Cloud & Infrastructure

Docker Consulting Services

Containerized applications that run the same image in development, staging and production, plus the pipeline that delivers it.

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How we work

Docker addresses a recurring problem: an application works on one machine and fails on another because of a mismatched library version, a missing environment variable or a different operating system. Our docker consulting services cover Dockerfiles that produce small, reproducible images, docker-compose setups for local development that mirror production dependencies, and the registry and CI pipeline that carry an image from a commit to a running container.

We treat Docker as packaging, not as an operations strategy by itself. A containerized application still needs somewhere to run: a single host, ECS, Cloud Run or a Kubernetes cluster, and the right choice depends on how many services exist and how much operational capacity the team has, not on Docker itself.

Most of our containerization work happens during a legacy modernization project or when a team standardizes environments across a growing group of developers. Getting the base image, layer caching and secrets handling right early saves real time later, particularly once a project has more than one or two services in play.

Fit

When Docker is the right choice, and when it is not

Good fit

You have more than one developer and want local environments to match staging and production instead of drifting apart over time.
You are moving an application toward a managed container platform such as ECS, Cloud Run or Kubernetes and need it packaged correctly first.
Your CI pipeline needs a consistent, reproducible build artifact rather than relying on the exact state of a build server.
You run several services with different language runtimes or dependency versions and want to isolate them from each other.

Consider something else

A single static site or a simple application with one clear, well-supported deployment target often does not need containerization at all.
Docker by itself does not solve scaling, service discovery or zero-downtime deployment; those require an orchestrator such as ECS or Kubernetes on top of it.
Teams with no existing CI/CD practice sometimes get more value from fixing deployment automation first, since a container without a pipeline to build and ship it adds process without much benefit.
Extremely resource-constrained environments, such as certain embedded or edge deployments, can find the overhead of a container runtime unnecessary compared with a native binary.

What we build

What we build with Docker

Dockerfile authoring
Multi-stage builds that separate compile-time dependencies from the runtime image, keeping final images small and reducing the attack surface that ships to production.
Local development environments
docker-compose configurations that bring up an application alongside its database, cache and message broker with one command, matching what runs in staging.
Image hardening
Minimal base images, non-root users inside containers, and dependency scanning with tools such as Trivy to catch known vulnerabilities before an image reaches production.
Registry and CI integration
Pushing built images to ECR, GCR, ACR or Docker Hub from a CI pipeline, with tagging conventions that make it clear which image is running where.
Legacy application containerization
Wrapping an existing application that was never designed for containers, handling file system assumptions, hardcoded paths and long-running processes that need adjustment first.

Ecosystem

Libraries and tools we pair it with

Docker Compose
BuildKit
Docker Hub
Amazon ECR
Trivy
Dive
Kubernetes
Amazon ECS

Multi-Stage Builds and Image Size

A common mistake is shipping a production image that still contains a full compiler toolchain, test dependencies and source files that were only needed to build the application. Multi-stage builds separate these concerns: one stage compiles or installs dependencies, and a final stage copies only the built artifact into a minimal runtime base image.

Smaller images pull faster during deployment, reduce the surface area for vulnerability scanners to flag, and make a container easier to reason about. We treat image size and content as something to review deliberately rather than accept whatever the default base image produces.

Separate build and runtime stages in every Dockerfile
Use minimal or distroless base images where the runtime allows it
Run containers as a non-root user by default

Docker Compose for Local Development Versus Kubernetes for Production

docker-compose is built for one machine: it starts a set of containers with defined networking and volumes, which is exactly what a developer needs locally. It is not designed for the concerns of a production cluster, rolling deployments, autoscaling, multi-node scheduling and self-healing after a node fails.

We use docker-compose for local development and CI test environments in almost every project, regardless of what runs in production. The production target is a separate decision: a small application might run the same images on ECS or Cloud Run without ever needing Kubernetes, while a larger platform with many services might run Kubernetes in production while developers still use docker-compose locally for speed.

Services

Services that use Docker

Industries

Where we use it

FAQ

Questions about Docker

Do we need Docker if we are already deploying directly to a cloud provider's platform?

Often yes, because most managed platforms, including ECS, Cloud Run and App Service, run container images rather than raw source code. Docker becomes the packaging format that makes your application portable across those platforms, and gives you a way to test the exact artifact that will run in production, on your own machine, before it ships.

How much does containerizing an existing application typically cost in engineering time?

A single, reasonably modern service usually takes a few days to containerize properly, including writing the Dockerfile, setting up docker-compose for local development and wiring the image into an existing CI pipeline. Older applications with hardcoded file paths, stateful assumptions or unusual startup scripts take longer, sometimes a few weeks, because the application itself needs adjustment first.

What is the difference between Docker Compose and Kubernetes, and do we need both?

Docker Compose runs containers on a single machine and is built for local development and simple deployments. Kubernetes coordinates containers across many machines, handling scheduling, scaling and recovery from node failure. Most teams use Compose locally regardless of what orchestrator, if any, runs in production.

How do you keep Docker images secure over time?

We scan images for known vulnerabilities in CI before they are pushed to a registry, rebuild base images regularly rather than letting them go stale, and avoid running containers as root unless the application specifically requires it. None of this is a one-time task; it needs to run on every build, not just at project handoff.

Can Docker help us reduce our cloud hosting costs?

Indirectly, yes. Smaller, well-built images deploy faster and use less memory and CPU overhead than a poorly configured container, which reduces the compute you provision to run the same workload. The bigger cost lever is usually the orchestration choice built on top of Docker, since ECS, Cloud Run and Kubernetes have very different baseline operating costs for the same application.

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