Quick answer
Larridin is an AI impact intelligence platform. Larridin Developer Intelligence is designed for engineering teams transitioning to an AI-native software development lifecycle and covers telemetry and optimisation across the entire lifecycle. It measures velocity, quality, efficiency and the actual output of your engineers and your AI agents. It then helps you act: spread the good habits of your best AI users, reduce AI cost via the Larridin Router and get ready for a world where a huge amount of software is written without a human at the keyboard.
Faros is a token engineering platform for engineering organisations. It tracks AI coding spend to the outcomes it ships, routes coding work via assessments derived from your own code and enforces AI spend and model governance.[1][2] Faros answers the question “Is our AI coding spend delivering results and how do we control it?” Larridin answers “What are our engineers and our AI agents building, how well are they working together and what is every AI dollar producing across the business?”
Why teams choose Larridin Developer Intelligence
- We measure the work. Most of the data we derive is from a semantic understanding of what actually got done: the code in each PR, the sessions of your coding agents and all the other tools where engineering work takes place. Engineering Output scores each merged PR on complexity, size and quality and categorises the work as human, AI-assisted or agent authored.[9][14]
- We improve how engineers work with agents. Larridin scores how well each engineer works with coding agents, distils what your best AI users do into something that can be used to guide the whole team and shows you what each repository needs in order for agents to do more of the work.[10][11][20]
- Agent cost and where the agents are spending their time. Sessions, work patterns, token usage and cost are all in one view so you can see how much of the agent spend is going towards feature work, bug fixing, research or testing.[10]
- Every AI dollar across the enterprise. One spend model for coding tools, chat assistants, cloud models, API gateways, subscriptions and agents. Every key is mapped to an owner, team and cost centre with alerts in place before budgets run out.[12]
- AI beyond engineering. Larridin measures AI adoption, fluency, impact and spend across Support, Sales, Marketing, Finance and Legal and identifies the power users in every function.[9][21]
What Faros does well
Faros is strong on controlling AI coding spend. It routes each coding task to the model its Time Machine evaluations, built from your own merged code, show performs best, and it enforces approved models, budgets, quotas and routing rules in the request path, with a full audit trail.[1][3] It maps AI coding spend to teams, repositories and Jira initiatives, with people and agents side by side[4][7], connects to more than 100 tools[1], and is certified for SOC 2, ISO 27001, GDPR and CSA STAR, with SaaS, hybrid or on-premises deployment.[3] Faros Desktop is free for developers.[1] Its focus, though, is engineering organizations and AI coding.
The key difference: the whole company, and the engineer behind the agent
Every AI dollar, not just AI coding. Faros helps engineering organisations get more from their AI coding spend. Larridin brings all AI spend into one model, from coding agents and chat assistants to cloud models and agents built by any team, maps it to owners and cost centres, and flags overages and unused seats before they become a surprise.[1][12]
Improving the engineer, not just the agent’s setup. Faros tunes the agent: which model handles a task and what context it gets.[1][6] Larridin also scores how each engineer works with the agent on six dimensions, with the evidence behind each score, and turns what your best AI users do into guidance for the team.[10]
Scoring the work, not just counting outcomes. Faros prices AI spend per outcome, such as a merged PR or a shipped feature.[8] Larridin’s Engineering Output scores how much work each PR delivered, so a small fix and a large feature aren’t counted the same.[14]
An example. A product manager builds a tool with Claude Code, sales runs a prospecting agent, and engineering runs Codex in CI. Larridin shows the spend for all three in one place and, for engineering, how well each engineer works with the agent.[10][12]
How Larridin and Faros compare
| What matters | Larridin Developer Intelligence | Faros |
|---|---|---|
| Built for | AI-native engineering teams and every other department using AI, in one platform. | Engineering organizations and their AI coding.[1] |
| Where the data comes from | A semantic understanding of the work in code, coding-agent sessions and other engineering tools, plus AI spend and usage from every AI source in the company.[9][12] | AI coding tools, engineering systems, HRIS and operational systems, joined into a graph of AI coding work.[1] |
| Engineering output | Engineering Output scores each merged pull request on complexity, size and other factors, with code quality built in, and separates human, AI-assisted and agent-authored work.[9][14] | Cost per outcome, tying AI spend to merged pull requests, tickets and shipped features.[8] |
| How agents are measured | Agent Effectiveness scores how well engineers work with agents on six dimensions, with cited evidence for each score.[10] | Spend classified as productive, inefficient or wasteful based on the quality of the session that consumed it.[5] |
