Sold by: IBM Software
Deployed on AWS
IBM Cloud Pak® for Data is a unified data and AI platform that runs on any cloud. Utilize a data fabric to automatically break down data siloes, improve data quality and enhance data privacy and security. Build and infuse trustworthy AI across your business to drive digital transformation.
4.2
Overview
For more information or customized pricing, please email us: cpd_on_aws@wwpdl.vnet.ibm.com
IBM Cloud Pak for Data is a unified data and AI platform that connects the right data, at the right time, to the right people anywhere. Available on AWS and running on Red Hat OpenShift, the platform simplifies data access, automates data discovery and curation, and safeguards sensitive information by automating policy enforcement for all users in your organization. Make better data driven decisions and lay the foundation for AI with a data fabric that connects siloed data on premises or across multiple clouds without data movement. Discover actionable insights and apply trusted data to build, run, automate and manage AI models.
Outcomes:
- Data access and availability: Eliminate data silos and simplify your data landscape to enable faster, cost-effective extraction of value from your data.
- Data quality and governance: Apply governance solutions and methodologies to deliver trusted, business data.
- Data privacy and security: Fully understand and manage sensitive data with a pervasive privacy framework.
- Batch data integration: Design, develop and run jobs that move and transform data with powerful automated integration capabilities.
- 360 entity data: Enable agility and accelerated ROI for consolidated and governed views of critical enterprise data.
Product Version 4.7.x
Standard Min: 48 VPCs Enterprise Min: 72 VPCs
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Highlights
- Deliver data responsibly with a data fabric. Unify and access disparate data with AutoSQL, a universal query engine. Discover and classify data in real time with Watson Knowledge Catalog. Protect sensitive data with automated policy enforcement.
- Scale trustworthy AI: Synchronize application and model pipelines while reducing drift, bias, and risk with ModelOps on Watson Studio. Monitor and govern AI models to meet regulations, manage risk and enhance transparency.
- Recognized by analysts as a Leader in core data and AI segments: The Forrester Wave™: Machine Learning Data Catalogs, Q4 2020; 2021 Gartner Magic Quadrant for Data Science and Machine Learning; The Forrester Wave™: Multi modal Predictive Analytics and Machine Learning, Q3 2020.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
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Dimension | Description | Cost/month |
|---|---|---|
Standard Option | Cloud Pak for Data Standard Option: 48 VPCs | $19,824.00 |
Enterprise Option | Cloud Pak for Data Enterprise Option: 72 VPCs | $59,400.00 |
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Updated weekly
By IBM Software
By Alation
By Collibra
Top
50
In Data Preparation
Top
10
In Data Catalogs, Data Governance, Master Data Management
Top
10
In Data Catalogs, Data Governance
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Universal Query Engine
AutoSQL provides a universal query engine for unified data access across disparate data sources.
Data Discovery and Classification
Watson Knowledge Catalog enables real-time discovery and classification of data with automated cataloging capabilities.
Automated Policy Enforcement
Pervasive privacy framework with automated policy enforcement for sensitive data protection across all users in the organization.
Model Operations and Governance
ModelOps on Watson Studio synchronizes application and model pipelines while monitoring and governing AI models to manage risk, reduce drift and bias, and enhance transparency.
Data Fabric Architecture
Data fabric technology connects siloed data on premises or across multiple clouds without requiring data movement, enabling consolidated and governed views of enterprise data.
Metadata Centralization
Centralizes metadata from disparate sources into a unified platform for discovering, describing, governing, and managing data assets including data, BI reports, and AI models.
Behavioral Analysis Engine
Incorporates a Behavioral Analysis Engine to provide advanced analytics and insights across data assets.
Data Lineage and Tracking
Enables documentation of insights and tracking of data lineage across teams for transparency and compliance purposes.
Self-Service Analytics
Supports self-service analytics capabilities allowing users to independently discover and analyze data assets.
