Docker & Kubernetes

Why Containers Matter

The “works on my machine” problem has killed more launches than bad code. Your application behaves one way on a developer’s laptop, another way in staging, and breaks entirely in production. Containers eliminate this chaos. They package your application with its exact dependencies, runtime, and configuration. What runs locally runs identically everywhere else.

Beyond consistency, containers change how teams work. Developers can spin up complex multi-service environments in seconds. New engineers onboard faster because they don’t spend days configuring their machines. Operations teams deploy with confidence because they know exactly what’s running. The abstraction layer containers provide makes everything more predictable.

The efficiency gains are real too. Unlike virtual machines that each need their own operating system, containers share the host kernel. You run more workloads on the same hardware. Startup time drops from minutes to milliseconds. Resource utilization improves dramatically. For startups watching cloud bills, this matters.

What We Build With Docker

We write Dockerfiles that production teams actually want to maintain. Multi-stage builds keep images small — often under 50MB for compiled applications. Proper layer ordering means rebuilds take seconds, not minutes. Non-root users and minimal base images reduce attack surface.

Specific things we build:

• Optimized application images — Node.js APIs, Python services, Go binaries, each with appropriate base images and build strategies
• Development environments — Docker Compose configurations that replicate production locally, complete with databases, caches, and message queues
• CI build images — Custom images with the exact tools your pipeline needs, cached and ready
• Private registry infrastructure — Harbor or cloud-native registries with vulnerability scanning and access controls

We’ve containerized monoliths, microservices, and everything in between. Some projects start with containers from day one. Others need migration strategies. We’ve done both.

What We Build With Kubernetes

Kubernetes orchestrates containers at scale. It handles the problems that appear when you have more than a few services: scheduling, networking, storage, secrets, and failure recovery.

We set up clusters that handle real production requirements:

• Stateless services — Web applications and APIs with horizontal pod autoscaling based on actual request metrics
• Background workers — Job processors that scale with queue depth
• Stateful workloads — Databases and caches with proper persistent volumes and backup strategies
• Scheduled jobs — CronJobs for reports, cleanups, and batch processing
• Internal tools — Admin dashboards, monitoring stacks, and development utilities

Every cluster includes proper observability from day one. Prometheus scrapes metrics. Logs flow to a central location. Network policies restrict traffic between namespaces. RBAC controls who can do what.

Our Experience Level

We’ve been running containers in production since 2017. That’s a lot of failed deployments, resource limits that weren’t quite right, and networking issues that seemed impossible until they weren’t.

We’ve deployed clusters on AWS EKS, Google GKE, DigitalOcean Kubernetes, and bare-metal setups. We’ve migrated monoliths to containers, broken monoliths into microservices, and sometimes convinced teams that their monolith was fine and just needed better deployment tooling.

Our infrastructure-as-code approach means we can recreate any cluster from scratch. Terraform for cloud resources. Helm for Kubernetes manifests. ArgoCD for GitOps deployments. Everything versioned, everything reviewable.

When to Use It (And When Not To)

Containers make sense when you need consistent deployments, environment parity, or you’re running multiple services. If you’re deploying a single Rails app to a single server, containers add complexity without proportional benefit.

Kubernetes makes sense when you have multiple services, need automatic scaling, or want zero-downtime deployments. For a small team running a handful of containers, Docker Compose or a managed service like AWS App Runner might be simpler.

We’ll tell you honestly which approach fits your situation. Sometimes the answer is “you don’t need Kubernetes yet.” Sometimes it’s “you should have adopted this six months ago.”

Common Challenges and How We Solve Them

Resource limits that don’t match reality. Applications crash or get throttled because CPU and memory limits are guesses. We profile actual usage and set limits based on real data. We configure vertical pod autoscalers to adjust over time.

Slow builds blocking deployment. Developers wait ten minutes for images to build. We restructure Dockerfiles for better layer caching, implement build caching in CI, and use multi-stage builds to parallelize work.

Networking complexity. Services can’t find each other. Traffic doesn’t flow where it should. We implement service meshes when complexity warrants it, but often simpler solutions like proper DNS configuration and health checks solve the problem.

Secret sprawl. Passwords and API keys end up in environment variables, config files, and places they shouldn’t be. We implement proper secrets management with tools like External Secrets Operator or HashiCorp Vault.

Upgrade anxiety. Teams run outdated Kubernetes versions because upgrades feel risky. We establish upgrade runbooks, test upgrades in staging, and keep clusters current. Running old versions is often riskier than upgrading.