In the first blog of our three-part HPE Data Centre Modernisation Series, we took a look at how flexible financial models can help unlock stalled refresh projects. In this second instalment, we dive into a comparison of the cost curve of AI testing versus production.
Across Australia, organisations are exploring artificial intelligence (AI) in various ways, from small-scale pilots in local councils, to research projects in educational institutions. These early tests usually take place in the public cloud because it provides immediate access to the GPU and compute resources required to get started. For small proofs of concept, the cost seems manageable, and the flexibility of cloud services makes it easy to experiment with new models and tools.
However, while cloud is ideal for AI testing, as those experiments move into production environments, costs can escalate quickly. GPU demand, data processing needs, and storage use all increase sharply, and monthly cloud bills soon exceed expectations.
This shift is prompting many organisations to rethink their AI infrastructure strategies. They have tested the technology and identified effective use cases but scaling them sustainably remains a challenge.
When it comes to moving beyond the pilot phase, organisations have several infrastructure options, each with its own trade-offs.
Public cloud remains the simplest way to get started, providing access to powerful compute resources on demand. The downside, as mentioned above, is that “costs can go through the roof” at scale, especially once models are trained and deployed continuously. Public Cloud also introduces important data considerations, such as: Where is the data stored? Is it structured to support the AI outcomes I want to achieve? Are there any data sovereignty implications when uploading company data to a Public Cloud service?
On-premises infrastructure offers control and predictability. Organisations can optimise performance, keep data local, and avoid fluctuating billing cycles. However, it requires a significant upfront investment and a long-term commitment to infrastructure that may not meet future AI needs.
Colocation can act as a compromise, housing your infrastructure in a third-party data centre while preserving ownership. However, it still involves capital expenditure and planning, along with the same risks of overinvesting in hardware that could become outdated, or unnecessary.
As-a-service models present an alternative. They allow organisations to start small and expand as workloads demand, without the risk of stranded investment. Consumption-based infrastructure means capacity can grow in line with project maturity, supporting the unpredictable nature of AI initiatives. Similar considersations need to be made for data as with Public Cloud.
HPE GreenLake exemplifies how consumption-based models can close the gap between early-stage testing and large-scale enterprise deployment. With this approach, GPU and compute capacity can be added gradually. Organisations can start with a small setup, like two or three nodes with storage in a colocation facility, and increase capacity as workloads grow. Billing is based on actual usage rather than fixed capacity, so you only pay for what you use each month. This flexibility enables teams to develop, test, and deploy AI workloads with financial confidence, without committing to hardware purchases or ongoing cloud expenses before the demand warrants them. While GreenLake is a well-known example, the same commercial principles apply across other consumption-based infrastructure models. The goal is not to emphasise product features, but to highlight how flexible financial arrangements make AI adoption more sustainable.
In Australia, the need for flexible AI infrastructure is increasing across various sectors. We’ve seen Councils experimenting with computer vision for traffic management, and educational institutions testing AI-supported learning tools, both facing the same issue: unpredictable growth in computing demands. These projects often start with small, targeted trials and then expand quickly once their value is recognised.
Flexible consumption models facilitate access to scalable GPU power without long-term commitments, making them ideal for challenges like VMware migrations and data centre modernisation projects. Organisations retain both financial and technical flexibility, whether the workload remains the same or grows, something traditional purchase models cannot offer.
For organisations moving from AI pilots to production, the next step is understanding how different investment models will impact cost, scalability, and control.
Data#3’s commercial model assessment helps IT managers and finance teams compare cloud, on-premises, and as-a-service options. The process simulates real workload growth and expenditure patterns to identify the most flexible and financially viable path forward.
If your AI ambitions are ready to move beyond the pilot phase, it may be time to reconsider not what you build, but how you pay for it. Let Data#3 help guide you on your journey.
This blog is just the midpoint in our three-part HPE Data Centre Modernisation Series. You can read the next blog, The smarter, more cost-effective path to modern virtualisation, today.
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