BigQuery Editions, CDN egress restructuring and GKE Spot, applied in the right order through See, Cut, Lock, Run. A representative profile of a Google Cloud engagement.
This is a representative engagement profile that reflects the levers and outcomes typical of our Google Cloud work. The monetary figures below are illustrative and are not the reported numbers of a specific named client. Our firm-wide results are 31% average reduction across more than 500 environments since 2019.
A growing media streaming platform was watching its Google Cloud bill rise faster than its audience. Two costs dominated: a BigQuery analytics workload scanning more data every week, and content delivery egress that scaled directly with viewing hours. A third, encoding compute on GKE, ran on-demand around the clock.
The platform ran its viewing analytics on BigQuery on-demand, where every query is billed by data scanned. As event volume grew, so did the scans, and nobody had partitioned or clustered the largest tables. Content delivery pushed large volumes of egress, and the routing was not optimized for cost. Encoding and transcoding ran on standard GKE node pools at on-demand rates, with requests set far above actual usage, so nodes sat half empty. The result was a bill that grew super-linearly with the business.
We worked the See, Cut, Lock, Run method. First we stood up the BigQuery billing export to attribute every dollar, which immediately showed the three cost centers and their trends.
On data, we partitioned and clustered the heavy tables and eliminated full-table scans, the work described in reducing BigQuery storage and query costs. With usage now steady and high, we moved analytics from on-demand to BigQuery Editions with a slot commitment, which is far cheaper than on-demand above the break-even.
On delivery, we restructured CDN and network egress, reducing inter-region traffic and aligning caching with viewing patterns. On compute, we rightsized encoding workloads and moved the fault-tolerant batch portion to GKE Spot with bin packing, cutting that compute line sharply. Finally we locked it in with budgets and anomaly alerts so a new feature launch could not quietly undo the work.
| Cost center | Lever | Direction |
|---|---|---|
| BigQuery analytics | Partition/cluster, move to Editions + commit | Large reduction |
| CDN and egress | Routing and caching restructure | Meaningful reduction |
| GKE encoding | Rightsize requests, move batch to Spot | Large reduction |
| Governance | Budgets, anomaly alerts, forecasting | Savings held |
The figures above are illustrative of the shape of the result, not a named client number. What is consistent across our Google Cloud engagements is the sequence: see the spend, cut the waste, then commit on a clean baseline, and lock it so it holds.
We will read your billing export, find the BigQuery, egress and GKE waste, and structure the commitments. Fixed fee, or pay only from realized savings. No savings, no fee.
Book a GCP cost audit →Read the full method in the complete guide to Google Cloud cost optimization, download the Google Cloud Cost Optimization Field Guide, or explore our other results on the case studies page.
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