NetApp buys DataPelago to boost AI data processing

NetApp has acquired AI data infrastructure company DataPelago, adding its data-processing technology to NetApp's storage business.

California-based DataPelago develops software that processes data where it is stored rather than moving it to separate computing systems. According to NetApp, that approach can cut infrastructure costs by up to 80% and deliver performance up to 10 times faster for AI and analytics workloads than conventional methods.

The acquisition brings DataPelago's Nucleus engine into NetApp's portfolio. The software uses CPUs and GPUs to process data at the storage layer, aiming to reduce a key hurdle in enterprise AI projects: the time and cost of preparing and moving data before models can use it.

Companies investing in AI systems often face a mismatch between spending on chips and models and the state of their underlying data estates. Large businesses typically store information across on-premises systems and multiple cloud environments, making data preparation, governance and access a slow and expensive part of AI deployment.

George Kurian, chief executive officer at NetApp, said the acquisition reflects growing pressure on customers to make stored data easier to use for AI work.

"As AI models and the chips that power them get ever more effective, enterprises need data infrastructure that is just as intelligent and powerful to harness the potential of their data," Kurian said. "NetApp is leading the industry in helping customers drive innovation and generate business value by giving them full command of their most important asset: their data. With DataPelago, we are extending our ability to help customers understand and process their data with the agility required to unleash competitive advantage."

Storage focus

At the centre of the deal is the idea of bringing compute closer to data. Instead of copying large datasets from operational systems into external compute clusters for analysis or AI training, DataPelago's software is designed to run processing tasks where the data already resides.

Copying data between systems can create delays, add cost and introduce governance issues, particularly in regulated industries or large organisations with fragmented data estates. By embedding data processing closer to storage, NetApp is seeking to simplify a part of the AI workflow that customers have struggled with.

Rajan Goyal, founder and chief executive officer of DataPelago, said the company had focused on removing those bottlenecks.

"DataPelago is on a mission to eliminate the data processing bottlenecks that prevent AI innovation from reaching its full potential," Goyal said. "Joining NetApp gives us the opportunity to combine our breakthrough processing technology with the industry's best data infrastructure portfolio. Enterprises have invested billions in GPUs and AI models, but their data remains fragmented, leaving valuable computing resources to sit idle rather than putting these investments to work. Together, we're positioned to help customers simplify and accelerate AI deployment at scale."

DataPelago is already used by large enterprises in several industries, according to NetApp, though the company did not identify customers or disclose financial terms. After the transaction, DataPelago will operate as a wholly owned subsidiary of NetApp.

AI bottlenecks

The move highlights how infrastructure suppliers are repositioning themselves around AI spending, which has so far centred heavily on semiconductors, cloud computing and model development. Storage and data management vendors are increasingly arguing that AI projects will stall if businesses cannot govern, locate and prepare their data more efficiently.

For NetApp, the acquisition expands its role in the AI stack beyond data storage and management into data processing itself. That may help it appeal to customers that want to avoid creating separate systems for data movement, preparation and analytics.

Syam Nair, chief product officer at NetApp, described DataPelago's software as a way to process and prepare data across CPUs and GPUs without moving it.

"DataPelago's Nucleus engine brings software-defined acceleration directly to the storage layer, processing data across CPUs and GPUs so enterprises can prepare, govern and activate their data for AI without moving it. This is true zero-copy activation," Nair said. "NetApp manages more enterprise data across more environments than anyone in the industry. The next phase of AI will be won by those who make that data work at the source, and the DataPelago team brings the technical depth and velocity to get us there faster."

The acquisition follows a series of alliances by NetApp with technology groups including Cisco, Google Cloud, Red Hat and SK Telecom, as it seeks to strengthen its position in data infrastructure for AI workloads.