IBM Cloud Pak for Data vs Teradata comparison

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IBM Cloud Pak for Data

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Executive SummaryUpdated on Sep 30, 2025

Teradata and IBM Cloud Pak for Data are key players in the enterprise data solutions space. While Teradata stands out for its data processing capabilities, IBM Cloud Pak for Data seems superior in offering integration and governance solutions.

Features: Teradata's parallel architecture efficiently handles large data volumes, provides scalability, security, and advanced analytics for structured data, along with respected optimization and workload management. IBM Cloud Pak for Data excels in containerization with seamless integration options, including Watson Studio and Machine Learning, supporting diverse data source governance requirements.

Room for Improvement: Teradata's high costs and complex licensing present challenges; enhancements in cloud capabilities, unstructured data support, SQL functionalities, and UI are needed. IBM Cloud Pak for Data requires improved integration for specific connectors, smoother on-premise to cloud migrations, and enhanced installation simplicity and performance consistency.

Ease of Deployment and Customer Service: Teradata offers on-premises and hybrid cloud deployment options, backed by generally positive but occasionally inconsistent technical support. IBM Cloud Pak for Data supports flexible public and hybrid cloud deployments but faces initial challenges with infrastructure requirements. Customer service is generally good, with room for improvement in response times and expertise.

Pricing and ROI: Both solutions face criticism for high costs, justified by advanced features and performance. Teradata's inflexible pricing model targets large enterprises, promising significant returns on investment in analytics and data processing. IBM Cloud Pak for Data, with complex licensing and setup costs, offers strong ROI for large organizations but may exclude smaller enterprises.

To learn more, read our detailed IBM Cloud Pak for Data vs. Teradata Report (Updated: September 2025).

IBM Cloud Pak for Data vs. Teradata

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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

IBM Cloud Pak for Data

Ranking in Data Integration

26th

Average Rating

7.8

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Virtualization (3rd)

Teradata

Ranking in Data Integration

17th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Customer Experience Management (6th), Backup and Recovery (21st), Relational Databases Tools (8th), Data Warehouse (3rd), BI (Business Intelligence) Tools (13th), Marketing Management (5th), Cloud Data Warehouse (4th)

Mindshare comparison

As of October 2025, in the Data Integration category, the mindshare of IBM Cloud Pak for Data is 1.8%, up from 1.7% compared to the previous year. The mindshare of Teradata is 0.8%, up from 0.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
Teradata	0.8%
IBM Cloud Pak for Data	1.8%
Other	97.4%

Data Integration

Featured Reviews

Michelle Leslie

Data asset management engineer at a tech services company with 1-10 employees

Starts strong with data management capabilities but needs a demo database

What I would love to see is an end-to-end, almost a training demo database of some sort, where one of the biggest problems with data management is demonstrated. There are so many components to data management, and more often than not, people understand one thing really well. They may understand DataStage and how to move data around, but they do not see the impact of moving data incorrectly. They also do not see the impact of everyone understanding a piece of data in the same way. I would love Cloud Pak to come with a demo database that illustrates the different components of data management in a logical way, so I can see the whole picture instead of just the area I'm specializing in. It would be great if Cloud Pak, from a data modeling point of view, allowed us to import our PDMs, for example. It would be ideal to import and create business terms in Cloud Pak. The PEA would be great to create the technical data. The association between the business and the technical metadata could then be automated by pulling it through from your ACE models. The data modeling component is available in Cloud Pak. Additionally, when it comes to Cloud Pak, even though it has the NextGen DataStage built into it, there is Cloud Pak for data integration as well. Currently, I do not think we have a full enough understanding of how CP4D and CP4I can enhance each other.

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Kirk Robinson

Interim and Fractional CIO and CTO at a consultancy with 11-50 employees

Has improved data quality, optimized reporting processes, and enabled predictive insights across business units

The limitation we encountered was related to speed, prompting us to increase our AWS cloud thresholds and benchmarks on the servers, adding more throughput. We have been testing that, but speed remains one of our key issues. The integration has been awesome because of the platforms and their usage, and usability is great. The most challenging aspect is finding Teradata resources, so we are focusing on internal training and looking for more Teradata experts. If Teradata could provide a list of certified experts, that would be fantastic. I rated Teradata a nine because the main area for improvement is the bandwidth of resources that could assist us in applying to our projects, specifically more certified and expert resources. The market share of Teradata professionals is limited, creating an issue for us.

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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

"Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF."

"The most valuable features of IBM Cloud Pak for Data are the Watson Studio, where we can initiate more groups and write code. Additionally, Watson Machine Learning is available with many other services, such as APIs which you can plug the machine learning models."

"What I found most helpful in IBM Cloud Pak for Data is containerization, which means it's easy to shift and leave in terms of moving to other clouds. That's an advantage of IBM Cloud Pak for Data."

"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."

"I love the way that I can start at a very basic level with my data management journey by capturing my policies, justifying my data, and putting them into different categories to say this is data relating to individuals, for example, or data relating to geography."

