Phantom Spend in Autoscaling Shadows

A $12,000 GCP ghost: How a forgotten instance haunted our budget (and how we banished it).

It started innocently enough. A small, unassuming GCP compute instance, spun up for a quick load test. We wrapped up the test, patted ourselves on the back, and promptly forgot about it. Fast forward a few weeks, and our cloud bill delivered a chilling surprise.

$12,000. For an idle instance. That nobody remembered creating.

Abstract image of a magnifying glass hovering over a complex network of interconnected nodes, representing the difficulty of manually searching for a specific resource in a large cloud environment.

Our initial (failed) solution: Manual Hunting

Our first instinct was to comb through the GCP console, hunting down the culprit. We sifted through logs, checked resource groups, and interrogated our team. This was like searching for a needle in a haystack made of virtual machines. It was time-consuming, frustrating, and ultimately, reactive. We needed a better way.

The "Aha!" Moment: Autoscaling's Shadow

We eventually realized the instance was part of an autoscaling group. While the group itself had scaled down to zero, this single instance, for reasons unknown, remained. It lingered in the shadows, quietly racking up charges while we focused on the active parts of our infrastructure. This highlighted a critical gap in our monitoring – we were blind to these "phantom" instances.

An abstract representation of a single, isolated node, disconnected from a larger network, symbolizing the forgotten instance within the autoscaling group.
A visual representation of a spotlight or beam of light illuminating a darkened area, symbolizing EazyOps detecting the hidden cost.

EazyOps: Shining a Light on Phantom Spend

This is where EazyOps stepped in. Its automated resource monitoring immediately flagged the idle instance as an anomaly. Not only did it pinpoint the resource, but it also provided crucial context: its connection to the autoscaling group, its cost history, and even suggested actions.

One-Click to $12,000 Saved

With a single click in the EazyOps dashboard, we terminated the instance, stopping the bleeding. More importantly, we configured EazyOps to proactively monitor for similar scenarios, preventing future phantom spend. The entire process, from detection to resolution, took minutes, not days.

An abstract image of a graph showing a sharp decline in cost, representing the savings achieved after terminating the idle instance.

Lessons Learned: Beyond the Instance

This experience taught us a valuable lesson about the hidden costs of autoscaling and the importance of comprehensive monitoring. It’s not enough to just track active resources. We need to be vigilant about the resources that lurk in the shadows.

What's Next: Proactive Cost Control

We’re now exploring deeper integrations with EazyOps to proactively identify and address potential cost anomalies before they impact our budget. This includes automated rightsizing, resource scheduling, and anomaly detection based on historical spending patterns.

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.