Combining DevOps practices, container workloads, and public cloud infrastructure is one of the most productive stacks available to modern engineering teams. Done well, it can compress the feedback loop between development and production to near zero. Done poorly, it generates performance problems, toolchain confusion, and cloud lock-in. Here are three concrete tips for getting it right.
1. Expect to Rebuild Part of Your DevOps Toolchain
Nearly every DevOps vendor claims container compatibility, but marketing copy and real-world integration are different things. When you introduce containers—and especially a container orchestrator such as Kubernetes—into your pipeline, you will almost certainly need tools that were designed with containers as a first-class citizen, not bolted on afterward.
Typical areas where you may need to evaluate new or replacement tooling include:
- Image build and registry management – tools that understand multi-stage Dockerfile builds, image signing, and vulnerability scanning.
- CI/CD pipelines – pipelines that can build, test, and push container images natively, and trigger Kubernetes rolling deployments or Helm chart upgrades.
- Secrets management – solutions that integrate with orchestrator APIs rather than injecting secrets as plain environment variables.
- Observability – monitoring and logging stacks that are cluster-aware and can correlate metrics across ephemeral container instances rather than long-lived VMs.
This toolchain expansion adds complexity. Acknowledge that up front, budget for it, and evaluate tools based on how well they fit a container-native workflow—not on how familiar they feel from your pre-container world.
2. Make Performance Testing a First-Class Concern
Container-based applications that are not well-designed frequently underperform, and poor container architecture is a primary reason. The most common mistake is treating containers the same way you would treat a monolithic process: packing too much functionality into a single container image.
Containers are designed to be distributed and independently scaled. When unrelated processing concerns—for example, an API handler, a background job processor, and a cache warmer—share a single container, you lose the ability to scale each concern independently. The result is either over-provisioning or resource contention.
Practical guidance:
- Apply the single-responsibility principle at the container level. Each container image should do one thing and do it well.
- Define resource requests and limits for every container so the orchestrator can schedule and throttle them appropriately.
- Run load and stress tests early in the pipeline, not just before production. A container application that looks fine under light traffic can degrade rapidly at scale if inter-service communication or data-layer access patterns are poorly designed.
- Profile inter-container latency. Network hops between containers add up. Distributed tracing tools help identify where latency is accumulating across service boundaries.
Architecture skills for container-native systems are still scarce. If your team is new to microservices or container design patterns, invest in structured training before you begin decomposing workloads. Retrofitting a poorly decomposed application is expensive.
3. Choose Your Cloud Endpoints Deliberately
All major public cloud providers offer managed Kubernetes services and container registries. That does not mean every provider is equally suited to your specific workload.
The portability that containers promise makes multi-cloud and cloud-agnostic deployments genuinely achievable in a way that VM-based workloads were not. Use that portability. Evaluate cloud providers on criteria relevant to your container workloads:
- Managed Kubernetes maturity – upgrade cadence, default networking and storage integrations, and node auto-provisioning capabilities vary significantly between providers.
- Egress and inter-region networking costs – container-based microservices can generate high volumes of inter-service traffic; understand the billing model before you commit.
- Ecosystem fit – some providers have deeper integrations with specific data services, ML infrastructure, or edge networking that may give your architecture a genuine advantage.
- Compliance and data residency – container orchestration does not eliminate regulatory requirements around where data is processed and stored.
Familiarity with a particular cloud provider is a valid factor, but it should not be the deciding one. Because containers abstract the underlying infrastructure, the switching cost of moving workloads is lower than it is with tightly coupled VM or PaaS deployments. Evaluate honestly and revisit the decision as your workload matures.
Summary
Containers, DevOps, and cloud computing genuinely accelerate software delivery when they are applied thoughtfully. The three areas that cause the most friction in practice are toolchain fit, application architecture and performance, and cloud provider selection. Address each of them intentionally rather than inheriting defaults from your pre-container workflow, and you will avoid the most common and costly mistakes.
Originally sourced from InfoWorld (2019). Some specifics may have evolved as the container and cloud-native ecosystem has matured since publication.