How Modern Applications Run on Cloud Infrastructure

Running an application used to mean plugging a server into a rack, installing an operating system by hand, and hoping nothing went wrong at 2 AM. Modern cloud infrastructure replaces that manual process with a repeatable, automated workflow that gets applications from code to production faster and with far fewer surprises.

This article explains the four key stages of that workflow. Each stage solves a specific problem, and together they form the foundation that serious engineering teams rely on to ship software reliably.

The Four Stages of a Modern Cloud Workflow

Think of deploying an application the way you would think about moving into a new office. First, you build the office space itself. Then you furnish and configure it. Next, you pack your work into portable boxes. Finally, you set up a system that keeps everything organized and running smoothly as you grow.

That is exactly what happens when modern applications move to the cloud:

Infrastructure is created with Terraform
Servers are configured with Ansible
Applications are packaged with Docker
Applications are run and managed with Kubernetes

Each tool handles one stage well. Together, they create a pipeline that is fast, consistent, and easy to scale.

Stage 1: Building the Infrastructure with Terraform

Before any application can run, you need infrastructure. That means servers, networks, storage volumes, firewalls, and load balancers. In a traditional setup, someone would log into a control panel and create each of these resources by clicking through menus. That works for one server. It falls apart when you need twenty, or when you need to rebuild everything after a failure.

Terraform solves this by letting you define infrastructure in code. You write a configuration file that describes exactly what resources you need, and Terraform creates all of them automatically. Need a Virtual Private Cloud (VPC) with three instances, a private network, and a block storage volume? Write it once, run it, and Terraform provisions everything in minutes.

The lasting benefit is repeatability. Because your infrastructure is defined in text files, you can store those files in version control just like application code. That means you have a complete record of every change, you can review infrastructure updates before they go live, and you can recreate your entire environment from scratch if needed. Teams that adopt Terraform stop worrying about "what did we configure last time?" because the answer is always in the code.

Learn more about Terraform in our glossary

Stage 2: Configuring Servers with Ansible

Terraform builds the servers. Ansible makes them useful.

Once your infrastructure exists, each server needs software installed, services configured, security settings applied, and users created. Doing this manually on one server is tedious. Doing it on ten or fifty servers is a recipe for inconsistency and mistakes.

Ansible automates server configuration using simple, readable text files called playbooks. A playbook describes the desired state of a server: install these packages, create this user, configure this firewall rule, start this service. Ansible connects to each server over SSH and makes it match the playbook. If you run the same playbook twice, Ansible only changes what needs changing. Everything that already matches gets left alone.

This approach eliminates the "works on my machine" problem at the infrastructure level. Every server configured by the same playbook ends up in the same state, whether you are setting up a development environment, a staging server, or a production cluster. When a new team member joins, they do not need to follow a setup guide with twenty steps. They run the playbook and their environment matches everyone else's within minutes.

Stage 3: Packaging Applications with Docker

With infrastructure built and servers configured, the next challenge is getting your application onto those servers in a way that is consistent and portable.

Applications have dependencies: specific language runtimes, libraries, configuration files, and system tools. Installing all of these directly on a server creates fragile environments where updating one library can break another application. Docker solves this by packaging your application and all of its dependencies into a single, portable unit called a container.

A Docker container includes everything the application needs to run, and nothing it does not. The container runs the same way on a developer's laptop, on a staging server, and in production. There is no gap between "it works in development" and "it works in production" because the container is identical in both places. This consistency is what makes Docker transformative for teams that have struggled with deployment failures caused by environment differences.

Containers are also lightweight. Unlike traditional virtual machines that each run a full operating system, containers share the host system's kernel and start in seconds rather than minutes. This makes it practical to run dozens or hundreds of containers on a single server, each handling a different part of your application.

Stage 4: Running and Managing Applications with Kubernetes

Docker packages your application into containers. But when you have dozens or hundreds of containers across multiple servers, you need something to manage all of them. That is where Kubernetes comes in.

Kubernetes is a container orchestration platform. You tell it what containers you want running, how many copies of each, and what resources they need. Kubernetes handles the rest: deciding which servers to place containers on, restarting containers that crash, scaling up when traffic increases, and scaling down when it drops. It turns a collection of individual servers into a single, manageable platform.

Consider what happens without it. Someone would need to manually track which containers run on which servers, restart failed containers, and redistribute workloads when a server goes down. Kubernetes automates all of that. It continuously monitors the state of your applications and takes corrective action without human intervention. When your application needs to handle a spike in traffic, Kubernetes can launch additional container copies in seconds and distribute incoming requests across them.

How the Stages Connect

These four tools are not isolated. They form a pipeline where each stage builds on the one before it.

Terraform creates the raw infrastructure: servers, networks, and storage. Ansible takes those freshly provisioned servers and configures them with the software and settings needed to run containers. Docker packages your applications so they run identically everywhere. Kubernetes takes those containerized applications and runs them at scale, handling failures and traffic automatically.

When something changes, the pipeline makes updates predictable. Need more capacity? Update the Terraform configuration to add servers, run Ansible to configure them, and Kubernetes automatically schedules containers onto the new resources. Need to deploy a new version of your application? Build a new Docker container and tell Kubernetes to roll it out gradually, replacing old containers with new ones without any downtime.

This is the workflow that allows small teams to manage infrastructure that would have required a large operations staff ten years ago.

Why This Matters for Your Business

Teams that adopt this workflow ship faster because infrastructure changes flow through the same code review process as application updates. A deployment that used to require a ticket, a meeting, and a manual checklist becomes a pull request that runs automatically.

The compounding benefit is reliability. Every environment built by this pipeline is identical, which means the category of bugs caused by "something was different in production" effectively disappears. When something does fail, Kubernetes detects and corrects it before anyone files a support ticket.

Scaling stops being a project and becomes a configuration change. Growing from ten users to ten thousand does not require rethinking architecture or hiring a larger operations team. You update a number in a Terraform file and the pipeline handles the rest.

On InMotion Cloud, this workflow runs on OpenStack infrastructure with transparent, predictable pricing. There are no surprise charges for network egress or API calls, and you get the architectural control to configure each layer exactly the way your team needs it. That combination of modern automation and cost transparency is what makes this approach practical for teams that cannot afford to overspend while they scale.

Where to Go from Here

Each stage of this workflow deserves a closer look. We have step-by-step guides that walk through the specifics on InMotion Cloud's OpenStack infrastructure:

Stage 1 — Infrastructure: How to Create Cloud Infrastructure with Terraform
Stage 2 — Configuration: How to Configure Servers with Ansible
Stages 3 and 4 — Packaging and orchestration: How to Deploy Applications with Docker and Kubernetes

More background:

Terraform: What is Terraform and how does it work?
Practical setup: How to create dev and QA environments in the cloud

If you are ready to see how this workflow runs on InMotion Cloud's infrastructure, get in touch with our team. We help teams design, build, and operate cloud environments that follow these modern practices from day one.