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Put BigQuery optimization on autopilot

Rabbit continuously tunes cost and performance across every BigQuery project and team, and applies one consistent policy at scale so standards do not drift as your estate grows. Recommendations by default. Automation runs only inside the guardrails you set.
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How Lufthansa Group Cut BigQuery Costs by 50% with Rabbit
The challenge

Manual tuning does not scale across your BigQuery estate

Slot priorities, commitment coverage, and storage settings drift out of policy team by team, with no single owner keeping them consistent. Contention and cost spikes become reactive scrambles because nobody catches the drift before it hits the bill.

Sparkle icon for automation illustrationThe Solution

Automated optimization, engineering guardrails, and drift visibility at scale

Rabbit gives engineers and their coding agents clear cost and performance insights in the workflow, so wasteful SQL never reaches production. Recommendations by default; automation only inside the policies you set.

platform capabilities

How Rabbit helps

Automated optimization, engineering guardrails, and drift visibility at scale

Automate

01

Continuous tuning across every project, without a bigger team.

Max Slot Optimizer, Job-level Pricing Model Optimizer, and Reservation Optimizer apply the same tuning logic to every project in the estate, from a new team's sandbox to your highest-spend production workload. What used to need a dedicated engineer per project now runs continuously, with one set of priority rules instead of hundreds of team-by-team decisions.

Build

02

Guidelines and guardrails in every engineering workflow.

Cost-aware code review and SQL cost estimation land on every pull request, so expensive changes get caught before merge, not after they hit the bill. Context Engine (MCP) brings the same BigQuery cost and performance context into IDEs and agent sessions, so junior and senior engineers alike ship efficient SQL against a shared standard.

Observe

03

See drift before your stakeholders do.

Performance Dashboard and cost insights roll up from the account level down to any project, team, or query, so you see where drift is concentrated across the estate before it surfaces as a support ticket or a budget conversation. Performance Alerting extends the same early warning to contention and runtime regressions, not only cost.
Customer stories

Results that hold up at scale

Nordstrom logo

At Nordstrom, Rabbit's slot optimizer alone cut BigQuery spend by 47% across hundreds of BigQuery projects, and automation reclaimed 400+ engineering hours a month that used to go into manual tuning.

47%

BigQuery spend reduction

$1M+

cumulative savings

400hrs+

engineering hours reclaimed monthly


Read the case study
Lufthansa Group logo

At Lufthansa Group, reservation planning and automated slot tuning combined for a 52% total reduction in BigQuery costs.

52%

BigQuery spend reduction

30%

savings from autoscaler automation

32%

reservation planning savings

See what Rabbit would automate across your BigQuery estate

Start with a metadata-only assessment. See where policy drifts across projects, which optimizations would move the needle first, and how much manual tuning you could hand off.

Calculate your savings

FAQ

Rabbit puts BigQuery optimization on autopilot across your entire estate: always-on tuning for slots, pricing, and reservations, one consistent policy for caps and commitments across every project and team, and near real-time visibility into drift before it reaches a stakeholder. Recommendations come first; automation runs only inside guardrails you set.

Slot priority rules, commitment planning, and spend caps apply the same way whether an estate spans 10 projects or 500. Rabbit enforces the policies you configure against live usage instead of leaving each team to tune independently, so standards do not drift as headcount and project count grow.

No. Every optimizer Rabbit runs is scoped to protect priority workloads first. Max Slot Optimizer's priority rules keep latency-sensitive jobs whole in every project, and SQL or pricing-model changes are validated before they ship, so no team trades speed for a platform-wide savings target.

Rabbit's forecast model flags cost anomalies and performance regressions near real-time, then drills from account rollups down to the exact project, query, or label behind a spike. Weekly and monthly digests keep leadership aligned without logging into a dashboard, so drift surfaces on your terms, not from another team's complaint.

No. Every optimizer and policy ships in recommend-only mode until you turn automation on. Always-on tuning, CUD Automation, and PR-based fixes are opt-in per workflow and per project, so you can pilot automation on one team's estate before extending the same guardrails everywhere else.

Results vary by starting point, but published outcomes include a 47% BigQuery spend reduction from slot optimization alone at Nordstrom, plus 400+ engineering hours reclaimed monthly, and a 52% total BigQuery cost reduction at Lufthansa Group combining reservation planning with automated tuning. A metadata-only assessment scopes your specific numbers first.

No. Rabbit reads billing export, job metadata, and BigQuery `INFORMATION_SCHEMA` through native GCP APIs, never underlying table data. There are no agents installed on your infrastructure, and access stays metadata-only for assessment and monitoring. See Enterprise security for SOC 2, ISO 27001, and residency detail.
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Get in touch to start saving

We help data teams cut BigQuery costs by 32% on average and see exactly where every slot hour goes.
The autonomous engine for BigQuery cost and performance
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