HCLTech and NetApp have expanded their strategic partnership to deliver an integrated hybrid cloud Storage-as-a-Service (STaaS) platform tailored for enterprise artificial intelligence, generative AI, and high-performance data architectures. It is the latest in a run of NetApp moves aimed at AI data pipelines, following its DataPelago acquisition in July. The offering links infrastructure consumption with operational demand, allowing IT organizations to scale storage performance and capacity alongside legacy enterprise applications without incurring large upfront capital expenditures.
Integrating NetApp Keystone with HCLTech U4X Architecture #
The joint offering combines HCLTech’s Utility for Everything (U4X) digital infrastructure framework with NetApp Keystone, the pay-as-you-go storage service. The companies describe the pairing as a way to align infrastructure with evolving workload requirements, so capacity and performance track demand rather than a purchase cycle. Neither company has detailed the underlying data services in this announcement.
The offering also ties into HCLTech’s AI Factory suite of offerings. In the companies’ framing, the combination strengthens data readiness and governance while letting organizations run workloads closer to where data resides, which they position as an efficiency and time-to-value gain rather than a throughput claim.
What the Companies Are Claiming #
“We believe strong partnerships are built by co-creating technology offerings, business models, solutions and services that deliver meaningful customer value,” said Alvaro Celis, Chief Partner and Ecosystem Officer at NetApp. “This collaboration gives customers access to NetApp’s intelligent data infrastructure through a flexible, accessible business model, backed by HCLTech’s hands-on expertise.”
“Our collaboration with NetApp reflects our focus on helping enterprises scale AI in a pragmatic and efficient way,” said Rampal Singh, Senior Vice President, Hybrid Cloud Business Unit at HCLTech. “By combining flexible infrastructure models with strong data management capabilities, we are enabling clients to unlock the value of their data and accelerate their AI journeys.”
HCLTech points to two deployments as evidence, both unnamed. A global food and beverage company adopted the consumption-based model to improve operational flexibility and reduce upfront investment, and a European telecommunications provider used it to improve scalability and resilience across distributed environments while meeting strict regulatory requirements. Shifting enterprise storage onto an OPEX footing is the clear pitch, letting organizations adjust capacity as AI workloads fluctuate instead of buying for peak.