0 / 0

Overview of IBM watsonx as a Service

Last updated: May 16, 2025
Overview of IBM watsonx as a Service

IBM watsonx as a Service is a secure and collaborative environment where you can access your organization's trusted data, automate AI processes, and deliver AI in your applications. The IBM watsonx.ai component provides a studio of integrated tools for building generative AI and machine learning solutions. The IBM watsonx.governance component provides end-to-end monitoring for AI solutions to accelerate responsible, transparent, and explainable AI workflows.

AI engineers, data scientists, and AI risk and compliance officers can accomplish the following goals with watsonx.ai and watsonx.governance:

Build generative AI solutions
Build solutions that include prompts, agents, retreival-augmented generation (RAG) patterns, and other capabilities of foundation models. Choose from IBM models, third-party models, open source models, or import custom foundation models. Tune foundation models to customize your prompt output.
Build machine learning solutions
Build models with open source frameworks and code-based, automated, or visual data science tools. Manage and automate the model lifecycle with integrated tools and runtimes to train, validate, and deploy machine learning models.
Govern AI
Track the detailed history of AI models, assess risks, and evaluate model output to help ensure compliance.

Data engineers can collect, store, query, and analyze enterprise data in a lakehouse architecture with IBM watsonx.data. See IBM watsonx.data documentation.

The following graphic shows the capabilities of the watsonx.ai and watsonx.governance components on top of the common core functionality that provides an integrated user experience. The watsonx.data experience is separate and not shown in the graphic.

Described in the surrounding text

Watsonx.ai

Watsonx.ai includes APIs and tools for building AI solutions, deployed foundation models for generative AI, and the hardware and software resources for computing and inferencing.

Watch this short video that introduces watsonx.ai.

This video provides a visual method to learn the concepts and tasks in this documentation.

Ways to prepare data and build AI solutions

For most tasks, you can choose between writing code and working with tools in the UI. Your watsonx.ai tools are in collaborative workspaces called projects.

You can prepare data and build AI solutions in the following ways:

Prepare data for AI
  • Refine and visualize your data files or data tables in remote data sources with Data Refinery.
  • Generate synthetic structured data for training machine learning models with Synthetic Data Generator.
  • Generate synthetic unstructured data for tuning foundation models or testing gen AI solutions with the synthetic data generation API.
  • Vectorize your unstructured data for RAG patterns with vector indexes.
See Preparing data.
Build generative AI solutions
  • Write generative AI solution code with Python SDKs, REST APIs, or Node.js.
  • Experiment with generative AI prompts in the Prompt Lab.
  • Automate RAG patterns with AutoAI.
  • Tune foundation models for your use case with Tuning Studio.
  • Build AI agents with Agent Lab.
See Developing generative AI solutions.
Build machine learning models
  • Automatically generate predictive model candidates wihh AutoAI.
  • Create machine learning model training flows with SPSS Modeler.
  • Write Jupyter notebooks to train models in Python or R.
  • Solve optimization problems with Decision Optimization.
  • Automate the machine learning lifecycle with Orchestration Pipelines.
See Data science solutions.

Deployed foundation models

IBM watsonx.ai has a range of deployed large language models for generative AI. The deployed foundation models include open source models and IBM foundation models. You can also deploy your own custom foundation models or deploy an on-demand model. You can customize foundation model behavior by tuning a foundation model.

For a list of supported foundation models that are deployed in watsonx.ai, see Supported foundation models.

Usage resources

Depending on your service plans, you might have a set amount of usage resources per month or you might be billed for the resources that you consume.

When you run tools or host models on watsonx.ai, you consume the following types of resources:

Compute usage
When you run jobs, notebooks, experiments that train or tune models, or deployments, your compute resource usage is calculated based on the rate for the runtime environment and its active duration. Compute resources include the appropriate hardware and software that are specific for the workload. Compute usage is measured in capacity unit hours.
Inferencing usage
When you run inferencing against foundation models, your inferencing usage is calculated as the sum of the tokens in the prompt input and output text multiplied by the rate for the foundation model. Tokens are basic units of text. Inferencing is measured in resource units.
Model hosting
When you deploy a custom foundation model or a deploy on demand foundation model, you are charged an hourly rate. Billing rates are according to model hardware configuration and apply for hosting and inferencing the model. Charges begin when the model is successfully deployed and continues until the model is deleted.
Text extraction
When you use text extraction to convert document files into an AI model-friendly JSON file format, you are charged per page.

Learn more about usage and billing:

Watsonx.governance

Watsonx.governance includes tools for governing models and the usage resources for evaluating and explaining models.

AI Governance capabilities differ depending on your deployment environment:

  • Watsonx.governance on IBM Cloud provides most AI governance capabilities. You can integrate the IBM OpenPages service to enable the Governance console. All solutions are available (licensing is required).
  • Watsonx.governance on Amazon Web Services (AWS) provides the Governance console with the Model Risk Governance solution.

Watch this short video that introduces watsonx.governance.

This video provides a visual method to learn the concepts and tasks in this documentation.

