Question

Your SRE team runs a payment API on GKE with a defined SLO of 99.9% availability measured over a 28-day rolling window. Developers push changes frequently, and leadership wants to maximize release velocity without endangering the SLO. During a new rollout, you want an automated mechanism that decides whether to continue promoting a release based on the release's real impact on the service's reliability signals, halting before the error budget is materially consumed. Which approach best balances change velocity and reliability?

Accepted Answer

Use a progressive canary rollout where a small percentage of traffic is shifted to the new version and promotion is gated on SLI measurements (error rate and latency) compared against thresholds derived from the error budget.

Answer

Deploy the full release to 100% of traffic immediately, then rely on the 28-day rolling SLO report to decide whether the next release should proceed.

Answer

Freeze all releases whenever any error budget has been consumed during the current window, regardless of the change's tested impact.

Answer

Increase the SLO target to 99.99% so any regression is caught faster, then deploy releases directly to production.