In this post, a case study of a low-cost airline in the APAC region, we walk you through how this company tackled unpredictable BigQuery costs with Rabbit: what the problem looked like at the workload level, how the engineering team regained control over Google Cloud spend, and what the outcome was in financial and operational terms.

For airlines running lean data teams at scale, BigQuery costs have a way of quietly becoming one of the largest line items in the cloud bill. Billing data arrives weeks late, granularity is poor, and by the time an anomaly surfaces, the spend is already locked in. This company’s experience is a useful case study in what it takes to go from reactive firefighting to continuous cost governance, without slowing down the release cadence that keeps operations running.

About the airline in this BigQuery case study

The company in this case study is a low-cost carrier serving millions of passengers across Asia-Pacific. With a reputation for efficiency and lean engineering teams, its strategy has always been about achieving more with less. As reliance on data and analytics grew, BigQuery quickly became central to daily operations but it also introduced a new challenge: how to keep cloud costs predictable without slowing engineering velocity.

What had once been manageable quickly became a source of friction: costs were unpredictable, visibility was limited, and engineering hours were drained by manual billing analysis instead of improving the passenger experience.

“Rabbit quickly became part of our team’s daily workflow. Engineers and analysts now have full transparency into the cost impact of their work, and using Rabbit’s insights, we achieved 23% cost reduction on our BigQuery workloads.”

— Engineering Leader, low-cost airline

The challenge: BigQuery costs without visibility

Engineering velocity was high, but BigQuery cost transparency lagged behind. The team was comfortable building pipelines and shipping new releases, yet the costs of those workloads often came as a surprise. Queries scaled up in production, engineers and analysts had no visibility into the costs they were generating, and anomalies could go unnoticed until the monthly bill arrived. The financial impact only showed up weeks later in billing exports, leaving the team without a reliable way to forecast the cost impact of a new release before it shipped.

Without job-level insights or workload baselines, investigations meant reactive log-checking, manual billing analysis, and maintaining custom dashboards and ad-hoc SQL against billing exports. That work was slow and repetitive, and it still left gaps, especially around whether repeating production queries should run on-demand or against reserved capacity.

Bills consistently overshot expectations. Stable, production-level workloads introduced financial risk because there was no mechanism to evaluate or optimize them before deployment. Instead of predictable cost per pipeline, the team was stuck in a cycle of post-mortem corrections rather than proactive cost governance.

The downstream effect was financial and cultural:

• BigQuery costs grew harder to predict as production pipelines and ad-hoc queries scaled without job-level visibility.
• Unclear pricing models meant that stable, production-level queries could carry dramatically different cost profiles depending on whether they ran on-demand or against reserved capacity, with no reliable way to evaluate that trade-off before deployment.
• Engineering velocity suffered, with developers pulled into cost firefighting instead of building features that improved passenger experience.

Curious about the actual cost of BigQuery slot hours? Learn more:
What Does a BigQuery Job Actually Cost on a Reservation?

The solution: Query-level observability and automated cost governance

The company needed more than dashboards: they needed granular, job-level visibility and automated levers for cost control. Rabbit delivered both, embedding cost intelligence directly into the engineering workflow without requiring process overhauls or long onboarding.

What began as a proof of concept quickly became part of daily practice. Rabbit plugged into existing pipelines and began surfacing insights that were previously invisible:

• Query-level cost attribution tied every BigQuery job to its financial footprint, eliminating the need for engineers to build and maintain custom SQL for spend analysis.
• Workload segmentation differentiated production pipelines from ad-hoc experimentation, exposing where reserved capacity was misaligned and where on-demand execution was driving unnecessary spend.
• Pre-release cost awareness gave engineers a way to understand the likely financial impact of new queries before they went live, turning cost checks into a standard step alongside performance and reliability testing.

“Rabbit became part of our release checklist. If a query was too expensive, we knew before it went live.”

— Engineering Leader, low-cost airline

Optimized engineering operations

On top of visibility, Rabbit introduced near real-time anomaly detection. Instead of discovering overspend weeks later in billing exports, engineers were alerted when queries deviated from baseline. This turned firefighting into continuous cost governance, where teams could intervene before inefficiencies compounded.

• Anomalies were flagged in near real time, ensuring rapid detection of unusual spending.
• Dashboards were ready to use, replacing homegrown solutions that required constant upkeep.
• Optimization recommendations highlighted the most impactful cost-saving actions — from query antipatterns to pricing model selection.

“Before every release, we can estimate the cost impact and prevent runaway BigQuery expenses. The anomaly detection alerts us before costs escalate, making Rabbit an essential part of how we operate.”

— Engineering Leader, low-cost airline

Rabbit’s Pricing Model Optimizer added an automated lever on top of visibility. By routing queries between on-demand and slot-based pricing models based on workload patterns, the platform reduced cost on selected workloads without requiring changes to existing queries or pipelines.

Related reading:
How to Cut BigQuery Autoscaling Costs When You Have a Commitment

This freed engineers from low-value cost investigations and allowed them to focus on delivering features and improving passenger experience instead of chasing down billing anomalies. During the proof of concept, the team logged 200+ Rabbit sessions across over a dozen users, a signal that cost intelligence had moved from a finance-side exercise into everyday engineering practice.

The results: lower costs, higher engineering efficiency

When the company adopted Rabbit, the change was visible almost immediately. What had once been a reactive struggle with billing exports became a clear, real-time view of how every query and workload affected spend.

From there, the financial impact followed:

• BigQuery spend dropped by 23% on optimized workloads through query-level attribution, optimization recommendations, and intelligent routing between On-Demand and Reservation models. Selected queries saw 20-30% lower costs once pricing model selection was aligned to workload patterns.
• Overall Google Cloud spend fell by 15% during the proof of concept, with BigQuery optimization as the primary driver.
• Engineering time was reclaimed at scale: automating dashboards, anomaly detection, and cost analysis saved the equivalent of 0.15 FTE each month: roughly a full day of engineering capacity per week that had previously gone to building cost reports and chasing billing surprises.

For engineers, the cultural change was just as significant. Before Rabbit, cost spikes could remain hidden for days, consuming a large portion of the monthly budget before anyone noticed. After adoption, that work was automated and streamlined. Instead of firefighting bills, the team focused on operations and passenger experience, knowing that cost governance was embedded directly into their workflows.

Rabbit didn’t just cut costs: it embedded predictability, automation, and workload-level cost intelligence into daily practice. The airline could keep scaling its analytics platform without the constant worry of unpredictable BigQuery bills.

If you’re managing BigQuery at scale and costs are growing faster than your workloads, see how Rabbit helps data teams get query-level visibility and control, or book a demo to walk through your environment.

Find out how other companies benefit from using Rabbit:
Read our case studies