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Karini.AI vs Qlik Talend Cloud comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Feb 22, 2026

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

Karini.AI
Ranking in Data Quality
12th
Average Rating
10.0
Reviews Sentiment
2.5
Number of Reviews
2
Ranking in other categories
AI Customer Support (11th), AI Procurement & Supply Chain (6th)
Qlik Talend Cloud
Ranking in Data Quality
2nd
Average Rating
8.0
Reviews Sentiment
6.5
Number of Reviews
56
Ranking in other categories
Data Integration (8th), Data Scrubbing Software (1st), Master Data Management (MDM) Software (2nd), Cloud Data Integration (7th), Data Governance (9th), Cloud Master Data Management (MDM) (3rd), Streaming Analytics (6th), Integration Platform as a Service (iPaaS) (7th)
 

Mindshare comparison

As of October 2026, in the Data Quality category, the mindshare of Karini.AI is 1.7%, up from 0.1% compared to the previous year. The mindshare of Qlik Talend Cloud is 6.8%, down from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Quality Mindshare Distribution
ProductMindshare (%)
Qlik Talend Cloud6.8%
Karini.AI1.7%
Other91.5%
Data Quality
 

Featured Reviews

reviewer2759967 - PeerSpot reviewer
Co-CEO at a tech services company with 51-200 employees
Has accelerated AI experimentation and simplified transition from prototype to production at scale
The Karini team is responsive and continuously innovating. Scaling this responsiveness is critical to meet the rapid development of generative AI technologies. Karini’s Forward-Deployed Engineers provide instant feedback to Karini’s engineers, and the deployment of enhancements or novel developments continues to keep pace with the overall acceptance of our customers. I expect that demand will intensify quickly, and Karini’s capability to provide near-real-time enhancements is critical to our ability to meet that demand.
Henrique Fsantos - PeerSpot reviewer
Data & Analytics Engineer at PicPay
Automated data pipelines have improved analytics workflows and provide trusted reporting data
The best features of Qlik Talend Cloud are its connectors to many systems, databases, APIs, and files, including both SQL and NoSQL databases. The scheduling feature allows me to run jobs automatically in the cloud, and the built-in data quality tools help clean and check data. The reusable components enable me to reuse jobs and parts, which saves time, making it easy to build good data pipelines. The feature that has made the biggest difference for me in Qlik Talend Cloud is the scheduling and automation, which helps me run ETL jobs automatically without manual work. This saves time, reduces errors, and makes the pipelines more stable. In my daily work, I can trust that the data is updated on time and ready for reports and analytics. Qlik Talend Cloud has had a positive impact on my organization by automating data integration processes, reducing manual errors, and ensuring reliable and up-to-date data for reports and business decisions. This has improved operational stability, productivity, and trust in the data. Since using Qlik Talend Cloud, we have reduced manual work, resulting in approximately 20% to 30% time savings in data load processes. We have also seen a reduction in operational errors, mainly due to automation and the data quality checks, and a clear improvement in data reliability since the pipelines became more standardized and monitored.

Quotes from Members

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

Pros

"Karini GenAI allowed us to achieve our goals to solve a customer problem, deliver value, and provide a successful entry point into our GenAI journey."
"The Karini team understands how to operationalize sophisticated GenAI business solutions at enterprise scale, allowing for rapid experimentation that does not require staffing up with data scientists, machine learning specialists, or AI practitioners."
"I would definitely recommend others to implement this product as it is really helpful, easy to learn, user friendly, provides lot of enhanced features, etc."
"I really like the fact that there are no out-of-the-box solutions regarding the development of jobs. Other vendors may have modules which cleanse your addresses. In Talend, you have the freedom to completely develop the process yourself. This can be tricky, but it also makes it fun."
"The product's integration with PostgreSQL and Jira has been helpful for us. Its performance is good. However, we do not use it for large data sets."
"We are able to get emails from URLs very easily using this function when others fail."
"The solution enables robust data matching, merging, survivorship, and Data Stewardship that can be a part of data quality workflows or true master data management."
"I like idea of storing the results of Data Quality jobs in a DB and having the ability to run reports in the DB to show a dashboard of quality metrics."
"The solution is connected to various phones. It retrieves the configuration from the system."
"Talend has allowed us to systematically organise/structure and deploy our staged ETL transformation processes from Development into production, we have tracked our data quality efforts during our runs and supplied comprehensive feedback during our development."
 

