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Rabbit for Data Engineers

Ship more efficient BigQuery pipelines faster

Rabbit reviews every BigQuery SQL change for cost and performance, autonomously tunes slots and reservations within your guardrails, and flags contention before it hits production. Spend less time firefighting and more time shipping.
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How Nordstrom Cut BigQuery Costs by 47% with Rabbit
The challenge

BigQuery's pricing and performance complexity slows you down

Slot contention, surprise production scans, expensive SQL in unflagged PRs, and dashboards that only explain last month's bill. Manual tuning can't keep up across a scaled enterprise estate: by the time you've fixed one hotspot, three more have moved.

Sparkle icon for automation illustrationThe Solution

Automated optimization from PR to production

Rabbit provides cost-aware code reviews, SQL cost estimation, actionable optimization recommendations by default. Merge-ready PRs and automation are available when you opt in.

platform capabilities

How Rabbit helps

Automated optimization, engineering guardrails, and drift visibility at scale

Build

01

Catch expensive SQL before it ships.

Cost-aware code on every pull request: slot-based cost estimates, partitioning/clustering flags, and antipattern checks inline, where you already review code. Context Engine (MCP) brings the same BigQuery cost and query context into your IDE and agent sessions.
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Automate

02

Less tuning toil, more autopilot.

Rabbit’s Max Slot Optimizer flattens spiky usage and protects priority jobs automatically. Reservation Optimizer and SQL Optimizations handle the rest, with a savings estimate on every validated rewrite.
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Observe

03

Signals that point to the job, not a vague ticket.

Slot contention flags and slow-query alerts point to the exact query and project behind a regression. Performance Dashboard ranks the biggest duration regressions first.
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Customer stories

Engineering hours, reclaimed

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

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See what Rabbit flags in your next PR

Start with a metadata-only assessment to see where BigQuery time and spend are going before you decide where to implement automated optimization.

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FAQ

Rabbit puts BigQuery cost and performance into your existing workflow: cost-aware review on every pull request, always-on optimizers that tune slots and reservations in the background, and contention or slow-query alerts tied to a specific job, not a vague dashboard complaint.

Rabbit estimates the cost of a SQL change from slot usage and reservation rates, not just bytes scanned, and posts that estimate on the pull request. Inline recommendations can flag partitioning, clustering, and common antipatterns, so cost becomes a first-class review signal where engineers already work.

No. Performance-protected tuning is the design goal. Max Slot Optimizer uses priority rules so latency-sensitive jobs stay protected while Rabbit flattens spiky slot usage elsewhere. SQL rewrites are tested for result-equivalence before they're proposed, so a cheaper query is never a slower or wrong one.

Context Engine gives coding agents cost-aware BigQuery context, not just schema. Through MCP, agentic plugins, and the CLI, Rabbit supplies table cost, query patterns, and cheaper-SQL guidance inside tools like Cursor, Claude Code, Copilot, and Gemini, tied to the files and queries you're already working on.

The Performance Dashboard tracks query performance by account, project, and label, and flags jobs that failed from slot contention. Performance Alerting extends anomaly detection to runtime regressions in dbt and Airflow pipelines, so you find out from an alert, not a Slack thread after the fact.

No. Build draws on metadata from your BigQuery environment and Git repo, table cost, query patterns, and schema, never underlying table data. Assessment and review access stays metadata-only. See Enterprise security for certifications and residency details.

No. Rabbit starts with recommendations: cost estimates on PRs, tuning suggestions, and contention alerts. Optimization PRs from Recommendation Applier and always-on optimizers are opt-in, per workflow. You review and merge through your normal Git approval before anything changes.
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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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