Q&A: How Infinidat Partners Embrace Storage for the AI Era

Infinidat is one of the largest providers of storage and infrastructure solutions, servicing partners focused on enabling the enterprise market. We spoke with Infinidat CMO Eric Herzog and Mark III Systems President Stan Wysocki to learn more about how enterprise storage and AI are intersecting for customers worldwide.

The following Q&A has been lightly edited for clarity.

Enterprise storage remains a complex but worthwhile conversation

What strategies can give a channel partner a competitive advantage in selling enterprise storage in 2025 and beyond?

WYSOCKI: To win in 2025 and beyond, channel partners must align with the realities of the modern AI stack. Storage platforms need the performance, scale, and flexibility to support a range of GPU-driven initiatives—from traditional ML to complex generative and agentic AI workflows. Partners that can architect and deliver scalable, AI-ready storage environments (including SuperPod-scale AI Factories) will create real, differentiated value in the enterprise.

HERZOG: AI is one of the biggest trends in the enterprise market, along with cyber and cost-effective, ‘green IT’ systems infrastructure. From a CIO perspective, AI is consistently one of the top priorities for IT spend this year. Storage plays a major role in the success of AI in enterprise environments.

What new opportunities for growth are Infinidat’s latest enterprise AI storage solutions creating in the channel?

WYSOCKI: Infinidat’s enterprise AI storage offerings are built for the scale, speed, and reliability

needed to support trillion-parameter Agentic AI systems. As enterprises operationalize these AI

factories, they need storage solutions that can handle the explosive growth in unstructured data and ensure low-latency access to systems of record. This opens up significant growth opportunities for partners who can integrate Infinidat with scalable pods and clusters.

HERZOG: Channel partners can gain a competitive advantage by selling Infinidat’s generative AI-centric Retrieval-Augmented Generation (RAG) workflow deployment architecture. Our RAG

architecture significantly improves the accuracy and relevancy of AI models with up-to-date, private data from multiple company data sources, including unstructured data and structured data, such as databases supporting the NFS. The beauty of our RAG-centric solution is that it does not require any specialized equipment. It uses existing Infinidat enterprise storage, hybrid multi-cloud storage, and even works on non-Infinidat storage arrays from our competitors, as long as the dataset supports the NFS protocol.

How can the channel that sells storage solutions take advantage of the rise of AI as a driving force in the enterprise market?

WYSOCKI: To stay relevant, the channel must move beyond the data center and get closer to the innovation layer—data scientists, ML engineers, and AI developers. By understanding how AI pipelines operate and by adding value at the top of the stack, partners can influence infrastructure decisions and define the storage stack that delivers on performance, availability, and speed.

HERZOG: It’s not just about AI servers, which clearly are important, too, but it is also about the right vector databases, LLMs and SLMs, and understanding what datasets are needed to have the AI workloads and workflows be accurate and constantly up to date. It’s about having the right enterprise storage that can handle AI applications and workloads. With Infinidat’s RAG architecture, enterprises utilize InfiniBox and InfiniBox SSA as the basis to optimize the output of AI models. We also provide flexibility of using RAG in a hybrid multi-cloud environment with our award-winning InfuzeOS Cloud Edition and non-Infinidat storage with NFS dataset support. RAG augments AI models using relevant and private data retrieved from any NFS dataset – file or database – all of which run very well in an Infinidat storage environment. RAG enables enterprises to auto-generate more accurate, more informed and more reliable responses to user queries. It enables AI learning models (i.e. LLM or a SLM) to reference information and knowledge that is beyond the data on which it was trained, continuously refining a RAG pipeline with new data.

The business approach that partners and vendors should keep in mind in 2025

What kinds of business models are working most effectively in the channel to adjust to the evolution of enterprise storage for AI?

WYSOCKI: Legacy business models centered on IT relationships and standard server builds are rapidly becoming obsolete. NVIDIA-powered AI initiatives are being driven by innovators outside traditional IT structures. Channel partners that shift their go-to-market to serve data science teams, embed themselves in AI development cycles, and deliver NVIDIA-optimized storage solutions are the ones best positioned to grow.

HERZOG: IT solution providers need to have their storage teams have at least a basic understanding of AI. If they have a server practice, they need those teams to understand how storage is critical for AI workloads. It’s compelling for partners to set up a business model − indeed a practice − with software, consulting, and software development, just as Mark III Systems has done very, very successfully.

What are your thoughts on how certain legacy storage vendors are weakening their commitment to storage innovation and focusing on other types of technologies to define their future? And what could it mean for the channel?

WYSOCKI: The enterprise doesn’t need more talk about “table stakes” like air gaps and cyber

resilience—those are expected. And claiming to have “AI in the platform” is no longer a differentiator. What matters now is: can your storage keep up with rapidly advancing GPUs and next-gen AI models? As NVIDIA accelerates compute performance, literally every year, storage must evolve just as rapidly to avoid becoming the bottleneck. The vendors—and partners—that lean into this challenge with genuine innovation will lead. Those that don’t will fall behind.

HERZOG: While some competitors have slowed down their storage innovation because they are focused on other tech priorities, Infinidat continues to be committed to storage innovation in the high-end enterprise. How we improve GenAI with our RAG solution is one of the latest examples of demonstrating how we are putting powerful tools into the hands of our partners to take advantage of the opportunities that AI is creating.

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