Build Images: Definition, How to Buit, Create Dockerfile, Why used

A Docker build image sits at the center of modern container workflows, and this article explains exactly what it is and how it works end to end. You’ll learn what a build image in Docker is, the difference between pulling an image vs building an image, how to create a Dockerfile and build a Docker image from it, how to build images for different architectures, how image layers and caching affect the build process, and how multi stage builds and multistage Dockerfile syntax optimise image size and security.

What is a build images?

A build image is the final container image produced after running a build process, typically using a Dockerfile, where your base image, source code, dependencies, and configuration are assembled into a single, versioned artifact. It is the immutable image you tag, store in a registry, and later use to create and run containers across different environments. A build image provides a consistent, reproducible package of your application, ensuring that the same code and dependencies behave identically across development, staging, and production. It also enables efficient deployments because the image becomes the single source of truth for creating containers anywhere your application runs.

Pulling an image VS Building an image: What is the difference?

Pulling an image means downloading a prebuilt container image from a remote registry to your machine. Building an image means creating a new image locally from a Dockerfile and your source code. Pulling reuses an existing artifact; building an image produces a custom one tailored to your application.

The following points are related to the key differences between pulling an image and building an image in Docker.

How to create a Dockerfile for building an image?

A Dockerfile is a plain docker file that tells Docker exactly how to assemble an image from your source code and an existing image (the base image). To create a Dockerfile that can build a Docker image, you define a series of instructions that the docker daemon executes during the build process.

A concise, practical guide to building a Dockerfile for your first image is:

• Create the Dockerfile in your project directory
  • In the root of your project, create a file named Dockerfile (no extension).
  • This directory becomes the default build environment and is used to build the image from the source.
• Choose a base image
  • Start with a base image using FROM, for example:
  • FROM ubuntu:22.04
  • This makes your Docker image based on Ubuntu or any other runtime you need (Node, Python, etc.).
  • You can pick available images from Docker Hub or from a private container registry.
• Configure the working directory and copy source code
  • Set where your app will live inside the image, for example:
  • WORKDIR /app
  • Copy your source code into the image:
  • COPY . .
  • This step ensures the image contains everything required to run the application.
• Install dependencies and configure the runtime
  • Use RUN instructions to install system or language dependencies, for example:
  • RUN apt-get update && apt-get install -y python3
  • Set environment variable values using ENV, such as ports or configuration flags.
  • Combining commands into fewer RUN layers helps reduce image size and improves build efficiency.
• Define how the container should start
  • Use CMD or ENTRYPOINT to define how the container starts.
  • Example:
  • CMD ["python3", "app.py"]
  • This command runs when a container is launched from the image.
• Optionally use multi-stage builds for smaller images
  • Multi-stage builds allow compiling in one stage and copying only the final artifacts into another image.
  • This helps create images that are smaller, faster, and more secure.
• Build your image from the Dockerfile
  • From the directory containing the Dockerfile, run:
  • docker build -t myapp:1.0 .
  • This command builds a Docker image that can be tagged and pushed to a container registry like Docker Hub.

What is a Dockerfile and why is it used?

A Dockerfile is a plain text build recipe that Docker uses to create an image. The Dockerfile contains step-by-step instructions that docker build executes to build the container image you want in a consistent, repeatable way.

The following points are related to the main reasons why a Dockerfile is used:

• Automation and reproducibility
  A Dockerfile lets you create an image automatically using docker build, so every run from the directory containing the Dockerfile produces the same result, reducing environment drift.
• Versioned with your code
  Stored in a git repository or GitHub repo, a Dockerfile changes alongside your source code, making it easy to track, review, and roll back image definitions.
• Consistent environments across systems
  One Dockerfile can build a Docker container identically on laptops, CI pipelines, and servers, minimising differences between Docker setups on different hosts.
• Customizable build process
  You can customize the build process (packages, configuration, optimisation) inside the Dockerfile, tuning image size and time to build for each image in a private or public registry.
• Standard, well-documented format
  The Dockerfile syntax is standardised and fully covered in the official Docker docs and each relevant documentation page, so tools and teams learn how to use it easily across modern container technology.

How to build a Docker image from a Dockerfile?

To build a Docker image from a Dockerfile, you place your application files and Dockerfile in a project directory, then run the docker build CLI from that directory so that Docker provides a reproducible way to turn your code into an image you can later use to create a container.

