Building a Data Platform for AI: Challenges, Opportunities, and Hype

Building a data platform for AI is both exhilarating and challenging. Data demands are soaring, new models are emerging constantly, and AI architectures are evolving at breakneck speed. With the rapid rise of LLMs and generative AI, innovation is accelerating even further. It’s an exciting yet intense moment, and while we’re still in the early stages of AI, today’s needs will continue to evolve as the technology matures.

The AI data platform market today resembles the early days of flash storage, where raw performance was everything. Many new entrants have focused their products as a “drag racer,” prioritizing speed over the architectural and platform components that enable sustainable growth. Now, however, we’re seeing AI architectures moving from drag racers to F1 cars with a maniacal focus on consistent performance across diverse workloads and a precise handling of complex demands with precision and balance. 

While there’s no one formula for success, one thing is clear: Just as F1 cars aren’t built for straight-line speed but dominate through precision engineering, AI data platforms require innovation across the entire storage stack. Success here will depend on a deeply innovative and co-engineered approach across both hardware and software, resulting in a seamless, consistent, and reliable solution delivered “as a service” to the market at large.

At Pure Storage, we support hundreds of AI customers across diverse stages of their innovation journeys, including some of the largest AI environments in existence. Through our collaboration with these customers, we’ve identified essential requirements they all share:

• Flexibility and the ability to evolve as requirements change: AI is evolving rapidly, and the last thing you want is to invest in technology that can’t grow with your business. A platform that not only keeps pace with change but also provides a strategic advantage is essential. While performance and scalability are key, in today’s fast-paced environment, flexibility becomes your greatest asset, enabling your AI team to adapt to any challenge ahead.
• Maximize GPU utilization across diverse workloads: Organizations manage varied workloads, from sequential data ingestion to high-concurrency tasks, all requiring dynamic management to keep GPUs fully utilized. This capability must extend across block, file, and object storage, both on premises and in the cloud.
• Scalable performance with flexible consumption: AI projects often begin as pilots and scale to production. Platforms must seamlessly and non-disruptively expand from terabytes to exabytes, with “pay-as-you-go” consumption for smooth, cost-efficient operations.
• Guaranteed uptime SLAs and long-term durability: Whether experimenting or running full production inference, resilience and uptime are essential. Proven resiliency and continuous availability for critical services prevent downtime, ensuring uninterrupted innovation and development.
• AI-driven automation and simplicity: Managing complexity at scale demands time and resources, especially with undefined scaling and the need for agility. Autonomic infrastructure with self-tuning performance, policy-based upgrades, and capacity rebalancing reduces operational overhead, removes unnecessary operational complexity, and frees up IT teams to focus on innovation.
• Efficiency and sustainability: Access to power, rack space, and cooling at scale is challenging and expensive, making it essential to balance performance and density. Optimizing flash management to reduce energy, cooling, and space requirements is critical to supporting sustainable, high-performance operations.
• Security and network flexibility: Your critical data drives training, RAG, and other processes, requiring both robust security and seamless network access. This must include end-to-end encryption, malware detection, and rapid breach recovery—all via standard Ethernet protocols.

The Truth behind the Hype: Avoiding Common Pitfalls

When you look beyond the marketing hype in today’s storage market, you’ll often find bold claims and “miracle” solutions promising to be the ultimate answer for AI needs. Some even claim to be the “Operating System for AI,” the panacea for all that ails! Unfortunately, we have all learned over the last few decades that “all that glitters is not gold” and feature velocity often fails in long-term reliability. Violin Memory serves as a cautionary tale: Despite creating the fastest hardware, they lacked the robust storage solution enterprises required for long-term success, ultimately failing to make a lasting impact. Unfortunately, customers were left with painful architectural debt which took years to resolve. 

Flash technology has driven transformative change over the past decade, but today’s claimed revolutionary offerings, such as flash/hard drive hybrid architectures, have failed by delivering mediocre performance across the board despite low acquisition costs. Storage class memory (SCM), combined with QLC hybrid tiering, has also delivered little true innovation. With Optane effectively DOA and the illusory “magic” of performance fading, vendors who relied on these technologies are left with significant architectural challenges and painful upgrades for customers. When coupled with few meaningful advances in commodity SSD performance and density, some vendors are leaning heavily on marketing promises, with a hope that engineering will be able to somehow deliver.

While performance—especially to keep GPUs fully utilized—is crucial, it’s only part of the solution. As the AI hype gives way to practical adoption across enterprises, we at Pure Storage believe that platforms that support a broad range of use cases with efficiency, reliability, and sustainability will become essential. Performance needs to go beyond speed, encompassing multi-dimensional capabilities like concurrent reads and writes, metadata scaling, resilience, and sustainability to meet diverse, real-world demands.

With that in mind, here’s a straightforward look at the current options available and how to assess them. You’ll see that all these systems fall short of a true as-a-service model, lacking performance guarantees to keep GPUs fully utilized, 25% capacity headroom, and the uptime assurance required for 99.9999% reliability. Efficiency and sustainability? Those are also left out of the equation.

