What is Docker?

Docker is a platform that packages an application with its dependencies into a container image that runs identically on a laptop, a build server and production. A container shares the host kernel rather than virtualising hardware, which is why images start in milliseconds, and the recurring mistakes are bloated base images, secrets baked into layers and unpinned version tags. PixelForce reduced a client's cloud hosting costs by over 50 percent, saving them more than $10,000 every month.

As of August 2026, Docker Hub still applies its long-standing limits of 100 pulls per six hours for unauthenticated users and 200 for Docker Personal accounts: the stricter limits announced for April 2025 were never enforced, and Docker has committed to six months notice before any change.

How does Docker work?

Docker packages an application and everything it needs to run - code, libraries, runtime and configuration - into a single unit called a container. The container is built from an image, a read-only template that defines exactly what is inside. Because the container carries its own dependencies, it runs identically whether on a developer's laptop, a test server or production. Unlike a virtual machine, a container shares the host operating system kernel rather than bundling a full operating system, which makes containers far smaller and faster to start.

This isolation is the key idea. Each container runs in its own self-contained space, so one application cannot interfere with another, and the environment a developer builds against is the same environment that runs in production. That consistency is what eliminates an entire class of deployment problems.

Why Docker matters

Software behaves differently across environments when dependencies and configuration drift. The familiar it works on my machine failure happens because the developer's setup does not match the server's. Docker solves this by shipping the environment alongside the application, so what is tested is what is deployed. It also makes applications portable across cloud providers, speeds up onboarding because a new developer can start a full environment with one command, and underpins modern scaling approaches such as microservices.

Containers versus virtual machines

Docker containers and virtual machines both provide isolation, but they differ in important ways:

  • Footprint - containers share the host kernel and are lightweight; virtual machines bundle a full operating system and are heavier.
  • Startup time - containers start in seconds; virtual machines take minutes.
  • Density - many more containers than virtual machines fit on the same hardware.
  • Isolation - virtual machines isolate more completely at the cost of overhead.

Docker best practices

Keep images small and based on minimal foundations so they build quickly and expose less to attack. Define images in version-controlled files so environments are reproducible and auditable. Run one main process per container so each can be scaled and replaced independently. Never store sensitive credentials inside an image, and avoid keeping important data inside a container's writable layer, since containers are designed to be disposable - persistent data belongs in a managed volume or external store.

How PixelForce approaches Docker

At PixelForce, containerisation supports Phase 2 - Development and the release pipeline that carries products into Phase 3 - Post Launch Support. Our in-house Adelaide team uses containers so the environment a developer builds against matches what runs in production, which removes a common source of deployment risk and contributes to the 99.99% crash-free reliability we sustain across live products. This work sits inside our broader AWS DevOps consulting, and for products that need to scale on managed cloud infrastructure it connects to our AWS app migration services.

Where this applies

The PixelForce services where Docker matters most - explore how we put it to work in client products.

Frequently asked questions

A virtual machine virtualises a full operating system, bundling its own kernel, which makes it large and slow to start. A Docker container shares the host's operating system kernel and packages only the application and its dependencies, making it lightweight and quick to launch. Containers are more efficient and let far more run on the same hardware, while virtual machines offer stronger isolation. Many systems use both, running containers inside virtual machines.

No. Docker is a platform for building and running individual containers. Kubernetes is an orchestration system that manages many containers across multiple machines - scheduling them, scaling them, restarting failed ones and handling networking between them. Docker creates the containers; Kubernetes coordinates large numbers of them in production. They are complementary rather than competing, and teams often use Docker to build images and Kubernetes to run them at scale.

Docker mainly improves consistency, portability and deployment speed rather than raw application performance. Containers add very little overhead because they share the host kernel, so a containerised application performs close to running directly on the host. The real gains are operational: faster, more reliable deployments, easier scaling and reproducible environments. Where performance improves, it is usually because Docker makes it easier to scale services and use infrastructure efficiently.

By default, data written inside a container lives in its writable layer and is lost when the container is removed, because containers are designed to be disposable. To keep data, it should be stored in a Docker volume or written to an external store such as a managed database or object storage. This separation of the application from its data is deliberate, and it is what makes containers safe to stop, replace and scale freely.

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