AI and machine learning are now embedded in everyday digital experiences—from recommendation engines to fraud detection and route optimization. At their core, these systems rely on identifying patterns across massive datasets. As AI adoption accelerates, projected to grow from $387 billion in 2022 to nearly $1.4 trillion by 2029, organizations are racing to modernize their infrastructure.
One of the most misunderstood components of AI/ML infrastructure is data storage. Persistent misconceptions often lead to over-engineered, expensive, or poorly balanced systems. Below are four of the most common AI/ML storage myths—and the realities behind them.
⚡ Myth 1: AI/ML Requires High-IOPS All-Flash Storage #
AI accelerators demand fast access to data, leading many teams to assume that only high-IOPS, all-flash or all-NVMe storage can meet AI/ML requirements.
Reality: AI/ML performance is not determined by storage speed alone. Different workloads place different demands on the data pipeline. In workloads such as image or object recognition, the compute time per sample can be long enough that hybrid storage architectures (HDD + SSD) perform comparably to all-flash systems at a much lower cost.
The goal is not maximum IOPS everywhere, but matching storage performance to workload behavior. Public benchmarks such as MLPerf provide useful guidance for identifying where premium flash storage delivers real benefits—and where it does not.
🧠 Myth 2: AI/ML Is Entirely GPU-Dependent #
GPUs and other accelerators are often viewed as the sole drivers of AI performance, with the rest of the infrastructure treated as secondary.
Reality: Accelerators are only as effective as the data pipeline feeding them. Storage and networking determine whether GPUs remain productive or sit idle. Slow or imbalanced storage leads directly to underutilized accelerators, while excessive over-provisioning wastes budget without improving outcomes.
Successful AI/ML systems are balanced systems, where compute, storage, and networking are designed together to ensure that data is always available when the accelerator is ready for it.
🏗️ Myth 3: AI/ML Needs Dedicated, Single-Purpose Storage Silos #
Because AI projects often begin as experiments, they are frequently deployed on isolated, purpose-built storage systems.
Reality: AI/ML delivers the most value when applied to core enterprise data—financial transactions, customer behavior, scientific research, or operational telemetry. Isolating AI workloads on separate storage platforms often creates data silos, increasing complexity and limiting access to the most valuable datasets.
Integrating AI/ML workloads into the core storage infrastructure improves data accessibility, simplifies governance, and accelerates the path from experimentation to production.
🧊 Myth 4: Storage Tiering Reduces AI/ML Costs #
Traditional storage tiering moves “hot” data to fast media and “cold” data to slower, cheaper tiers. This strategy is widely used in enterprise IT.
Reality: In AI/ML training, almost all data is effectively hot. Training jobs typically iterate over the entire dataset across multiple epochs. If even a small portion of the data resides on slower storage, the entire training process can stall.
- The bottleneck: If 20% of training data is slow, 100% of the GPU cluster waits.
- The correct focus: Instead of tiering, AI/ML storage should emphasize linear scalability, ensuring that capacity and throughput grow together as datasets expand.
Tiering may still play a role for archival or post-training data, but it is generally unsuitable for active training pipelines.
📌 Summary #
AI and ML are reshaping industries, but their success depends on solid infrastructure foundations. Storage systems designed around outdated assumptions—such as universal all-flash requirements or traditional tiering models—often increase costs without improving performance.
By aligning storage design with workload behavior, integrating AI pipelines into core infrastructure, and focusing on balanced, scalable architectures, organizations can build AI-ready storage platforms that grow efficiently alongside their models and data.