Sold by: IBM Software
Deployed on AWS
IBM watsonx.data is an open, hybrid data lakehouse with built-in data fabric and multi-engine optimization to prepare structured and unstructured data for AI.
4.4
Overview
IBM watsonx.data as a Service is an open, hybrid-cloud data lakehouse on AWS that combines lakehouse storage with integrated data fabric capabilities for governance, lineage, and data quality. Using open formats such as Apache Iceberg and Parquet, and engines including Presto SQL and Apache Spark, the platform provides governed access to structured, semi-structured, and unstructured data across hybrid, multi-cloud, and on-premises environments.
watsonx.data is GenAI-ready, automating ingestion, preparation, and retrieval of unstructured data to fuel accurate generative AI. With vector search and multi-model capabilities through Cassandra (Astra DB) and Milvus, watsonx.data supports advanced RAG, similarity search, and real-time operational workloads. Internal testing shows improved accuracy over vector-only RAG by leveraging retrieval governance and integrated metadata.
watsonx.data offers enterprise-grade deployment flexibility and security, including VPC-based deployments, AWS PrivateLink, and support for FedRAMP (Medium) and HIPPA for AWS GovCloud. Native AWS integrations, such as AWS Lake Formation and the Common Policy Gateway (CPG) for unified access control, enable real-time policy synchronization and full auditability. With multi-engine optimization across Presto and Spark, organizations can reduce data warehouse costs while scaling analytics and AI across their AWS footprint.
Q: How does watsonx.data integrate with AWS-native services?
The platform integrates with AWS Lake Formation for access management and metadata alignment, supports AWS PrivateLink for secure connectivity, and uses the Common Policy Gateway (CPG) for unified access control with real-time policy synchronization and full audit tracking.
Q: What security and compliance capabilities are available?
watsonx.data offers enterprise-grade deployment flexibility and security, including VPC-based deployments, AWS PrivateLink, and support for FedRAMP (Medium) and HIPPA for AWS GovCloud. to support regulated workloads.
Q: What deployment options does watsonx.data support?
IBM watsonx.data supports SaaS on AWS, in-customer VPC deployments on AWS and Azure, multi-cloud architectures, and on-premises deployments on Red Hat OpenShift. On-premises deployments can take advantage of existing IBM Power and IBM Fusion HCI environments to deliver optimized performance, while maintaining flexibility for data residency, security, and compliance requirements.
Q: How does watsonx.data improve GenAI and RAG accuracy?
watsonx.data enhances generative AI results by combining governed retrieval with integrated vector databases such as Milvus and Cassandra (Astra DB), enabling fusion of unstructured, structured, and metadata-rich context. Internal testing shows higher answer correctness compared to vector-only RAG by applying data fabric governance and optimized retrieval strategies.
Highlights
- Unify hybrid-cloud analytics through a single entry point: Access all enterprise data across AWS, on-premises, and multi-cloud environments through a shared metadata layer that supports open table formats such as Apache Iceberg and Parquet, enabling consistent analytics and governance without ETL.
- Deploy and connect to AWS data sources in minutes: Begin querying data quickly by connecting AWS storage (e.g. Amazon S3) and analytics environments - including Db2 Warehouse on AWS and Netezza on AWS - within minutes, supported by built-in governance, security automation, and multi-engine execution through Presto and Spark.
- Reduce the cost of your data warehouse by up to 50% through workload optimization: Lower analytics spend by offloading and optimizing workloads across fit-for-purpose engines (Presto, Spark) and storage tiers, enabling measurable cost reductions of up to 50% when augmenting traditional warehouse workloads.
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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 |
The following dimensions are not included in the contract terms, which will be charged based on your usage.
Dimension | Description | Cost/unit |
|---|---|---|
Overage charge for overconsumption of contracted resource units | Overage charge for overconsumption of contracted resource units | $1.10 |
Astra_Ovg | Astra Streaming 1000RU Overage | $1.10 |
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Open Table Format Support
Supports open table formats including Apache Iceberg and Parquet for consistent analytics and governance across hybrid-cloud environments without requiring ETL processes.
Multi-Engine Query Optimization
Provides multi-engine optimization across Presto SQL and Apache Spark for executing analytics and AI workloads with workload-specific engine selection and storage tier optimization.
Vector Database Integration
Integrates vector search and multi-model capabilities through Cassandra (Astra DB) and Milvus to support advanced retrieval-augmented generation (RAG), similarity search, and real-time operational workloads.
Enterprise Security and Compliance
Offers VPC-based deployments, AWS PrivateLink connectivity, FedRAMP (Medium) certification, and HIPAA compliance support for regulated workloads on AWS GovCloud.
