What is a contributor?

A contributor is a software developer included in a team for which LinearB builds metrics. Pricing and credit bundles are based on the number of contributors.

What happens if I exceed my monthly credit allocation?

You may exceed your allocation at no additional charge in any two months of a year, up to a 120% limit, to prevent inadvertent runaway use.

Do you offer monthly billing?

No. At this time, all LinearB subscriptions are billed annually.

Which payment methods are accepted?

Wire transfer, ACH, credit card, or through the AWS Marketplace.

What workflow automations are not supported on Azure DevOps and Bitbucket on-prem?

Workflow automations via gitStream are not supported on Azure DevOps and Bitbucket on-prem, though AI Iteration Summaries are supported.

Is there an on-premise deployment option?

Yes, LinearB offers an On-Premise Agent (OPA) for organizations with strict data residency or air-gapped environment requirements.

Directory/Automation/Engineering Analytics/LinearB

LinearB

LinearB is an engineering productivity platform that helps teams measure and improve their software delivery pipeline, with a core focus on quantifying the impact of AI coding tools. It provides AI-powered code reviews, DevOps workflow automation, developer experience optimization, and executive reporting. The platform integrates with major Git providers (GitHub, GitLab, Bitbucket, Azure DevOps), project management tools (Jira, Azure Boards), and communication platforms (Slack, Microsoft Teams). Key capabilities include tracking DORA metrics, forecasting project delivery with Monte Carlo simulations, automating PR workflows, and generating audit-ready reports for R&D cost capitalization. It targets engineering leaders, managers, and DevOps teams at companies with 50+ developers who need to prove ROI on engineering investments and AI adoption. LinearB is SOC 2 Type II, SOC 1 Type II, GDPR, and ISO 27001 compliant. It offers a freemium model with Essentials ($29/user/month) and Enterpr

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Sep 19, 2026

Engineering productivityAI code reviewsWorkflow automationDeveloper surveysDORA metricsSOC 2 Type IIGDPR compliant

Product information

Everything worth knowing about LinearB

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

AI Code Reviews

Automated analysis to catch security risks, bugs, performance issues, and spec mismatches before code is merged.

AI & Developer Productivity Insights

Track and measure the impact of AI coding tools on delivery velocity, code quality, and team health.

DevOps Workflow Automation

Policy-based PR routing, approvals, and test enforcement to automate developer workflows.

Developer Experience Optimization

Identify friction points with delivery metrics, qualitative feedback, and MCP-driven insights.

Resource Allocation & Cost Capitalization

Shape investment strategies and generate audit-ready reports to capitalize engineering costs.

Technical capabilities

MCP Server

A Model Context Protocol server allowing chat with your data to spot patterns and boost output.

API Integrations

REST API for measurements, deployments, incidents, and external custom metrics.

Must watch videos

Software Engineering Intelligence with LinearB

3:13

Tutorial3:13

Software Engineering Intelligence with LinearB

Software engineering leaders create the value that drives revenue. To succeed, they have to fulfill a dual mandate: maximize operational efficiency and align their resources to business goals. Answering the dual mandate begins with LinearB's software engineering intelligence solutions. Combining software delivery insights, engineering investment visibility, and workflow automation, LinearB helps leaders deliver more business value, improve developer efficiency, and foster a culture of predictable software delivery. Using LinearB’s 5-step engineering success model, engineering leaders will see better outcomes. Learn more about each step in the links below: Step 1: Engineering Metrics Program ►►https://linearb.io/resources/engineering-leader-guide-to-building-a-metrics-program?utm_source=Youtube&utm_medium=social&utm_campaign=202402+-+Video+-+SEI Step 2: Resource Allocation ►► https://linearb.io/resources/engineering-leader-guide-to-resource-allocation?utm_source=Youtube&utm_medium=social&utm_campaign=202402+-+Video+-+SEI Step 3: Business Goals & Reporting ►► https://linearb.io/resources/engineering-leader-guide-to-goals-and-reporting?utm_source=Youtube&utm_medium=social&utm_campaign=202402+-+Video+-+SEI Step 4: Programmable Workflows ►► https://linearb.io/resources/engineering-leader-guide-to-programmable-workflows?utm_source=Youtube&utm_medium=social&utm_campaign=202402+-+Video+-+SEI Step 5: Predictable Project Delivery ►► https://linearb.io/resources/engineering-leader-guide-to-predictable-project-delivery?utm_source=Youtube&utm_medium=social&utm_campaign=202402+-+Video+-+SEI If you want to take the first steps to predictable project delivery, reach out and we'll be happy to help: https://linearb.io/book-a-demo?utm_source=Youtube&utm_medium=social&utm_campaign=202402+-+Video+-+SEI

