Atlas, a restaurant operating system builder, uses hundreds of isolated Cloud SQL for PostgreSQL databases, one per merchant, to maintain data separation, predictable performance, and independent scaling.

The company initially ran on the standard Cloud SQL Enterprise edition but encountered operational bottlenecks: connection pooling required a separate layer to manage, performance debugging was slow due to limited query visibility, and a lean team without dedicated database engineers felt the maintenance load acutely.

Migrating to Cloud SQL Enterprise Plus edition removed those pain points. Managed connection pooling is now built into Cloud SQL, eliminating a separate service to run and secure.

Query insights show exactly which queries are expensive and which merchant triggered them, turning performance tuning from guesswork into concrete action.

Data cache keeps read performance consistent as merchant datasets grow, and near-zero downtime scaling allows scaling instances without disrupting service.

After seeing results on a new instance, Atlas migrated all existing databases to Enterprise Plus.

The impact includes 30% less time spent on database operations, faster merchant onboarding (provisioned in seconds with a ready-to-use database), and a more proactive stance on catching performance issues before they affect merchants.

Atlas reports 200%-300% year-over-year growth. Looking ahead, Atlas is investing in AI-powered tools for restaurant operators and using AI-assisted development workflows internally.

The company credits Cloud SQL and Google Kubernetes Engine as a battle-tested foundational layer that enables fast innovation without infrastructure worries.

The post is a sponsored case study promoting Cloud SQL Enterprise Plus.