Quick answer
Larridin is an AI impact intelligence platform. Larridin Developer Intelligence is for engineering teams moving to an AI-native software development lifecycle. It covers telemetry and optimization across the entire lifecycle. It measures velocity, quality, efficiency and the actual output of your engineers and your AI agents. It enables you to spread the habits of your best AI users and reduce AI cost via the Larridin Router. It also gets you ready for a world where a lot of software is being written without a human at the keyboard.
Swarmia is an engineering intelligence platform built on delivery metrics and AI tool cost. It also runs developer surveys and publishes reports on engineering spend.[1][3] It’s designed to answer the question “Is AI accelerating our delivery and what is the cost?” Larridin’s platform answers “How much did our engineers and AI agents build? How well are they working together? What should we change?”
Why AI-native teams pick Larridin
We measure the work. Most of the data we derive comes from a semantic understanding of what was actually done. Engineering Output scores each merged PR on complexity, size and quality and keeps human work separate from AI-assisted and agent-authored work.[10][15]
We look at the coding-agent sessions as well as the pull requests. Larridin scores how well each engineer works with the agent and takes what the best AI users do and turns it into guidance for the team.[11][12] The same view will show you the cost of the agents and where their time is going (from feature work to bug fixing).[11]
We act on cost. The Larridin Router selects the model for each coding request according to your cost and quality priorities, with budgets per engineer and per team.[14] Agent Readiness then looks at each repository and tells you the shortest path to fixing what is holding the agents back.[13]
Swarmia is strong on delivery habits and engineering investment
Swarmia tracks code and DORA metrics against benchmarks and its working agreements allow teams to set targets and get nudges in Slack or Microsoft Teams.[3] It runs developer experience surveys with team heatmaps and benchmarks.[22] It can report on where engineering time is going across initiatives and can forecast when those initiatives will finish. Its software capitalization uses AI to suggest which work is capitalizable (including the cost of AI coding tools).[21][24]
On AI, Swarmia breaks down the tool cost by person and by team and flags unused licenses. It can trace the cost of AI back from an initiative to a single PR by combining AI usage with FTE and compute cost.[2][5] It maps AI token usage to investment categories, measures AI review agents and can determine whether a repo is ready for AI agents.[24] Swarmia AI can answer questions in plain English, flag the likely root causes of org-wide changes and can set up follow-ups (e.g. working agreements) in Swarmia or via MCP.[1][6][24] It is SOC 2 Type 2 and SOC 1 Type 2 audited and has a free plan for small teams.[3][4]
What Swarmia doesn’t reveal is the amount of work each pull request actually delivered, or indeed how much an engineer was involved in guiding a coding agent through a particular session.
The key difference: the work itself, and the engineer behind the agent
Scoring the work, not just the flow. Swarmia’s commit analysis works from change statistics such as file names and line counts, and doesn’t read the code itself.[4] Its AI impact view compares AI-assisted pull requests with the rest on cycle time, throughput and batch size.[3] Engineering Output reads the code in each merged pull request and scores the work in it.[15]
Measuring the engineer, not just the agent’s pull requests. Swarmia tracks throughput, merge rate and batch size for each coding agent.[3][7] Larridin also reads each coding-agent session and scores how the engineer guided the agent, with the evidence behind each score.[11] Scoring how the engineer guided each agent session is Not shown for Swarmia.
Acting on cost, not just reporting it. Swarmia reports AI cost by tool, team and person, and maps token usage to investment categories.[5][24] The Larridin Router chooses the model for each coding request under your cost and quality priorities, with budgets per engineer and per team.[14] A model router is Not shown for Swarmia.
A simple example. Two pull requests each change 300 lines and merge on the same day. One adds a new payment flow with tests. The other reformats files after a linter update. Line counts and cycle time treat them the same. Engineering Output doesn’t.