4:50

Tutorial4:50

AI Insights Dashboard Demo

Understanding the true impact of AI tools on productivity is a key challenge for engineering leaders. It's essential to know if these tools are improving performance or creating downstream issues, such as increased code rework. In this quick demo, Andrew Zigler from LinearB shows you how to use the AI Insights Dashboard to get a comprehensive view of how AI tools affect your software delivery lifecycle. You'll see how to measure AI adoption and its impact on your engineering organization's performance. In this video, you'll learn how to: • Compare AI-generated code vs. human-driven activity (commits, comments, PRs) to see a clear distribution of AI's footprint. • View trends on AI tool adoption and code acceptance rates over time. • Correlate AI usage with key quality metrics like Rework Rate, Refactor Rate, and PR Size to understand its downstream impact. • Combine these quantitative metrics with qualitative developer feedback from Surveys to get a complete, holistic view of AI's effectiveness. To get these actionable insights for your own team, start a free trial: ➡️ https://linearb.io/start-free-trial Questions? 💻 Learn more about LinearB: https://linearb.io/ 📩 Book a demo: https://linearb.io/book-a-demo?utm_source=YouTube&utm_medium=social&utm_campaign=202504-ai-product-announcement&utm_content=Workshop Chapters: 00:00 Introduction 00:18 Compare AI vs. Human Activity (Commits, Comments & PRs) 01:32 Track Developer Adoption & Acceptance Rates 02:26 Combine with Qualitative Survey Feedback 03:36 Correlate AI Impact on Rework & Refactor Rates 04:38 Summary

3:43

Tutorial3:43

LinearB MCP Server Prompt Demo

This demo showcases the LinearB MCP server, a tool that connects with Anthropic's Claude to help engineering teams gain deep insights into their data. By conversing with your LinearB data, you can quickly get actionable reports on things like high-impact pull requests, team lead times, and merge rates, all presented in a consistent and repeatable format. Chapters: 00:00 Introduction 00:05 Connecting with Claude 00:20 Querying LinearB Data 00:41 Using the MCP Server for Insights 00:59 Creating Consistent User Experiences 01:18 Fetching PR Information 01:34 Exploring Lead Time Dashboard 02:01 Consistent Metrics Reporting 02:34 Predefined Prompts for Dashboards 03:17 Demo Wrap-Up and Future Potential

Who uses it

Real use cases, no hype

Measuring AI Tool ROI

Quantify the impact of tools like GitHub Copilot or Cursor on cycle time, PR throughput, and code quality.

Engineering Cost Capitalization

Automate the creation of audit-ready reports to capitalize R&D costs for financial reporting.

Automating PR Governance

Enforce PR routing, required reviews, and test policies via programmable, code-based rules.

Predicting Delivery Timelines

Use Monte Carlo forecasting and project tracking to set and meet delivery commitments.

Improving Developer Experience

Surface team friction points with delivery metrics and anonymous satisfaction surveys.

Executive Reporting & Budgeting

Visualize engineering investment, resource allocation, and cost profiles for leadership.

AI Code Review & Quality Gates

Catch security vulnerabilities, bugs, and specification mismatches before code merges.

What's great

Deep AI impact measurement and reporting for tool ROI.
Comprehensive DORA metrics and delivery forecasting.
Powerful policy-based PR workflow automation (gitStream).
Strong security compliance (SOC 2 Type II, ISO 27001).
Unified view of AI and human code delivery.

Technical strengths

MCP Server enables natural language data queries.
On-prem agent option for air-gapped environments.

Where it falls short

Expensive for smaller teams; starts at 50+ developers.
Essentials plan lacks core insights and forecasting.
Annual billing only; no monthly flexibility.
Credit system adds complexity to cost management.
Essentials plan is GitHub Cloud only.

Technical limitations

Workflow automations via gitStream unsupported on Azure DevOps/Bitbucket on-prem.
API rate limits may constrain heavy custom integration use.

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

It’s a 45-day free trial, with no commitment or payment method required, of the full LinearB platform including all AI actions, automation, and MCP server capabilities.