Quick answer
Larridin is an AI impact intelligence platform. Larridin Developer Intelligence is designed for engineering teams moving to an AI-native software development lifecycle. It provides telemetry and optimization across the entire lifecycle. It measures velocity, quality, efficiency and the actual output of your engineers and your AI agents. It helps you spread the habits of your best AI users and reduce AI cost via the Larridin Router. It also gets you ready for the future where a huge amount of software will be written without a human at the keyboard.
LinearB is an engineering productivity platform built around DORA delivery metrics. It uses gitStream to automate pull request workflows and reports on AI adoption and cost per effective PR.[1][2] LinearB answers the question “How fast is work moving and what does each merged PR cost?” Larridin answers “How much did our engineers and AI agents build, how well are they working together and what should we change?”
Why teams choose Larridin Developer Intelligence
We measure the work. A lot of the data we derive is from a semantic understanding of what was done. Engineering Output scores each merged PR on complexity, size and quality and it’s the only way we keep human work separate from AI-assisted and agent authored work.[7][12]
We’re looking at coding-agent sessions rather than just pull requests. Larridin scores how well each engineer is working with the agent and uses that to provide guidance to the team on what the best AI users are doing.[8][9] The same view will show you what the agents are costing and where their time is going (from feature work to bug fixing).[8]
We act on cost. The Larridin Router picks the model for each coding request based on your cost and quality priorities, with budgets per engineer and per team.[11] Agent Readiness then looks at each repository and tells you the shortest path to fixing what is holding the agents back.[10]
What LinearB does well
LinearB tracks DORA and delivery metrics against benchmarks from over 8.1 million pull requests. It also splits the work between people and agents. gitStream routes and approves PRs by policy and AI code reviews are included with every paid plan.[16][17][18]
LinearB runs anonymous developer surveys in Slack. It has an AI assistant which can explain the metrics and suggest changes (in LinearB or via MCP in Claude, ChatGPT, Cursor and other tools). It has an AI ROI dashboard which reports cost per effective PR and its metrics are purely based on Git metadata.[4][19][20][2][3]
For enterprises, LinearB adds policy-as-code rules for agent code and reports for cost & capitalization and resource allocation. It can run in the cloud, on-premises or in a hybrid.[21][22]
On LinearB the size of the work in each PR and how much an engineer guided the coding agent in a particular session is not displayed.
The key difference: Larridin scores the work; LinearB counts it
LinearB’s cost per effective PR is total engineering cost (people and AI) divided by the number of merged PRs, with rework discounted. That’s LinearB’s own definition of it and it means every PR is worth the same.[2] Engineering Output looks at the code in each merged PR and scores the work that’s in it.[12]
On agents, LinearB operates at the PR level. It shows who did the work and the merge rates by agent, and it ranks the tools in terms of autonomy.[16][19] Larridin also reviews each coding agent session and scores how the engineer guided the agent, with the evidence for each score.[8] The scoring of how the engineer guided each agent session is not shown in LinearB.
Here’s a hypothetical example (not actual customer data). Imagine two teams each merging 40 PRs in a month with the same rework rate, so their cost per effective PR is the same. One team shipped a new checkout flow. The other team essentially just renamed fields and bumped dependencies. Engineering Output would show the difference.
