search
10th
Average Rating
7.8
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of May 2026, in the Indexing and Search category, the mindshare of Elastic Search is 10.9%, down from 24.9% compared to the previous year. The mindshare of IBM Watson Discovery is 3.3%, down from 4.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Indexing and Search Mindshare Distribution
ProductMindshare (%)
Elastic Search10.9%
IBM Watson Discovery3.3%
Other85.8%
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.
Geraldo Lima - PeerSpot reviewer
Sales Director at Kukac
Stable, scalable, and has testing and conversational AI features
The total time it takes to deploy IBM Watson Discovery depends on the documents you'll be working with. For example, I was in a situation where I was working with some painting files and folders for a painting store. The store had PDF documents, but the information was mixed up, so I had to treat the documents on IBM Watson Discovery, and discovering and understanding each PDF file took longer. The process is more straightforward for plain documents, and you have to work with questions that will help IBM Watson Discovery understand the documents. The time to deploy the product depends on the quantity and type of documents you'll be working with.

Quotes from Members

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

Pros

"Using real-time search functionality to support operational decisions has been helpful."
"The product is scalable with good performance."
"A positive feature of ELK is that it directly interacts with Elasticsearch, the UI is very nice, and performance wise it's quite good too."
"The search speed is most valuable and important."
"The most valuable feature for us is the analytics that we can configure and view using Kibana."
"Elastic Enterprise Search is scalable. On a scale of one to 10, with one being not scalable and 10 being very scalable, I give Elastic Enterprise Search a 10."
"The most valuable features are the ease and speed of the setup."
"The Attack Discovery feature helps to dig into incidents from where they occurred to determine how the incident originated and its source; it gives an entire path of attack propagation, showing when it started, what happened, and all events that took place to connect the entire cyber incident."
"Being able to have some rules to extract the entities is valuable. The capability to crawl external sites and internal documents, and then draw internal information with external contents is also valuable."
"The most valuable features of IBM Watson Discovery are the integration with the rest of the Watson Suite and the Watson Assistant capability. If you use Watson Assistant, the ability for it to be able to determine the accuracy of your voice models and your voice response systems is a benefit."
"It has allowed us to provide longer service availability for support to users."
"The most valuable feature of IBM Watson Discovery is testing, mainly because the product applies conversational AI, which means I can ask questions to get the information I want from a specific test area."
"Being able to have some rules to extract the entities is valuable."
"The most valuable features of IBM Watson Discovery are the integration with the rest of the Watson Suite and the Watson Assistant capability."
"Language support and the ability to build a natural language of speech recognition are the most valuable features."
 

Cons

"I have not been using the solution for many years to know exactly the improvements needed. However, they could simplify how the YML files have to be structured properly."
"Apart from the good things, what I would like to see improved or enhanced in Elastic Search is the storage cost."
"I would rate the stability a seven out of ten. We faced a few issues."
"They should improve its documentation. Their official documentation is not very informative."
"The pricing of this product needs to be more clear because I cannot understand it when I review the website."
"There are some features lacking in ELK Elasticsearch."
"There should be more stability."
"Something that could be improved is better integrations with Cortex and QRadar, for example."
"The pricing is an area for improvement in IBM Watson Discovery because the customer initially used the free version. Still, when he needed more questions and documents, he had to move to a different version, which was paid and cost $500 per month. That change in pricing made my company lose many customers."
"There are probably other chatbots out there that were built for specific use cases and are easier to deploy than this."
"It needs a lot of memory. Our index is very big. It is around 100 gigabytes. So, we need more than 100 gigabytes of memory to use Watson."
"IBM Watson Discovery has moved to a newer version and it is very difficult to train the newer version."
"There are probably other chatbots out there that were built for specific use cases and are easier to deploy than this. Having said that, Watson is way more flexible. While it may require a greater amount of effort, it is not substantially more than some of the other ones that are kind of prebuilt for a specific use case. It would be good to have more prebuilt and specific use cases and specific business models. It can have better phone integration, even though I think that it is actually becoming less of an issue. Most people are online nowadays."
"It needs a lot of memory. Our index is very big."
"The support from IBM Watson Discovery is good but could improve to make it great."
 

Pricing and Cost Advice

"I rate Elastic Search's pricing an eight out of ten."
"We are using the free open-sourced version of this solution."
"We are paying $1,500 a month to use the solution. If you want to have endpoint protection you need to pay more."
"The pricing model is questionable and needs to be addressed because when you would like to have the security they charge per machine."
"The version of Elastic Enterprise Search I am using is open source which is free. The pricing model should improve for the enterprise version because it is very expensive."
"ELK has been considered as an alternative to Splunk to reduce licensing costs."
"The price could be better."
"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."
"IBM Watson Discovery is an expensive product."
"Cost-wise, it is very reasonable because it is cloud-based."
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
9%
Manufacturing Company
9%
Retailer
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business39
Midsize Enterprise12
Large Enterprise47
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for ELK Elasticsearch?
When it comes to pricing, I think we had to pay AWS approximately 1,000 to 1,200 per month for the overall stack. I am not quite certain about how much Elastic Search costs specifically because I w...
What needs improvement with ELK Elasticsearch?
Elastic Search has many features, including Kibana and Logstash, which we regularly use. However, one downside in our product is cost, as it can be expensive when maintaining multiple shards and in...
What is your primary use case for ELK Elasticsearch?
As a developer, I use Elastic Search in developing one of my applications, basically integrating the back-end with Elastic Search. Our main use case for Elastic Search is for Logstash, which is a s...
Ask a question
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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.
Prudential, Bradesco, Woodside
Find out what your peers are saying about Elastic Search vs. IBM Watson Discovery and other solutions. Updated: April 2026.
893,244 professionals have used our research since 2012.