How Larridin and Swarmia compare
| What matters | Larridin Developer Intelligence | Swarmia |
|---|---|---|
| Built for | AI-native engineering teams, with AI across the rest of the company in the same platform. | Engineering organizations, from startups to enterprises.[1] |
| Where the data comes from | A semantic understanding of the work in code, coding-agent sessions and other engineering tools.[10] | Connected code hosts, issue trackers, CI, AI tools and HR systems. Commit analysis uses change statistics, not the code itself.[3][4][20] |
| Engineering output | Engineering Output scores each merged pull request on complexity, size and other factors, with code quality built in.[10][15] | Code, issue and DORA metrics, such as cycle time, merged PRs per FTE and batch size, plus an AI ROI view.[3][24] |
| How agents are measured | Agent Effectiveness scores each coding-agent session on six dimensions, with cited evidence for each score.[11] | Pull request throughput, merge rate and batch size for each coding agent, including Devin and in-house agents, plus metrics for AI review agents.[2][3][7][23] 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.[11][13] | Working agreements with Slack and Teams nudges, signals with likely causes, a developer overview for coaching, an AI readiness check for each repository, and Swarmia AI, which can set up follow-ups for you.[3][6][24][25] |
| 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.[10][14] | Not shown. |
| AI cost | Agent sessions, token usage and cost in one view, plus one spend model for AI across the company.[11][16] | AI tool cost by person, team and tool, unused licenses, cost per initiative down to a single pull request, and AI token usage by investment category.[2][5][24] Company-wide AI spend is Not shown. |
| Engineering finance | Effort Allocation estimates each team’s share of captured effort across seven work categories.[19] | Investment balance, initiative forecasts, and software capitalization with AI-suggested classifications, including AI coding tool costs.[21][24] |
| Developer surveys | Not a developer survey tool. | Developer experience surveys with benchmarks and Slack reminders.[3][22] |
| Deployment | Cloud, hybrid or on-premises. | Cloud web app, hosted in the EU by default; US hosting by agreement. On-premises integrations through a proxy agent on the Enterprise plan.[3][4][24] On-premises deployment is Not shown. |
| 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. | Code metrics come from change statistics, so they show how work flows, not how much each change delivered.[4] |
Similar features, different names
| What you want to measure | Larridin Developer Intelligence | Swarmia | How they differ |
|---|---|---|---|
| Value of the work | Engineering Output | AI impact metrics | Larridin scores the work in each PR; Swarmia compares cycle time, throughput and batch size |
| Agent measurement | Agent Effectiveness | Coding agent metrics | Larridin scores each session; Swarmia measures each agent’s pull requests |
| Asking questions | Ask AI | Swarmia AI and MCP | Similar purpose; both suggest next steps |
| Where time goes | Effort Allocation | Investment balance | Similar purpose; Swarmia also ties effort to initiatives and capitalization |
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, external penetration test and data flow diagram are all available via the Larridin Trust Center.[17]
Recommendations by role
A CTO sees what the engineers and the AI agents actually build rather than just how quickly the pull requests get merged. That’s the perspective you need if you’re going to drive the transition to AI-native development.
A VP of Engineering acquires the agent habits of the very best engineers and is primed to make that the new team standard.
An AI platform lead has agent sessions being scored with evidence and the Larridin Router, which selects the model for each request.
A CFO has AI coding spend set against how much was actually built. Larridin also has the full company AI spend in one place.
Security-constrained teams can run Larridin on-premises or in a hybrid environment. We’d recommend you have a look at our SOC 2 and SOC 3 reports and data flow diagram first.
Moving from Swarmia
Larridin Developer Intelligence doesn’t require Swarmia or its data to get going. It simply connects to your Git hosts, your coding agents and your AI tool APIs. If you’re currently running Swarmia then run a pilot on two teams and compare what each product shows you. Then you can make your decision with real-world data in front of you.
Questions to test in a pilot
- Pick two PRs of a similar size: a large reformat and a real feature. See which product can tell them apart.