Cons

"Karini is still expanding its list of features. As we add new features, additional connections and technologies around AI must be incorporated to ensure we stay current and continue to improve our platform."
"Scaling this responsiveness is critical to meet the rapid development of generative AI technologies."
"Due to using the open-source version of Talend Data Integration, which lacks a scheduler, our current approach involves developing jobs in Talend, exporting them as Java packages, and utilizing an external scheduler, such as Windows Scheduler, to manage the scheduling process."
"Account for Java developers/custom development efforts apart from DQ functional/technical expertise, to use Talend DQ product to the fullest."
"Not enough material is available for beginners."
"The sales and market department could improve the Talend Data Management Platform."
"We'd like to see more connectors it the future."
"Finding assistance with issues can be spotty. With Python, there are literally millions of open source answers which are recent and apply to the version that we are using."
"The product's setup process could be simpler."
"NullPointerExceptions are going to be the death of me and are a big reason for our transition away from Talend. One day, it is fine with a 1000 blank rows, then the next day, it will find one blank cell and it breaks down."
 

Pricing and Cost Advice

Information not available
"The pricing is a little higher than what I had expected, but it's comparable with I-PASS competitors."
"The product pricing is considered very good, especially compared to other data integration tools in the market."
"The price of the Talend Data Management Platform is reasonable. The other competing solutions are priced high. Gartner Magic Quadrant identified other solutions, such as Informatica, that are far more expensive."
"The tool is cheap."
"I have been using the open-source version."
"It's a subscription-based platform, we renew it every year."
"The licensing cost is about 40,000 Euros a year."
"License renewal is on a yearly basis."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
14%
Outsourcing Company
12%
Comms Service Provider
11%
Construction Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business21
Midsize Enterprise12
Large Enterprise20
 

Questions from the Community

What is your experience regarding pricing and costs for Karini.AI?
Karini’s pricing was attractive, with an all-in model that allowed us to deploy three environments aligned with our development instances. We subscribed to Karini’s Forward-Deployed Engineer progra...
What needs improvement with Karini.AI?
The Karini team is responsive and continuously innovating. Scaling this responsiveness is critical to meet the rapid development of generative AI technologies. Karini’s Forward-Deployed Engineers p...
What is your primary use case for Karini.AI?
We created a talent intelligence platform called MAIA. MAIA fuses four advanced AI technologies: Reactive AI, Generative AI, Reasoning AI, and Agentic AI to transform how organizations discover, as...
What needs improvement with Talend Data Quality?
I don't use the automated rule management feature in Talend Data Quality that much, so I cannot provide much feedback. I may not know what Talend Data Quality can improve for data quality. I'm not ...
What is your primary use case for Talend Data Quality?
It is for consistency, mainly; data consistency and data quality are our main use cases for the product. Data consistency is the primary purpose we use it for, as we have written rules in Talend Da...
What advice do you have for others considering Talend Data Quality?
Currently, I'm working with batch jobs and don't perform real-time data quality monitoring because of the large data volume. For real-time, we use a different product. I cannot provide details abou...
 

Also Known As

No data available
Talend Data Quality, Talend Data Management Platform, Talend MDM Platform, Talend Data Streams, Talend Data Integration, Talend Data Integrity and Data Governance
 

Overview

 

Sample Customers

Information Not Available
Aliaxis, Electrocomponents, M¾NCHENER VEREIN, The Sunset Group
Find out what your peers are saying about Karini.AI vs. Qlik Talend Cloud and other solutions. Updated: September 2026.
917,433 professionals have used our research since 2012.