Weave Engineering Intelligence
Weave · Coding · Productivity
Weave Engineering Intelligence is an engineering analytics platform that measures how AI changes the way your team builds software. It combines LLM-driven analysis with DORA, SPACE, and survey data to score token spend, code quality, and output engineer by engineer. Teams use it to see where AI money actually pays off, route prompts to cheaper models, and ask plain-language questions about their engineering org. The goal is simple. Know where every AI dollar goes.
About Weave Engineering Intelligence
What Is Weave Engineering Intelligence
Weave Engineering Intelligence is a service for engineering leaders who want proof that their AI spend is worth it. Most tools count the tokens a team burns. Weave goes further: it scores cost, efficiency, and quality for each engineer, then benchmarks those numbers against thousands of other organizations.
The pitch is simple. Tokens show consumption, not value, and Weave tries to close that gap. It reads your commits, pull requests, reviews, deploys, and AI telemetry, then folds everything into a single view that flags where a team is stuck. A built-in router and a research agent called Wooly round out the platform.
The core of the product is SDLC metrics. Weave watches the whole software development life cycle, from the first prompt to a production deploy, and turns each step into a number you can act on. That covers AI usage tracking in detail, so you can tell a confident adoption story instead of a vague one.
The main limits are scope and price. Weave is built for software teams, so it does nothing for non-engineering work. The paid tier runs $50 per engineer per month, which adds up fast for a large org, and the most sensitive compliance features sit behind a custom Enterprise contract.
Getting Started
- Sign up at the Weave site and connect your code host, such as GitHub, so Weave can read commits, pull requests, and reviews.
- Link your AI provider billing and telemetry so the platform can trace token spend back to real work.
- For the router, generate a bearer token and run the installer, which detects your clients and points them at Weave automatically.
- Open the dashboard to review output scores, AI ROI, and quality metrics across the org.
- Ask Wooly a question in plain language, or call it through the MCP, to dig into a specific team or sprint.
Product Information
A quick look at Weave Engineering Intelligence's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Engineering leaders
- Platform and DevEx teams
- Finance and ops partners
Tasks
- Benchmarking AI ROI
- Cutting token spend
- Investigating a metric drop
Scenarios
- Rolling out AI coding tools and needing to prove they helped
- Preparing a quarterly budget review
- Running an engineering health check
Key features
AI impact, engineer by engineer
Weave assigns each engineer an AI usage percentage, an AI score, a quality score, and an output change against baseline. A team column view shows at a glance who is leaning on AI and whether that shows up in shipped work. It's the per-person view most token dashboards skip. Not a summary. A scorecard.
Token intelligence and Dev FinOps
The platform traces where every token goes and benchmarks your return on token spend against 1,000-plus other organizations. Instead of a raw consumption number, you get cost tied to output, which is the figure finance actually asks for.
Prompt Router
The Weave router inspects each prompt, classifies it, and sends it to the model with the best quality-per-token balance. It learns from feedback at both the individual and org level. Setup is one installer that detects your clients and writes one environment variable per provider, and the client never knows a proxy sits in the path.
Wooly research agent
The Wooly AI agent is a research agent you query in plain language, either in the app or through an MCP. Ask how AI-assisted output compares across teams, or why cycle time jumped last sprint, and it returns a grounded answer with citations to the pull requests, reviews, and agent runs behind it. No dashboard digging required.
SDLC measurement from prompt to production
Weave pulls commits, tokens, pull requests, reviews, deploys, and AI telemetry into one pane. It blends its own AI metrics with established frameworks like DORA and SPACE, plus surveys, so engineering health and AI impact live in the same place.
Enterprise security controls
SOC 2 Type II certification, regular third-party audits, SSO, and role-based access cover the compliance side. Enterprise plans add GitHub Enterprise support, a dedicated Slack channel, and custom invoicing and data processing terms.
Pros and cons
Pros
- Scores AI value per engineer instead of just counting tokens, which answers the "is this worth it" question directly.
- Benchmarks against a large pool of engineering orgs, so a score has context.
- The prompt router runs as a transparent proxy, so developers don't change how they work.
- Wooly answers questions with citations, which makes its claims checkable.
- A free Starter tier and a $50 per engineer Pro tier let a small team try it before committing.
Cons
- It's built only for engineering teams, so non-technical departments get nothing from it.
- At $50 per engineer per month, cost scales with headcount and gets steep for a large org.
- The deepest security and compliance features require a custom Enterprise deal, so smaller teams can't buy them off the shelf.
- Router quality leans on feedback loops, so early results improve only after the system has seen your traffic.
Frequently asked questions
It measures how AI changes engineering output. Weave scores cost, efficiency, and quality for each engineer and each team, then benchmarks those scores against thousands of other engineering orgs so you can see where you stand.
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