AI Governance Framework
Provides an AI governance framework that ensures data quality, transparency, and compliance for AI initiatives.
AI Governance Framework
Active metadata-based governance with rules, processes and responsibilities to ensure ethical AI practices, mitigate risk, adhere to legal requirements, and protect privacy
Automated Data Lineage
End-to-end lineage tracking providing transparency into data transformation and flow across systems, including both summary-level business lineage and detailed technical lineage
Unified Data Catalog
Multi-cloud and hybrid environment data discovery with business context including data origin, ownership, usage patterns, and access to reports, AI models and data products
Data Quality Automation
Automated monitoring and rule management system for enterprise-wide data quality management replacing manual processes
Privacy and Compliance Workflow
Centralized automation of privacy workflows to operationalize privacy requirements and address global regulatory compliance
Standard contract
No
No
No
Customer reviews
Aleksander M.
Unified Data Integration and Governance for Analytics and AI on OpenShift
Reviewed on Jul 10, 2026
Review provided by G2
What do you like best about the product?
The most helpful aspect is the ability to connect different data sources, prepare and govern data, and then use it for analytics or AI workloads within a consistent environment. Its integration with Red Hat OpenShift is also valuable because it provides flexibility for hybrid cloud deployments and allows organizations to run the platform in their own infrastructure.
What do you dislike about the product?
The main drawback is the complexity of deploying and maintaining the platform, particularly in self-managed environments. Installation, upgrades, capacity planning, and troubleshooting can require significant OpenShift and infrastructure expertise, which may make adoption difficult for smaller teams.
What problems is the product solving and how is that benefiting you?
IBM Cloud Pak for Data helps solve the problem of fragmented data and AI environments by bringing data integration, governance, analytics, and AI development into a single platform. It allows teams to connect data from different sources, prepare and catalog it, apply access controls, and make it available for analytics and machine learning workloads.
Amr a.
Comprehensive solution for data-intensive workflows
Reviewed on Jun 17, 2026
Review provided by G2
What do you like best about the product?
IBM Cloud Pak for Data provides a comprehensive solution for data-intensive workflows. It allows my team to manage almost all aspects of the data workflow, including connecting data sources and tools, because it brings together access, governance, cataloging, analytics, and AI services in one environment.
The platform supports data virtualization, allowing teams to work across distributed sources without creating unnecessary copies, while providing greater control over ETL processes and seamless integration with Watson Knowledge Catalog. The Auto-Discovery feature and automated metadata tagging workflows are massive time-savers, especially when mapping data lineage and automatically applying governance policies.
The platform supports data virtualization, allowing teams to work across distributed sources without creating unnecessary copies, while providing greater control over ETL processes and seamless integration with Watson Knowledge Catalog. The Auto-Discovery feature and automated metadata tagging workflows are massive time-savers, especially when mapping data lineage and automatically applying governance policies.
What do you dislike about the product?
IBM Cloud Pak for Data can make service selection and implementation feel a bit challenging and more time-consuming than expected for smaller teams or companies without dedicated data platform expertise, especially when it comes to service deployment, management, and troubleshooting.
What problems is the product solving and how is that benefiting you?
IBM Cloud Pak for Data solves the problem of fragmented data across on-premises and Azure cloud environments, particularly when team members need trusted access but would otherwise have to wait for IT teams to properly manage privacy and access permissions. It helps analysts find certified data faster and gives data science teams a cleaner path from data preparation to model deployment and monitoring.
It enables faster access to distributed data across various environments and improves our ETL efficiency through data virtualization.
The access controls and dynamic data masking features help our data analytics team reduce data compliance risks to near zero, as sensitive information is automatically masked based on user roles and predefined criteria.
It enables faster access to distributed data across various environments and improves our ETL efficiency through data virtualization.
The access controls and dynamic data masking features help our data analytics team reduce data compliance risks to near zero, as sensitive information is automatically masked based on user roles and predefined criteria.