"Its data preparation capabilities are highly valuable."

"It is a scalable solution, and we have had no issues with its scalability in our company. I rate the solution's scalability a nine out of ten."

"Scalability-wise, I rate the solution a nine or ten out of ten."

More IBM Cloud Pak for Data pros

"Teradata's pretty fast."

"Teradata's best feature is its speed with historical data."

"Teradata features high productivity and reliability because it has several redundancy options, so the system is always up and running."

"The most valuable feature is the ease of uploading data from multiple sources."

"The most valuable feature of Teradata is the quick processing of large data."

"A conventional and easily defined way to build a data warehouse or a layer of data marts."

"It is very stable. It's 100% uptime. Speed and resilience are one of the greatest features of this product. In almost twenty years we've never had downtime, except for outages for patches and upgrades. We've never had a system failure in twenty years."

"It has a solid set of tools and consulting services."

More Teradata pros

Cons

"One challenge I'm facing with IBM Cloud Pak for Data is native features have been decommissioned, such as XML input and output. Too many changes have been made, and my company has around one hundred thousand mappings, so my team has been putting more effort into alternative ways to do things. Another area for improvement in IBM Cloud Pak for Data is that it's more complicated to shift from on-premise to the cloud. Other vendors provide secure agents that easily connect with your existing setup. Still, with IBM Cloud Pak for Data, you have to perform connection migration steps, upgrade to the latest version, etc., which makes it more complicated, especially as my company has XML-based mappings. Still, the XML input and output capabilities of IBM Cloud Pak for Data have been discontinued, so I'd like IBM to bring that back."

"The setup cost is very expensive. The cost depends on the pieces of the solution I'm using, how much data I have, and whether it's on the cloud or on-prem."

"One thing that bugs me is how much infrastructure Cloud Pak requires for the initial deployment. It doesn't allow you to start small. The smallest permitted deployment is too big. It's a huge problem that prevents us from implementing the solution in many scenarios."

"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve."

"The product must improve its performance."

"The tool depends on the control plane, an OpenShift container platform utilized as an orchestration layer...So, we have communicated this issue to IBM and asked if it is feasible to adapt the solution to work on a Kubernetes platform that we support."

"There is a solution that is part of IBM Cloud Pak for Data called Watson OpenScale. It is used to monitor the deployed models for the quality and fairness of the results. This is one area that needs a lot of improvement."

"The solution's user experience is an area that has room for improvement."

More IBM Cloud Pak for Data cons

"An additional feature I would you like to see included in the next release, is that it needs to be more cloud-friendly."

"Their level of technical support is adequate. It could be better."

"Data synchronization to the DR site."

"The following could be better: licensing, architecture openness, integration with other tools."

"The scalability could be better. The on-premises solution is always more complicated to scale."

"Teradata is an expensive tool. Like, if you're already using Microsoft products like Windows, they'll market all their products together. And with the rise of cloud technologies, companies will adopt solutions that offer them some privileges or facilities. Similar to how SAP does it in the market, so do Microsoft and other companies. Even Oracle and other such tools are quite commonly seen compared to Teradata's competitors in everyday solutions."

"Teradata's UI could be improved."

"Teradata needs to pay attention to the cloud-based solution to make sure it runs smoothly."

More Teradata cons

Pricing and Cost Advice

"Cloud Pak's cost is a little high."

"IBM Cloud Pak for Data is expensive. If we include the training time and the machine learning, it's expensive. The cost of the execution is more reasonable."

"It's quite expensive."

"I don't have the exact licensing cost for IBM Cloud Pak for Data, as my company is still finalizing requirements, including monthly, yearly, and three-year licensing fees. Still, on a scale of one to five, I'd rate it a three because, compared to other vendors, it's more complicated."

"I think that this product is too expensive for smaller companies."

"For the licensing of the solution, there is a yearly payment that needs to be made. Also, since it is expensive, cost-wise, I rate the solution an eight or nine out of ten."

"The solution is expensive."

"The solution's pricing is competitive with that of other vendors."

"The initial cost may seem high, but the TCO is low."

"The cost is significantly high."

"In the past, it turned out that other solutions, in order to provide the full range of abilities that the Teradata platform provides plus the migration costs, would end up costing more than Teradata does."

"I am using the free version of Teradata."

"Price is quite high, so if it is really possible to use other solutions (e.g. you do not have strict requirements for performance and huge data volumes), it might be better to look at alternatives from the RDBMS world."

"The price of Teradata is on the higher side, and I think that it where they lose out on some of their business."

"Teradata is currently making improvements in this area."

"The cost is substantial, totaling around $1.2 million, solely dedicated to upgrading the hardware."

