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Elastic Search vs IBM Cloud Pak for Integration comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 3, 2024

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 Cloud Data Integration
5th
Average Rating
8.2
Reviews Sentiment
6.5
Number of Reviews
96
Ranking in other categories
Indexing and Search (1st), Search as a Service (1st), Vector Databases (2nd)
IBM Cloud Pak for Integration
Ranking in Cloud Data Integration
19th
Average Rating
8.6
Reviews Sentiment
7.0
Number of Reviews
5
Ranking in other categories
API Management (27th)
 

Mindshare comparison

As of May 2026, in the Cloud Data Integration category, the mindshare of Elastic Search is 1.7%, down from 1.8% compared to the previous year. The mindshare of IBM Cloud Pak for Integration is 1.3%, down from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Integration Mindshare Distribution
ProductMindshare (%)
Elastic Search1.7%
IBM Cloud Pak for Integration1.3%
Other97.0%
Cloud Data Integration
 

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.
Igor Khalitov - PeerSpot reviewer
Owner/Full Stack Software Engineer at Maraphonic, Inc.
Manages APIs and integrates microservices with redirection feature
IBM Cloud Pak for Integration includes monitoring capabilities to track the performance and health of your integrations. You can quickly roll back to a previous version if an issue arises. Additionally, it supports incremental deployments, allowing you to shift traffic to a new version of an API gradually. For example, you can start by directing 10% of traffic to the new version while the rest continue using the legacy version. If everything works as expected, you can gradually increase the traffic to the new version over time. IBM Cloud Pak for Integration has a client base that includes numerous organizations using AI and machine learning technologies. We leverage an open-source machine learning framework and integrate it with Kafka to help create and manage various products and data retrieval processes. For companies with private data, the framework first retrieves relevant data from a GitHub database, which is then combined with the final request before being sent to a language model like GPT. This ensures that the language model uses your specific data to generate responses. Kafka plays a key role by streaming real-time data from file systems and databases like Oracle and Microsoft SQL. This data is published to Kafka topics, then vectorized and used with artificial intelligence to enhance the overall process. It's like an old-fashioned approach. The best way is to redesign it with products such as Kafka. Overall, I rate the solution an eight out of ten.

Quotes from Members

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

Pros

"Elasticsearch helps us to store the data in key value pairs and, based on that, we can produce visualisations in Kibana."
"The most valuable features are the detection and correlation features."
"You have dashboards, it is visual, there are maps, you can create canvases. It's more visual than anything that I've ever used."
"The solution has great scalability."
"Aggregation is faster than querying directly from a database, like Postgres or Vertica, and it's much faster if I want to do aggregation, which allows me to store logs and find anomalies effectively."
"Elastic Search has excellent features, particularly its scalability and speed."
"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 analytics with Elastic benefits us due to the huge traffic volume in our organization, which reaches up to 60,000 requests per second. With logs of approximately 25 GB per day, manually analyzing traffic behavior, payloads, headers, user agents, and other details is impractical."
"I appreciate that Elastic Enterprise Search is easy to use and that we have people on our team who are able to manage it effectively."
"The most valuable aspect of the Cloud Pak, in general, is the flexibility that you have to use the product."
"It is a stable solution."
"Redirection is a key feature. It helps in managing multiple microservices by centralizing control and access."
"The most preferable aspect would be the elimination of the command, which was a significant improvement. In the past, it was a challenge, but now we can proceed smoothly with the implementation of our policies and everything is managed through JCP. It's still among the positive aspects, and it's a valuable feature."
"Cloud Pak for Integration is definitely scalable; that is the most important criteria."
"Cloud Pak for Integration is definitely scalable. That is the most important criteria."
"In general, the solution works very, very well."
 

Cons

"Technical support should be faster."
"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."
"We'd like more user-friendly integrations."
"I found an issue with Elasticsearch in terms of aggregation. There is a maximum of 10,000 entries, so the limitation means that if I wanted to analyze certain IP addresses more than 10,000 times, I wouldn't be able to dump or print that information."
"There are challenges with performance management and scalability."
"I would rate technical support from Elastic Search as three out of ten. The main issue is a general sum of all factors."
"I would like to see more integration for the solution with different platforms."
"I want the solution to improve the graph feature because it is a little bit poor."
"Setting up Cloud Pak for Integration is relatively complex. It's not as easy because it has not yet been fully integrated. You still have some products that are still not containerized, so you still have to run them on a dedicated VM."
"Enterprise bots are needed to balance products like Kafka and Confluent."
"Setting up Cloud Pak for Integration is relatively complex. It's not as easy because it has not yet been fully integrated."
"The pricing can be improved."
"What needs to be improved is the restriction that they have on the product."
"Its queuing and messaging features need improvement."
"The initial setup is not easy."
 

Pricing and Cost Advice

"we are using a licensed version of the product."
"The price could be better."
"We are using the free open-sourced version of this solution."
"The price of Elasticsearch is fair. It is a more expensive solution, like QRadar. The price for Elasticsearch is not much more than other solutions we have."
"The tool is not expensive. Its licensing costs are yearly."
"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."
"We are paying $1,500 a month to use the solution. If you want to have endpoint protection you need to pay more."
"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."
"It is an expensive solution."
"The solution's pricing model is very flexible."
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
9%
Manufacturing Company
9%
Retailer
6%
Financial Services Firm
15%
Government
11%
Manufacturing Company
8%
Insurance Company
8%
 

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...
What needs improvement with IBM Cloud Pak for Integration?
Enterprise bots are needed to balance products like Kafka and Confluent.
What is your primary use case for IBM Cloud Pak for Integration?
It manages APIs and integrates microservices at the enterprise level. It offers a range of capabilities for handling APIs, microservices, and various integration needs. The platform supports thousa...
What advice do you have for others considering IBM Cloud Pak for Integration?
IBM Cloud Pak for Integration includes monitoring capabilities to track the performance and health of your integrations. You can quickly roll back to a previous version if an issue arises. Addition...
 

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.
CVS Health Corporation
Find out what your peers are saying about Elastic Search vs. IBM Cloud Pak for Integration and other solutions. Updated: April 2026.
894,738 professionals have used our research since 2012.