Your release strategy is a cost-of-failure decision.
The industry often debates Blue/Green versus Rolling updates on purely technical merits. This perspective is incomplete. The choice is fundamentally an economic one, balancing infrastructure expenditure against the business impact of a failed deployment.
Rolling updates are frequently seen as the default for their operational simplicity and lower resource footprint. A new version gradually replaces the old, instance by instance. The common anti-pattern here is the “slow-burn” failure. A critical bug discovered mid-rollout creates a complex state, where rollback is not an instant switch but a full, often panicked, redeployment of the previous version.
Blue/Green, by contrast, is engineered for rapid recovery. An entire parallel stack is deployed, and traffic is switched atomically. This offers near-instantaneous rollback, a critical feature for systems with low tolerance for downtime. However, its primary trade-off is stark: a near doubling of infrastructure costs. This pattern also introduces significant complexity around stateful services and database schema management, which is often underestimated.
I believe the defining factor is not the mechanism, but the system’s blast radius. For a critical, high-revenue service, the cost of doubling infrastructure is often a rounding error compared to the cost of an extended outage. For a non-critical internal service, the cost of Blue/Green is unjustifiable.
The decision is therefore less about technology and more about risk modeling.
Beyond the obvious cost and rollback speed, what are the most subtle second-order effects you’ve seen influence the choice between these two deployment patterns?
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