Can private cloud AI challenge hyperscaler dominance? Lessons from the HPE–OpenText model

Open Text Corporation (NASDAQ: OTEX) has deepened its enterprise AI strategy by joining Hewlett Packard Enterprise’s (NYSE: HPE) Unleash AI partner program. This expansion integrates OpenText’s Aviator AI suite with HPE Private Cloud AI, a turnkey “AI factory” co-developed with NVIDIA to support large-scale generative AI workloads.

The partnership represents more than a technology announcement. It is a case study in how private cloud AI stacks are trying to carve space in a market long dominated by hyperscaler giants such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. The central question is whether these turnkey private stacks can seriously challenge hyperscalers — or whether they will remain niche offerings confined to highly regulated industries.

Why is the hyperscaler model still the default for enterprise AI adoption in 2025?

Hyperscalers continue to dominate the global AI market because of three central advantages: scale, accessibility, and ecosystem depth. AWS provides near-infinite compute elasticity, Microsoft Azure powers OpenAI deployments, and Google Cloud’s Vertex AI platform has become the default for developers seeking advanced model training and fine-tuning. Together, these providers account for the lion’s share of enterprise AI workloads, backed by R&D budgets that exceed what most competitors could match in a decade.

For early adopters of generative AI, the hyperscaler path offered speed and innovation. Enterprises could experiment with large language models, image recognition, or predictive analytics without investing in on-premise infrastructure. This flexibility fueled the explosive adoption curve of AI in 2023–2024. Yet, hyperscalers are not without drawbacks: lock-in risk, opaque pricing, and compliance hurdles have pushed many regulated enterprises to reconsider whether the public cloud is always the safest bet.

What specific gaps in hyperscaler AI offerings are private cloud players like HPE and OpenText trying to exploit?

The cracks in hyperscaler dominance have grown more visible as enterprises scale up AI deployment. One pain point is data sovereignty. Regulations such as the EU AI Act, GDPR, HIPAA, and various financial compliance rules increasingly require organizations to know exactly where data is stored, how it is processed, and who has access. Hyperscaler data centers, often spanning multiple jurisdictions, complicate this picture.

HPE’s Private Cloud AI “factory,” built in collaboration with NVIDIA, directly targets this concern. It offers pre-validated AI environments that can be deployed on-premise, at the edge, or within colocation facilities. OpenText’s Aviator AI stack, already known for compliance-ready document and information management, integrates seamlessly on top of this infrastructure. The model emphasizes predictable cost, localized control, and enhanced security — a pitch designed to resonate with CIOs navigating regulatory minefields.

Institutional investors have pointed out that such partnerships are not simply about technology. They are about offering choice in an ecosystem where many buyers feel pressured to rely solely on hyperscaler platforms.

How does the HPE–OpenText–NVIDIA model position itself differently from Dell, IBM, and Oracle’s AI strategies?

The AI infrastructure market is evolving into a patchwork of alliances. Dell Technologies has partnered with NVIDIA to bring AI-ready servers and storage systems to enterprise clients. IBM has pursued industry-specific AI with SAP and leaned on its hybrid-cloud Red Hat ecosystem. Oracle has doubled down on Cohere partnerships, layering generative AI into its verticalized cloud for financial services, healthcare, and retail.

HPE, however, has staked its claim by branding Private Cloud AI as a factory model — a standardized, repeatable framework to deploy AI workloads in secure environments. Adding OpenText strengthens this proposition because of its historical role in managing unstructured data and providing compliance-aligned analytics tools. Unlike Dell’s hardware-first approach or Oracle’s SaaS-driven play, the HPE–OpenText collaboration positions itself as a verticalized full-stack AI alternative.

Industry analysts have noted that such hardware-software co-engineering is one of the few ways private AI stacks can differentiate. Rather than compete on scale, they compete on trust, compliance, and integration.

In which industries could private cloud AI adoption gain faster traction compared to hyperscaler platforms?

If hyperscalers remain the default for generic workloads, private cloud AI may find its strongest foothold in sectors where data sensitivity is existential. Healthcare providers, bound by HIPAA and regional patient privacy laws, are unlikely to risk exposing sensitive records to multi-tenant public cloud environments. Financial institutions, grappling with fraud detection and real-time trading analytics, increasingly value low-latency on-premise AI. Manufacturers, meanwhile, are turning to edge AI for robotics and industrial automation, where proximity to operations is crucial.

Government and public sector agencies also present a large addressable market. From defense to social security, workloads that require sovereign data storage and deterministic AI governance align neatly with what HPE and OpenText are offering.

By targeting these niches, private stacks do not need to displace hyperscalers everywhere. They only need to dominate in the industries where compliance risk outweighs the flexibility of the public cloud.

Can turnkey private AI solutions match hyperscalers on scale, innovation, and ecosystem depth?

Matching hyperscalers on raw scale is unrealistic. AWS and Azure operate data centers across dozens of regions, supporting millions of workloads daily. Their ecosystems — thousands of ISVs, APIs, and developer communities — are entrenched. However, private stacks can compete in more focused ways.

The Unleash AI model addresses the problem of time-to-deployment by offering pre-validated solutions. Instead of building bespoke AI stacks over months, enterprises can deploy compliant solutions in weeks. The focus is less on infinite scale and more on fit-for-purpose innovation.

Analysts generally agree that enterprises will pursue hybrid strategies: using hyperscalers for experimentation and high-volume training, while relying on private stacks for production workloads involving sensitive data. This dual-track adoption reinforces the notion that private cloud AI will not replace hyperscalers but instead co-exist as a parallel ecosystem.

What are the investor and institutional signals from OpenText’s AI push within the HPE Unleash AI program?

For Open Text Corporation, the strategic upside is visibility in a crowded AI market. OTEX shares, trading in the USD 34–36 range in August 2025, reflect a cautious balance of optimism and execution risk. Institutional investors note that the Micro Focus integration has given OpenText a larger enterprise base to sell into, but the real test lies in converting its AI positioning into recurring revenue.

Some buy-side sentiment suggests that participation in HPE’s Unleash AI program could improve perception of OpenText as more than just a legacy information management vendor. If traction materializes, analysts believe the company could attract fresh institutional inflows, particularly from funds seeking mid-cap AI exposure without the volatility of pure-play startups.

For HPE, the implications are different. Its pivot from legacy servers toward AI-ready infrastructure has been welcomed by investors looking for growth beyond commoditized IT hardware. By aligning with NVIDIA and software partners like OpenText, HPE strengthens its case as a key player in the AI infrastructure race, directly competing with Dell, Cisco, and Lenovo.

What lessons does the HPE–OpenText collaboration offer for the future of enterprise AI competition?

The clearest lesson is that private cloud AI is not about replacing hyperscalers — it is about complementing and correcting them. Enterprises want hyperscaler innovation but need private stack governance. They want access to frontier AI models but require sovereignty for mission-critical data.

Partnerships like HPE–OpenText–NVIDIA show how private stacks can carve a sustainable niche. By offering AI sovereignty stacks that are scalable, compliant, and pre-validated, they move the debate beyond public vs. private cloud into a hybrid model of coexistence.

For enterprises, the path forward will likely be dual-track. Hyperscalers will remain indispensable for scale and ecosystem innovation. Private stacks will grow indispensable for trust, compliance, and vertical specialization. For investors, the message is equally clear: private cloud AI is not a sideshow. It is becoming a permanent layer in the AI value chain.