Insights
Cloud Engineering Analysis
Technical articles, research papers, and infrastructure methodologies from the CLOUDKYO engineering team, covering AWS, Microsoft Azure, Google Cloud, and the tooling that runs them.
Featured analyses
Deep dives by technology stack
Engineering breakdowns of the platforms and practices our teams run in production every day.
AWS, Azure, and Google Cloud optimization
How workload placement, reserved capacity, and instance right-sizing change the cost and performance profile across the three major platforms.
Kubernetes, Docker, and Terraform in production
Container orchestration, image hygiene, and infrastructure-as-code patterns that keep environments reproducible from development through production.
Linux foundations and CI/CD pipelines
Hardening Linux hosts and building delivery pipelines that test, promote, and roll back changes without manual intervention.
Architecture perspectives
Hybrid design, compliance, and cost governance
Hybrid infrastructure paradigms keep sensitive workloads close to the systems they depend on while letting elastic capacity run where it fits best. The architecture decision is rarely about one platform; it is about how data gravity, latency, and operational ownership align across environments.
Regulatory requirements for banking and healthcare add concrete constraints on residency, audit trails, and access control. Designing those controls into the platform from the start is far less costly than retrofitting them after migration.
Cloud cost governance depends on visibility first. Tagging standards, allocation models, and regular reviews of idle and oversized resources turn spend from an unexplained line item into a measurable engineering input.
Our perspectives draw on the same environments our teams operate, so each recommendation is tested against the realities of running production infrastructure rather than a theoretical reference architecture.