Cloud & Infrastructure
Docker for environments that behave the same everywhere
Reproducible application images that remove "works on my machine" from the vocabulary and make deployments boring in the best possible way.
1 image
Identical artefact promoted across environments
<10 min
Typical build-to-deploy pipeline time
-80%
Environment-specific defects
Minutes
New developer onboarding to a running stack
Definition
What Docker is
Docker packages an application with its runtime, libraries and configuration into an immutable image. The same artefact runs in development, staging and production, so environment differences stop causing incidents.
When to use it
Where Docker is the right call
Recognise your situation before committing engineering budget.
Capabilities
How we work with Docker
What is actually delivered, beyond the logo on a stack slide.
Image engineering
Multi-stage builds, minimal base images and layer caching for fast, small artefacts.
Local parity
Compose stacks that reproduce databases, queues and services on any laptop.
Supply-chain security
Image scanning, signed builds, pinned dependencies and non-root runtimes.
CI/CD
Automated build, test, scan and promote pipelines with rollback on failure.
Strengths
- Environment parity from laptop to production
- Fast, repeatable deployments and clean rollbacks
- Portable across clouds and on-premise hosts
- Foundation for autoscaling and orchestration later
Trade-offs to plan for
- Careless images become large and insecure without discipline
- Stateful services still need deliberate volume and backup design
- Containers alone do not solve orchestration — that is Kubernetes' job
Use cases
Docker in production
Representative systems we build and support on this technology.
Legacy modernisation
Containerising an ageing application to move it off unsupported servers safely.
Multi-service platform
API, workers, cache and database orchestrated consistently across environments.
Regulated deployments
Immutable, scanned images with provenance for audit requirements.
Comparison
Docker vs virtual machines vs bare deployment
| Dimension | Docker | Virtual machines | Bare deployment |
|---|---|---|---|
| Startup time | Seconds | Minutes | Varies |
| Resource overhead | Low | High | None |
| Environment parity | Excellent | Good | Poor |
| Rollback | Image swap | Snapshot | Manual and risky |
Engineering practices
Non-negotiables on every engagement
Decision guide
Which direction fits your situation?
Match your constraints to the recommended approach.
| If this sounds like you | We recommend |
|---|---|
| Single small application, one server | Docker with Compose and automated deploys |
| Many services and variable load | Docker images orchestrated by Kubernetes |
| Strict compliance and audit needs | Signed, scanned images with provenance records |
Delivery
Services built on Docker
Pairs well with
Industries running this
FAQ
Docker questions, answered
Related technologies
What we usually pair with it
Kubernetes
Declarative orchestration for containerised workloads: self-healing deployments, autoscaling and zero-downtime releases governed by version-controlled configuration.
Node.js
An event-driven runtime that handles thousands of concurrent I/O-bound requests efficiently — ideal for API gateways, integrations and real-time features.
PostgreSQL
An open-source relational database with genuine ACID guarantees, deep SQL, JSONB flexibility and vector search — the default system of record for serious business data.
Planning a build or migration on Docker?
Share your requirements and current systems. We will return an architecture recommendation, trade-offs and a phased plan — under NDA.