Artificial intelligence (AI) has rapidly moved from experimental projects to strategic business initiatives. Organizations now expect AI to improve productivity, accelerate decision-making, increase revenue opportunities, and create competitive advantages. As AI deployments mature, however, enterprises frequently discover that GPUs and AI models alone do not determine success. Increasingly, the underlying storage infrastructure dictates whether AI initiatives can efficiently scale into production.
A recent DCIG Competitive Intelligence Report helps organizations better understand the storage technologies that support modern AI workloads. This report, available at no cost from HPE, compares HPE Alletra Storage MP X10000 and Dell PowerScale for hosting AI workloads.
The New Object Storage Platform Requirements for AI Workloads
More enterprises look to object storage platforms to address the evolving requirements of AI-centric applications. To do so, these platforms must support more than Amazon S3 APIs and scale to tens of petabytes. This must also support new AI workflow capabilities and data management features.
These architectural features and operational considerations increasingly serve as a point of differentiation between object storage systems. They separate object storage platforms originally developed for conventional enterprise workloads from those designed to handle AI workloads.
Unlike traditional storage comparisons that emphasize capacity, performance, or scalability alone, this report begins with a broader question:
- What characteristics should organizations evaluate when selecting storage specifically for AI?
As enterprises bring more AI initiatives on premises, storage becomes a foundational technology. It directly influences AI responsiveness, operational efficiency, governance, and long-term scalability. The report helps organizations understand why storage architecture has become just as important as the compute infrastructure powering AI applications.
AI Changes What Enterprises Should Expect from Storage
Many organizations continue evaluating storage using criteria developed for specific use cases. These may include analytics, archiving, backup, file sharing, transactional, or virtualization workloads. AI follows the same pattern, but it introduces a different set of priorities.
Modern AI environments demand storage platforms capable of supporting extremely large datasets. However, they must also simultaneously provide consistent low-latency access, high throughput, intelligent metadata handling, and efficient data movement. Further, enterprises increasingly require storage platforms to integrate with AI frameworks and support growing AI pipelines without unnecessary complicating operations.
The report explains why these changing requirements have elevated object storage into a central role for AI infrastructure. It also explores how today’s object storage platforms have evolved well beyond simple repositories. They have become intelligent data platforms that increasingly participate in preparing, organizing, governing, and accelerating AI data workflows.
More Than a Traditional Product Comparison
The report compares two well-known enterprise storage platforms:
- HPE Alletra Storage MP X10000
- Dell PowerScale
This report examines how each platform approaches the challenges organizations face when building AI infrastructure. The major areas that this report covers include the following:
- Product architecture
- AI workflow optimization
- Data protection, governance, and security
- Management and operational capabilities
Each section explains why these areas matter to enterprises deploying AI and outlines the practical considerations organizations should evaluate before making long-term infrastructure decisions.
Why Architecture Matters More Than Ever
Storage architecture increasingly influences AI performance long before organizations begin tuning models or adding GPUs. Modern AI workloads require storage systems capable of scaling both performance and capacity while minimizing operational disruption as environments grow.
This report explores how architectural choices affect an object storage system’s adaptability, flexibility, upgradability, and utilization as AI initiatives expand. These design aspects come into play as the X10000 and PowerScale utilize different architectures.
The report explains the tradeoffs associated with their two different design approaches. It then provides guidance on evaluating those tradeoffs in the context of enterprise AI strategies. These insights become particularly valuable as each product’s architecture impact future decisions in multiple ways many years after product deployment.
AI Storage Becoming Increasingly Intelligent
Object storage platforms increasingly do much more than simply store information. They now participate in:
- Organizing data
- Improving discoverability
- Supporting governance
- Preparing information for AI consumption
- Helping AI applications retrieve relevant information more efficiently.
The report examines these emerging capabilities and connects them directly to practical enterprise AI deployments. Further, it explains the importance of intelligent storage services as organizations attempt to operationalize AI across more of their business.
Data Quality Still Determines AI Success
Data Quality Still Determines AI SuccessSuccessful AI depends upon high-quality data. Poorly organized, inconsistent, or incomplete information can undermine AI initiatives regardless of how much compute power organizations deploy.
Instead of focusing only on storage capacity or performance, the report discusses the growing importance of preparing, organizing, governing, and maintaining enterprise data before AI models ever begin processing it. Organizations evaluating AI infrastructure will find this perspective refreshing because it shifts attention toward one of AI’s most overlooked success factors: data quality.
Look Beyond Performance Benchmarks
While performance certainly matters, enterprises must also consider other operational factors. These include simplicity, scalability, governance, management, security, interoperability, and future flexibility. Further, AI environments evolve rapidly, making today’s purchasing decisions significantly more strategic than previous storage refresh cycles.
This report examines multiple dimensions of AI infrastructure rather than emphasizing only throughput or latency. In so doing, it provides organizations with a broader framework for evaluating storage platforms intended to support production AI workloads.
A Valuable Resource for AI Infrastructure Planning
Organizations evaluating enterprise AI infrastructure face an increasingly complex set of decisions. Traditional storage evaluation criteria remain important, but AI introduces additional considerations that many infrastructure teams have only begun to address.
This DCIG Competitive Intelligence Report offers a structured framework for understanding those considerations. Further, it compares the HPE X10000 and Dell PowerScale through the lens of AI rather than conventional enterprise storage.
In evaluating them, it minimizes overwhelming readers with detailed and sometimes difficult to grasp product specifications. Rather, it explains why specific capabilities matter and how architectural decisions influence AI deployment success.
This report serves as a valuable resource for any organization exploring AI. Whether just beginning its AI journey, modernizing its existing infrastructure, or preparing for larger production AI deployments, it will provide valuable insight into the questions that enterprises should ask before selecting storage for AI workloads.
Download this complimentary DCIG report to obtain meaningful comparisons and DCIG’s assessment of how each platform addresses modern AI requirements.
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Editor’s note: HPE is a client of DCIG