Estimated reading time: 11 minutes
Table of contents
- Colocation vs Cloud: The Fundamental Difference
- The Real Cost Comparison of Colocation vs Cloud: 5-Year TCO
- When Cloud Is the Better Choice
- When Colocation Is the Better Choice
- The Hybrid Approach: Why “Both” Is Usually the Right Answer
- How to Decide: A Practical Framework
- Common Mistakes in the Colocation vs Cloud Decision
- How Reboot Monkey Supports the Transition
- Frequently Asked Questions
Does your company also get stuck in the colocation vs cloud debate for small businesses?
Two years ago, one of our clients, a SaaS company with about 200 employees and a growing customer base across Europe, was running their entire production infrastructure on AWS. Monthly bill: roughly €38,000. The workloads were stable and predictable. They weren’t using auto-scaling. They weren’t bursting. Moreover, they were paying on-demand cloud rates for servers that ran at 60–70% utilisation, 24 hours a day, 365 days a year.
We helped them move their production workloads to a colocation facility in Amsterdam. They kept AWS for development environments, staging, and disaster recovery — workloads that genuinely benefit from cloud elasticity. Their new monthly spend: approximately €16,000 for colocation (including rack space, power, connectivity, and smart hands support), plus about €6,000 for the remaining AWS services.
Total: €22,000 per month. That’s a 42% reduction — roughly €192,000 saved per year — without sacrificing performance. In fact, their application latency improved because they were no longer sharing compute resources with other tenants.
This isn’t an unusual outcome. Cloud repatriation — the strategic movement of workloads from public cloud back to private infrastructure — has become one of the defining infrastructure trends of 2026. According to industry surveys, over 80% of enterprise IT leaders are planning to repatriate at least some workloads from public cloud. But that doesn’t mean cloud is wrong or colocation is always better. The answer depends entirely on what you’re running, how predictable it is, and what you’re optimising for.
This guide gives you the honest comparison — with real numbers — so you can make the right call for your business.
Colocation vs Cloud: The Fundamental Difference
Before comparing costs and trade-offs, let’s make sure the definitions are clear.
Cloud (public cloud) means renting virtualised compute, storage, and network resources from a provider like AWS, Azure, or Google Cloud. You don’t own hardware. You pay based on usage — per hour, per GB, per request. The provider manages the physical infrastructure, and you manage everything from the operating system up (in IaaS) or just your application and data (in PaaS/SaaS).
Colocation means housing your own physical servers in a third-party data centre facility. You own the hardware. The facility provides the space, power, cooling, physical security, and network connectivity. You manage the equipment — or you partner with a provider like Reboot Monkey to manage it through managed colocation services.
The core trade-off: cloud gives you flexibility and speed at a premium price. Colocation gives you control and predictable costs at the expense of upfront investment and operational responsibility.
The Real Cost Comparison of Colocation vs Cloud: 5-Year TCO
This is where most “colocation vs cloud” articles fall short — they compare monthly rates without accounting for the full cost picture. Here’s a realistic 5-year total cost of ownership comparison for a mid-sized production workload.
Scenario: 8 servers (dual-socket, 64 cores, 256 GB RAM each), 50 TB usable storage, redundant 10 Gbps connectivity, 24/7 operations.
Cloud (AWS — On-Demand)
| Cost Component | Monthly | 5-Year Total |
| EC2 instances (8x m6i.16xlarge, on-demand) | ~€22,000 | €1,320,000 |
| EBS storage (50 TB gp3) | ~€4,000 | €240,000 |
| Data transfer (5 TB egress/month) | ~€450 | €27,000 |
| Support (Business tier) | ~€2,500 | €150,000 |
| Monitoring and management tools | ~€500 | €30,000 |
| Total | ~€29,450 | €1,767,000 |
Note: Reserved Instances or Savings Plans can reduce EC2 costs by 30–40%, bringing the 5-year total to approximately €1,200,000–€1,350,000. But you’re committing to 1–3 years upfront, which partially negates cloud’s flexibility advantage.
Colocation
| Cost Component | Monthly | 5-Year Total |
| Hardware (8 servers, amortised over 5 years) | ~€2,400 | €144,000 |
| Colocation (2 racks, power, cooling) | ~€4,000 | €240,000 |
| Network connectivity (10 Gbps, IP transit) | ~€1,500 | €90,000 |
| Smart hands + remote hands support | ~€1,200 | €72,000 |
| Hardware monitoring | ~€500 | €30,000 |
| Hardware refresh (year 4 budget) | ~€800 | €48,000 |
| Spare parts and maintenance | ~€300 | €18,000 |
| Total | ~€10,700 | €642,000 |
The Gap
| Cloud (On-Demand) | Cloud (Reserved) | Colocation | |
| 5-Year TCO | €1,767,000 | ~€1,275,000 | €642,000 |
| Monthly average | €29,450 | ~€21,250 | €10,700 |
| Savings vs on-demand cloud | — | ~28% | ~64% |
For stable, predictable workloads, colocation costs roughly 50–65% less than public cloud over a 5-year horizon. That’s consistent with what we see across our client base and aligns with industry data showing 30–60% cost reductions through strategic repatriation.