Christopher Ola
Data-driven decisions have become faster as I predict trends from unified structured and unstructured data
Reviewed on Jun 01, 2026
Review from a verified AWS customer
What is our primary use case?
It is easy to transform structured and unstructured data into analytics insights. This ensures I am able to make data-driven decisions.
I build and test models with best-in-class AI and analytics.
I also use it to store utility data to build a smart utility solution for the prediction of future trends.
How has it helped my organization?
We have significantly reduced data footprint and enhanced AI/ML analysis for predictive analytics. There is better inbuilt integration with many systems to store data.
What is most valuable?
The valuable features include cloud storage, AI/ML capabilities, data infestation, and a 360-degree understanding.
What needs improvement?
There is room for more out-of-box integration.
For how long have I used the solution?
I have used the solution for five months.
Which solution did I use previously and why did I switch?
We previously used SAP Data Intelligence. IBM Cloud Pak for Data had better inbuilt integration with many systems to store data.
What's my experience with pricing, setup cost, and licensing?
If I need to connect multiple data sources, ingest data, and run AI/ML algorithms, IBM Cloud Pak for Data is a very good solution.
Which other solutions did I evaluate?
I have considered Concur Travel and Expense, and Microsoft 365 as alternate solutions.
What other advice do I have?
It manages to store a high volume of both structured and unstructured data and churns out the desired result in optimal time.
HarryJude
Unified data workspace has enabled secure collaboration and improved automated AI lifecycle
Reviewed on May 13, 2026
Review from a verified AWS customer
What is our primary use case?
I use IBM Cloud Pak for Data to connect it with our product for integration with the cloud, and it helps to enable all of our data users to collaborate from a single, unified interface that supports many services that are designed to work together.
I do use the data virtualization features in IBM Cloud Pak for Data , and this has positively impacted my data analysis operations.
What is most valuable?
What I appreciate most about IBM Cloud Pak for Data is the all-in-one cloud-native data and AI platform in a single platform. It enables us to collect, organize, and analyze data and provides unprecedented simplicity and agility within a pre-configured and governed environment.
What needs improvement?
Areas within IBM Cloud Pak for Data that have room for improvement include user interface design and integration capabilities.
For how long have I used the solution?
I have been using IBM Cloud Pak for Data since 2020, so that is approximately five years.
What do I think about the scalability of the solution?
For scalability, I rate it a nine out of ten because it is a very scalable solution that has been able to handle my organization's growth efficiently.
How are customer service and support?
I rate the technical support from IBM a nine out of ten because the support has been very top-notch, unparalleled, and also very professional.
It is much more cost-effective compared to other solutions such as SAP, and the customer and technical support have always been very proactive and helpful.
Which other solutions did I evaluate?
I compare IBM Cloud Pak for Data with other solutions and find it stands out in several key aspects.
What other advice do I have?
My relationship with IBM Cloud Pak for Data is that I am a customer.
I use IBM Data Stage for ETL processes, and this has helped my data integration significantly.
The Watson Knowledge Catalog has helped improve my data governance by providing several benefits.
I assess the impact of the automated AI lifecycle management on my project development times as quite significant.
IBM Cloud Pak for Data is a great tool because if you need to connect to or get data from multiple data sources, it is the best solution, enabling data-driven decisions that are very accurate. I would rate this solution a nine out of ten overall.
Khaled AlKadi
Integrated data tools have unified governance and AI workflows for complex enterprise projects
Reviewed on Apr 08, 2026
Review provided by PeerSpot
What is our primary use case?
I believe IBM Cloud Pak for Data is suitable for mid-size to bigger companies. It is not tailored for smaller customers.
My customers use IBM DataStage for ETL processes.
One client has implemented automated AI lifecycle management, and their journey with IBM Cloud Pak for Data was very successful. They are one of the first banks in Jordan to implement AI in achievable and beneficial use cases that benefit their internal and external clients. The implementation was very successful.
What is most valuable?
