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Rabbit Platform · Build

Build optimized BigQuery pipelines from the first commit

Shift BigQuery cost and performance left into development: IDEs, AI coding sessions, and pull requests. Rabbit’s Context Engine helps engineers and agents write leaner, more efficient queries on an optimized infrastructure. Opt in for automated, validated SQL rewrites and ready-to-merge optimization PRs.
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36%

average cost waste caught before production

40%

improvement in performance

-60%

LLM (token) cost

inside build

Shift-left BigQuery cost and performance into the coding workflow

Cost decisions happen in code, not on a dashboard. Rabbit grounds your team and their AI agents in what tables cost, how queries run, and what to change before merge.

Cost-aware code review

Put the true cost of every SQL change on the pull request.

Rabbit estimates cost from slot usage and reservation rates, not just bytes scanned. SQL optimization recommendations land inline, where engineers already review.

  • Cost estimate per SQL change in the PR

  • Slot-based costing on capacity, not on-demand assumptions only

  • Partitioning, clustering, and SQL optimization recommendations

Reduce query costs by 38%, LLM costs by 60%

Context Engine (MCP)

Give coding agents cost-aware BigQuery context, not just schema.

Rabbit supplies deep BigQuery insights on what your tables cost, how they are queried, and how to write cheaper SQL. MCP, agentic plugins, and the CLI bring that context into the tools your team already uses.

  • MCP for Cursor, Claude Code, Copilot, Gemini, and compatible agents

  • Your BigQuery environment, Git repo, and Rabbit BigQuery expertise in one context layer

  • Recommendations tied to files and queries in context

60% lower LLM costs

Automatic optimization PRs

Turn validated Rabbit platform recommendations into ready-to-merge pull requests.

Rabbit's Recommendation Applier converts existing Rabbit recommendations into reviewable pull requests, so approved fixes ship in Git instead of sitting in a backlog.

  • Applies existing Rabbit recommendations with a savings estimate attached

  • Reviewable diff, same approval flow as any other PR

  • Pairs with SQL Optimizations on Automate

32% average BigQuery cost reduction

Explore the platform
Safety & Reliability

Shift-left without losing control

Rabbit puts cost signals in your workflow. You decide what merges. For certifications, residency, and how Rabbit handles access, see Security.

PR-based changes

Optimizations arrive as reviewable pull requests, not silent production edits.

01

SQL validated

Rewrites are tested on synthetic data before Rabbit proposes them.

02

Opt-in automation

Optimization PRs and agent workflows run on your terms. Learn more →

03

Solutions

For the teams that run BigQuery

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

Data Platform Leaders

Cost guardrails in the engineering workflow and clarity on the optimal reservation & pricing setup – without slowing delivery.
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Data Engineers

PR cost review, SQL optimization, and agent context where you already write code.
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FinOps & Finance

Shift-left cost awareness: catch expensive SQL before it hits the bill.
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Catch expensive SQL before it ships, in your IDE and PRs

See what Rabbit would flag in your next pull request, then turn validated recommendations into reviewable changes.

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FAQ

Rabbit’s Build feature set shifts BigQuery cost and performance into the engineering workflow so teams build optimized pipelines earlier: cost-aware review on pull requests, context for engineers and AI agents in the IDE, and optional ready-to-merge optimization PRs. The goal is more efficient SQL in development, not after it hits the bill.

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

It 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 such as Cursor, Claude Code, Copilot, and Gemini, tied to the files and queries in context.

Recommendation Applier turns existing Rabbit platform recommendations into automated, ready-to-apply pull requests. Engineers review and merge through normal Git approval.

No. Optimizations arrive as reviewable pull requests. SQL rewrites are tested for identical results before Rabbit proposes them. Optimization PRs and agent workflows are opt-in. You decide what merges. Rabbit does not make silent production edits.

Rabbit’s Build feature set runs inside your normal engineering controls: reviewable PRs, validated SQL, and opt-in automation. For certifications, residency, and access detail, see Enterprise security.

Book a demo to see what Rabbit would flag on your repositories and pull request workflow.
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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
Book a demoTry it free

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