Ways to govern AI

You can use watsonx.governance APIs and tools to govern AI in the following ways:

Monitor and evaluate AI assets
  • Monitor machine learning model output and explain model predictions.
  • Evaluate and compare generative AI prompts.
Your watsonx.governance model monitoring and evaluation tools are in projects and deployment spaces.
See Evaluating AI assets.
Track and document AI use cases
View model lifecycle status, general model and deployment details, training information and metrics, and deployment metrics with AI use cases.
See Governing assets in AI use cases.
Manage governance activity
  • Sync data from factsheets with the Governance console.
  • Extend governance capabilities with workflows and other compliance tools with the Governance console.
You must integrate with the watsonx Governance console from IBM OpenPages.
See Managing risk and compliance with Governance console.

Usage resources

When you run model evaluations and explanations with watsonx.governance, you consume resources. Depending on your service plans, you might have a set amount of usage resources per month or you might be billed for the resources that you consume. Your resource usage is calculated based on the number of model evaluations and explanations. Evaluations and explanations are measured in resource units.

See IBM watsonx.governance plan options.

Shared functionality

Watsonx includes the following functionality that is shared between services and experiences for secure and scalable collaboratation:

  • Connectivity
  • Administration
  • Storage
  • Workspaces
  • Resource hub

Connectivity

You can create connections to remote data sources and import connected data. You can configure connections with personal or shared credentials. For a list of supported connectors, see Connectors.

You can share connections with others across the platform in the Platform assets catalog.

Administration

The following administration features provide security and flexibility:

Software and hardware

Watsonx is fully managed by IBM on IBM Cloud. Software updates are automatic. Scaling of usage resources and storage is automatic.

Security, compliance, and isolation

The data security, network security, security standards compliance, and isolation of watsonx are managed by IBM Cloud. Data is encrypted at rest and in motion. You can set up extra security and encryption options.

Your work on watsonx, including your data and the models that you create, are private to your account. Your data and models will never be accessible or used by IBM or any other person or organization.

Learn more about security and your options:

Services provisioning

You can add services from the watsonx services catalog. See Creating and managing IBM Cloud services.

You access some services in other experiences. For example, if you add the watsonx.data intelligence service, you must switch to the Data Fabric experience to use it.

You can add data source services and create connections to them from watsonx experiences, but you manage data source services from the IBM Cloud console.

User management

You add users and user groups and manage their IBM Cloud account roles and permissions with IBM Cloud Identity and Access Management. You assign roles within each collaborative workspace across the platform. See Managing users and access.

Storage

When you sign up for a watsonx experience, a IBM Cloud Object Storage service instance is automatically provisioned to provide storage for the assets that you create or add to workspaces. The IBM Cloud Object Storage service instance is shared across experiences. Information that is stored in IBM Cloud Object Storage is encrypted and resilient. Each workspace has its own dedicated bucket. See Object storage for workspaces.

Workspaces

Watsonx is organized as a set of collaborative workspaces where you can work with your team or organization. Each workspace has a set of members with roles that provide permissions to perform actions.

Most users work with assets, which are items that are created or added to workspaces by users. Assets can represent data, models, or other types of code or information. Data assets contain metadata that represents data. Assets that you create in tools, such as models, run code to work with data. You can also create assets that contain information about other assets, such as model use cases that contain metadata, history, and reports about models. See Asset types and properties.

You can work in these types of workspaces in the watsonx experience:

  • Projects
  • Deployment spaces
  • Platform connections
  • Inventories

You can search for assets across all workspaces that you belong to.

Projects

Projects are where your data science and model builder teams work with data to create assets, such as, saved prompts, notebooks, models, or pipelines. Your first project, which is known as your sandbox project, is created automatically when you sign up for watsonx.ai. See Your sandbox project.

Your projects are shared across the integrated experiences. However, you can view and run only those assets that are valid in the current experience. For example, in the watsonx experience, you can't enrich the metadata of a data asset.

The following image shows what the Overview page of a project might look like.

Overview page for a project

Deployment spaces

Deployment spaces are where your ModelOps team deploys models and other deployable assets to production and then tests and manages deployments in production. After you build models and deployable assets in projects, you promote them to deployment spaces. See Deployment spaces.

The following image shows what the Overview page of a deployment space might look like.

Overview page for a deployment space

Platform connections

Platform connections is a view of the Platform assets catalog that lists connection assets. You can access platform connections in any project or deployment space. The Platform assets catalog is shared across integrated experiences.

The following image shows what the Connections page of the Platform connections might look like.

Connections page for Platform connections

Inventories

Inventories store AI use cases. Use cases track the details for AI assets in factsheets. You can also view all AI use cases in all inventories.

The following image shows what the AI use cases page might look like.

AI use cases

Resource hub

Watsonx includes an integrated collection of samples that provides deployed foundation models, data assets, prompts, notebooks, and sample projects. Sample notebooks provide examples of data science and machine learning code. Sample projects contain sets of data, models, other assets, and detailed instructions on how to solve a particular business problem.

The following image shows what Resource hub looks like.

Samples page

Watch this video to see a tour of the Resource hub.

This video provides a visual method to learn the concepts and tasks in this documentation.

Learn more