Sold by: IBM SoftwareÂ
Deployed on AWS
Built on a lakehouse architecture, IBM watsonx.data is an open, hybrid, and governed data store optimized for all data, analytics, and AI workloads.
Overview
IBM watsonx.data is an open, hybrid, and governed data store built on an open data lakehouse architecture. The data lakehouse is an emerging architecture that offers the flexibility of a data lake with the performance and structure of a data warehouse. Watsonx.data is an enterprise-ready data store that enables hybrid cloud analytics workloads such as data engineering, data science and business intelligence, through open-source components with integrated IBM innovation.
Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines such as Presto and Spark across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. It also offers built-in governance, automation and integrations with an organization's existing databases and tools to simplify setup and user experience.
Db2 Warehouse and Netezza on AWS natively integrate with watsonx.data with shared metadata and support for open formats such as Parquet and Iceberg to share and combine data for new insights without ETL. Watsonx.data allows customers to augment data warehouses such as Db2 Warehouse and Netezza and optimize workloads for performance and cost.
For trials and customized IBM watsonx.data pricing contact your IBM Sales Representative or email us at watsonx_on_AWS@wwpdl.vnet.ibm.comÂ
Visit https://www.ibm.com/products/watsonx-data to learn more about our consumption model and product editions.
For more information on IBM watsonx.data visit https://www.ibm.com/products/watsonx-data .
Highlights
- Access all your data across hybrid-cloud: Access all data through a single point of entry with a shared metadata layer across clouds and on-premises environments.
- Get started in minutes: Connect to storage and analytics environments in minutes and enhance trust in data with built-in governance, security, and automation.
- Reduce the cost of your data warehouse by up to 50% through workload optimization: Optimize costly data warehouse workloads across multiple query engines and storage tiers, pairing the right workload with the right engine.
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Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Extra-small Watsonx.data installation | Watsonx.data Resource Units annual Contract "pack" of 2000 Resource Units | $2,000.00 |
Small Watsonx.data installation | Watsonx.data Resource Units annual Contract "pack" of 20000 Resource Units | $20,000.00 |
Medium Watsonx.data installation | Watsonx.data Resource Units annual Contract "pack" of 50000 Resource Units | $50,000.00 |
Large Watsonx.data installation | Watsonx.data Resource Units annual Contract "pack" of 100000 Resource Units | $100,000.00 |
Dimension | Cost/unit |
|---|---|
Overage charge for overconsumption of contracted resource units | $1.10 |
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All orders are non-cancellable and all fees and other amounts that you pay are non-refundable.
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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Updated weekly
By IBM Software
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Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
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Positive reviews
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AI generated from product descriptions
Data Lakehouse Architecture
Built on an open data lakehouse architecture that combines data lake flexibility with data warehouse performance and structure
Multi-Engine Query Support
Supports multiple fit-for-purpose query engines like Presto and Spark across different IT environments
Open Format Data Sharing
Natively supports open data formats like Parquet and Iceberg for seamless data sharing and integration across databases
Hybrid Cloud Data Access
Enables unified data access through a single point of entry with a shared metadata layer across hybrid cloud and on-premises environments
Integrated Governance
Provides built-in governance, automation, and security capabilities for enterprise data management and integration
Data Platform Architecture
Unified platform integrating data engineering, analytics, business intelligence, data science, and machine learning on a single architecture
Open Source Foundation
Built on open source data projects with support for open standards and data formats
Lakehouse Infrastructure
Provides a common data management approach using a lakehouse architecture running on Amazon S3
Data Intelligence Engine
Advanced engine capable of interpreting organizational data context and enabling broad data access across teams
Collaborative Workflow
Native collaboration capabilities enabling cross-functional data and AI workflow integration
Data Lake Query Performance
Provides sub-second query response times using SQL query service on data lake platforms
Open Standards Support
Utilizes community-driven standards like Apache Iceberg and Apache Arrow for processing engines
Multi-Source Data Integration
Enables joining data from data lakes and external databases without data movement
Compute Engine Management
Automatically handles compute engine lifecycle including provisioning, scaling, pausing, and decommissioning
VPC-Based Data Processing
Deploys compute engines within customer's Amazon Virtual Private Cloud for secure data processing
Standard contract
No
No
No
Customer reviews
0 ratings
0 AWS reviews
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67 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Akash J.
