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DevOps & CloudConfidential SaaS platform

Infrastructure Automation at Scale

Engineered an automated cluster lifecycle system that cut infrastructure costs by 40% while maintaining 99.9% uptime across services running for millions of users.

40%

Infrastructure cost reduction in the first quarter

99.9%

Uptime maintained throughout the transition

0

Maintenance windows needed for database migrations

The challenge

A SaaS platform serving millions of users had infrastructure costs growing faster than revenue. Clusters were provisioned by hand, sized for peak load around the clock, and nobody could safely turn anything off.

The catch: the platform had a 99.9% uptime commitment, so cost work couldn't introduce risk. Every optimization had to be provably safe before it touched production.

Our approach

We built an automated cluster lifecycle system: environments scale down or hibernate based on actual usage patterns and scale back up before demand returns, with manual override always available.

Database migrations were re-engineered to run online, under load, with verified rollback — removing the maintenance windows that had been blocking infrastructure changes.

Cost observability was wired into the engineering workflow, so the team sees the dollar impact of architectural decisions before merging them.

The results

Infrastructure costs dropped 40% within the first quarter after rollout.

Uptime held at 99.9% throughout the transition — including during the database migrations.

Stack

KubernetesTerraformPostgreSQLPrometheusGo

Building something with the same stakes?

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