GCP Autopilot: The $3,800 "Autoscaling" Surprise
When 'set it and forget it' turned into 'pay it and regret it.'
We drank the Autopilot Kool-Aid. Promised scalability, simplified management, reduced operational overhead—it ticked all the boxes. We migrated several key workloads, patted ourselves on the back, and waited for the cost savings to roll in.
They didn't.
Instead, our GCP bill ballooned. What was supposed to be a cost-effective solution ended up costing us $3,800 more per month than our old, manually managed clusters. It was time for some serious FinOps forensics.
The Autopilot Assumption
Our initial assumption was simple: Autopilot would magically scale our resources up and down based on demand. We envisioned a perfectly optimized system, humming along, only consuming what it needed. The reality, however, was far less elegant.
It turns out, Autopilot is great at scaling *up*. It's less enthusiastic about scaling *down*. Unused development environments, forgotten test deployments, and long-finished batch jobs continued to chug along, racking up charges. Autopilot diligently kept them running, blissfully unaware that they were no longer needed.
The EazyOps Epiphany
Enter EazyOps. We started using it to gain deeper visibility into our Kubernetes spend, and that’s when the lightbulb went off. EazyOps wasn't just showing us the costs; it was pinpointing the *why* behind them. It flagged the misconfigured workloads, the orphaned pods, and the perpetually running development environments that were quietly draining our budget.
Schedule-Based Shutdown: The Simple Solution
The solution, as it often is, was surprisingly simple. EazyOps allowed us to enforce schedule-based shutdowns for non-production environments. Development and testing clusters now automatically power down outside of working hours, eliminating the idle time that was driving up our costs. No more orphaned pods, no more forgotten deployments, no more weekend waste.
The $1,710 Savings
Implementing schedule-based shutdowns with EazyOps delivered a 45% reduction in our Autopilot cluster costs—a cool $1,710 saved every month. Not bad for a few lines of configuration.
Beyond the cost savings, we gained something even more valuable: peace of mind. We no longer had to constantly monitor our clusters for runaway costs. EazyOps provided the guardrails we needed to confidently embrace the scalability of Autopilot without the fear of unexpected bills.
About Shujat
Shujat is a Senior Backend Engineer at EazyOps, working at the intersection of performance engineering, cloud cost optimization, and AI infrastructure. He writes to share practical strategies for building efficient, intelligent systems.