How Larridin and LinearB compare
| What matters | Larridin Developer Intelligence | LinearB |
|---|---|---|
| Built for | AI-native engineering teams, with AI across the rest of the company in the same platform. | Engineering organizations.[1] |
| Where the data comes from | A semantic understanding of the work in code, coding-agent sessions and other engineering tools.[7] | For metrics, Git metadata plus project management, CI/CD, incident and AI tool data; no code is scanned for metrics. gitStream and AI code reviews read code to run rules and reviews.[3][16][17][18] |
| Engineering output | Engineering Output scores each merged pull request on complexity, size and other factors, with code quality built in.[7][12] | Merged pull requests, cycle time and DORA metrics. Effective PRs discount rework.[1][2] |
| How agents are measured | Agent Effectiveness scores each coding-agent session on six dimensions, with cited evidence for each score.[8] | Every pull request attributed to a person, AI-assisted work or an agent, across 50+ AI tools with no local install. Merge rate by agent and which tools earn autonomy.[16][19][22] Scoring how the engineer guided each agent session is Not shown. |
| Acting on it | Coaching and practice guidance shared across teams, plus Agent Readiness fixes for each repository.[8][10] | gitStream automation, policy-as-code rules for agent code, AI code reviews, and an AI assistant that suggests changes.[17][18][19][22] |
| Model choice and cost | The Larridin Router chooses the model for each coding request, with budgets per engineer and per team, and compares routed work with pinned work on quality.[7][11] | Gives coding agents context to match effort to how risky the code is.[1] |
| AI cost | Agent sessions, token usage and cost in one view, plus one spend model for AI across the company.[8][13] | AI tool cost and cost per effective PR for engineering.[1][2] Company-wide AI spend is Not shown. |
| Engineering finance | Effort Allocation estimates each team’s share of captured effort across seven work categories.[23] | Cost capitalization reporting, resource allocation dashboards, and project cost and investment profiles.[21] |
| Developer surveys and code review | Not a developer survey or code review tool. | Anonymous developer surveys in Slack, scored by team, and AI code reviews.[18][20] |
| Deployment | Cloud, hybrid or on-premises. | Cloud, hybrid or on-premises, with customizable data retention.[21] |
| What to know | Engineering Output reads the work in each pull request to score it. Agent scores depend on which agents and sessions are captured. | Metrics come from metadata, so they show how work moves, not how much each change delivered.[3] |
Similar features, different names
| What you want to measure | Larridin Developer Intelligence | LinearB | How they differ |
|---|---|---|---|
| Value of the work | Engineering Output | Effective PRs | Larridin scores the work in each PR; LinearB counts PRs, discounting rework |
| AI ROI | AI spend against Engineering Output | Cost per effective PR | Different way of counting the work |
| Asking questions | Ask AI | AI assistant and MCP server | Similar purpose; Larridin also recommends what to change |
| Agent cost | Larridin Router | Effort recommendations to agents | The Router chooses the model per request and checks quality |
What data Larridin collects
Larridin Developer Intelligence reads Git metadata, coding agent session data and usage from AI tool APIs. It doesn’t record keystrokes or take screenshots. Our SOC 2 Type 2 and SOC 3 reports, our external penetration test and data flow diagram are all available via the Larridin Trust Center.[14]
Recommendations by role
A CTO sees what the engineers and the AI agents actually build, not just how many PRs get merged. That’s the perspective you need to be in if you’re going to lead the move to AI-native.
A VP of Engineering will have the agent habits of the very best engineers and be perfectly set up to become the team’s practice.
An AI platform lead is getting agent sessions scored with evidence and a router that chooses the model for each request.
A CFO has AI coding spend set against how much was actually built. Larridin also has the AI spend for the whole company in one place.
Security-constrained teams can run Larridin on-premises or in a hybrid environment and will of course want to review our SOC 2 and SOC 3 reports and data flow diagram first.
Moving from LinearB
Larridin Developer Intelligence doesn’t require LinearB or its data at all. It just connects to your Git hosts, your coding agents and your AI tool APIs. If you’re currently running LinearB then run a pilot on two teams and compare what each product shows you. Then you can make up your mind with real numbers in front of you.
Questions to test in a pilot
- Pick a team whose combined PR count was flat over a two-month period while the work itself had changed. See which product shows the difference.
- Open up one agent session in each product. Check that it shows how the engineer guided the agent and what they could have done better the next time.
- Ask each vendor what it does about cost. Does it advise the agent or does it pick the model for the next request and then check the result?
- After 30 days, ask each vendor what they’d recommend you change and what that would save.
Proof
For the measured findings from Larridin’s own data, see the AI Impact in Engineering Report, Q3 2026. That’s Larridin’s own research, not a comparison against LinearB.[15]
Larridin data AI Impact in Engineering Report, Q3 2026 Read the reportFAQ
What is LinearB?
LinearB is an engineering productivity platform built around DORA delivery metrics. It uses gitStream to automate the pull request workflow and reports on AI adoption and cost per effective PR.[1][2]
Is Larridin a better fit than LinearB for AI-native engineering teams?
