This week, NetApp acquired an AI data infrastructure company, paving the way for data processing closer to storage repositories and expanding the options available to companies facing AI cost and data management challenges.
NetApp’s acquisition this week of California-based DataPelago for an undisclosed sum will further push NetApp into AI data management. NetApp began the transition from AI-enabled data storage to a data management layer last year with the launch of AI Data Engine, a set of software tools that collect, manage, synchronize, protect, and prepare data for use in AI applications. We also shipped new AFX all-flash arrays, our first foray into distributed storage that separates storage controllers from storage capacity.
Rob Strechay, founder and president of Smuget Consulting, said DataPelago will help NetApp build on that foundation and go deeper into areas that competitors like Dell, Nutanix, Everpure and Vast are also seeking.
Rob Strechay
“We believe this has the potential to evolve into a connectivity layer between NetApp’s file and object storage platform, multimodal enterprise data, and modern compute engines such as Apache Spark and other open analytics frameworks,” Strechay said. “By bringing GPU- and CPU-accelerated processing directly into the storage layer, which is the direction NVIDIA is moving in the stack, organizations will be able to perform analytics and AI data preparation without constantly copying data to a separate compute cluster.”
This appears to be the direction NetApp intended with DataPelago and its Nucleus data processing engine, according to the company’s press release. By processing data at the storage layer rather than moving it to an external compute cluster, infrastructure costs are reduced by up to 80% and performance is up to 10x faster, the release states.
AI data management intensifies among enterprise challenges
As the industry transitions to agent AI over the past year, data management has emerged as a key challenge for enterprise AI, along with security and governance. Market research shows that deploying enterprise AI agents at scale remains challenging, with data management being the main culprit.
According to Simon Robinson, an analyst at Omdia, a division of Informa TechTarget, a majority (68%) of 449 IT leaders surveyed by Omdia in September 2025 cited data management as the most challenging aspect of production AI implementation.
As data storage vendors rush to seize opportunities to expand into new markets, “the Venn diagrams of ‘storage management’ and ‘data management’ are increasingly overlapping,” Robinson said. “All other storage vendors are making similar moves.”
NetApp’s longevity in storage management could be an advantage as it moves between specialties, he said.
“They understand, store, and manage vast amounts of unstructured data at scale,” Robinson said. “Customers want to unify data across distributed environments, and NetApp has a good story on this. DataPelago has the potential to enhance that.”
Competitors such as Vast and Dell share that vision, but “If NetApp can realize that vision, DataPelago’s architecture and potential for integration across NetApp’s broader ecosystem could provide a more open and broadly integrated execution model,” Strecay said.
At least initially, DataPelago will operate as a wholly owned subsidiary of NetApp, according to a company press release.
DataPelago is “very focused on the data processing layer, which appeals to a different persona that NetApp is trying to target because it’s not a storage engine, so it makes sense to keep it separate, at least for now,” Robinson said. “But we’re going to look at how NetApp intends to integrate the technology.”
Beth Pariseau, senior news writer at Informa TechTarget, is an award-winning IT journalism veteran. Any tips? send an email to her or connect linkedin