More Teradata pricing and cost advice

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Comparison Review

it_user232068

Senior Data Architect at a pharma/biotech company with 1,001-5,000 employees

Aug 5, 2015

Netezza vs. Teradata

Original published at https://www.linkedin.com/pulse/should-i-choose-net Two leading Massively Parallel Processing (MPP) architectures for Data Warehousing (DW) are IBM PureData System for Analytics (formerly Netezza) and Teradata. I thought talking about the similarities and differences…

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Top Industries

By visitors reading reviews

Financial Services Firm

29%

Manufacturing Company

10%

Computer Software Company

Government

Financial Services Firm

25%

Computer Software Company

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	7
Large Enterprise	8

By reviewers
Company Size	Count
Small Business	26
Midsize Enterprise	12
Large Enterprise	49

Questions from the Community

What is your experience regarding pricing and costs for IBM Cloud Pak for Data?

The setup cost is very expensive. The cost depends on the pieces of the solution I'm using, how much data I have, and whether it's on the cloud or on-prem.

What needs improvement with IBM Cloud Pak for Data?

What is your primary use case for IBM Cloud Pak for Data?

My primary use case for Cloud Pak is that I am the reference Data steward for the Africa regions in the banks where I work. My main objective is to capture the reference data in Caltech or Data and...

Comparing Teradata and Oracle Database, which product do you think is better and why?

I have spoken to my colleagues about this comparison and in our collective opinion, the reason why some people may declare Teradata better than Oracle is the pricing. Both solutions are quite simi...

Which companies use Teradata and who is it most suitable for?

Before my organization implemented this solution, we researched which big brands were using Teradata, so we knew if it would be compatible with our field. According to the product's site, the comp...

Is Teradata a difficult solution to work with?

Teradata is not a difficult product to work with, especially since they offer you technical support at all levels if you just ask. There are some features that may cause difficulties - for example,...

IBM InfoSphere DataStage vs IBM Cloud Pak for Data

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Compared 11% of the time

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Compared 8% of the time

Snowflake vs Teradata

Compared 7% of the time

SQL Server vs Teradata

Compared 7% of the time

OpenText Analytics Database (Vertica) vs Teradata

Compared 6% of the time

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Product Reports

IBM Cloud Pak for Data

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Also Known As

Cloud Pak for Data

IntelliFlex, Aster Data Map Reduce, , QueryGrid, Customer Interaction Manager, Digital Marketing Center, Data Mover, Data Stream Architecture, Teradata Vantage Enterprise (DIY)

Overview

IBM Cloud Pak® for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud-native by design, the platform unifies market-leading services spanning the entire analytics lifecycle. From data management, DataOps, governance, business analytics and automated AI, IBM Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions while providing the information architecture you need to implement AI successfully.

Building on the streamlined hybrid-cloud foundation of Red Hat® OpenShift®, IBM Cloud Pak for Data takes advantage of the underlying resource and infrastructure optimization and management. The solution fully supports multicloud environments such as Amazon Web Services (AWS), Azure, Google Cloud, IBM Cloud™ and private cloud deployments. Find out how IBM Cloud Pak for Data can lower your total cost of ownership and accelerate innovation.

IBM

Teradata is a powerful tool for handling substantial data volumes with its parallel processing architecture, supporting both cloud and on-premise environments efficiently. It offers impressive capabilities for fast query processing, data integration, and real-time reporting, making it suitable for diverse industrial applications.

Known for its robust parallel processing capabilities, Teradata effectively manages large datasets and provides adaptable deployment across cloud and on-premise setups. It enhances performance and scalability with features like advanced query tuning, workload management, and strong security. Users appreciate its ease of use and automation features which support real-time data reporting. The optimizer and intelligent partitioning help improve query speed and efficiency, while multi-temperature data management optimizes data handling.

What are the key features of Teradata?

Parallel Processing Architecture: Enables efficient handling of substantial data volumes.
Cloud and On-Premise Deployment: Offers seamless adaptability across environments.
Query Tuning: Improves speed and performance of data retrieval processes.
Workload Management: Ensures optimal resource allocation for enhanced performance.
Robust Security: Provides high levels of data protection and system integrity.
Data Integration: Facilitates cohesive data management from multiple sources.
Automation for Real-time Reporting: Enhances timely data insights and decision-making.
Optimizer and Intelligent Partitioning: Further increase query speed and system efficiency.
Multi-Temperature Data Management: Optimizes the handling and retrieval of data.

What benefits and ROI do users look for?

Efficient Data Management: Facilitates smooth integration and processing of diverse data sets.
Scalability: Easy adjustment to increasing or decreasing data requirements.
Improved Performance: Fast query speeds and optimized resource use.
Comprehensive Security: Safeguards data integrity and accessibility.
Ease of Use: Simplifies data handling and operational processes.

In the finance, retail, and government sectors, Teradata is employed for data warehousing, business intelligence, and analytical processing. It handles vast datasets for activities like customer behavior modeling and enterprise data integration. Supporting efficient reporting and analytics, Teradata enhances data storage and processing, whether deployed on-premise or on cloud platforms.

Sample Customers

Qatar Development Bank, GuideWell, Skanderborg Music Festival

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IBM Cloud Pak for Data vs. Teradata