Ask most businesses whether they're "cloud" or "co-location" and you'll get a confident answer. But that confidence often masks a misunderstanding: co-location and cloud are not competing alternatives in the way people assume. They're infrastructure models suited to different types of workloads — and the most sophisticated organisations use both deliberately.
Here's a practical framework for thinking through which model is right for each part of your infrastructure, rather than treating it as an all-or-nothing decision.
First — what each model actually means
Co-location
In a co-location arrangement, you own the hardware. Your servers, storage arrays, and networking equipment are physically housed in a data centre facility. The data centre provides the things that are expensive and impractical to replicate in an office environment: redundant power (including UPS and generator backup), precision cooling, physical security, fire suppression, and high-capacity internet connectivity.
You are responsible for the hardware itself — purchasing it, maintaining it, and eventually replacing it. The data centre is responsible for the facility that hosts it.
Cloud computing
In a cloud model, the provider owns the hardware. You rent compute capacity, storage, or specific services from a provider like AWS, Microsoft Azure, or Google Cloud. You pay for what you use, you don't manage physical equipment, and you can scale your resource allocation up or down based on demand.
The trade-off is that ongoing costs are variable and can compound significantly at sustained usage levels — particularly for compute-intensive workloads that run continuously.
When co-location is the better choice
Predictable, consistent resource usage
Cloud pricing is optimised for workloads that are intermittent, variable, or hard to size in advance. When a workload runs at consistent high utilisation — say, a database server that's busy all day, every day — the economics shift. The cloud provider's margins are built around the flexibility they offer; when you don't need that flexibility, you're paying for something you're not using.
Hardware that you own in a co-location facility has a fixed cost that doesn't vary with utilisation. Once it's paid for, the marginal cost of running it harder is essentially zero. For many organisations with mature, well-understood infrastructure, this makes co-location substantially cheaper over a three-to-five year horizon.
Low-latency requirements
Physical proximity matters for latency. If your application needs to communicate with other equipment — storage systems, databases, networking hardware — placing it all in the same data centre eliminates the round-trip latency of going out to a cloud region and back. For latency-sensitive applications such as financial processing, real-time communications infrastructure, or high-frequency database queries, co-location can deliver measurably better performance.
Data sovereignty and regulatory requirements
Some industries operate under regulatory or contractual obligations that specify where data must be stored and processed. Co-location gives you absolute certainty: your hardware is in a known, specific physical location. With cloud infrastructure, data can be distributed across regions in ways that require careful configuration to control — and even then, the provider's infrastructure may not satisfy some regulatory frameworks.
Industries where this matters most include healthcare, legal, government, and financial services.
Protecting an existing hardware investment
If your business has recently invested in server hardware — or has equipment with several years of useful life remaining — migrating that workload to cloud means paying for compute twice: once for the hardware you already own, and again for cloud resources. Moving that hardware into a co-location facility lets you continue using the investment while dramatically improving the environment it operates in.
When cloud is the better choice
Variable or spiky workloads
Cloud infrastructure was built for workloads that don't run at consistent levels. If your business experiences significant peaks — seasonal traffic spikes, month-end processing, burst compute for reporting — cloud lets you scale resources to match demand and scale back when it passes. Provisioning co-located hardware for peak capacity means that capacity sits idle most of the time.
New applications with unknown resource requirements
When you're building or adopting a new application and can't accurately predict its infrastructure demands, cloud removes the risk of buying too much or too little hardware. You can start small, observe actual usage, and adjust. Once the application is mature and its resource profile is well understood, that's often the right time to evaluate whether it belongs in the cloud long-term or whether co-location becomes more cost-effective.
SaaS-first organisations
Many modern businesses have little or no on-premise software. Their email is Microsoft 365, their CRM is Salesforce, their accounting is Xero. For these organisations, there may be no meaningful workload to co-locate — everything already runs in someone else's cloud. Trying to force co-location into this model adds complexity without benefit.
The hybrid model — the right answer for most businesses
The most common outcome for businesses that think through this carefully is a deliberate hybrid: co-locate the workloads that benefit from it, and use cloud for the workloads where cloud's strengths are relevant.
The key is that neither model is chosen by default or out of trend. Each workload is evaluated on its actual characteristics: usage patterns, latency sensitivity, data requirements, cost at scale, and the existing hardware position.
Questions worth asking before you decide
- Does this workload run continuously, or does it have significant idle periods?
- Is the resource requirement well understood, or is this a new workload where sizing is uncertain?
- Does this application have latency sensitivity to other systems it communicates with?
- Are there regulatory or contractual requirements about where the data sits?
- Do we have existing hardware investment that still has useful life?
- What does the total cost of ownership look like over three years, not just the monthly bill?
Working through these questions systematically — rather than defaulting to whichever model is currently fashionable — typically leads to a better infrastructure outcome and lower long-term cost. If you'd like to work through it for your own environment, the Caznet team is happy to have that conversation.