Yes. If you’re moving to AI-native development then Larridin Developer Intelligence is the way to go. It’s measuring what the engineers and AI agents actually build from the work. The Larridin Router is acting on the cost of every coding request.[3][11][12]
How is Engineering Output any different from LinearB’s cost per effective PR?
LinearB takes total engineering cost and divides it by the effective PRs (merged PRs with rework discounted, each one counted the same). Engineering Output is a measure of how much work each merged PR actually delivered so a config change and a new feature are not equivalent.[2][12]
Does LinearB read the code?
For its metrics LinearB says it uses Git metadata only and scans no code at all. Its gitStream automations and AI code reviews do read code in order to run their rules and reviews. Engineering Output is derived from a semantic understanding of the work in each PR, which is how it distinguishes a small change from a large one.[3][12]
Do both act on AI cost?
Yes, in different ways. LinearB provides coding agents with the context to match their effort to the risk of the code. The Larridin Router picks the model for each coding request under your cost and quality criteria (with individual budgets per engineer and per team) and then compares the routed work against the pinned work on both quality and cost.[1][11]
Does Larridin do developer surveys or code reviews?
No. Larridin measures what the engineers and the AI agents build and how they work together from the telemetry. LinearB provides developer surveys and AI code reviews.[1][7]
What data does Larridin Developer Intelligence collect?
Git metadata, coding agent session data and usage via AI tool APIs. Nothing to do with keystroke logging or screen capture. Security reports and a data flow diagram are available at trust.larridin.com.[14]
What are the alternatives to LinearB?
Teams generally compare LinearB against Larridin, DX, Jellyfish, Swarmia, Weave and Span. For an AI-native engineering team we’d point you at Larridin Developer Intelligence. It looks inside each PR and inside each agent session which a simple PR count simply can’t do.[7][8]
Can we go from LinearB to Larridin?
Yes, Larridin connects directly to your Git hosts and your coding agents so you can kick off a pilot on two teams with absolutely no LinearB data.
Sources and method
Written by Larridin using the LinearB public website and Larridin’s own product pages. This is a documentation review, not a hands-on benchmark of LinearB. If something is not described on the LinearB pages we’ll just put “Not shown”. LinearB facts were reviewed on November 11, 2026. To report an error please email support@larridin.com and we will fix the page within 3 business days.
- LinearB homepage: linearb.io
- LinearB, Measure AI ROI: linearb.io/use-case/measure-ai-roi
- LinearB Security & Compliance: linearb.io/security
- LinearB MCP server: linearb.io/platform/mcp-server
- Larridin platform overview: larridin.com
- Larridin AI Fluency: larridin.com/ai-fluency
- Larridin Developer Intelligence: larridin.com/developer-intelligence
- Larridin Agent Effectiveness: larridin.com/developer-intelligence/agent-effectiveness
- Larridin, Agent Effectiveness Score: larridin.com/measurement-guide/agent-effectiveness/agent-effectiveness-score
- Larridin Agent Readiness: larridin.com/developer-intelligence/agent-readiness
- Larridin Router: larridin.com/router
- Larridin Engineering Output methodology: larridin.com/developer-intelligence/methodology
- Larridin Token Spend & Insights: larridin.com/tokens-spend-insights
- Larridin Trust Center: trust.larridin.com
- Larridin, AI Impact in Engineering Report, Q3 2026: larridin.com/data/engineering-ai-impact-q3-2026
- LinearB Engineering Metrics: linearb.io/platform/engineering-metrics
- LinearB Workflow Automation (gitStream): linearb.io/platform/workflow-automations
- LinearB AI Code Reviews: linearb.io/platform/ai-code-reviews
- LinearB AI Assistant: linearb.io/platform/ai-assistant
- LinearB Developer Surveys: linearb.io/platform/developer-surveys
- LinearB Enterprise: linearb.io/solutions/enterprise
- LinearB for AI enablement teams: linearb.io/teams/ai-enablement
- Larridin Effort Allocation: larridin.com/developer-intelligence/effort-allocation
Not just engineering
Larridin isn’t just an engineering tool. The same platform can be used to measure AI adoption, fluency, impact and spend across Finance, Support, Marketing, Sales and Legal. Leadership gets one view of what AI is costing and what it’s producing across the business.[5][6]