Reliable Data Platform ,Still Evolving Though
Reviewed on Aug 10, 2025
Review provided by G2
What do you like best about the product?
1.Flexible data handling and fast searches.
2. Queries run quickly and handle diverse data well.
3. I like how fast it processes and manages big data.
2. Queries run quickly and handle diverse data well.
3. I like how fast it processes and manages big data.
What do you dislike about the product?
1. Learning takes time for a beginner.
2. Initial setup also takes time.
2. Initial setup also takes time.
What problems is the product solving and how is that benefiting you?
For me, IBM watsonx.data helps address two main challenges — ensuring data quality and making it easily accessible for AI and analytics. In Responsible AI work, having a trusted, well-governed dataset is critical to avoiding bias and ensuring compliance. The platform’s governance tools make it easier to maintain lineage, manage permissions, and apply consistent policies across multiple data sources.
It also streamlines access to both structured and unstructured data, so instead of spending hours gathering and cleaning data, I can focus on building and testing AI models. Using it as a data warehouse has reduced the time it takes to prepare datasets for machine learning, which speeds up experimentation and shortens project cycles. Overall, it’s given me a more reliable foundation for developing AI systems that are transparent, scalable, and ethically sound.
It also streamlines access to both structured and unstructured data, so instead of spending hours gathering and cleaning data, I can focus on building and testing AI models. Using it as a data warehouse has reduced the time it takes to prepare datasets for machine learning, which speeds up experimentation and shortens project cycles. Overall, it’s given me a more reliable foundation for developing AI systems that are transparent, scalable, and ethically sound.
Anandu R.
Reliable
Reviewed on Aug 10, 2025
Review provided by G2
What do you like best about the product?
What I really like about IBM watsonx.data is its ability to handle and analyze large amounts of structured and unstructured data from different sources all in one place. It’s flexible, integrates well with existing tools, and helps turn raw data into meaningful insights much faster. I also appreciate how it’s built for scalability, so it can grow with the business needs
What do you dislike about the product?
One thing I’ve noticed is that, because IBM watsonx.data is such a powerful and feature-rich platform, there can be a learning curve for new users to fully leverage all its capabilities. Also, depending on the size of the datasets and complexity of queries, performance tuning might be needed to get the best results. But once you get familiar with it, the benefits outweigh the initial challenges
What problems is the product solving and how is that benefiting you?
IBM watsonx.data is solving the problem of having data scattered across multiple systems and formats. Instead of spending a lot of time moving and preparing data, I can query and analyze it directly from where it resides, whether it’s in a data lake, warehouse, or external source. This saves time, reduces duplication, and makes it easier to get real-time insights for decision-making. It’s also helping improve collaboration, since different teams can work off the same unified view of the data
DEEPAK REDDY K.
IBM Watsonx Usage Experience
Reviewed on Aug 10, 2025
Review provided by G2
What do you like best about the product?
I have Watsonx for IBM Call for code as it is a Pre-requisite of the Competiton to use the IBM Watsonx. IBM Watsonx has a wide range of AI Products which aligns well with the different usecases. It has it's own Foundation Models Like Granite which we used in our IBM Cal for code Project it's integration with the multiple other models is also easy liek for example Hugging Face Repo and DB Connections as well code Deployment in IBM Cloud. One good thing was the have documentation and walkthrough docs/videos for each and every AI model/functionalty Implementation. These docs/videos helped reduce some time in getting started as they are to the point. Talking about the customer support it is very quick i got problem with my account and got resolved in within a day or so. I have used these IBM Watsonx Three times and alway feel the Functionality and the power of AI integerated tools is amazaing.
