The rapid development of AI presents unprecedented challenges to data storage, demanding massive data volumes and high-performance infrastructure. Enterprises must build efficient, scalable, and secure storage systems—such as all-flash storage and data lakes—to support emerging AI workloads. At the same time, organizations must handle data security, privacy protection, and cost control. IT teams face the challenge of balancing performance, scalability, security, and operational simplicity with limited resources. Selecting infrastructure aligned with business characteristics is crucial for accelerating AI adoption and improving data management efficiency.
Overview #
AI is transforming industries at an extraordinary pace, and organizations increasingly view it as a strategic asset. However, capturing AI’s value requires more than compute and algorithms—it depends heavily on how effectively data is managed and delivered to the AI pipeline.
As AI applications expand, organizations must find ways to bring valuable, high-quality data into model training workflows. Balancing data accessibility, performance, and security is becoming a central challenge for IT teams.
Cloud-based AI development is convenient, but enterprises often prioritize data sovereignty and compliance. This forces IT teams to provide secure, high-performance on-premises environments for AI workflows. With limited resources, IT departments must determine how to deliver efficient, reliable local AI infrastructure that meets the demands of data scientists and AI engineers.
Beyond GPUs and accelerators, storage is the foundation of any AI system. High-throughput, low-latency, and scalable storage infrastructure can significantly accelerate training, reduce costs, and improve data scientist productivity. To achieve this, IT organizations must understand AI workflow characteristics and select technologies that address performance, availability, and operational requirements.
New Requirements for AI Infrastructure #
For traditional IT teams, AI infrastructure introduces unfamiliar challenges. Teams may have years of experience with storage and data management but lack expertise in GPUs, heterogeneous architectures, and high-bandwidth data pipelines.
AI workflows often rely on data from diverse, heterogeneous sources—databases, file systems, object storage, and even external datasets. Formats vary, and data may be scattered across systems. Data engineers must consolidate and prepare these datasets for training, which becomes increasingly difficult as data volume grows.
While data scientists manage data quality, IT must supply the technical foundation, including:
- High-throughput, low-latency storage
- Efficient GPU interconnects
- Reliable data replication and protection
- Seamless access to databases and data services
Most AI development today is cloud-based. Teams commonly use pretrained foundation models, fine-tuned with private enterprise data. Retrieval-Augmented Generation (RAG) is one example, enhancing LLMs by injecting domain-specific knowledge.
Because cloud development abstracts away infrastructure details, data teams may overlook storage performance or protection mechanisms. This creates a gap IT must fill—evaluating solutions, clarifying trade-offs, and advocating for on-prem capabilities such as:
- Security and compliance controls
- Predictable performance and cost
- Local data governance
- High-throughput file/object workflows
As AI projects grow, enterprises increasingly migrate workloads from cloud to on-prem or hybrid models due to:
- High and unpredictable public cloud bills
- Data security and privacy concerns
- Need for GPU and storage resource control
- Rise of infrastructure and Storage-as-a-Service solutions
Characteristics of Local AI Storage #
AI training datasets combine structured and unstructured data and are typically stored in Data Lakes or Data Lakehouses. These architectures support massive scale and high-throughput IO for AI/ML workflows.
Data science teams require:
- High bandwidth
- Low latency
- Large storage capacity
- Predictable performance under load
As AI adoption accelerates, requirements diversify. All-flash storage is emerging as the default choice because of its consistent performance characteristics.
Storage Needs by AI Maturity Stage #
- Initial or Maturing Stage: High-performance file storage plus scalable object storage
- Production Stage: Large-scale file + object storage with consistent, high performance
Core Characteristics for AI Storage Systems #
- Performance: Predictable low latency and high bandwidth
- Reliability & Protection: Strong fault tolerance to avoid data loss
- Security: Robust mechanisms for data confidentiality and integrity
- Kubernetes-Native: Seamless integration for containerized AI/ML workflows
- MLOps Acceleration: Self-service access to storage, vector databases, and ML services
- Scalability: Linear expansion of capacity and performance
- Simplicity: Easy configuration and management
- Cost-Effectiveness: Efficient performance-to-cost ratio
- Energy Efficiency: Lower power consumption and reduced TCO
Conclusion #
The rapid growth of AI is reshaping enterprise infrastructure, especially data storage. AI introduces new and complex challenges that differ significantly from traditional IT workloads. AI platform teams often lack experience with enterprise storage or on-prem operational requirements, especially when they come from cloud-native backgrounds.
For enterprises, choosing the right storage platform is the most critical decision in building AI infrastructure. An ideal AI storage platform should:
- Accelerate AI Adoption: Shorten deployment and development cycles
- Deliver Comprehensive Performance: Meet diverse requirements across reliability, efficiency, scalability, and ease of use
- Provide Advanced Features: Streamlined operations, multi-protocol access, and rich performance capabilities
- Be Container-Friendly: Integrate cleanly with Kubernetes and modern AI platforms
- Shorten Model Development Time: Enable rapid deployment of training environments and private AI workloads
- Inspire Confidence: Provide consistent reliability and reduce operational risk
A well-designed AI storage foundation enables organizations to fully unlock the power of AI—transforming data into insight, innovation, and competitive advantage.