Cloud Deployment & Scaling

We deploy services as containers on managed cloud platforms — AWS, GCP, or Azure, chosen for your constraints rather than our preference — with the entire environment defined in Terraform. Networking, TLS, DNS, secrets, databases, and compute exist as reviewed code, which means a new environment is a plan-and-apply away and 'how is production configured?' has an answer in version control instead of in someone's memory.

The architecture leans on managed services where they reduce operational load — managed databases with automated backups, managed load balancers, container platforms — and keeps portability in view so you're using the cloud, not married to one vendor's proprietary glue. Staging mirrors production closely enough that a deploy that works in staging works in production, and Nginx or the platform's native routing fronts services with TLS and sane headers from day one.

CI/CD is how releases become boring: every commit builds, tests, and packages automatically — typically in GitHub Actions — then promotes through staging to production with the same artifact at every step. Deploys are one action, rollbacks are one action, and nobody's laptop is part of the release process. The pipeline enforces what code review can't: nothing unbuilt, untested, or unscanned reaches production.

Around the pipeline we set up the operational practices that keep a team fast: secrets management so credentials never live in code, database migrations that run as part of deployment, environment parity so 'works locally' means something, and deployment history that answers 'what changed?' in seconds during an incident. Your developers keep shipping the way they already work — the pipeline meets them at git push.