Matching a Cloud Provider to Your Actual Workload

Provider comparisons usually proceed feature by feature, which produces a
table and not a decision. A better approach runs the other way: describe the
workload precisely, find the one constraint that actually binds, and let that
eliminate most of the field.

Step one: describe the workload in four lines

Write down, concretely:

• Shape: a web application, a batch pipeline, a database, an
  API, a static site.
• Size: how many servers, roughly, and how much memory each
  needs.
• Variability: steady, daily peaks, or genuinely
  unpredictable.
• Dependencies: the managed services you will actually use in
  the next six months.

Most people cannot complete the fourth line, and that is itself the answer:
if you cannot name a managed service you need, you do not need a platform that
specialises in having many.

Step two: find the binding constraint

Almost every real decision is dominated by one factor. Identify yours:

• Legal. Data must stay in a jurisdiction, or an auditor
  requires a specific certification.
• Cost. The budget is fixed and modest.
• Capability. You need something specific, such as managed
  Kubernetes, a data warehouse or GPU instances.
• Latency. Users are spread across continents and the
  application is sensitive to distance.
• Team. Nobody has operated infrastructure before, or
  everybody already knows one platform.

If two of these feel equally binding, the legal one wins, because it is the
only one that cannot be traded against anything else.

Step three: eliminate on hard requirements

Hard requirements are the ones where a platform either qualifies or does
not. Run them first, because they shrink the field fastest.

EU-only data residency rules out platforms without suitable regions and
points towards OVHcloud and
Hetzner. A requirement for a
mature managed data warehouse points at
Google Cloud. Deep
Active Directory integration points at
Azure. A need for the widest
possible service catalogue points at
AWS.

After this step most people are looking at two or three options rather than
ten.

Step four: decide the platform question honestly

The real fork is not which hyperscaler. It is whether you need a hyperscaler
at all.

You need one when load genuinely varies by a lot, when you will use managed
services you would otherwise have to operate, when you need many regions, or
when fine-grained access control is a requirement.

You do not need one when the deployment is a predictable number of servers
running an application and a database, which describes a very large share of
real systems. In that case a flat-rate provider is cheaper, simpler and
genuinely adequate, as set out in
when a cheap VPS beats a
hyperscaler and
the developer VPS
comparison.

Step five: weigh what your team already knows

This is the criterion most comparisons omit and most outcomes depend on.
Platform familiarity is worth more than a modest price difference, because the
alternative is paying for the learning in incidents and misconfigurations.

If your team knows one platform well and it is not disqualified by step
three, that is a strong reason to choose it. If nobody knows any of them, weight
documentation quality and interface clarity heavily, which favours
DigitalOcean and
Linode.

Step six: verify you can actually open the account

A platform you cannot register with is not an option, however well it scores.
Verification strictness varies considerably, and Oracle, Hetzner and Kamatera
are the strictest of the ten. Check the
verification
comparison before committing to a plan built around a specific provider.

What not to let decide it

Market share is not a technical argument. Free tier generosity is a reason to
evaluate, not a reason to build, because the credit ends and the platform
remains. And a feature you might use in two years should not outweigh the one
you need next week.