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Rabbit Platform · Observe

Observability for deep BigQuery cost and performance insights

Rabbit provides near real-time, actionable cost and performance visibility across your estate. Drill from account rollups to individual jobs, catch forecast divergence, and act before the bill lands.
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<24hrs

anomalies caught within hours, not days

100hrs+

engineering hours reclaimed / month

40%

improvement in performance

inside observe

Visibility across cost and performance

Dashboards show last month's bill. Rabbit gives you live observability across cost and performance, and points to the query, label, or project behind every spike. Cost insights, anomaly alerts, and performance monitoring each have their own surface.

Near real-time cost anomaly detection

Catch spend spikes and linear drift near real-time, not on the monthly bill.

Rabbit learns your usage patterns, builds a forecast model, and notifies you when spend diverges. Tune sensitivity and mark false positives as your team learns what matters.

  • Project and service · account and label

  • Forecast-based spike and drift detection

  • Email and Pub/Sub alerting, sensitivity levels 1–5

Cost issues caught within hours, not days

Deep cost insights

See where BigQuery spend actually goes, from account rollups down to individual jobs.

Rabbit joins billing export with job metadata so you can compare on-demand vs capacity cost, slot usage, and bytes processed per query.

  • Cost and slots by query, project, label, and reservation

  • On-demand vs capacity breakdown per job

  • Dbt/Airflow label enrichment and unlabeled resource highlights

50 engineering hours saved on average

Weekly and monthly digests

Keep finance and platform teams aligned without logging in every day.

Scheduled email summaries roll up cost trends, flagged anomalies, and top drivers for the cadence you choose.

  • Weekly or monthly delivery

  • Highlights anomalies and largest cost movers

  • Complements live alerts and in-product views

Performance Dashboard

Turn "BigQuery feels slow" into specific queries and contention failures.

See how queries perform by account, project, and label. Surface jobs that failed from slot contention and pinpoint the largest duration regressions.

  • Performance by query, account, project, and label

  • Slot contention failure visibility

  • Biggest duration regressions ranked and drillable

40% improvement in performance

Performance Alerting

Get notified when pipelines slow down or slot contention spikes.

Rabbit extends anomaly detection to performance signals. Same alert channels you use for cost, pointed at contention spikes and runtime regressions.

  • Slot contention spike detection

  • Pipeline run-time regression alerts (dbt, Airflow)

  • Route from alert to tuning in Automate or fixes in Build

Anomalies caught near real-time

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Safety & Reliability

Detection without disruption

Rabbit surfaces drift with actionable insights and direct recommendations, so your team knows exactly what to fix and where to start. For certifications, residency, and how Rabbit handles access, see Security.

Recommendations, not just charts

Drift signals ship with next steps: tune a reservation, rewrite SQL, or investigate a contention spike.

01

Opt-in to automate

Apply fixes in Automate or Build on your terms; automation never runs without approval.

02

Solutions

For the teams that run BigQuery

Same platform. Different priorities. Pick the path that matches your role.

Data Platform Leaders

Drift visibility and early warning before cost or performance becomes a stakeholder issue.
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Data Engineers

Slot contention flags and slow-query signals that point to the job, not a vague ticket.
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FinOps & Finance

Anomaly detection and attribution-ready cost views, not a surprise on the monthly bill.
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See what Rabbit would surface in your BigQuery environment

Start with a metadata-only assessment. See where cost is drifting and which queries need attention.

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FAQ

Deep cost and performance visibility across your BigQuery estate: account rollups down to individual jobs, forecast-based anomaly alerts, weekly or monthly digests, and a performance dashboard for slow queries and slot contention. Cost insights, anomaly alerts, and performance monitoring each have their own surface.

Rabbit learns usage patterns, builds a forecast, and notifies you when spend diverges. Detection covers project and service as well as account and label scopes, with spike and linear drift signals. Alerting uses email and Pub/Sub, with sensitivity levels 1-5 and false-positive marking as your team learns what matters.

Cost anomalies surface same-day once billing export data is available from GCP (typically a few hours' lag). Performance signals (slot contention and pipeline regressions) come from INFORMATION_SCHEMA and update more frequently.

Dashboards show what already happened. Rabbit gives you near real-time observability, joining billing export with job metadata so you can drill from rollups to queries, compare on-demand vs capacity cost, enrich dbt and Airflow labels, and get forecast-based alerts plus performance signals for contention and duration regressions.

No. Rabbit's Observe feature set serves detection and insight with metadata-only access. Recommendations point to next steps such as tuning a reservation or rewriting SQL. Fixes run when you opt into using features described in Automate or Build. Automation never runs from Observe alone without approval.

Yes. Performance Alerting covers slot contention spikes and pipeline run-time regressions for dbt and Airflow, on the same alert channels you use for cost. From an alert you can move to tuning in Automate or fixes in Build.

Platform leaders use Rabbit for early warning before cost or performance becomes a stakeholder issue. Data engineers get contention and slow-query signals tied to specific jobs. FinOps gets anomaly detection and attribution-ready cost views instead of a surprise on the monthly bill.
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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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