Integrating DevOps and Containers: Practical Toolchain Adjustments

This article is based on reporting originally published on TechTarget.com. It has been expanded and rewritten for clarity and technical accuracy.

Why DevOps and Containers Belong Together

Organizations adopt DevOps methodologies to reduce application delivery time and improve code quality, consistency, and security. They adopt containers for many of the same reasons. Both approaches prioritize automation, repeatability, and the elimination of environment-specific inconsistencies—making them a natural pairing.

A typical DevOps implementation chains together multiple tools covering development, testing, and deployment. That toolchain must accommodate:

• Multiple programming languages such as C, Python, and Go
• Different application targets, including mobile and web
• Diverse deployment platforms—virtual machines (VMs), cloud services, and containers

Because a well-designed toolchain decouples its components—separate tools for version control, build automation, integration testing, and deployment—teams can swap individual elements without replacing the entire pipeline. When developers migrate from VMs to containers, they update specific toolchain stages rather than starting from scratch.

Choosing a Container Orchestration Strategy

The most consequential decision in any container strategy is the choice of orchestration and cluster management software. For the container runtime itself, Docker has long been the de facto standard. For orchestration, Kubernetes has emerged as the dominant choice—especially for large enterprises and online businesses that need scalable, highly available infrastructure.

Kubernetes is not the right fit for every team. It can be complex to install and operationally demanding to manage. Several commercial products are built on top of Kubernetes specifically to reduce that operational burden. Other organizations prefer packaged container management systems that integrate with existing virtual infrastructure—such as VMware vSphere Integrated Containers or Windows Server Containers.

Some teams opt for a structured Platform-as-a-Service (PaaS) stack such as Cloud Foundry or Google App Engine, which use containers as the underlying runtime by default while abstracting away cluster management responsibilities.

Adapting Your DevOps Pipeline for Containers

A basic DevOps pipeline typically includes these stages:

1. Code repository with version management (e.g., Git, GitHub, GitLab)
2. Build automation
3. Continuous integration and continuous delivery (CI/CD)
4. Configuration management
5. Infrastructure deployment

Migrating from VMs to containers primarily affects the configuration management and infrastructure deployment stages, with possible customization to the CI/CD pipeline. Developers can continue using existing version control and build systems with minimal disruption. The container management system takes over image packaging, resource configuration, and deployment coordination.

Teams that migrate entirely to containers may be able to retire traditional infrastructure automation tools. In practice, most enterprises run a mix of deployment targets—containers, VMs, and cloud services—so the pipeline must support all of them.

Example: A Docker-Based DevOps Workflow

Consider a team using Docker for container-based applications. The workflow might look like this:

• Developers push code to a Git repository.
• A webhook triggers the CI/CD tool, which runs integration tests.
• On a successful test run, the CI/CD tool automatically builds a Docker image and pushes it to a private registry (e.g., Docker Hub or a self-hosted registry).
• The orchestration layer—Docker Swarm or Kubernetes—detects the new image and either deploys it automatically to the container cluster or alerts the operations team that a new release is ready for promotion to production.

This model keeps deployment fast and consistent while preserving human oversight at the production promotion gate if required.

Deploying Containers in the Cloud

Shifting to cloud-based containers within a DevOps model is similar in structure, but the key question is whether the toolchain itself lives in the cloud or only the runtime environment does.

Hybrid toolchain implementations are common for teams that already have established build automation and CI/CD tools but want to change the deployment target. In this case, only the image registry and deployment tooling need to change. The CI/CD tool continues to handle build and test cycles, then pushes images to a cloud registry and uses cloud APIs or CLI scripts to trigger a rolling deployment on the container cluster.

Example: CI/CD to Google Kubernetes Engine (GKE)

A team using a CI/CD tool such as Codefresh can define a pipeline that:

1. Builds and tests a Docker image on each code commit.
2. Uses a YAML deployment template with gcloud and kubectl CLI commands to push a successfully tested image to Google Container Registry.
3. Triggers a rolling update across all pods in a GKE Kubernetes cluster.

Example: Fully Cloud-Native Pipeline on AWS

AWS provides a suite of managed developer services that can run the entire pipeline without self-managed infrastructure:

• CodeCommit stores the source code repository.
• CodePipeline detects new commits and orchestrates the pipeline.
• CodeBuild builds and tests the Docker image.
• Elastic Container Registry (ECR) stores the tested image.
• A Lambda function (or CodePipeline action) instructs Kubernetes to pull the new image and perform a rolling update across container instances.

This fully automated flow enables rapid application updates without requiring manual intervention or production changes between releases.

Using PaaS to Simplify Container Operations

For teams that want to avoid managing Kubernetes clusters directly, PaaS platforms abstract away much of the operational complexity:

• Azure App Service and Google App Engine handle container configuration, deployment, and autoscaling automatically.
• Cloud Foundry, a container-based private PaaS, integrates with popular developer tools including private Git servers and hosted GitHub repositories. However, some teams report limitations with certain CI pipeline tools and have adopted Concourse CI as an alternative that integrates more tightly with Cloud Foundry’s deployment model.

Ops teams that use Cloud Foundry are generally advised to evaluate Concourse CI to automate the full code-to-image-to-deployment pipeline reliably.

Key Takeaways

• Migrating from VMs to containers does not require rebuilding your entire DevOps toolchain—only the configuration management and deployment stages need significant changes.
• Kubernetes is the dominant orchestration platform but adds operational complexity; commercial distributions and PaaS options reduce that burden.
• Cloud providers offer integrated services (AWS CodePipeline, GCP Container Registry + GKE, Azure Container Service) that can replace self-managed pipeline components.
• Container image portability makes hybrid and multi-cloud deployment strategies significantly easier to implement within an automated CI/CD pipeline.
• Choosing the right orchestration strategy—self-managed Kubernetes, a managed Kubernetes service, or a PaaS—is the foundational decision that shapes all downstream toolchain choices.