The cloud is, simply, someone else’s computers — compute, storage, and services you rent on demand over the internet instead of buying and running your own. You pay for what you use, scale in minutes, and let a provider handle the data center.
Owning servers means buying for your peak — and paying for idle capacity the rest of the time. The cloud flips that: capacity becomes elastic, you pay only for what you use, and the upfront capital cost (CapEx) turns into a flexible operating cost (OpEx).
Add or remove capacity in minutes to match real demand.
No big upfront purchase; cost tracks usage.
Databases, AI, networking — ready to use, no setup.
The catch: elastic doesn’t mean cheap by default. Without governance, cloud bills balloon — which is why cost optimization is a discipline of its own, not an afterthought.
The big three cloud models differ by where the handoff is — how much of the stack you run versus the provider. Switch models and watch the line move.
A separate question from service model: whose infrastructure, and how many?
Shared infrastructure run by a provider (AWS, Azure, GCP), rented by many customers. Maximum elasticity and the lowest barrier to entry. Best for variable workloads and speed.
Cloud-style infrastructure dedicated to a single organization — in your own data center or hosted. More control and isolation for strict compliance or data-residency needs, at higher cost.
A deliberate mix of public and private, connected together. Keep sensitive workloads private, burst to public for scale. The most common enterprise reality.
Using more than one public provider — for resilience, to avoid vendor lock-in, or to use each provider’s best service. Powerful, but adds real operational complexity.
Moving to the cloud isn’t one move. These are the common “R” strategies, from least to most effort. Expand each.
Move applications as-is onto cloud servers. Fastest and lowest-risk, but you don’t yet gain cloud-native benefits. A common, sensible first step.
Make a few cloud optimizations during the move — e.g. swap a self-run database for a managed one — without rewriting the app. A balanced effort-to-payoff ratio.
Redesign the application to be cloud-native (containers, serverless, microservices). The most effort and the biggest long-term payoff in scale and cost efficiency.
Not everything should move. Retire what’s no longer needed, and retain on-premises whatever genuinely belongs there (latency, compliance, or cost reasons). A good migration deletes as much as it moves.
The economics flip depending on how steady your workload is. Steady and predictable favors owning; spiky and variable favors cloud. Model it below.
On-prem must buy for peak; cloud scales to average. On-prem modeled at ~$6,000 / server amortized over 36 months plus power & admin; cloud at ~$0.20 / server-hour. A simplified illustration, not a quote.
Four questions on cloud fundamentals.
Migration and landing zones, cost optimization, and 24/7 managed operations across AWS, Azure & Google Cloud — RHC Solutions has engineered and operated cloud infrastructure for regulated, high-uptime enterprises since 1994.
Next guide → AI: turning data into decisions