qarunbook vs Weave: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of qarunbook and Weave — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

qarunbook

Freemium

qarunbook is an app testing runbook where AI drafts a test plan from your code and your team or AI works through it, tracking issues and retests.

AI-Drafted Test Plans: The /test-checklist skill looks through your app and drafts checks with steps and expected results already filled in.
MCP Connection: Connect Claude Code, Claude Desktop, Codex, Cursor or any MCP client so the assistant can read the plan and record results.
Issue-to-Check Linking: Every issue sits on the check that found it with the affected platforms, and raising one removes the pass.
Retest Flow: When an issue is fixed the check waits for someone to look again before being marked passed.
Roles and Join Links: Give people access to only the projects they need and invite whole groups with a join link.
Multi-App Workspaces: Keep each app's sections and platforms separate so web, driver and marketing sites are tested side by side.
GitHub and Linear Integration: Connect issue trackers so problems found in testing flow into existing engineering workflows.
QA Runbook Skill: The /qa-runbook skill works through the plan on web and mobile, recording each result and re-checking fixes.

Release Testing: Run a structured test plan before each release across web, Android and iOS.
AI-Assisted QA: Let a coding assistant execute checks and record pass/fail results through MCP.
Team Test Coordination: Split test checklists among teammates and see progress and results in one workspace.
Regression Follow-up: Track bugs against specific checks and retest them after fixes ship.
Solo Developer Testing: Generate a test plan from the codebase so a one-person team knows what to check.
Agency Multi-Client QA: Manage separate apps and clients with scoped access and a flat workspace price.

WorkWeave

Freemium

Engineering intelligence platform that measures the ROI of AI coding spend and routes every prompt to the most cost-efficient model.

Prompt-to-Production Analysis: LLM and ML models analyse commits, tokens, pull requests, reviews, deploys, and AI telemetry as a single pipeline rather than isolated metrics.
AI ROI Scoring: Token consumption is scored for cost, efficiency, and quality, benchmarked against thousands of engineering organisations, so spend is measured by value rather than volume.
Per-Engineer AI Impact: A breakdown of AI usage rate, AI score, code quality, and output change versus baseline for each engineer over a rolling window.
Weave Prompt Router: Classifies every prompt and routes it to the most cost-efficient model without compromising speed or quality, learning from individual and organisation-level feedback.
One-Command Router Install: Running npx @workweave/router detects your existing clients and writes one env var per provider for Anthropic, OpenAI, and Google, with the bearer token staying on your device unless you export it.
Wooly Engineering Agent: An AI agent that reviews all your engineering data to suggest where and how to improve, answering questions grounded in your own records with citations, available in-app or over MCP.
Standard Framework Reporting: DORA and SPACE metrics plus survey data combined with AI-specific measures in one pane of glass for executive reporting.
Enterprise Compliance Controls: SOC 2 Type II certification with regular third-party audits, GDPR and HIPAA compliance, SSO via SAML and OIDC, SCIM provisioning, and role-based access.

Justifying AI Tooling Spend: Producing an executive report on what a Claude Code or Cursor rollout actually returned, benchmarked against peer organisations.
Cutting Inference Costs: Routing routine edits to cheaper models and reserving frontier models for work that needs them, without changing how developers work.
Finding SDLC Bottlenecks: Identifying where pull requests, reviews, or deploys stall using DORA and SPACE metrics alongside AI telemetry.
Coaching Engineers on AI Use: Seeing which engineers get real quality and output gains from AI assistance and which are consuming tokens without effect.
Agent Observability: Tracking what autonomous coding agents contribute to the codebase separately from human-authored work.
Ad-Hoc Engineering Questions: Asking Wooly where deployment cycles are getting stuck and receiving an answer cited back to the organisation's own records.