• Parallel file systems: While these systems offer high performance, they come with complex management, frequent updates, and lack guaranteed SLAs. They excel in specific use cases but often falter when scaled to enterprise environments, where the management burden can quickly outweigh their performance benefits. Do you really want your highly skilled AI team bogged down by maintenance? Is it even financially and operationally feasible to maintain these systems at scale?
• DIY disaggregated hybrid architectures: Disaggregated hybrid architectures may sound promising, but they often fail to deliver in practice. Storage class memory (SCM), once hailed as revolutionary, has proven costly and limited in capability. Pairing SCM with QLC flash creates only a temporary illusion of speed—performance drops as capacity fills and flash ages, leading to inconsistency over time. AI workloads require dependable, burst-ready performance that caching systems struggle to provide. The complexity only grows with “bring your own hardware” approaches, custom Linux distributions, and networking intricacies, making the operational experience potentially nightmarish. Thoroughly test these systems “at scale” before buying into the marketing claims. Ironically, many of these products are now removing SLC from their designs and writing directly to flash. What a concept! Welcome to 2016! 
• Hyperconvergence hopes and dreams: Offloading too many non-storage tasks to storage systems creates CPU contention, making the dream of running non-storage operations on storage hardware a pipedream. Plus it locks customers in. The most reliable approach is to rely on dedicated software for non-storage tasks, ensuring consistent performance and avoiding dependence on watered-down solutions bundled by storage vendors.
• Reliability and scalability: Rapid adaptation and innovation demands both reliability and scalability. Yet, the market feels like it’s moving backward. Sure, GPU utilization is crucial, but many platforms still can’t handle quick, non-disruptive firmware upgrades without downtime. Capacity expansions often hit performance or require downtime. Worse still, adding performance nodes requires data resharding, leading to more interruptions. Customers tell us daily about performance hits as high as 80%, or outright downtime, when a single drive, node, or SLC caching device fails. It feels like we’re back in the early 2000s when it comes to user experience.

That leads us to the next logical question to ask: Why is Pure Storage best positioned to solve the challenges in enterprise AI? Let’s dig in.

The Pure Storage Platform for AI: The Future of AI Infrastructure

The Pure Storage platform delivers a unified, multi-dimensional solution built on 15 years of relentless software innovation and flash technology. It empowers organizations to seamlessly execute every stage of the AI pipeline, from data curation and model training to serving and inference, with autonomously tuned, high-performance storage, all with Pure Storage efficiency and simplicity in a single, powerful platform. More than just storage, it’s engineered to accelerate AI outcomes at the enterprise level, offering a seamless, cloud-like experience through an integrated data platform that supports many access patterns by many clients on the same data all at once (throw in integrated data versioning and we have ourselves a hat trick). 

A Data Platform, Not a Storage Array

While others in the data storage industry love to talk about their storage array’s performance, features, and functionality, our customers tell us all the time that the real problem we solved for them was that they no longer have to worry about managing their storage. Our platform is different in a few fundamental ways. 

• Multi-dimensional performance at scale: AI workloads generate diverse I/O profiles, making a consistent, multi-dimensional storage infrastructure vital for scalable, consolidated data and performance. Our “AND, not OR” approach combines scale-out solutions for scalability with scale-up architectures needed for low-latency, transactional workloads like vector databases. Unified FlashArray™ and FlashBlade® consolidate block, file, and object storage for high scalability and performance. DirectFlash® technology removes SSD inefficiencies, centralizing IO path management for peak performance, while DirectFlash Modules (DFMs) offer high density (150TB today, 300TB soon) with top-tier resiliency (<0.2% annual return rate) and zero downtime.

• Flexibility that evolves with you: Our Evergreen//One™ storage-as-a-service solution is a long-term, comprehensive service built on our unique Evergreen® architecture, providing continuous innovation, seamless upgrades, and predictable costs. With industry-first SLAs covering performance, capacity, efficiency, and uptime—backed by unmatched technology—Pure Storage handles power, cooling, and rack space, so you pay only for the service, not the hardware maintenance. AI-optimized SLAs ensure easy throughput sizing to keep GPUs fully utilized, and everything is outlined in a straightforward, under-five-page contract—no fluff, no surprises.