Important caveat: These numbers assume stable workloads. If your compute needs swing wildly from day to day, or if you’re a startup that might scale 10x in six months, the cloud’s pay-as-you-go model may actually cost less because you’re not paying for capacity you don’t use.
When Cloud Is the Better Choice
Cloud isn’t losing the colocation vs cloud debate, it’s being used more strategically. Here are the scenarios where cloud genuinely wins:
Variable and unpredictable workloads. If your traffic patterns look like a heartbeat monitor — quiet most of the time with massive spikes — cloud auto-scaling delivers value that colocation can’t match. E-commerce during Black Friday, media streaming during live events, and batch processing jobs that run for hours then go idle are all textbook cloud use cases.
Rapid prototyping and development. Spinning up a test environment in minutes, running it for a week, then tearing it down is where cloud shines. We tell our clients: keep dev/staging in the cloud, run production in colo. Best of both worlds.
Global distribution with low latency. If you need to serve users on every continent with sub-50ms latency, cloud providers’ global edge networks are hard to replicate with colocation alone. Content delivery, global SaaS applications, and real-time multiplayer gaming benefit from cloud’s geographic reach.
Startups in early growth phase. When you don’t know what your infrastructure needs will look like in 12 months, committing to hardware and colocation leases is risky. Cloud lets you experiment, pivot, and scale without capital expenditure. The economics shift toward colocation once your workloads stabilise — usually around the Series B or C stage.
Managed services you can’t replicate. Cloud-native services like AWS Lambda, Google BigQuery, Azure Cognitive Services, and managed Kubernetes (EKS, GKE, AKS) provide capabilities that would take significant engineering effort to build and maintain on your own hardware. If your architecture depends heavily on these, the migration cost to colocation may not justify the savings.
When Colocation Is the Better Choice
Colocation wins when predictability, control, and long-term economics are your priorities:
Stable, predictable production workloads. This is colocation’s sweet spot. Databases, application servers, file storage, and internal tools that run 24/7 at consistent utilisation are dramatically cheaper in colo. Our SaaS client’s 42% cost reduction is typical for this profile.
Data sovereignty and compliance. GDPR, HIPAA, PCI DSS, and sector-specific regulations are increasingly forcing organisations to know exactly where their data sits and who can access the physical hardware. In a colocation facility, you control the rack, the drives, and the physical access list. In the cloud, your data sits on shared infrastructure in a region — and under the US CLOUD Act, data hosted by US-owned hyperscalers may be accessible to US law enforcement even if the servers are physically in Europe. For regulated European businesses, this is becoming a deal-breaker.
High-performance and low-latency requirements. Multi-tenant cloud environments share physical resources. During peak periods, you may experience variable I/O latency, network jitter, and the “noisy neighbour” effect. Dedicated hardware in colocation delivers consistent, predictable performance because nobody else is sharing your resources.
AI and GPU-intensive workloads. Running GPU clusters in the cloud is extremely expensive — and getting more so as AI demand drives cloud GPU pricing up. Building dedicated GPU infrastructure in colocation, while requiring upfront investment, often proves 50–70% cheaper for consistent AI workloads. We’re seeing more clients move AI training and inference workloads to colocation while keeping data pipelines and orchestration in the cloud.
Long-term cost control. If you know you’ll need this infrastructure for 3–5 years, colocation’s economics are hard to beat. The hardware pays for itself within 18–24 months, and after that, your ongoing costs are just power, connectivity, and support — a fraction of equivalent cloud spend.
The Hybrid Approach: Why “Both” Is Usually the Right Answer
Here’s what we tell most clients: the colocation vs cloud question is the wrong question. The right question is “which workloads belong where?”
The most successful infrastructure strategies we see in 2026 follow a pattern we call “steady in colo, variable in cloud”:
- Colocation handles production databases, core application servers, storage, and any workloads with predictable, consistent resource needs
- Cloud handles development and staging environments, disaster recovery, burst capacity, globally distributed content delivery, and any workloads that use cloud-native managed services
- Connectivity between the two is handled through direct cloud on-ramps at the colocation facility — most major colo providers offer direct connections to AWS, Azure, and GCP with low-latency private links
This hybrid model gives you the cost predictability and performance of colocation where it matters most (production), combined with the flexibility and global reach of cloud where those advantages apply (dev, DR, burst, global distribution).
One of our clients — a media company running video transcoding — uses this exact model. Their transcoding servers run on dedicated GPU hardware in a Frankfurt colocation facility. Completed files are pushed to AWS S3 for global CDN distribution. The transcoding workload is consistent and GPU-intensive (perfect for colo). The distribution is global and bursty (perfect for cloud). Together, they spend about 55% less than they would running everything in AWS.
