Veeam Software announced at HPE Discover in Las Vegas an expansion of its collaboration to help organizations modernize and scale the private cloud, from AI-ready infrastructure and validated designs to replicable, partner-ready solutions that enable faster deployments.
Building on the partnership between the two companies and Data Resilience by Design, a framework that establishes resilience as the foundation for secure, governed and recoverable data, Veeam and HPE are driving new capabilities focused on accelerating private cloud adoption with simpler architectures, more secure AI data flows and rapid time-to-value.
“The private cloud is evolving rapidly as customers seek cloud agility with greater control, stronger governance, and the ability to operate AI closer to their data,” said John Jester, CRO at Veeam. “AI will transform the business, but only if leaders can trust the data that powers it. That trust starts with resilience: knowing that data is protected, recoverable, and governed end-to-end. “Together with HPE, Veeam is bringing that layer of trust to private clouds with validated designs and partner-ready solutions that help customers protect and manage their data, reduce risk in AI workflows, and accelerate business outcomes without sacrificing performance or agility.”
AI-ready private cloud deployments
Veeam and HPE will launch new validated designs for HPE Private Cloud AI, helping customers deploy secure, AI-ready private environments. HPE Private Cloud AI is a comprehensive AI platform, developed in collaboration with NVIDIA as part of the NVIDIA AI Computing by HPE portfolio. It is designed to offer a complete and secure AI work environment, with a unified data lakehouse, deployable models and agentic AI use cases. With data sovereignty requirements increasing globally and AI ethics evolving from statements of principle to operational mandates, organizations need practical architectures that protect data, improve resilience, and strengthen trust in the processing of AI data.
“Enterprises are turning to the private cloud to meet performance and sovereignty requirements while putting the right controls in place for AI,” said Patrick Osborne, senior vice president of technology acceleration for hybrid cloud at HPE. “With these new validated designs for HPE Private Cloud AI and our expanded collaboration with Veeam, we offer customers a ready-made solution to deploy AI closer to their data, combining proven infrastructure with stronger governance and greater confidence in how data is prepared, managed and protected. AI as they move from pilot testing to real production.”
Validated designs will include:
- Veeam Data Platform and Veeam Kasten, both part of the Veeam DataAI Command Platform, ensure operational continuity for virtualized and Kubernetes-based workloads that drive AI initiatives.
- Secure data ingestion to help organizations prepare and ingest data for AI use cases with stronger controls and greater confidence in data processing, complementing HPE AI Essentials, which manages the model development and deployment lifecycle.
These validated designs reduce deployment complexity and help customers move from AI pilots to real production more quickly while maintaining appropriate operational controls for the enterprise private cloud.
Resilient HPE Private Cloud
In line with HPE’s recent announcement of its unified private cloud, HPE and Veeam offer partners intelligent sizing tools and templates to facilitate the implementation and delivery of resilient private cloud deployments using HPE Private Cloud PC3000 with HPE Morpheus Software VM Essentials. The goal is to help partners standardize designs, optimize reach and accelerate delivery so customers can expand their private cloud infrastructures with greater predictability and lower operational costs thanks to Veeam and HPE Private Cloud.
To further simplify adoption and modernization, Veeam also offers a practical migration guide for moving virtual machines from vSphere to virtual machines on HPE Morpheus. Using the Veeam Data Platform on HPE Private Cloud helps customers and partners plan and execute these transitions, while maintaining the resilience and recovery needed for critical workloads.
Security, governance and trust in AI
As AI moves from an assistive tool to an autonomous agent that operates on enterprise data at machine speed, organizations face a growing gap between confidence in their AI readiness and the ability to generate auditable evidence that data flows, access controls, governance, and recovery meet audit, governance, and regulatory requirements. Trust in AI is high, but trust alone doesn’t scale, so Veeam and HPE are focused on helping customers realize trust in AI, not just aspire to it.
Veeam and HPE to Launch New Validated Designs for HPE Private Cloud AI, Helping Customers Deploy Secure, AI-Ready Private Environments
Veeam DataAI Command Platform is the industry’s first unified trusted data and AI infrastructure for the agent era. The platform unifies key domains, such as security, governance, compliance, privacy and DataAI resilience, thanks to the DataAI Command Graph, Veeam’s intelligence layer that powers the entire platform and includes hundreds of connectors across cloud environments, SaaS applications and on-premises.
Building on the Data Resilience by Design approach, both companies are expanding their collaboration to connect resilience outcomes (data protection, recoverability, and clean restoration) with the governance and controls necessary for trustworthy AI. To help close this execution gap, HPE Services will serve as a pilot partner for Veeam’s new Data and AI Trust Maturity Model, a research-based, customer-validated framework that offers leaders a clear, objective way to assess where they stand, benchmark progress, and prioritize the capabilities needed to strengthen trust readiness across four critical pillars: Understanding, Security, Resilience, and Potential.
Through this collaboration, Veeam and HPE will help customers move from AI experimentation to trusted execution by improving visibility and governance, reducing risk in AI data streams, and ensuring the ability to recover clean, reliable data when needed.