The following points are related to the exact steps for how to build a Docker image from a Dockerfile:

• Prepare your project directory
  Ensure the Dockerfile and application code are in the same folder; this folder becomes the context build using the docker build command.
• Run the build command from the project directory
  In a terminal, navigate to the project folder and run the command from the directory containing the Dockerfile, for example:
  docker build -t myapp:1.0 .
  Here, -t myapp:1.0 tags the image and . sets the current directory as the build context.
• Understand what the build provides
  The docker build step build provides a new image layer stack exactly as the Dockerfile contains, whether the project is proprietary or open source, and this works across many dockerfiles and projects once you learn how to build them.
• Use the built image to create a container
  After the build completes, you can create a container from the image with:
  docker run --name myapp-container myapp:1.0
  This is how you create Docker workloads from the image you just built.
• Accounts and registries (optional)
  You do not need to create an account to build locally; an account is only required when you later push the image to a remote registry (for example, Docker Hub), which is outside the basic build flow.

How to build images for different architectures?

To build images for different architectures, you use Docker’s multi-platform build tooling (primarily docker buildx) and --platform flags to produce separate images (for example, linux/amd64 and linux/arm64) and optionally publish them as a single multi-arch manifest.

The following points are related to the practical steps for building images for multiple architectures:

• Enable BuildKit and buildx
  Make sure BuildKit is enabled (for example, by setting DOCKER_BUILDKIT=1) and use docker buildx, which adds native support for multi-platform builds.
• Create and use a buildx builder instance
  Run something like docker buildx create --use to initialise a builder that can target multiple architectures, often backed by QEMU emulation or a remote node.
• Specify the target platforms explicitly
  Use the --platform flag to define architectures, for example:
  docker buildx build --platform linux/amd64,linux/arm64 -t myorg/myapp:1.0 .
  Here, one Dockerfile is used to build images for both amd64 and arm64.
• Push multi-arch images to a registry
  When you add --push, buildx creates architecture-specific images plus a manifest list, then pushes them to your registry (for example, myorg/myapp:1.0). The registry tag becomes a single reference that automatically serves the right architecture to each client.
• Use --load only for a single local architecture
  If you specify --load, Docker loads only the image for the host architecture into your local engine. For true multi-arch distribution, you typically use --push instead.
• Keep Dockerfile architecture-agnostic where possible
  Avoid hardcoding architecture-specific binaries or base images. Use architecture-neutral base images or parameterised tags, so that the same Dockerfile cleanly builds for all target platforms.
• Integrate multi-arch builds into CI
  Configure your CI pipeline (GitHub Actions, GitLab CI, etc.) to call docker buildx build --platform ... so every release automatically produces and publishes multi-architecture images from the same source and Dockerfile.

What is the difference between build image and runtime image?  

How do Docker image layers and caching affect the build process?

Docker image layers and build caching determine how fast, how repeatable, and how large your image builds are.

A Docker image is built as stacked layers, where each Dockerfile instruction creates a new layer. During the build process, Docker uses a cache: if an instruction and its inputs have not changed, the existing layer is reused instead of rebuilt, which can cut build time from minutes to seconds when iterating.

The following points are related to the most important ways layers and caching affect builds:

• Instruction order impacts cache reuse
  Putting stable steps (base packages, runtimes) first and frequently changing steps (COPY ., app code) later lets Docker reuse more cached layers and speeds up rebuilds.
• File changes control cache invalidation
  When files referenced by COPY or ADD change, Docker invalidates that step and all layers after it, forcing them to rebuild.
• Base image changes cascade
  Updating the base image or a key RUN step invalidates downstream layers, slowing that build but ensuring the new image includes updated dependencies or security fixes.
• Cache flags trade speed for freshness
  Using --no-cache disables reuse and forces a full rebuild, which is slower but guarantees everything is rebuilt from scratch.

How to optimise layers for smaller image sizes?

To optimise layers for smaller image sizes, use a minimal base image (for example, an Alpine or slim variant), install only the packages needed to run the application, and combine installs plus cleanup in a single RUN instruction so caches and temporary files never persist in earlier layers. Add multi stage builds so compilers and build tools stay only in a builder stage, and use a focused .dockerignore with targeted COPY instructions so unnecessary files (logs, tests, local caches) never enter the image, resulting in smaller, more efficient Docker images, which are the packaged application artifacts you later run as containers.

The following points are related to practical ways to optimise layers for smaller Docker image sizes:

• Use a minimal base image
  Prefer slim, Alpine, or distroless base images over full OS images, so every layer starts from a smaller footprint. Distroless container images include only the essential application runtime without a traditional operating system, which helps reduce size and minimize security risks.  
• Combine related RUN commands and clean up
  Chain package installs and cleanup in a single RUN (for example, apt-get update && apt-get install ... && rm -rf /var/lib/apt/lists/*) so temporary files never persist as separate layers.
• Use multi stage builds
  In multi-stage builds, keep compilers, build tools, and test dependencies only in the builder stage, then COPY only the final binaries or artefacts into a small runtime stage.
• Avoid copying unnecessary files
  Use .dockerignore to exclude logs, node_modules, test data, and build artefacts you don’t need in the image, and use targeted COPY instructions instead of copying the entire context.
• Remove caches and temporary artefacts
  Delete language-specific caches (for example, pip, npm, Maven, Gradle) and build directories inside the same RUN step that creates them, so they don’t remain in previous layers.
• Install only required runtime dependencies
  Install the smallest set of packages needed to run the application, not to develop it; keep debugging tools and shells out of production images wherever possible.