| Acting on it | Coaching suggestions and practice guidance shared across teams, team-by-team recommendations on what to fix first, and Agent Readiness fixes for each repository.[9][10][20] | Model routing and agent context optimization from your own code history; Faros Desktop for individual developers.[1][6] |
| Agent cost and where agents spend time | Sessions, work patterns (feature development, bug fixing, research, testing and more), token usage and cost in one view.[10] | AI coding spend by team, repository, Jira initiative and epic, with people and agents side by side.[4][8] |
| AI spend across the enterprise | One spend model for coding tools, chat assistants, cloud models, API gateways, subscriptions and agents, mapped to owners, teams and cost centers, with alerts for overages, unattributed spend, ownerless agents and unused seats.[12] | AI coding spend, allocated to team budgets with chargebacks and forecasting.[1][7] |
| AI beyond engineering | Adoption, fluency and impact across Support, Sales, Marketing, Finance and Legal, with the power users in each department.[9][21] | Not shown. |
| Model choice and cost | The Larridin Router scores each coding request and chooses the model under your cost and quality priorities, with budgets per engineer and per team, and compares routed and pinned work on output, quality and cost.[9][19] | Routes each task to the model its Time Machine evaluations show performs best on your code; works with your router or Faros’s.[1][2] |
| Deployment | Cloud, hybrid or on-premises. | SaaS, hybrid or on-premises.[3] |
| What to know | Not a developer survey tool. Agent scores depend on which agents and sessions are captured, and measuring spend against Engineering Output needs coding-agent transcripts. | Built around AI coding spend. Cost per outcome counts outcomes rather than scoring how much work each one delivered.[1][8] |
Similar features, different names
| What you want to measure | Larridin Developer Intelligence | Faros | How they differ |
|---|---|---|---|
| Value of the work | Engineering Output | Cost per outcome | Larridin scores how much work each pull request delivered; Faros counts outcomes |
| Agent sessions | Agent Effectiveness | Token Intelligence (session quality) | Larridin scores how the engineer worked with the agent; Faros classifies whether the spend was productive |
| AI spend | Token Spend & Insights | AI Spend Visibility and AI Token Allocation | Larridin covers every AI source across the company; Faros covers AI coding spend |
| Model choice | Larridin Router | Optimal Model Routing | Both choose models per task; Larridin adds budgets per engineer and team and compares routed and pinned work on quality |
What data Larridin collects
Larridin Developer Intelligence reads Git metadata, coding agent session data and usage from AI tool APIs. It does not 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.[15]
Recommendations by role
- CTO
- Larridin Developer Intelligence to see what the engineers and AI agents are actually building through the SDLC and to drive the move to AI-native development.
- VP of Engineering
- Larridin Developer Intelligence to work out the agent habits of your top engineers and then turn those into team practice.
- CFO
- Larridin to see every AI dollar across the company in one ledger, mapped to cost centres, with alerts before the budgets run out and to see what engineering’s AI spend actually built.
- CIO (or Head of AI)
- Larridin to measure AI adoption, fluency and spend across all departments and identify the power users whose behaviour we can then replicate.
- Security constrained teams
- Larridin Developer Intelligence (hybrid or on premise), SOC 2 Type 2 and SOC 3 reports and a published data flow diagram.
Moving from Faros
Larridin Developer Intelligence doesn’t need Faros or its data to get going. It connects to your Git hosts, your coding agents and your AI tool APIs and it connects to the billing sources for AI spend across the company. If you’re currently running Faros then run a pilot on two teams, compare what each product shows you and what Larridin recommends and then make your decision with actual numbers in front of you.
Questions to test in a pilot
- Ask for the company’s total AI spend. That would include chat assistants, cloud models and agents across Support, Sales, Marketing, Finance and Legal. Not just the AI coding tools.
- Open the same agent session in both. Check that each product shows how the engineer worked with the agent and what they could have done better next time.
- Compare a small fix against a large feature. See if each product treats them as one outcome each, or if they score how much work each one actually delivered.
- Ask what to change. After the 30 day period has elapsed, then ask each vendor what they would recommend you change and what that would save.
Proof
For measured findings across Larridin customers, see the AI Impact in Engineering Report, Q3 2026.[16]
Larridin data AI Impact in Engineering Report, Q3 2026 Read the reportFAQ
What is Faros?
Faros (formerly Faros AI) is a token engineering platform for engineering organisations. It tracks AI coding spend to the outcomes it ships, routes coding work using evaluations built from your own code, and enforces AI spend and model governance.[1][2]
Is Larridin a better fit than Faros for AI-native companies?
