Elastic Search vs IBM Cloud Pak for Integration comparison

Elastic Search vs. IBM Cloud Pak for Integration

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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

Ranking in other categories

Indexing and Search (1st), Search as a Service (1st), Vector Databases (6th)

IBM Cloud Pak for Integration

Ranking in Cloud Data Integration

20th

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

API Management (28th)

Mindshare comparison

As of July 2026, in the Cloud Data Integration category, the mindshare of Elastic Search is 1.7%, down from 1.9% compared to the previous year. The mindshare of IBM Cloud Pak for Integration is 1.2%, down from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Integration Mindshare Distribution
Product	Mindshare (%)
Elastic Search	1.7%
IBM Cloud Pak for Integration	1.2%
Other	97.1%

Cloud Data Integration

Featured Reviews

reviewer2817942

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.

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Igor Khalitov

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.

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Quotes from Members

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

Pros

"The flexibility and the support for diverse languages that it provides for searching the database are most valuable, and we can use different languages to query the database."

"Using real-time search functionality to support operational decisions has been helpful."

"The most valuable feature is the out of the box Kibana."

"The most valuable feature of Elastic Enterprise Search is user behavior analysis."

"The products comes with REST APIs."

"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."

"Elastic Enterprise Search is a nonstructured database that can manage large amounts of nonstructured data."

"The search speed is most valuable and important."

More Elastic Search pros

"The most valuable aspect of the Cloud Pak, in general, is the flexibility that you have to use the product."

"In general, the solution works very, very well."

"It is a stable solution."

"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."

"Redirection is a key feature. It helps in managing multiple microservices by centralizing control and access."

"Cloud Pak for Integration is definitely scalable. That is the most important criteria."

Cons

"There is another solution I'm testing which has a 500 record limit when you do a search on Elastic Enterprise Search. That's the only area in which I'm not sure whether it's a limitation on our end in terms of knowledge or a technical limitation from Elastic Enterprise Search. There is another solution we are looking at that rides on Elastic Enterprise Search. And the limit is for any sort of records that you're doing or data analysis you're trying to do, you can only extract 500 records at a time. I know the open-source nature has a lot of limitations, Otherwise, Elastic Enterprise Search is a fantastic solution and I'd recommend it to anyone."

"According to me, as far as I have seen, people will start moving from Elastic Search sooner or later. Why? Because it is expensive."

"This product could be improved with additional security, and the addition of support for machine learning devices."

"The real-time search functionality is not operational due to its impact on system resources."

"There should be more stability."

"Elastic needs to work on their Machine Learning offering because currently they have been trying to make it a black box which doesn't work for a serious user (a Data Scientist) as it doesn't give any control over the underlying algorithm."

"I see that there are areas in Elastic Search that have room for improvement, such as user documentation and onboarding processes."

"I think the first area for improvement is pricing, as the cluster cost for Elastic Search is too high for me."

More Elastic Search cons

"The pricing can be improved."

"The initial setup is not easy."

"Enterprise bots are needed to balance products like Kafka and Confluent."

"What needs to be improved is the restriction that they have on the product."

"Its queuing and messaging features need improvement."

"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."

Pricing and Cost Advice

"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."

"The tool is an open-source product."

"Elastic Search is open-source, but you need to pay for support, which is expensive."

"We are using the free open-sourced version of this solution."

"ELK has been considered as an alternative to Splunk to reduce licensing costs."

"Although the ELK Elasticsearch software is open-source, we buy the hardware."

"It can be expensive."

"The basic license is free, but it comes with a lot of features that aren't free. With a gold license, we get active directory integration. With a platinum license, we get alerting."

More Elastic Search pricing and cost advice

"The solution's pricing model is very flexible."

"It is an expensive solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

Computer Software Company

Retailer

Financial Services Firm

13%

Government

11%

Manufacturing Company

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	40
Midsize Enterprise	12
Large Enterprise	49

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.

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?

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Compared 11% of the time

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Compared 10% of the time

IBM App Connect vs IBM Cloud Pak for Integration

Compared 9% of the time

IBM DataPower Gateway vs IBM Cloud Pak for Integration

Compared 8% of the time

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Product Reports

Elastic Search

Download Elastic Search product report

API Management

Download IBM Cloud Pak for Integration product report

Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud

No data available

Overview

Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.

Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.

Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.

At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.

Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.

In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.

Elastic

IBM Cloud Pak for Integration offers multi-cloud management and automation, focusing on connecting core systems for seamless integration using advanced technologies and efficient API management.

IBM Cloud Pak for Integration provides flexibility, automation, and accounting tools on the OpenShift Container Platform, easing license and management. Among its notable features are effective streaming capabilities, simplified policy implementation via JCP, and enhanced control with redirection, particularly beneficial for microservices management. Its stability and practical container management are major advantages. However, limitations include exclusive IBM product deployment over OpenShift and complex initial setup. Users seek better pricing, enhanced streaming features, improved queuing and messaging, and integration with monitoring tools. Enhanced enterprise bots are needed for balance with Kafka and Confluent.

What are IBM Cloud Pak for Integration's key features?

Flexibility: Supports multi-cloud management and automation for seamless integration.
Streaming: Effective streaming feature for data processing and policy implementation.
Redirection: Enhances control and access for microservices management.
OpenShift Platform: Simplifies licensing and management with container technology.

What benefits should users look for in reviews?

Efficiency: Simplified policy implementation and automation reduce operational challenges.
Control: Enhanced management of microservices and APIs for streamlined operations.
Stability: Robust container management for consistent performance.
Integration: Supports integration with multiple technologies like Kafka and DataPower.

Implemented across industries like travel and banking, IBM Cloud Pak for Integration aids in connecting core systems and enables effective automation and API management. It supports data conversion, format alterations, and comprehensive management of numerous APIs, providing deployment options based on specific needs.

IBM

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

Elastic Search vs. IBM Cloud Pak for Integration