How to Decide: A Practical Framework
If you’re evaluating colocation vs cloud right now, run through this decision matrix for each major workload:
| Question | If Yes → | If No → |
| Is the workload stable and predictable? | Lean toward colo | Lean toward cloud |
| Does it run 24/7 at >50% utilisation? | Lean toward colo | Lean toward cloud |
| Are you committed to this workload for 3+ years? | Lean toward colo | Lean toward cloud |
| Do you need guaranteed, consistent performance? | Lean toward colo | Cloud is fine |
| Do strict data sovereignty rules apply? | Lean toward colo | Either works |
| Does the workload rely on cloud-native managed services? | Stay in cloud | Either works |
| Do you need to scale 10x within months? | Stay in cloud | Either works |
| Is this a dev, staging, or test environment? | Stay in cloud | Depends on workload |
Most businesses find that 40–60% of their workloads are better suited to colocation (the stable, predictable, high-utilisation ones) and 40–60% are better in the cloud (the variable, distributed, managed-service-dependent ones). The savings from moving the colo-suited workloads typically cover the entire colocation investment and then some.
Common Mistakes in the Colocation vs Cloud Decision
Only comparing monthly costs. A cloud instance’s monthly price looks comparable to colo until you factor in data egress, premium support, monitoring tools, and 5-year compounding. Always compare 3–5 year TCO.
Forgetting the operational burden of colocation. Colocation is cheaper but not easier. If you move workloads to colo without a plan for managing the hardware, you’re trading one problem for another. This is exactly why managed colocation services and smart hands support exist — they close the operational gap without the cost of hiring full-time on-site staff.
Moving everything or nothing. The all-or-nothing approach is almost always wrong. We’ve seen companies move their entire infrastructure to colo, including workloads that genuinely benefit from cloud elasticity. And we’ve seen companies stay fully in cloud because “moving some stuff to colo seems complicated.” Both extremes leave money and performance on the table.
Ignoring vendor lock-in before it’s a problem. Cloud-native services like proprietary databases, serverless functions, and AI platforms create deep dependencies that make future migration extremely expensive. If you’re designing new applications, consider portability: containers, open-source databases, and standard APIs reduce lock-in risk regardless of where you deploy.
Not accounting for exit costs. Moving data out of cloud (egress) is expensive. AWS charges roughly €0.09 per GB for data transfer out. If you’re moving 100 TB, that’s €9,000 just in egress fees — before you’ve paid for anything at the destination. Factor exit costs into your migration budget.
How Reboot Monkey Supports the Transition
Whether you’re moving from cloud to colocation, expanding into a hybrid model, or optimising an existing colocation footprint, we provide the support layer that makes colocation work without the operational burden:
- Server migration — planning and executing the physical move from cloud to colo, including migration best practices we’ve refined across dozens of projects
- Smart hands and remote hands — 24/7 on-site technical support across 22+ global locations so you don’t need local IT staff at every facility
- Hardware monitoring — proactive 24/7 monitoring with automated alerting
- Rack and stack — deployment of new hardware at the destination facility
- Data destruction and hardware recycling — end-of-life management when hardware is retired
We work across major European and US data centre markets — Amsterdam, London, Frankfurt, Paris, New York, Dallas, Northern Virginia, and more. One provider, one support model, wherever your racks are.
Book a free consultation and we’ll help you identify which workloads belong in colo, estimate your TCO savings, and build a migration plan.
Frequently Asked Questions
For stable, predictable workloads running 24/7, colocation typically costs 50–65% less than public cloud over a 5-year period. The savings come from avoiding cloud markup on compute, eliminating data egress fees, and amortising hardware costs over a longer lifespan. However, for variable, bursty, or short-lived workloads, cloud’s pay-as-you-go model can be more cost-effective.
Cloud repatriation is the strategic process of moving workloads from public cloud providers (AWS, Azure, GCP) back to private infrastructure — either colocation facilities, on-premises data centres, or alternative cloud providers. It’s driven primarily by cost optimisation, data sovereignty requirements, and performance needs. Over 80% of enterprise IT leaders are planning some form of repatriation in 2026.
Yes, and most successful businesses do. A hybrid approach places stable production workloads in colocation for cost and performance, while using cloud for development environments, disaster recovery, burst capacity, and globally distributed services. Direct cloud on-ramps at colocation facilities provide low-latency private connectivity between the two environments.
Workloads that benefit from cloud elasticity, global distribution, or cloud-native managed services are best kept in the cloud. This includes development and staging environments, applications with highly variable traffic, workloads relying on proprietary cloud services (Lambda, BigQuery, etc.), and any applications requiring global edge presence for low-latency delivery.
A typical cloud-to-colo migration for a mid-sized environment (10–30 servers) takes 8–16 weeks including planning, hardware procurement, facility setup, data migration, and validation. Smaller environments can be completed in 4–6 weeks. The timeline depends on hardware lead times, application complexity, and the migration approach (swing, phased, or big bang).
Leave a Reply