Explore how HPE Services enable secure, conflict-free AI by giving each user isolated workspaces to run notebooks, AI models, and pipelines safely at scale.

The multitenancy challenge in enterprise AI

 In today’s enterprise AI landscape, organizations are increasingly looking to scale their machine learning (ML) and analytics workloads across multiple teams. While platforms such as Kubeflow simplify the orchestration of AI workflows on Kubernetes, supporting multiple users within the same environment introduces new operational challenges.

Without proper isolation mechanisms, notebooks, experiments, and models from different users may interfere with each other. This can lead to:

• Resource conflicts
• Security concerns
• Reproducibility issues
• Lack of governance across teams

To address these challenges, enterprise AI platforms such as Red Hat OpenShift AI, SUSE AI, and NVIDIA AI Enterprise focus heavily on multitenancy capabilities.

These include:

• Per-user isolation
• Access control
• Resource management within the cluster

This model is commonly referred to as soft multitenancy.

A vanilla Kubeflow deployment, however, does not fully address these capabilities out of the box.

 The cost factor in enterprise-flavored AI platforms

 Enterprise AI distributions provide these multitenancy capabilities as part of their platform, but they typically come with premium licensing or subscription costs.

Organizations, therefore, face a common dilemma:

Should they invest in a fully packaged enterprise AI platform, or build an open, flexible solution while maintaining governance and security?

This leads to a second major challenge that many organizations face during AI initiatives:

How do we maximize the number of GPU resources available within a fixed budget?

GPU infrastructure is the backbone of any AI factory. When budgets are limited, organizations must carefully balance:

• Platform licensing costs
• Infrastructure investment
• Operational overhead
• Governance and security requirements

Reducing platform licensing costs can allow organizations to allocate more budget toward GPU capacity, enabling a more capable AI environment for data scientists and engineers.

 The Kubeflow ecosystem

 Despite these platform differences, many enterprise AI offerings rely heavily on the same open-source ecosystem originally defined by Kubeflow.

Key components include:

• Jupyter Notebooks
  Used for data exploration, model prototyping, and experimentation
• ML pipelines
  Orchestrates the end-to-end lifecycle of ML workflows, typically built on Argo Workflows
• KServe
  Provides production-grade model inference supporting multiple frameworks
• TensorFlow
  An open-source framework for training deep-learning models with support for distributed computing across CPUs and GPUs
• PyTorch
  Another widely adopted open-source framework for ML and deep learning, known for its flexible design and strong research community

Because many enterprise platforms build on this same ecosystem, the core AI/ML capabilities are often comparable.

 Kubeflow vs. enterprise-backed AI platforms

Kubeflow and enterprise AI distributions are both highly capable platforms for building AI factories. However, they differ in several operational dimensions.

Typical evaluation criteria include:

• Ease of deployment
• Kubernetes integration
• Enterprise support
• Customization flexibility
• Built-in MLOps pipelines
• Security and governance

Figure 1. Comparison between Kubeflow and OpenShift AI

Among these criteria, ease of deployment is often a one-time hurdle that can be addressed through collaboration with an experienced system integrator.

This is where HPE Services plays a key role, helping organizations deploy and operationalize Kubeflow efficiently.

Addressing enterprise support requirements

Enterprise support is another important factor when choosing a platform. The perceived value of a commercial subscription often depends on:

• The organization's internal expertise
• Its tolerance for upstream open-source innovation
• Its operational maturity

HPE offers a flexible model that allows organizations to adopt open platforms while still benefiting from enterprise-grade support.

Through managed services, HPE can handle day-two operations of the AI platform, allowing customers to focus on developing models and extracting value from their AI initiatives rather than managing platform infrastructure.

 Solving the multitenancy challenge

 The most critical decision point for many organizations remains multitenancy, particularly in relation to security and governance.

To address this challenge, HPE Services enables per-user isolated workspaces within Kubeflow.

Each user or team operates within a dedicated environment where they can safely run:

• Notebooks
• Training workloads
• Pipelines
• Inference services

This approach ensures that users cannot interfere with each other’s workloads while maintaining efficient resource utilization.

The solution combines Kubeflow with open-source identity and access management (IAM) technologies such as Keycloak, enabling:

• Secure authentication
• Role-based access control
• Streamlined onboarding of new users

Each user is automatically assigned to a dedicated Kubernetes namespace with defined resource quotas, ensuring predictable resource allocation and governance.

 Enabling the enterprise AI factory

 HPE Services helps organizations get the optimal value out of their enterprise AI strategy.

In an AI factory deployment, tenant isolation is a critical requirement. Careful platform design is necessary to ensure that the selected AI framework complies with the organization’s security, governance, and operational standards.

Through HPE Cloud Native Computing Services—Container Adoption integration for ML with Kubeflow, organizations can design and deploy a production-ready Kubeflow platform from day zero.

HPE supports the entire AI lifecycle, including:

• Platform architecture and design
• Kubeflow implementation
• Integration with enterprise IAM and security systems
• MLOps enablement
• Day-two operations and platform management

With the right architecture and operational support, organizations can unleash the full potential of their AI factory while maintaining security, scalability, and cost efficiency.

Learn more at HPE Cloud Native Computing Services—Container Adoption solution brief.

Meet the author:

Alex Tesch—Principal Solutions Architect