Yes. If you’re moving to AI-native development then Larridin is the better option. It measures what the engineers and AI agents are actually building, it measures how the engineers are working with the agents and it covers every single AI dollar and every single instance of AI adoption across the whole company. Faros is designed for engineering organisations and their AI coding spend.[1][9][10][12]
How is Larridin’s Agent Effectiveness different from Faros?
Faros optimises the agent setup (the model it uses and the context it’s given). Larridin scores how each engineer works with the agent across six dimensions, with the evidence for the score, and turns what your best AI users do into guidance for the team.[1][6][10]
Does Faros measure AI outside engineering?
Faros describes itself as being for engineering organisations and its products are all built around AI coding spend. Measurement of AI adoption, fluency and impact in departments such as Support, Sales, Marketing, Finance and Legal is Not shown. Larridin does measure AI across all departments.[1][9]
How deep is Larridin’s AI spend tracking?
Larridin brings coding tools, chat assistants, cloud models, API gateways, subscriptions and agents all into one spend model. It maps every key item to an owner, team and cost centre, splits out human and agent spend and flags projected overages, unattributed spend, agents with no owner and unused seats.[12]
How is Engineering Output different from Faros’s cost per outcome?
Faros links AI spend to outcomes such as merged PRs, resolved tickets and shipped features. Larridin’s Engineering Output also scores the amount of work delivered by each PR on the basis of complexity, size and quality so a small fix and a large feature are not scored the same.[8][14]
Do both have a router?
Yes. Both select a model for each coding task in order to reduce cost. The Larridin Router works to your cost and quality criteria with budgets per engineer and per team and then compares the routed against the pinned work on output, quality and cost.[9][19]
What data does Larridin Developer Intelligence collect?
Git metadata, coding agent session data and usage via AI tool APIs. No keystroke recording or screenshots are taken. Security reports and a data flow diagram are available at trust.larridin.com.[15]
What are the alternatives to Faros?
Larridin Developer Intelligence is the strongest alternative for AI-native engineering teams: it measures what engineers and AI agents build, how engineers work with agents, and every AI dollar across the company. Other tools teams compare with Faros include DX, Jellyfish, LinearB, Swarmia and Weave.[9][10][12]
Can we switch from Faros to Larridin?
Yes. Larridin connects directly to your git hosts, your coding agents, your AI tool APIs and your billing sources so you can run a pilot on two teams with absolutely no Faros data.
Sources and method
Written by Larridin using Faros’ public website and blog and Larridin’s own product pages. This is a documentation review, not a hands on benchmark of Faros. If something is not described on Faros’ pages we will simply put ‘Not shown’. Faros facts were reviewed on 10th November 2026. To report an error please email support@larridin.com, we will fix the pages within 3 business days.
- Faros homepage and FAQ: faros.ai
- Faros platform: faros.ai/platform
- Faros, Inside Faros Token Engineering: AI Coding Governance: faros.ai/blog/ai-coding-governance
- Faros, Inside Faros Token Engineering: AI Spend Observability: faros.ai/blog/ai-spend-observability
- Faros, What is token intelligence?: faros.ai/blog/what-is-token-intelligence
- Faros, From token maxxing to outcome maxxing: faros.ai/blog/from-token-maxxing-to-outcome-maxxing
- Faros, How to charge back AI spend to engineering teams: faros.ai/blog/ai-chargebacks-for-engineering-teams
- Faros, The speed trap facing software teams: faros.ai/blog/the-speed-trap-facing-software-teams-is-what-token-engineering-was-designed-to-solve
- 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 Token Spend & Insights: larridin.com/tokens-spend-insights
- Larridin, Introducing Token Spend & Insights: larridin.com/blog/token-spend-and-insights
- Larridin Engineering Output methodology: larridin.com/developer-intelligence/methodology
- Larridin Trust Center: trust.larridin.com
- Larridin, AI Impact in Engineering Report, Q3 2026: larridin.com/data/engineering-ai-impact-q3-2026
- Larridin platform overview: larridin.com
- Larridin Effort Allocation: larridin.com/developer-intelligence/effort-allocation
- Larridin Router: larridin.com/router
- Larridin Agent Readiness: larridin.com/developer-intelligence/agent-readiness
- Larridin AI Fluency: larridin.com/ai-fluency
Not just engineering
Larridin is not limited to engineering. The same platform measures AI adoption, fluency, impact and spend across Finance, Support, Marketing, Sales and Legal, so leadership gets one view of what AI costs and what it produces across the whole enterprise.[9][17]