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Colocation vs Public Cloud
Key Takeaways
Colocation houses hardware you own in a professional data center, trading upfront equipment cost for predictable monthly fees and full control. Public cloud rents compute on demand, ideal for variable workloads but expensive at steady state scale once egress is counted. Most businesses in 2026 land on hybrid, and ObsidianX models both economics vendor neutrally before anything moves.
The colocation versus cloud question has changed. Cloud bills that grew faster than the businesses paying them, egress fees, and AI workloads that need dedicated GPUs have pushed steady workloads back toward owned hardware, while data center capacity itself has become scarce. Here is the honest 2026 decision guide.
At a glance
Colocation
Your hardware in a professional data center that provides power, cooling, space, and bandwidth.
Public Cloud
Compute and storage rented on demand from a hyperscale provider.
Head-to-head comparison
| Criterion | Colocation | Public Cloud |
|---|---|---|
| Cost model | Hardware upfront, then predictable monthly power and space fees | No upfront cost; consumption billing that varies month to month |
| Steady state economics | Usually cheaper at meaningful, constant scale | Premium for capacity you hold around the clock |
| Bursty workloads | Poor fit; you buy for peak | Excellent; scale up and down on demand |
| Egress and data gravity | No egress fees; you pay for circuits you control | Egress fees that grow with data volume and surprise at exit |
| Control and customization | Full control of hardware, hypervisor, and network | Control ends at the provider's service boundary |
| Compliance and residency | You choose the facility, controls, and exact data location | Strong certifications, but shared responsibility and less location precision |
| AI and GPU workloads | Dedicated GPUs at high density where facilities support it | Instant access, but sustained GPU rental compounds quickly |
| Speed to start | Weeks for space, hardware, and migration | Minutes to first workload |
Why this decision looks different in 2026
Two market shifts reframed the question. First, cloud maturity: after a decade of migration, finance teams can finally see what steady state cloud actually costs, and egress fees plus always on capacity have pushed disciplined teams to re place their steadiest workloads. Second, scarcity on the other side: CBRE puts data center vacancy at record lows, with grid power in most major markets largely booked through 2030, so colocation capacity itself is now something you secure early rather than shop casually.
The result is that placement discipline, deciding workload by workload where things belong, has replaced platform loyalty in both directions. The businesses overpaying most are the ones that never modeled the question at all.
The three questions that decide most workloads
- Is the demand steady or variable? Steady utilization favors owned hardware; spiky demand favors renting capacity by the hour.
- How much data leaves? Egress heavy workloads, analytics, media, backups serving restores, bleed money in cloud and cost nothing extra on your own circuits.
- Who has to attest? When auditors need to know exactly where data lives and who can touch the hardware, a certified facility you control answers faster than a shared responsibility diagram.
Bring us one cloud bill and one hardware inventory. The workload placement model is free, vendor neutral, and usually finds at least one workload sitting in the wrong place.
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The verdict
This is not an either or decision anymore. Steady state, data heavy, and compliance bound workloads tend to win in colocation, while variable, experimental, and rapidly scaling workloads tend to win in cloud. The durable answer for most mid market businesses is a hybrid core: owned hardware in a carrier neutral facility with direct cloud on ramps beside it. What matters is modeling your actual workloads, including egress, before committing either way.
Frequently Asked Questions
Questions buyers actually ask about colocation vs public cloud, answered plainly.
Is colocation cheaper than the cloud?
At steady state scale, usually yes, once hardware is amortized and egress fees are counted. For small footprints or variable demand, cloud usually wins because you pay only for what you use. The crossover depends on workload profile, which is why ObsidianX models your actual estate rather than quoting a universal percentage. Be skeptical of any page that promises one.
What is cloud repatriation and is it really happening?
Repatriation means moving workloads from public cloud back to owned infrastructure, usually in colocation. It is real but selective: industry research finds most enterprises repatriating something, typically steady workloads with painful egress bills, while very few leave cloud entirely. The honest trend is hybrid placement discipline, not a cloud exodus.
Can I connect my colocation racks directly to the cloud?
Yes. Carrier neutral facilities offer direct private on ramps to the major cloud platforms, so your hardware and your cloud workloads can sit milliseconds apart on a private connection instead of the public internet. This is exactly how a hybrid core works, and on ramp quality is one of the criteria we weigh when picking your facility.
Should AI workloads run in the cloud or in colocation?
Experimentation and spiky training demand favor cloud, where GPUs are rented by the hour. Sustained training and inference on GPUs you keep busy favor owned hardware in high density colocation, because around the clock GPU rental compounds brutally. Facility choice matters here: GPU racks draw far more power than traditional ones and increasingly need liquid cooling, and not every data center can deliver that.
What about disaster recovery: colocation or cloud?
Both patterns work and they combine well. Cloud based DR keeps costs low for standby capacity you hope never to use, while a colocation DR site gives you deterministic recovery on hardware you control in a different risk region. Many businesses run production in colocation and DR in cloud, or the reverse. The right answer follows your recovery time objectives and data volumes.
How do I actually compare the costs fairly?
Build a three year model per workload, not per platform. On the colocation side, count hardware amortization, power commitment, space, cross connects, circuits, and remote hands. On the cloud side, count compute, storage, licensing, support tiers, and, critically, egress at your real data volumes. ObsidianX builds this model with your numbers and is paid the same whichever way it comes out.
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