Blue-Green Deployment

Blue-green deployment works by having two complete production environments behind a load balancer or DNS switch. The blue environment runs the current production version while the green environment receives the new deployment. After smoke tests verify the green environment, traffic is switched from blue to green. If problems emerge, traffic can be instantly reverted to blue, which still runs the known-good version.

For AI product teams, blue-green deployment is valuable for model updates because it provides instant rollback capability. If a new model version produces unexpected outputs in production, switching back to the previous version takes seconds rather than the minutes or hours needed to redeploy. Growth teams appreciate blue-green deployments because experiment rollbacks are equally fast: if a feature flag change causes metric degradation, the entire deployment can be reverted while the team investigates. The main cost is maintaining two full production environments, which doubles infrastructure expenses during the deployment window. For expensive GPU-equipped inference infrastructure, teams may optimize by sharing the model serving layer and only blue-greening the application code.