The $1,200 Logging Sink That Broke the Bank (and How We Fixed It)

When "verbose logging" turned into a very expensive mistake.

Last month, we had a little surprise on our GCP bill: a $1,200 charge for BigQuery storage. Now, we use BigQuery for a lot of things, so a little extra cost isn’t unusual. But this was different. This wasn't a gradual increase, it was a sudden spike. My FinOps Spidey-sense started tingling.

An abstract representation of inefficient database queries consuming excessive resources.

Our First Suspect: Inefficient Queries

Our initial thought was that someone had written a poorly optimized query that scanned excessive data. We've all been there, right? A missing `WHERE` clause, a forgotten `LIMIT`, and suddenly you're processing terabytes instead of gigabytes. So, we started auditing recent queries, looking for the culprit. We found a few suboptimal ones, but nothing that could explain a $1,200 jump.

The 'Aha!' Moment: Drowning in Logs

Then we looked at the BigQuery table sizes. One table, specifically a logging table, had ballooned to over 500GB in the past month. That was it. Our logging sink, responsible for piping logs from our GCP project into BigQuery, had gone haywire. It was ingesting *everything* without filtering, essentially turning BigQuery into a giant, expensive syslog server.

A visual metaphor for an overflowing data stream representing unfiltered logs.
An abstract image depicting automated filtering and control of a data flow.

EazyOps to the Rescue

Luckily, we had recently started using EazyOps. We configured it to monitor our logging sinks and alert us on unusual ingest volumes. Not only did it flag the problematic sink immediately, but it also allowed us to automatically apply filters to the sink through its automation engine. Within minutes, we had restricted the sink to only ingest logs related to specific services and error levels, significantly reducing the volume of data being sent to BigQuery.

The Results: From Waste to Savings

After implementing the changes, we saw a 90% reduction in log volume ingested into BigQuery. This translated directly to cost savings – our BigQuery storage bill for the following month was back to normal levels, saving us approximately $1,100. Beyond the immediate cost savings, we also realigned our log retention policies, further optimizing our BigQuery usage and preventing this issue from recurring.

A conceptual visualization of cost savings and optimized resource utilization.

Lessons Learned (and Applied)

  • Unfiltered logging sinks are a silent budget killer: Regular monitoring and proactive management are crucial.
  • Automation is key to FinOps: Manual intervention is slow and error-prone. Automate remediation whenever possible.
  • EazyOps can be a lifesaver (and a budget saver): Identifying and addressing cost anomalies before they become significant problems is essential for efficient cloud operations.

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.