- Memory and storage shortages are forcing organisations to reconsider how and when to scale their infrastructure.
- Figure out where capacity is underused and make better use of what you have.
- Opt for active-active rather than active-passive architecture.
- Incorporate real-time data sharing.
For years, infrastructure strategy has followed a simple rule: when demand increases, add more capacity, more storage, more compute and more headroom. That approach has defined cloud adoption and digital transformation alongside years of rapid data growth. It’s worked until now, because supply was relatively flexible and demand was mostly predictable. If something ran out, you scaled it.
That assumption is under pressure now. Memory and storage shortages predicted to last until at least through 2027 and into 2028, in addition to AI workloads and rising infrastructure costs mean that, increasingly, organisations are deciding how to scale and when to commit. The lucky ones have been able to bring those decisions forward.
Constraint is changing the timing of decisions
As compute and storage become more expensive and harder to obtain, projects have been accelerated just to secure capacity and avoid even higher costs, rather than taking it in stages as they used to. Teams have committed earlier in the cycle, sometimes before demand is fully understood, as waiting risks not getting what they need later.
While IT teams are trying to scale for the future, they are increasingly struggling to support the demand they already have.
So as well as asking “how do we add more?”, we should be asking “how far can we go with what we already have?” For organisations who have not been able to bring their projects forward, this becomes even more important.
Making existing infrastructure work harder
Rather than viewing constraints as a limitation, they can be an opportunity to look differently at existing infrastructure. For example, by identifying where capacity is being underused, organisations may be able to unlock more from what they already have. This frees up budget for other priorities and extending the value of current investments.
This does not necessarily require a major transformation. In some cases, the capacity already exists, IT teams simply need to find a way to use it more effectively.
Active-passive leaves capacity unused
Many organisations still rely on active-passive architecture. This means that one environment runs the workload while another sits in reserve in case of failure. On paper, this is a clean and reliable model but in practice, it often leads to inefficiency.
The active environment is typically run at high utilisation, with little room for spikes or unexpected shifts in demand. Meanwhile, the passive environment remains largely under-used until something goes wrong.
There is spare capacity in the system, but it’s not being used in a way that improves flexibility and it is not helping with day-to-day pressure.
In a world where storage and memory is limited and expensive, that unused headroom becomes harder to justify.
Active-active uses capacity more evenly
This is where active-active models can make a difference. Instead of one system carrying the full load and another waiting in reserve, workloads are distributed across multiple live environments.
This leads to improved resilience and a better use of existing capacity. With work spread more evenly, no single system carries all the pressure, reducing strain during peak demand and improving overall stability.
For example, a team running at around 80% to 90% CPU and memory utilisation on the active side of an active-passive setup can redistribute workloads across environments by implementing active-active capabilities. With no additional storage or compute, utilisation could fall significantly simply by making better use of existing infrastructure.
Active-active is often described as a resilience or file collaboration strategy. However, it is increasingly being used as a way to uncover hidden capacity in systems that are already stretched.
Real-time data sharing makes it work
Active-active systems only function effectively if data can move quickly and consistently between environments. If systems are out of sync, workloads cannot be distributed safely because systems could be operating with different or outdated information.
Real-time data sharing keeps environments aligned, removing the delays, manual intervention and bottlenecks that often crop up in traditional synchronisation. This allows data to flow freely between locations.
As a result, organisations can make better use of existing compute and storage resources, improving resilience and scalability without having to continually expand infrastructure.
Rethinking what scale actually means
For years, scale has meant more infrastructure, but that definition is becoming less useful.
As AI demand increases and capacity constraints tighten, the challenge is changing. This forces organisations to make better use of the systems already in place. To do this, you must improve how data moves and how systems are connected as well as figuring out how evenly workloads are distributed across environments.
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