What do you dislike about the product?
The things that i felt could have been more better is the limited Third party Resources and integrations though it has few popular tools and integration for some use cases the watsonx does not support them. The Pricing is more compared to other open resources example if i need Large Model Training or multi model usage in watsonx AI the cost increases there is no proper tanspaernecy in Cost upfront as comaprted to AWS. If i want to use the Watsonx AI with non IBM Tools custom connectors which by user needs to be build up is required which is time taking and some times the implementation goes waste.
What problems is the product solving and how is that benefiting you?
The AI Models it has huge computatuion and capable of Handeling Large amounts of data sets, example : Granite Models. These Granite Models already pretrianed with large amounts of data and for use case we have used LLM for passing our use case data as context for the Training Models to generate the results for us. The Results are 75-80% accurate. Teh IBM Granite Models have language support where it support large number of Languages across the world. Since it is integrated with the IBM Cloud everything becomes easy from development to Deployment But, if we want get the Third part tools which not supported by IBM is a bit complex to get it working. Rest it is dtraight forward approach if we are using everything like tools, models and apps from the IBM Cloud.
amar c.
Makes working with data much easier
Reviewed on Aug 09, 2025
Review provided by G2
What do you like best about the product?
I like how easy it is to manage and search large datasets using the platform. The AI-assisted data preparation tools help me clean and organize data much faster than doing it manually. The interface is user-friendly, and the integration with other IBM products makes it easy to fit into our existing workflow. It also handles large amounts of data without slowing down, which is a big plus for my team.
What do you dislike about the product?
Some of the more advanced analytics features have a steep learning curve and require extra training to use effectively. Also, the cost might be on the higher side for smaller companies. Lastly, it needs a stable internet connection for most operations, so offline work is limited.
What problems is the product solving and how is that benefiting you?
IBM watsonx.data helps us centralize and manage large volumes of data from multiple sources in one platform. It reduces the time needed for data preparation, cleaning, and organization, allowing our team to focus on analysis and decision-making instead of manual processing. The platform’s AI-driven tools improve the accuracy of our datasets, which leads to more reliable insights for our business. Overall, it has increased productivity and made our data operations much smoother.
Computer Software
Data as a service, i think this is something fresh and new
Reviewed on Aug 09, 2025
Review provided by G2
What do you like best about the product?
The reason i explored IBM watsonx is, in my current org, we were also building a similiar kind of product, not at this scale but many of the funcitonalitier are common, the feature i liked specially is their prompt lab and how well it is easy to implement, and that actually provides a very good simulation for building different kinds of usecases a person may have. in terms of integration, the data source integration feels seemless a wide variety mainstream connectors are present and easy to integrate, didnt ineracted with the customer support as i didnt have to use it much
What do you dislike about the product?
This not a beginner friendly tool, a person should be well aware of the current AI-scenario, technical terms and how LLMS works upto some level, the UI is clean and minimal but many time i found a bit of difficulty in navigation between different screens, and sometimes i felt everything is given to me, and that made me confused what should i pick, the point is since there is big chunk of business and non-tech professionals are also adopting the use of LLMs into their workflows, and they could be a user of this platfrom, then the platform should hide some of the configuration and handle it via some assumptions, although this is just an opinion i am not very sure of the target audiene of watsonx. for my use i dont see much of use within my team, and current org, there are already many tools which are free and opensource for instance openmetadata, people who want production ready and readiness to scale within their org as they have that much data to take leverage, and exclusive proprietary platform, which is catered for them then this could be a good choice.
What problems is the product solving and how is that benefiting you?
the first is its proprietary nature with ease of integration with my data, that will help organization to quickly bootstrap their products, next is the fine tuning and its simulation with prompt labs, this will actually gives the user an idea how his model will behave without wasting much of his resources on billing and computing,