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NetApp Introduces Intelligent Data Infrastructure

NetApp announced new capabilities that maximize the potential of generative artificial intelligence (Gen AI) projects and build competitive advantage for users.

Customers can now take their AI projects to the next level by combining NetApp’s intelligent data infrastructure with high-performance compute, networking and software from NVIDIA.

“NetApp is the intelligent data infrastructure company, with solutions optimized to maximize the potential of our customers’ AI investments,” said Arunkumar Gururajan, Vice President of Data Science & Research at NetApp. “Our unique approach to AI gives customers complete access and control over their data throughout the data pipeline, moving seamlessly between their public cloud and on-premises environments. By tiering object storage for each phase of the AI process, our customers can optimize both performance and costs exactly where they need them. Our unified approach delivers the performance, productivity, and protection customers need to quickly innovate with AI.”

To support companies leveraging Gen AI to improve their operations and strategic decision-making, NetApp released updates to its intelligent data infrastructure capabilities including:

- NetApp AIPod™ is NetApp's AI-optimized converged infrastructure for organizations’ highest priority AI projects, including training and inferencing. NetApp AIPod powered by NVIDIA DGX is now a certified NVIDIA DGX BasePOD solution using NVIDIA DGX H100 systems integrated with NetApp AFF C-Series affordable capacity flash systems to drive a new level of cost/performance while optimizing rack space and sustainability. NetApp AIPod powered by NVIDIA DGX also continues to support NVIDIA DGX A100 systems.

- New FlexPod for AI reference architectures extend the leading converged infrastructure solution from NetApp and Cisco. FlexPod for AI now supports the NVIDIA AI Enterprise software platform. FlexPod for AI can now be extended to leverage RedHat OpenShift and SuSE Rancher. New scaling and benchmarking have been added to support increasingly GPU-intensive applications. Customers can use these new FlexPod solutions as an end-to-end blueprint to efficiently design, deploy, and operate the FlexPod platform for AI use cases.

- NetApp is now validated for NVIDIA OVX systems. NetApp storage combined with NVIDIA OVX computing systems can help streamline enterprise AI deployments, including model fine-tuning and inference workloads. Powered by NVIDIA L40S GPUs, validated NVIDIA OVX solutions are available from leading server vendors and include NVIDIA AI Enterprise software along with NVIDIA Quantum-2 InfiniBand or NVIDIA Spectrum-X Ethernet, and NVIDIA BlueField-3 DPUs. NetApp is one of the first partners to complete this new storage validation for NVIDIA OVX.

NetApp also is announcing new cyber-resilience capabilities including one of the first uses of AI/ML embedded in storage to fight ransomware. The new Autonomous Ransomware Protection with AI (ARP/AI) will provide the next generation of machine learning in ONTAP, giving the increased accuracy and performance required to detect and mitigate new, more sophisticated cyber threats.

“AI powers mission-critical use cases in every industry, from healthcare to manufacturing to financial services,” said Tony Paikeday, Senior Director of AI Systems at NVIDIA. “NetApp AIPod certified for NVIDIA DGX BasePOD provides a powerful reference architecture that helps enterprises eliminate design complexity, reduce deployment time frames, and simplify ongoing operations.”

“GenAI has massive potential to help organizations harness their data to uncover business insights and improve operational efficiency,” said Archana Venkatraman, Research Director, Cloud Data Management, IDC. “NetApp has continuously adapted to deliver the services and solutions customers need to effectively manage their data pipelines. These updates further illustrate NetApp’s willingness to evolve and bring innovations to customers that unlock the full potential of AI.”

NetApp delivers a unified approach to infrastructure and data management that eliminates data silos, brings enhanced performance and trusted data protection to customers’ AI turnkey solutions, and helps customers accelerate the time to results for their AI projects.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

NetApp Introduces Intelligent Data Infrastructure

NetApp announced new capabilities that maximize the potential of generative artificial intelligence (Gen AI) projects and build competitive advantage for users.

Customers can now take their AI projects to the next level by combining NetApp’s intelligent data infrastructure with high-performance compute, networking and software from NVIDIA.

“NetApp is the intelligent data infrastructure company, with solutions optimized to maximize the potential of our customers’ AI investments,” said Arunkumar Gururajan, Vice President of Data Science & Research at NetApp. “Our unique approach to AI gives customers complete access and control over their data throughout the data pipeline, moving seamlessly between their public cloud and on-premises environments. By tiering object storage for each phase of the AI process, our customers can optimize both performance and costs exactly where they need them. Our unified approach delivers the performance, productivity, and protection customers need to quickly innovate with AI.”

To support companies leveraging Gen AI to improve their operations and strategic decision-making, NetApp released updates to its intelligent data infrastructure capabilities including:

- NetApp AIPod™ is NetApp's AI-optimized converged infrastructure for organizations’ highest priority AI projects, including training and inferencing. NetApp AIPod powered by NVIDIA DGX is now a certified NVIDIA DGX BasePOD solution using NVIDIA DGX H100 systems integrated with NetApp AFF C-Series affordable capacity flash systems to drive a new level of cost/performance while optimizing rack space and sustainability. NetApp AIPod powered by NVIDIA DGX also continues to support NVIDIA DGX A100 systems.

- New FlexPod for AI reference architectures extend the leading converged infrastructure solution from NetApp and Cisco. FlexPod for AI now supports the NVIDIA AI Enterprise software platform. FlexPod for AI can now be extended to leverage RedHat OpenShift and SuSE Rancher. New scaling and benchmarking have been added to support increasingly GPU-intensive applications. Customers can use these new FlexPod solutions as an end-to-end blueprint to efficiently design, deploy, and operate the FlexPod platform for AI use cases.

- NetApp is now validated for NVIDIA OVX systems. NetApp storage combined with NVIDIA OVX computing systems can help streamline enterprise AI deployments, including model fine-tuning and inference workloads. Powered by NVIDIA L40S GPUs, validated NVIDIA OVX solutions are available from leading server vendors and include NVIDIA AI Enterprise software along with NVIDIA Quantum-2 InfiniBand or NVIDIA Spectrum-X Ethernet, and NVIDIA BlueField-3 DPUs. NetApp is one of the first partners to complete this new storage validation for NVIDIA OVX.

NetApp also is announcing new cyber-resilience capabilities including one of the first uses of AI/ML embedded in storage to fight ransomware. The new Autonomous Ransomware Protection with AI (ARP/AI) will provide the next generation of machine learning in ONTAP, giving the increased accuracy and performance required to detect and mitigate new, more sophisticated cyber threats.

“AI powers mission-critical use cases in every industry, from healthcare to manufacturing to financial services,” said Tony Paikeday, Senior Director of AI Systems at NVIDIA. “NetApp AIPod certified for NVIDIA DGX BasePOD provides a powerful reference architecture that helps enterprises eliminate design complexity, reduce deployment time frames, and simplify ongoing operations.”

“GenAI has massive potential to help organizations harness their data to uncover business insights and improve operational efficiency,” said Archana Venkatraman, Research Director, Cloud Data Management, IDC. “NetApp has continuously adapted to deliver the services and solutions customers need to effectively manage their data pipelines. These updates further illustrate NetApp’s willingness to evolve and bring innovations to customers that unlock the full potential of AI.”

NetApp delivers a unified approach to infrastructure and data management that eliminates data silos, brings enhanced performance and trusted data protection to customers’ AI turnkey solutions, and helps customers accelerate the time to results for their AI projects.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.