What are multi stage builds and how do they optimise build images?

Multi stage builds are a Docker build pattern where a single Dockerfile defines multiple sequential stages (for example, a builder stage and a runtime stage) and each stage uses a different FROM image. You compile, test, or bundle your application in one or more heavyweight stages that contain compilers, SDKs, and build tools, then copy only the final artefacts (binary, static files, app bundle) into a much smaller final image. Docker discards the intermediate stages from the final output, so the resulting build image contains only what is required to run the application, not to build it.

The following points are related to how multi stage builds optimise build images:

• They shrink image size by excluding compilers, package managers, and temporary build artefacts from the final stage.
• They improve security by reducing the attack surface: fewer tools and libraries are present in production images.
• They speed up deployments because smaller images pull and start faster in CI/CD and orchestrators like Kubernetes, the platform that automates container deployment, scaling, and management.  
• They keep Dockerfiles maintainable, letting you define build, test, and runtime concerns in one file while still producing a clean, minimal runtime image.

How does multistage Dockerfile syntax work?

Multistage Dockerfile syntax works by defining multiple stages in a single Dockerfile, each starting with its own FROM instruction and optionally a named alias using AS. The early stages (for example, FROM golang:1.22 AS builder) contain compilers, SDKs, and build tools, while the final stage (for example, FROM alpine:3.19) is a minimal runtime image. You then use COPY --from=<stage> to copy only the necessary artefacts (binary, static files, bundles) from a previous stage into the final one, and Docker discards the intermediate stages from the resulting image.

In practice, the syntax pattern is: define a builder stage with FROM and AS, run RUN steps to compile or bundle the app, then define a runtime stage with a new FROM line and use COPY --from=builder <source> <target> to bring in just the build outputs. Each FROM resets the layer stack for that stage, but all stages share one Dockerfile, which is what allows multistage Dockerfile syntax to produce a small, production-ready image while keeping full build logic in the same file.  

FAQ’S

Q 1. How do I choose the right base image for my Docker build image?

Choose a base image that matches your runtime requirements as closely as possible while staying minimal. For example, Node.js microservice → node:22-alpine (entity: Node.js app, attribute: base image, value: node:22-alpine, predicate: runs, temporal: current LTS). Prefer official images with regular security updates, and avoid full OS images unless you need system packages that slim or Alpine variants do not provide.  

Q 2. Where are Docker build images stored on my machine, and can I clean them safely?

Locally, Docker build images are stored in Docker’s internal image cache (typically under /var/lib/docker on Linux, but treated as an opaque store). You can safely remove unused images with commands like docker image prune (dangling) or docker image prune -a (all unused), as long as no running containers depend on them; Docker will refuse deletion when an image is still in active use.  

Q 3. How should I tag Docker build images for different environments (dev, staging, prod)?

A robust tagging scheme uses semantic versioning plus environment labels, for example myapp:1.4.2-dev, myapp:1.4.2-staging, myapp:1.4.2-prod (entity: myapp image, attribute: environment, values: dev/staging/prod). Tie tags to git commit SHAs or release branches (for example, myapp:1.4.2-abc1234) so you can trace every running container back to the exact source revision and rebuild the same image when needed.  

Q 4. How can I debug a failing Docker build without slowing every build down?

To debug a failing Docker build, first re-run with detailed output (docker build --progress=plain) to see exactly which Dockerfile instruction fails. You can insert temporary RUN steps (for example, RUN ls -R /app) near the failing layer, then later remove them. Use targeted --no-cache only around suspect steps (by temporarily reordering layers) instead of globally, so you keep most build cache speed while isolating the error.  

Q 5. How do I ensure my Docker build images are secure before pushing them to a registry?

Before pushing, scan each build image with a vulnerability scanner (for example, docker scan or a registry-integrated scanner) to catch known CVEs in the base image and installed packages. Combine this with minimal base images, least-privilege users (using USER instead of root), and regular rebuilds from updated bases. In CI, enforce a policy such as “block push if severity ≥ high” (entity: image, attribute: allowed severity, value: below “high”) to keep unsafe images out of production registries.  

Q 6. What is the difference between a Docker build image and a snapshot of a running container?

A Docker build image is a versioned template created by docker build, designed to be immutable and reused to start many containers. A snapshot of a running container (for example, docker commit <container>) captures the container’s current filesystem state, including manual changes. The first is declarative and reproducible (defined by the Dockerfile); the second is imperative and ad-hoc, and should be avoided for long-term builds because it is harder to track, audit, or recreate.