For many years, cloud systems have separated storage and compute functions. Storage was just where data was kept, while processing and analysis happened in the compute layer. This setup worked well for traditional analytics tasks using structured data stored in tables. These jobs are predictable, scheduled, and involve only a few compute engines working over the data. But as AI transforms business IT, this old model faces new challenges.

Why AI Demands a New Approach

AI workloads are different from traditional analytics. Instead of working with structured tables, AI processes large amounts of unstructured and multimodal data. It generates complex data like embeddings, vectors, and metadata. Also, AI tasks are often continuous, with multiple compute engines repeatedly accessing the same data. Each time data is pulled from storage and reshaped for a specific purpose, it adds extra work and costs.

This leads to more data movement and redundancy. For example, the same dataset might be read multiple times—once for training, again for inference, and yet again for testing. Each of these steps involves transferring data and transforming it, which adds up quickly. This inefficiency is often invisible at small scales but becomes a major economic barrier as AI workloads grow.

The Hidden Costs of Traditional Storage and Compute

One major issue is underutilized hardware. Many organizations report that up to 93% of their GPUs sit idle much of the time. High-end GPUs cost several dollars per hour on cloud platforms, so wasted compute time can mean millions of dollars lost each year. When hardware spends more time waiting on data transfer than doing actual work, it’s hard to justify the investment.

This mismatch between storage and compute creates a cycle of inefficiency. GPUs and other processors are ready to work, but they’re waiting for data from storage. This results in wasted resources and higher costs. As AI becomes more central to business operations, these inefficiencies threaten to slow down progress and increase expenses.

Moving Toward Smarter Storage Solutions

The limitations of the old model highlight a need for change. Storage should no longer be a passive place just for holding data. Instead, it needs to become smarter and more integrated with compute. To support modern AI workloads, data must be closer to the processing power that uses it.

Making storage more intelligent means enabling compute to access data more quickly and efficiently. When data is stored in platforms that combine storage and processing, companies can reduce unnecessary data movement. This approach can lower costs, speed up workflows, and make AI projects more scalable and practical.

Ultimately, shifting to integrated storage and compute architectures helps organizations get more value from their data. It allows AI models to train faster, perform better, and cost less. As AI continues to evolve, rethinking how data is stored and processed will be key to unlocking its full potential.

Inspired by

• https://www.infoworld.com/article/4138058/why-ai-requires-rethinking-the-storage-compute-divide.html

Sources

• datapelago.ai