In the age of AI and usage-based billing, optimising IT to reduce overspend is a strategic imperative. This is even more relevant when 79% of organisations believe a significant portion of their cloud spend is wasted. The ability to align costs tightly with business value is now a board-level concern.
Most organisations have learned that simply shifting workloads to the cloud does not work. Instead, they need a consumption-based model combined with intelligent data management that ensures every terabyte, transaction or service level is justified.
From cloud-first to workload-first
The rapid rise of public cloud proved the value of on-demand consumption, but it also exposed how easily costs can spiral when capacity is over-provisioned or poorly governed. A workload-first strategy counters this by matching each application to the most appropriate infrastructure – on-premises, hybrid or public cloud – based on performance, compliance or cost requirements.
Instead of defaulting to “cloud-first”, organisations design a workload-first architecture that delivers the right service levels at the lowest sustainable cost.
The uSCALE model takes this principle on-premises, providing infrastructure with monthly pay-per-use billing. Capacity is deployed with a built-in buffer to absorb growth, but billing reflects actual usage (subject to an agreed minimum). uScale removes the traditional trade-off between over-provisioning for peak or under-provisioning for day-to-day demand. Users benefit from a cloud-like financial model without surrendering control over data location, latency or regulatory compliance.
Intelligent data management is the ‘secret sauce’
Consumption-based pricing alone does not guarantee efficiency; how data is stored, moved or protected directly shapes the bill. Intelligent data management is actually the main tool for optimising spend.
Primary storage platforms such as Fujitsu ETERNUS DX, AB or HB are designed to align performance with business priority, using features like automated quality-of-service controls or tiering across SSD or HDD media to keep “hot” data on fast tiers. Meanwhile, “lukewarm” or archival data is moved to more cost-effective disk drives. This avoids wasting premium flash capacity on low-value workloads while still meeting stringent SLAs.
Software-defined file or object storage solutions such as Qumulo or NetApp StorageGRID extend this intelligence to massive unstructured data sets. Qumulo uses real-time analytics or machine learning to provide visibility into who is using which data, how or when, enabling teams to make informed decisions about placement, retention or performance tiers.
StorageGRID applies policy-driven, object-level management to determine how data is distributed across geographies or tiers, balancing durability, access time or cost while supporting hybrid or multi-cloud workflows. The result is that capacity growth can be channelled to the lowest-cost locations that still meet business needs.
Automation, visibility or operational efficiency
True optimisation requires continuous adjustment, which is impossible to achieve manually at petabyte scale. Automation, therefore, plays a central role.
Fujitsu Eternus platform integrates automation for tiering, failover or quality-of-service, reducing the operational overhead of keeping performance or availability within SLA thresholds. Qumulo’s built-in analytics surface hot spots, orphaned data or inefficient workloads, turning what used to be forensic storage analysis into a routine operational task. uSCALE’s price estimator adds a financial lens, allowing your storage architects to model different configurations or understand cost implications before committing to an investment.
This combination of automation and transparency transforms capacity management from reactive firefighting into proactive optimisation. Your team can identify under-utilised resources, reclaim stranded capacity or adjust retention or protection policies based on actual usage patterns rather than static assumptions. That in turn keeps the pay-per-use bill closer to the theoretical minimum required to support your strategic goals.
Funding next-generation workloads
The strategic value of these savings goes far beyond lowering operational costs. As AI, machine learning, real-time analytics or high-performance edge workloads become mainstream, they demand high-performance, secure or scalable infrastructure – often including all-flash arrays, GPU-accelerated compute or high-bandwidth networks. These investments tend to be expensive, especially for organisations that must maintain legacy estates alongside new technologies.
By adopting consumption-based models like uSCALE or tightening control of data placement or protection through intelligent storage platforms, your business can release budget tied up in idle capacity or inefficient cloud usage. The ability to “only pay for what you use” on both primary or secondary infrastructure means that cost curves flatten even as data grows, freeing funds to invest in next-generation platforms optimised for AI, analytics or other transformational workloads.
In effect, smarter consumption or data management becomes a self-funding mechanism for innovation.
If your business can combine consumption-based infrastructure with intelligent, automated data management, you will be well placed to navigate the twin pressures of explosive data growth and rising cloud costs. You will not only keep spend under control, but also redirect the savings into strategic initiatives that differentiate your business with a resilient, data-driven foundation for future success.
To learn more about how your business can free funds for future investment by better managing your workloads with Fujitsu Eternus, please give the WTL team a call.