- 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 in terms of AI cost. Does it report the spend? Does it also make the decision as to which model to use for the next request?
- 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 Swarmia.[18]
Larridin data AI Impact in Engineering Report, Q3 2026 Read the reportFAQ
What is Swarmia?
Swarmia is an engineering intelligence platform based around delivery metrics and AI tool cost. It also runs developer surveys and publishes reports on engineering spend.[1][3]
Is Larridin a better fit than Swarmia for AI-native engineering teams?
Yes. If you’re moving to AI-native development then Larridin Developer Intelligence is the better option. It’s measuring what the engineers and AI agents are actually building from the work. The Larridin Router is also acting on the cost of every coding request.[4][14][15]
How is Engineering Output different from Swarmia’s AI impact metrics?
Swarmia compares AI-assisted PRs against the rest on cycle time, throughput and batch size. Engineering Output is used to score how much work each merged PR delivered (so a reformat and a new feature don’t count the same).[3][15]
Does Swarmia read the code?
Swarmia says their commit analysis works from change stats (file names and line counts) and doesn’t actually read the code. Engineering Output comes from a semantic understanding of the work in each PR, which is how it can tell the difference between a small and a large change.[4][15]
Does Swarmia have a model router?
A model router is not shown on Swarmia’s site. Swarmia reports AI cost by tool, by team and by person and will flag unused licenses. The Larridin Router picks the model for each coding request under your cost and quality preferences, with budgets per engineer and per team.[5][14]
Does Larridin run developer surveys?
No. Larridin is all about measuring what engineers and AI agents build and how they work together from the telemetry. Swarmia provides developer experience surveys with benchmarks.[3][10]
What data does Larridin Developer Intelligence collect?
Git metadata, coding agent session data and usage from AI tool APIs. No keystrokes are recorded, no screenshots are taken. Security reports and a data flow diagram are available at trust.larridin.com.[17]
What are the alternatives to Swarmia?
Teams generally compare Swarmia to Larridin, DX, Jellyfish, LinearB, Weave and Span. For AI-native engineering teams we’d point you to Larridin Developer Intelligence. That one looks inside each PR and inside each agent session, something the delivery metrics simply can’t do.[10][11]
Can we switch from Swarmia to Larridin?
Yes. Larridin connects directly to your Git hosts and coding agents so you can run a pilot on two teams with absolutely no Swarmia data.
Sources and method
Written by Larridin using Swarmia’s public website and help center and Larridin’s own product pages. This is a documentation review rather than a hands-on benchmark of Swarmia. If something is not described on the Swarmia pages we will simply state “Not shown” and that will not be interpreted as it being missing. The Swarmia facts have been reviewed on November 11, 2026. To report an error please email support@larridin.com and we will correct the pages within 3 business days.
- Swarmia homepage: swarmia.com
- Swarmia AI impact: swarmia.com/product/ai-impact
- Swarmia pricing and plans: swarmia.com/pricing
- Swarmia security: swarmia.com/security
- Swarmia AI adoption and cost: swarmia.com/product/ai-adoption-and-cost
- Swarmia AI (help center): help.swarmia.com/features/swarmia-ai
- Swarmia cloud agents (help center): help.swarmia.com/features/ai-tools/cloud-agents
- 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
- Larridin Effort Allocation: larridin.com/developer-intelligence/effort-allocation
- Swarmia product overview: swarmia.com/product
- Swarmia software capitalization: swarmia.com/product/software-capitalization
- Swarmia developer experience surveys: swarmia.com/product/developer-experience-surveys
- Swarmia integrations: swarmia.com/integrations
- Swarmia changelog: swarmia.com/changelog
- Swarmia developer productivity: swarmia.com/product/developer-productivity
Not just engineering
Larridin isn’t just an engineering tool. The same platform is used to measure AI adoption, fluency, impact and spend across Finance, Support, Marketing, Sales and Legal. Leadership gets a single view of the AI spend and the AI output across the business.[8][9]