• Zero tuning and always efficient and performant: While others may market simplicity, our platform is truly autonomous by design, offering self-tuning performance and continuously optimized data layouts without human intervention. Built on our own purpose-built operating system—the Purity Operating Environment—and DirectFlash hardware, it ensures peak efficiency and sustainability, scaling effortlessly with near-zero management. It also delivers peak performance without the need for complex HPC science projects or the complications of hybrid systems, seamlessly supporting multiple access patterns at once.
• Simple automation, lifecycle management, and orchestration: Our built-in AIOps—Pure1®—simplifies automation, lifecycle management, and orchestration by offering enterprise-wide visibility and management in a single interface, with an AI co-pilot that removes guesswork. Set policies once, and it manages compliance, automated upgrades, and real-time security and sustainability tracking. A single control plane—Pure Fusion™—enables instant resource access, allowing admins to configure services once so developers and business users can access them without IT delays. This reduces wait times from months to seconds, empowering teams to innovate and focus on high-impact work.
• Never take a downtime ever again: Evergreen is more than a concept—it’s a continuous innovation model powered by the unique architecture of Pure Storage. When I joined Pure Storage from EMC, I quickly realized that the heart of Pure Storage arrays is Purity, not the controllers. Pure Storage’s stateless architecture enables non-disruptive hardware upgrades, eliminating the need for migrations or forklift upgrades. After experiencing my first seamless hardware swap, I knew this was revolutionary. With stateless controllers and plug-and-play simplicity, Evergreen allows easy density and performance upgrades, keeping the platform adaptable and modern with zero planned downtime.
• Container orchestration and optimized Kubernetes support: Orchestrating an AI pipeline requires seamless coordination, with Kubernetes at its core. Our platform leverages Portworx®—a cloud-native data solution designed for Kubernetes and containerized applications—on a unified, scalable, and secure storage platform. It provides persistent storage for stateful workloads, zero-downtime disaster recovery, and seamless data portability, empowering enterprises to manage data-intensive applications across hybrid and multi-cloud environments with agility. This flexible platform integrates with any Kubernetes-enabled solution, from Kubeflow on Red Hat OpenShift to Milvus on Rancher, allowing clients to optimize their Kubernetes stack of choice.
• The most sustainable platform: AI is a power-hungry endeavor. Many AI innovators are looking for ways to decrease power consumption to allow for more GPU power to be stacked in the data center. Pure Storage has a proven track record of efficiency and sustainability:
  • Unmatched efficiency, using only 10% of the power of legacy HDD systems
  • Reduces floor and rack space needs by up to 95%, slashing cooling costs by up to 75%
  • Cutting-edge design and recycling practices reduce e-waste by 3x, advancing a sustainable future

Validated Solutions and Reference Architectures

Pure Storage focuses on delivering top-tier storage, providing validated, certified reference architectures for the best performance and reliability. We partner with leaders like Arista, Cisco, NVIDIA, and Supermicro for flexibility and seamless interoperability. Unlike restrictive hyperconverged platforms, Pure Storage’s open architecture offers freedom of choice without vendor lock-in, ensuring our storage evolves with AI demands.

These solutions include:

• Pure Storage GenAI RAG with NVIDIA
• AIRI® – AI-ready infrastructure with NVIDIA DGX BasePOD
• NVIDIA OVX validation – OEM servers with NVIDIA L40s GPUs
• Certified storage for NVIDIA DGX SuperPOD: Pure Storage and NVIDIA have certified the Ethernet-based FlashBlade//S™ with NVIDIA DGX SuperPOD, offering enterprises a high-speed, energy- and space-efficient platform for large-scale AI needs. This turnkey solution accelerates AI training and inference while enabling enterprises to leverage existing Ethernet infrastructure for demanding AI workloads. Whether or not customers require DGX SuperPOD’s scale, data challenges for AI arise at all sizes. Read the blog, which explores the unique storage opportunities and challenges in AI environments, cutting through the hype.
• FlashStack® for AI – Cisco Validated Designs for GenAI, inferencing, and MLOps
• GenAI Pods: This full-stack solution provides turnkey designs built on the Pure Storage platform. Organizations can use the Pure Storage GenAI Pod to accelerate AI-powered innovation and reduce the time, cost, and specialty technical skills required to deploy generative AI (GenAI) projects.
• Validated AI Design Library – See our entire portfolio of validated designs

What’s Next for the Pure Storage Platform

Our platform sets the industry standard for performance, reliability, efficiency, and sustainability, but we’re never content to stop there. We continuously innovate to push boundaries, enabling hundreds of AI customers, from small-scale deployments to some of the world’s largest GPU cloud, to achieve transformative AI results. Notably, our recent investment and partnership announcement with CoreWeave supports customers operating at the scale of tens of thousands of GPUs, alongside hyperscale customers like Meta’s AI Research SuperCluster. As AI innovation continues to evolve, we’re committed to continuing to build next-generation solutions that redefine what’s possible. Stay tuned for exciting updates ahead!

Conclusion: Unleash AI Innovation with Pure Storage

AI workloads need more than speed; they demand a platform that is resilient, scalable, and efficient for all workloads, especially as your AI demands evolve. The Pure Storage platform provides consistent performance, unbeatable density, and zero downtime through our unique Evergreen architecture, software innovation, and DirectFlash. Whether running transactional AI or massive, high-concurrency pipelines, Pure Storage ensures top performance without sacrificing scalability. With cloud-like flexibility and a partner committed to your success, Pure Storage helps you fully realize AI’s potential.

Don’t fall for the hype—let Pure Storage keep you ahead of the curve.

Until next time……stay flashy, my friends! (It’s good to be back!)

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