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Infinidat is one of the largest providers of storage and infrastructure solutions, a service partner focused on enabling the enterprise market. We spoke with Infinidat CMO Rob Hertzog and Stan Wysocki, president of Mark III Systems, to learn more about how enterprise storage and AI intersect with customers around the world.
The following Q&A has been edited lightly for clarity.
Enterprise Storage remains a complex yet valuable conversation
What strategies will give channel partners a competitive advantage when selling enterprise storage after 2025?
Wysocki: To win after 2025, channel partners must fit the reality of modern AI stacks. Storage platforms require performance, scale and flexibility to support a wide range of GPU-driven initiatives, from traditional ML to complex generation and agent AI workflows. Partners who can architect and provide scalable, AI-enabled storage environments (including Super Podscale AI factories) create real, differentiated value in the enterprise.
HERZOG: AI, along with cyber and cost-effective “Green It” systems infrastructure, is one of the biggest trends in the enterprise market. From a CIO's perspective, AI has consistently been one of the top costs of this year. Storage plays a major role in AI success in enterprise environments.
New opportunities for growth are creating Infinidat's latest enterprise AI storage solutions in the channel?
Wysocki: Infinidat's enterprise AI storage products are built for the scale, speed, and reliability needed to support trillion parameter agent AI systems. To operate these AI plants, businesses need storage solutions that can handle the explosive growth of unstructured data and ensure degraded access to the records system. This opens up significant growth opportunities for partners that can integrate Infinidat with scalable pods and clusters.
HERZOG: Channel partners can gain a competitive advantage by selling Infinidat's generative AI-centric search rise (RAG) workflow deployment architecture. Our RAG architecture greatly improves the accuracy and relevance of AI models with modern private data from multiple corporate data sources, such as databases that support NFS, including unstructured and structured data. The beauty of a lug-centric solution is that it does not require special equipment. As long as your dataset supports the NFS protocol, it uses existing Infinidat Enterprise Storage, hybrid multicloud storage, and also works with non-finidat storage arrays from competitors.
How can channels selling storage solutions leverage the rise of AI as a driving force behind the enterprise market?
Wysocki: To stay relevant, channels must move beyond the data center and approach innovation layers, Data Scientists, ML Engineers, and AI developers. By understanding how an AI pipeline works and adding values to the top of the stack, partners can define a storage stack that impacts infrastructure decisions and provides performance, availability and speed.
HERZOG: It's not just AI servers. This is obviously important, but it is about the right vector database, LLMS, SLM, and it is also about understanding the datasets needed to keep your AI workloads and workflows accurate and up to date. Having the right enterprise storage to handle AI applications and workloads. With Infinidat's RAG architecture, enterprises utilize Infinibox and Infinibox SSA as the basis for optimizing the output of AI models. It also offers the flexibility to use RAG in a hybrid multi-cloud environment with award-winning Infuzeos Cloud Edition and NFS dataset support. rag augments your AI models using related and private data retrieved from NFS datasets (files or databases). All of these run very well in Infinidat storage environments. RAG allows businesses to automatically generate more accurate, more informed, and more reliable responses to user queries. AI learning models (i.e. LLM or SLM) can refer to information and knowledge beyond the trained data and use new data to continuously refine the rag pipeline.
Business Approach Partners and Vendors Should Keep in mind in 2025
What business models work most effectively on the channel to adapt to the evolution of AI's enterprise storage?
Wysocki: Legacy business models centered around IT relationships and standard server builds are rapidly becoming obsolete. The AI initiatives that powered NVIDIA are driven by innovators outside of traditional IT structures. Serving data science teams, embedding them into the AI development cycle and providing NVIDIA-optimized storage solutions are the perfect channel partner to grow.
HERZOG: IT solution providers need to have their storage teams understand AI fundamentally. If you have server practice, you need a team to understand how storage is important to your AI workload. Just as the Mark III system is very successful, it is convincing for partners to set up a business model, with software, consulting and software development – to set up a practice -.
What do you think about how certain legacy storage vendors weaken their commitment to storage innovation and define the future by focusing on other types of technology? And what does that mean for the channel?
wysocki: Enterprises don't need to talk more about “table stakes” such as air gaps and cyber resilience. And claiming that they have “AI on the platform” is no longer a differentiator. What's important now is: can storage keep up with GPUs and next-generation AI models quickly? As Nvidia literally accelerates its computational performance each year, storage needs to evolve just as quickly to avoid becoming a bottleneck. Vendors and partners are guided to lean towards this challenge with genuine innovation. Those who don't will fall behind.
Herzog: While some competitors are slowing down storage innovation as they focus on other tech priorities, Infinidat continues to work on storage innovation for high-end companies. How to improve Genai with RAG solutions is one of the latest examples showing how AI is placing powerful tools in the hands of partners to take advantage of the opportunities they are creating.
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