Dockerfile redesign
Layer ordering, multi-stage targets and cache mounts arranged so the slow steps sit at the bottom and rebuild only when their inputs change.
Platform & Containers
Docker & Containerization
Container images built fast, kept small and shipped without known vulnerabilities.
The work
What this actually does
Docker problems are usually build problems in disguise. We rewrite Dockerfiles around multi-stage builds and layer ordering so a change to application code reuses the expensive dependency layers instead of rebuilding the world every time. Builds move to a shared cache and a repeatable base image, which keeps laptops and CI producing the same thing.
The other half is what ends up in the image. We strip build toolchains and package managers from the runtime layer, run as a non-root user, pin digests where supply-chain risk matters, and produce a software bill of materials for each release. Images that were once a gigabyte routinely come down by more than half, which speeds up every pull and narrows the patching surface.
If any of these sound familiar
- A one-line change triggers a fifteen-minute image rebuild
- Images carry compilers, package managers and root users into production
- It works on a laptop and fails in the pipeline
- Nobody can say what packages are inside a running image
Scope
What's included
Every engagement on this page covers the following, sized to your setup rather than delivered as a fixed package. If something here is not relevant to you, it comes off the scope and off the price.
Image slimming
Runtime layers built from distroless or Alpine bases with build tooling removed, dropping image size and the number of packages you have to patch later.
Hardened runtime defaults
Non-root users, read-only root filesystems and dropped Linux capabilities, with a documented exception path when a workload genuinely needs to run as root.
Registry and tagging
A tag convention that maps every image back to a commit, immutable digests for production, and retention rules so the registry does not fill with untagged layers.
Local development parity
Compose files and dev containers that mirror the deployed image closely enough that environment differences stop being the first suspect in every bug report.
Build verification
Scans for base-image CVEs, secrets and licence issues wired into the build, so a bad image is caught before it reaches a registry production can pull from.
What changes
What teams typically see
60%Typical image size cut
2 minWarm rebuild time
0Root containers shipped
Handover
What you keep
Everything produced during the engagement is yours: the repositories, the accounts, the documentation. There is no proprietary layer and nothing to unlicense if you take the work in-house.
- Rewritten Dockerfiles with documented layer strategy
- Shared cache configuration for local and CI builds
- Image size and vulnerability baseline report
- Tagging, retention and registry policy document
- Local compose setup matching the deployed image
Tooling
Tools we use here
A starting point, not a requirement. We work in whatever you already run wherever it does the job.
Docker
Kubernetes
GitHub
Trivy
Snyk
Node.js
Python
How it runs
From first call to handover
The same four steps on every engagement. You see each one before it starts and can stop at any of them.
- 01
Audit the build images
We time a cold and a warm build, trace where the layers come from, and check what the runtime image actually contains before changing anything.
- 02
Rebuild for caching
Dockerfiles are restructured so dependency layers change rarely and code layers change often, with cache mounts for package managers that support them.
- 03
Harden the runtime
We switch to minimal bases, remove build tooling, add a non-root user and fix whatever breaks, which is usually a file permission or a missing shell.
- 04
Wire it into the pipeline
Scans, digests and retention run on every build, and we hand over the Dockerfile conventions so new services start from a good image.
Questions
Asked before we start
Will slimming images break our application?
Occasionally, yes — normally when something relied on a shell, a package manager or a library that was present by accident. We stage the change, run the test suite against the new image and add anything genuinely required rather than abandoning the approach.
Do we need to move off our current base image?
Not always. If a maintained base already gives you a small runtime and timely security updates, the gains from switching are modest. The bigger wins usually come from layer ordering, cache use and what you copy into the final stage.
How does this relate to our Kubernetes setup?
Better images make the cluster easier to run: faster pulls, smaller nodes, fewer CVEs to patch and less chance of a container needing privileged access. The two pieces of work reinforce each other, but the image changes stand alone if you are not on Kubernetes yet.
Can you work with our existing Dockerfiles?
Yes, and that is the usual starting point. Most Dockerfiles have a sound shape and two or three expensive mistakes. We keep the structure, fix the ordering and caching, and rewrite only the stages that are clearly working against you.
Platform & Containers
Often needed alongside this
Platform & Containers
Kubernetes & Containers
Production-grade clusters with sane defaults, safe rollouts and an operator experience your team will actually enjoy.
- EKS, AKS, GKE and self-managed clusters
- Helm, Kustomize and GitOps-driven deployments
- Node autoscaling, resource tuning and cost control
Security & Compliance
Container & Image Scanning
Block images with known critical CVEs before they ever reach a registry your production cluster can pull from.
- Trivy, Grype and Snyk in the build pipeline
- SBOM generation and provenance attestation
- Registry admission policies that block bad images
Delivery Automation
CI/CD Pipeline Engineering
Automated build, test and deploy pipelines that turn every commit into a repeatable, auditable release.
- GitHub Actions, GitLab CI, Jenkins & Azure Pipelines
- Parallel builds, caching and artifact promotion
- Quality gates, approvals and rollback on failure
Worth a conversation about Docker & Containerization?
Bring the specific problem. We will tell you honestly whether this is the service that fixes it, and what it would take.