What is Continuous Deployment? - Explanation & Meaning

Definition

Continuous deployment (CD) is a software practice where every code change that passes all automated tests and quality checks is automatically deployed to the production environment without manual intervention.

Technical explanation

Continuous deployment differs from continuous delivery: with continuous delivery, code is always deployment-ready but requires manual approval for production, while continuous deployment is fully automated. The deployment pipeline consists of multiple stages: code compilation, unit tests, integration tests, security scans (SAST/DAST), staging deployment, smoke tests, and production deployment. Blue-green deployments maintain two identical environments and switch traffic instantly. Canary deployments roll out new versions to a small percentage of users. Rolling deployments gradually replace instances. Feature flags decouple deployment from release. Rollback mechanisms automatically restore the previous version on failing health checks. GitOps workflows use Git as the single source of truth for both application and infrastructure configuration. Observability (metrics, logs, traces) is essential for quickly detecting issues after deployment. The average deployment frequency for organizations with continuous deployment is multiple deploys per day.

How MG Software applies this

MG Software implements continuous deployment pipelines for all projects where the reliability and speed of the release process are crucial. Our pipelines in GitHub Actions or Vercel include automated tests, security scans, and deployment to staging and production. We combine this with feature flags for safe rollouts and monitoring for rapid detection of post-deployment issues.

Practical examples

• A SaaS company deploying to production an average of ten times per day, where every commit automatically runs through a pipeline of tests, scans, and staged deployments.
• A team using blue-green deployments to immediately switch back to the previous version if monitoring detects anomalies after deployment.
• A startup that has configured their deployment pipeline so that a merge to main is running in production within five minutes, provided all tests and quality gates pass.

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