The features I find most valuable in IBM Cloud Pak for Data are the data warehouse, data repository, and the data governance tools that exist there, including data masking, data quality, and ETL. The best thing about IBM Cloud Pak for Data is that you are getting all the products or all the needs in one license, and as you utilize it, you can add more licenses.
IBM DataStage has helped their data integration efforts by providing enhanced ETL tools. It is not just extracting the data and loading it, but it has some advanced tools to classify the data and transform the right format of the data, helping my customers have more cleansed data. It enables the customer to cleanse their data and have the right data to utilize. This was a value-add for the customers and it is very good. It connects quickly, so the experience with the implementation integration is stable. Since 2019, my customers have been working with no issues, and it is serving them well.
Watson Knowledge Catalog has helped improve data governance in my customers' organizations. I have some customers who needed data masking, and the WKC supported them in that need. I have a customer who only purchased IBM Cloud Pak for Data for this requirement of data masking, and it helped them a lot.
What needs improvement?
I see room for improvement in IBM Cloud Pak for Data, as it lacked the lake house. However, IBM issued the new product which is Watsonx.data. This is a new product for IBM and it provides all the missing capabilities that were lacking because this technology was released before the concept of lake house was established. The new product covers all those requirements.
I believe IBM Cloud Pak for Data could learn from its competitors in terms of data governance tools. Previously, Informatica had a more robust data governance offering, but with their sale to Salesforce , I believe they are not a threat to IBM. IBM has an even better story. Oracle has better go-to-market options, but it is not an issue with the product itself, rather the way they do their sales. I believe we have a strong product. Nothing is missing except the strategy from IBM on how to sell, not the product itself.
For how long have I used the solution?
Since its release, I have been dealing with IBM Cloud Pak for Data. It was previously a different product earlier, known as Netezza and different products. Since we named it IBM Cloud Pak for Data, we have sold it. I believe the first one was in 2019.
What do I think about the stability of the solution?
The overall performance of IBM Cloud Pak for Data, particularly with IBM DataStage for ETL processes, is very good. The customers I have are all very happy. They are looking for expansion and the experience is very good with it. It is providing them with the results needed and more. This was one of the successful products that I have worked on with IBM.
How are customer service and support?
I would rate the technical support by IBM as an eight to nine.
The response time for IBM's technical support is excellent. I gave them an eight to nine to cover the market here, but they really are excellent. I had a case a couple of days ago where the IBM team was with us 24/7 until the issue was closed. They need more enhancement in level one support, but overall it is good and effective.
How was the initial setup?
I believe the initial setup and configuration of IBM Cloud Pak for Data is straightforward. The implementation was needed first because it was from the first project I implemented with IBM. However, the support and implementation afterward, and all the enhancements, were done by the customer themselves. It was straightforward.
Which other solutions did I evaluate?
I think IBM Cloud Pak for Data is the best option on the market at the moment with the addition of Watsonx.data. For the current needs, it depends on what you need. It is not a magic ingredient. You need to understand your business requirements and accordingly select the right product for your need. It is a tool, but it has all the ingredients to succeed. You need to select the right thing in order to implement it well.
What other advice do I have?
I believe the licensing model and pricing of IBM Cloud Pak for Data is fair. If you get the right discount, it is fair and competitive.
Not all of my customers utilize data virtualization features. Mostly, my customers had their own data visualization tools earlier, so they kept using what they have in order not to buy more. They have seamlessly integrated the product they have with IBM Cloud Pak for Data.
I would not expect additional functionalities regarding AI from them in the future. It is already there, and the customers who purchased IBM Cloud Pak for Data do not need to change or replace the product. They can keep what they have and utilize Watsonx.data on top of it to enable them for the new features, so they do not lose anything.
My customers usually do not purchase IBM Cloud Pak for Data from the AWS Marketplace . They purchase it from partners and implement it on-premises, not on cloud.
I would rate this review a nine overall.