GCP Cloud SQL Backups Hoarded: How We Saved $1,600/Month From Our Digital Dustbin

It started as a minor blip in our monthly GCP cost report – a line item for Cloud SQL storage that had been steadily climbing. "Nothing to worry about," I initially thought, "databases grow, right?" But when that blip turned into a consistent, unexplainable $1,600/month increase, my eyebrow started to twitch. This wasn't just database growth; this was something else entirely.

Digging deeper, the culprit wasn't elusive compute power or egress bandwidth, but something far more mundane, yet insidious: backups. Our GCP Cloud SQL instances, critical for our core applications, were faithfully creating backups. The problem? They were too faithful. Like digital hoarders, they were clinging onto every single snapshot, some dating back years, long past their utility or regulatory requirement.

We were effectively paying premium storage rates for digital dust. It was a classic "set it and forget it" scenario gone wrong, where the default backup configurations, combined with a lack of active pruning, had created a substantial, recurring financial drain. Our engineering team was focused on building new features, not auditing historical database snapshots. This was a silent, costly problem festering beneath the surface.

Manual Cleanup: A Sisyphean Task

Our first instinct was to tackle the problem head-on, manually. We envisioned a grand sprint: log into every single Cloud SQL instance, navigate to the backup tab, identify the ancient relics, and hit 'delete'.

This quickly proved to be a Sisyphean task. With dozens of instances, each with potentially hundreds of backups, the effort was staggering. Not only was it mind-numbingly repetitive, but it was also fraught with risk. What if we deleted a backup needed for a compliance audit? Or a crucial point-in-time recovery for a historical data analysis project?

We tried to implement blanket policies: "All backups older than 30 days should be deleted." But this was too blunt. Some critical databases required 90-day retention for regulatory purposes. Others, like staging environments, needed only 7 days. Applying a one-size-fits-all rule would either violate compliance or continue to incur unnecessary costs.

Asking individual development teams to manage their own backup pruning also fell short. With release cycles and feature development taking precedence, backup hygiene became an afterthought, leading to inconsistent application and, inevitably, continued cost creep. We needed a solution that was intelligent, automated, and policy-driven.

EazyOps: Unearthing Value from the Backup Graveyard

This is where EazyOps entered the picture. We needed more than just a reporting tool; we needed an active, autonomous solution that could identify, manage, and prune our Cloud SQL backups according to precise, configurable policies. EazyOps offered exactly that.

The core of EazyOps' solution for this problem lay in its ability to:

• Intelligent Discovery: Automatically scan all GCP Cloud SQL instances and their associated backups, regardless of how they were created (automated or on-demand).
• Granular Policy Enforcement: Allow us to define specific retention policies based on tags, instance names, or even database types. For example, 'prod-db-*' instances get 90-day retention, while 'dev-db-*' instances get 7-day retention.
• Historical Pruning: Actively clean up historical backups that fell outside the defined retention policies, immediately recovering valuable storage space.
• Compliance Assurance: Provide audit trails and reports to demonstrate that retention policies were being met, removing the guesswork from regulatory adherence.
• Anomaly Detection: Flag instances where backup costs or volumes suddenly spike, indicating potential misconfigurations or unmanaged data growth.

Implementing EazyOps felt like flipping a switch from chaotic manual intervention to seamless automation. We configured our policies once, and EazyOps took over, turning our digital dustbin into a clean, cost-efficient data repository.