The 7th Annual Nutanix Enterprise Cloud Index: the US Federal Government's GenAI and Containerization Ambitions Clash with Data Security, Privacy and IT Skills Gap Concerns

The 7th Annual Nutanix Enterprise Cloud Index (ECI) Report for the US Federal Government reveals candid assessments by survey respondents on the state of adoption of GenAI and containers, as well as noting key concerns and challenges to their successful deployment. 

These technologies are part of a wave that is fundamentally reshaping the technology landscape for all levels of government, bringing new capabilities and benefits. 

GenAI: Promising But Challenging

Over 90% of US federal respondents believe GenAI either already supports or could support their organization’s strategic goals. Key use cases of GenAI include:

• Chatbots for automating and enhancing constituent and employee engagement 
• Intelligent document processing
• Fraud detection. 

Despite the high degree of enthusiasm for GenAI, the ECI report indicates that only 77% of respondents are leveraging GenAI workloads, well behind the global average of 98%. 

Federal agencies face strong hurdles for GenAI adoption, including:

• Evolving federal guidelines and requirements - as they operate under much stricter regulatory oversight. 
• Infrastructure limitations - 81% of respondents voiced concerns that their current infrastructure requires at least moderate improvement to fully support cloud-native apps/containers
• Security around GenAI models and applications - especially using LLMs with sensitive organizational data, as well as data security and privacy concerns. 
• Skills shortage - only 20% of respondents believe their organization has the necessary skills to support GenAI.   

The Rise of Containers

The report shows a strong push toward containerization, with 85% of respondents in the process of containerizing applications. Containers are lightweight, portable packages that include everything needed to run an app, code, runtime, system tools, software libraries, configuration files, and settings. Unlike traditional virtual machines, which require entire operating systems (OSs) for each app, containers share the host OS kernel, making them significantly more resource-efficient and faster to start. 

Because container images hold everything needed for an application, developers do not need to code applications for new environments, and deployment is greatly streamlined. Generally, applications have multiple containers that function like isolated, secure building blocks for the application’s software. Open-source tools allow organizations to build, test, and deploy applications through containers. 

Citizen service portals that handle everything from permit applications to tax filings are being modernized through containerization, enabling agencies to scale resources dynamically based on demand while maintaining strict security standards.

US federal government respondents report they are leveraging containers for: 

• Dev/test applications 
• Databases
• GenAI workloads
• Citizen service portals.

In terms of Kubernetes® deployment, 64% of US Federal government respondents report they operate multiple Kubernetes environments; 27% use a single Kubernetes environment; and 9% said they do not use Kubernetes environments. Kubernetes is the standard for container orchestration, automating the deployment, scaling, and management of containerized applications.

These data points show that the US Federal Government lags significantly behind global (all industries) and global public sector averages. The reasons from the respondents include challenges from limitations of their current IT infrastructure, cloud-native/container-native application development, data silos that prevent seamless data access and sharing across different parts of the organization, and portability of applications between clouds and on-premises environments. Skills shortages and legacy infrastructure remain significant challenges.

While US federal agencies have varying degrees of skills and experience with containers, what is inescapable is that the applications their organization depends on are increasingly being delivered via containers. In addition, containers have become the preferred method for deploying AI and machine learning applications because they solve several critical challenges. AI workloads are notoriously resource-intensive and often require specific software dependencies, libraries, and runtime environments. Containers package all these requirements together, ensuring that an AI model trained on a data scientist’s laptop will run identically in production environments.

The Convergence of AI and Containers

The convergence of AI and containers marks a pivotal shift in how federal agencies approach modernization. As highlighted in the ECI Report, US Federal agencies are rapidly adopting containerization to overcome many of the operational challenges inherent in deploying AI at scale. Containers offer the portability and scalability needed to run AI workloads across diverse environments — from data centers to the tactical edge. While the benefits of containerization are clear, managing these environments can introduce new complexities. This is where Kubernetes has emerged as a critical enabler, simplifying the orchestration and lifecycle management of containers and empowering agencies to focus on mission outcomes rather than infrastructure overhead.

Final Recommendations

The report concludes with recommendations for US Federal agencies that apply to both application containerization and GenAI implementation. 

• Address the skill gaps: Invest in training to support GenAI and containerized workloads 
• Modernize infrastructure: Ensure cloud-native readiness with a focus on data security, privacy, and compliance
• Align with mission outcomes: Prioritize use cases that deliver measurable improvements and efficiencies to build support and secure resources

The 7th annual US Federal ECI Report is available here. 

©2025 Nutanix, Inc. All rights reserved. Nutanix, the Nutanix logo, and all Nutanix product and service names mentioned are registered trademarks or trademarks of Nutanix, Inc. in the United States and other countries. Kubernetes is a registered trademark of The Linux Foundation in the United States and other countries. All other brand names mentioned are for identification purposes only and may be the trademarks of their respective holder(s). Certain information contained in this content may link or refer to, or be based on, studies, publications, surveys, and other data obtained from third-party sources and our own internal estimates and research. While we believe these third-party studies, publications, surveys, and other data are reliable as of the date of publication, they have not independently verified unless specifically stated, and we make no representation as to the adequacy, fairness, accuracy, or completeness of any information obtained from a third-party. Our decision to publish, link to or reference third party data should not be considered an endorsement of any such content