Unified Access Control and Governance
Implements integrated data fabric with governance, lineage, and data quality capabilities, including AWS Lake Formation integration and Common Policy Gateway (CPG) for unified access control with real-time policy synchronization and audit tracking.
Lakehouse Architecture
Unified data foundation built on lakehouse architecture providing open, unified foundation for data and governance with support for open standards and formats
Data Intelligence Engine
Powered by Data Intelligence Engine that enables organization-wide access to data and insights across all users and roles
Multi-Workload Unification
Consolidates data engineering, analytics, business intelligence, data science and machine learning workloads on a single common platform
Open Source Foundation
Built on open source data projects and open standards to maximize flexibility and interoperability across data ecosystem
Collaborative Capabilities
Native collaboration features enabling unified data teams to collaborate across entire data and AI workflow
Auto-scaling Capability
Intelligently autoscales workloads up and down across hybrid and public cloud environments for optimized cloud infrastructure utilization.
Multi-function Analytics Platform
Provides integrated data warehouse, machine learning, and custom analytics services with standardized user experience for ingesting, transforming, querying, optimizing, and making predictions from data.
Unified Security and Governance Framework
Implements Shared Data Experience (SDX) technology that sets security and governance policies once and applies them consistently across all data and workloads regardless of infrastructure.
Cross-infrastructure Policy Portability
Maintains security and governance policies with data as it moves across multiple public clouds, private cloud, and hybrid cloud environments.
Centralized Data Management and Discovery
Provides unified metadata management enabling efficient data curation, discovery, and sharing while eliminating data silos and redundant data copies.
Standard contract
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Customer reviews
Christopher P.
Excellent Managed Services Support and Smooth Upgrade Assistance
Reviewed on Oct 28, 2026
Review provided by G2
What do you like best about the product?
Managed services support and assistance with upgrades.
What do you dislike about the product?
The contract options feel overly complex, and there isn’t enough flexibility in how they can be set up or adjusted.
What problems is the product solving and how is that benefiting you?
datastax database for storing large datasets and resilient db systems
Legal Services
Datastax Delivered Excellent Vector DB Throughput and Low Latency at Scale
Reviewed on Oct 28, 2026
Review provided by G2
What do you like best about the product?
We used Datastax to scale vector db for one of our large clients. Focused on taxation and law. We were able to get really good through puts and latency.
What do you dislike about the product?
Nothing really, the learning curve was a bit high but we had to get help from the account support team and that was helpful.
What problems is the product solving and how is that benefiting you?
Primary use case was use of AstraDB from Datastax for RAG pipelines.
Theo A. S.
Easy Information Ingestion That Strengthens User Support
Reviewed on Oct 27, 2026
Review provided by G2
What do you like best about the product?
What I like most is how easy it is to ingest information and then use it to better support our users.
What do you dislike about the product?
I haven’t come across anything I dislike so far, but if I do, I’ll let you know.
What problems is the product solving and how is that benefiting you?
It’s helping us uncover relationships in our data that we previously missed. It also gives us a smoother, more cohesive view of our data overall, which helps the business make sound decisions that influence the direction we’ll take in the future.
Vishwanath P.
Easy, High-Performance Data Management Across Multiple Sources
Reviewed on Aug 26, 2026
Review provided by G2
What do you like best about the product?
I like how easy it makes data management, its solid performance, and the way it helps me bring data from different sources together in one place.
What do you dislike about the product?
The interface can feel a little complex at first, and some features take time to understand and get used to. After spending more time with it, navigation becomes easier, but there’s still a noticeable learning curve.
What problems is the product solving and how is that benefiting you?
It helps me manage data from different sources in one place, making it much easier to access, organize, and analyze everything. As a result, I save time, and my day-to-day data work feels more efficient, streamlined, and easier to stay on top of.
Manshi S.
Powerful and Flexible Data Management for AI and Analytics
Reviewed on Aug 21, 2026
Review provided by G2
What do you like best about the product?
I like IBM watsonx.data’s ability to bring data from different sources into a single, open, governed platform, which makes it easier to manage, analyze, and prepare data for AI. Its hybrid-cloud support, strong data governance, and multi-engine architecture also add flexibility and scalability, so it can handle different workloads more smoothly.
What do you dislike about the product?
One thing I dislike about IBM watsonx.data is that the initial setup and overall learning curve can feel a bit complex, especially for beginners. Certain configurations and integrations take extra technical know-how, and I found the documentation could be clearer, more straightforward, and more practical to follow.
What problems is the product solving and how is that benefiting you?
IBM watsonx.data helps address challenges such as data silos, complex data management, and the difficulty of accessing trusted data for analytics and AI. It brings data from multiple sources together into a unified platform, adds governance and security, and enables querying data without unnecessary duplication or movement.