Quick answer
Larridin is an AI impact intelligence platform. Larridin Developer Intelligence is built for engineering teams moving to an AI-native software development lifecycle, and it covers telemetry and optimization across that whole lifecycle. It measures velocity, quality, efficiency and the real output of your engineers and your AI agents. Then it helps you act: spread the habits of your strongest AI users, cut AI cost with the Larridin Router, and get ready for a future where much more software is written without a human at the keyboard.
Weave is an engineering intelligence platform that traces coding-agent sessions, estimates the expert hours behind each code change and offers an open-source model router.[2][4][5] Weave answers “What did our engineering team and its agents do?” Larridin answers “How well are our people working with agents, what is every agent costing across the company, and what should we change?”
Why teams choose Larridin Developer Intelligence
- Agent effectiveness, not just agent tracing. Larridin scores how well engineers work with coding agents, from how clearly they set the task to how they verify the result, and turns the practices of your best AI users into guidance the rest of the team can adopt.[9][10]
- Agent cost, and where agents spend their time. Coding-agent sessions, work patterns, token usage and cost sit in one view, so you can see how much agent spend goes to feature work, bug fixing, research or testing.[9]
- Every AI dollar across the enterprise. Larridin pulls AI spend from coding tools, cloud models, LLM gateways, employee-built agents, desktop apps and invoices, and attributes it to teams, agents, projects and workflows, with forecasting and budgeting.[11][12]
- AI beyond engineering. Product managers, analysts and operations teams now build with coding agents too. Larridin measures AI adoption, fluency and spend in every department, in the same platform.[13][17][18]
- We measure the work. Engineering Output scores each merged pull request on complexity, size and other factors, separates human, AI-assisted and agent-authored work, and builds code quality into the score.[14][18]
What Weave does well
Weave gives engineering teams detailed agent observability: each session’s prompts, model calls, tool calls and tests on one timeline, loop detection, and session cost joined to the work that merged.[2] It tracks per-user cost for 11 AI coding tools and offers Dev FinOps reporting, including R&D capitalization[3], explains the score its Silk 1 model gives each code change[1][4], and publishes benchmarks for its open-source Router.[5][6] It also offers SOC 2 Type II and on-premises deployment.[8] Its focus, though, stays on engineering teams and their coding tools.
The key difference: effectiveness and spend across the enterprise
Agent effectiveness, not just observability. Weave traces what the agent did: each step, its time and its cost.[2] Larridin also measures how the engineer and the agent worked together. Each session is scored on six dimensions (prompt clarity, session steering, sophistication, prompt quality, verification discipline and task outcomes), with the evidence behind each score.[9] As our measurement guide puts it, most AI coding platforms measure agent performance; Larridin measures engineer performance with agents, which is what tells you where to coach and what to change.[10]
Agent cost by the work it does. Larridin shows agent sessions, work patterns, token usage and cost together, including prompt-cache costs, so you can see that agents spent most on feature development this month, or that codebase research could run on a cheaper model.[9] Weave joins session cost to merged work and per-user tool cost.[2][3] A breakdown of agent cost by type of work is Not shown.
Every AI dollar, every department. Weave’s Dev FinOps covers AI coding tools and engineering finance, such as R&D capitalization.[3] Larridin covers every AI source across the company: coding tools, cloud models such as Bedrock, GCP and OpenAI, LLM gateways, employee-built agents and desktop apps such as Cowork, attributed to teams, projects, workflows and use cases.[11][12] Company-wide AI spend outside engineering is Not shown for Weave.
A simple example. A product manager builds an internal tool with Claude Code. An analyst runs agents in Cowork. The platform team runs Codex in CI. An engineering-only view sees the platform team’s coding tools. Larridin shows the adoption and spend for all three, so leadership gets one answer on what AI costs and what it produces.[11][13]
How Larridin and Weave compare
| What matters | Larridin Developer Intelligence | Weave |
|---|---|---|
| Built for | AI-native engineering teams and every other department using AI, in one platform. | Engineering teams.[7] |
| How agents are measured | Agent Effectiveness scores how well engineers work with agents on six dimensions, with cited evidence for each score.[9] | Agent observability traces each session’s prompts, model calls, tool calls and tests, with loop detection and six signals including accepted output and cost per accepted change.[2] |
| Acting on it | Coaching suggestions and reviewable practice guidance, such as agent-guide changes and model comparisons, shared across teams.[9] | Wooly, an AI agent that answers questions about engineering data.[7] Coaching on how engineers work with agents is Not shown. |
| 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, including prompt-cache costs.[9] | Session cost joined to merged work, and per-user cost for 11 AI coding tools.[2][3] Cost by type of work is Not shown. |
| AI spend across the enterprise | Every AI source, from coding tools and cloud models to LLM gateways, employee-built agents, desktop apps and invoices, attributed to teams, agents, projects, workflows and use cases, with forecasting and budgeting.[11][12] | Dev FinOps for AI coding tools: subscription spend per tool, cost per active user, per-user cost and R&D capitalization reports.[3] Company-wide AI spend outside engineering is Not shown. |
| AI beyond engineering | Adoption, fluency and impact across Support, Sales, Marketing, Finance and Legal, alongside each department’s AI spend.[13][18] | Not shown. |
| 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.[14][18] | Silk 1 estimates the expert hours behind each code change.[4] |
| Model choice and cost | The Larridin Router chooses the model for each coding request, inside the platform that measures Engineering Output and spend, and compares routed and pinned work on output, quality and cost.[18] | Weave Router, open source, sold separately from the platform.[1][5] |
| Deployment | Cloud, hybrid or on-premises. | Cloud or on-premises, including air-gapped.[8] |
| 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. | Cost attribution depends on the tool; some tools report only organization-level spend. Expert hours estimate human effort, not what was delivered.[3][4] |
Similar features, different names
| What you want to measure | Larridin Developer Intelligence | Weave | How they differ |
|---|---|---|---|
| Agent sessions | Agent Effectiveness | Agent observability | Larridin scores how the engineer and agent worked together and suggests practices; Weave traces the agent’s steps |
| AI spend | Token Spend & Insights | Token Intelligence and Dev FinOps | Larridin covers every AI source across the company; Weave covers AI coding tools |
| Engineering output | Engineering Output | Estimated expert hours (Silk 1) | Larridin scores what was built; Weave estimates human effort |
| Model choice | Larridin Router | Weave Router | Larridin’s runs inside the platform that measures Engineering Output and spend; Weave’s is sold separately |
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 available through the Larridin Trust Center.[15]
Recommendations by role
- CTO
- Larridin Developer Intelligence, to see how engineers and AI agents actually work together and to steer the move to AI-native development.
- VP of Engineering
- Larridin Developer Intelligence, to find the agent habits of your strongest engineers and turn them into team practice.
- CFO
- Larridin, to see every AI dollar across the company in one place, attributed to teams and projects, with forecasting and the Router to cut cost.
- CIO or head of AI
- Larridin, to measure AI adoption, fluency and spend in every department, including teams outside engineering that now build with coding agents.
- Security-constrained teams
- Larridin Developer Intelligence, with hybrid or on-premises deployment, SOC 2 Type 2 and SOC 3 reports, a published data flow diagram, and one platform for engineering and company-wide AI data.
Moving from Weave
Larridin Developer Intelligence does not need Weave or its data to start. It connects to your Git hosts, coding agents and AI tool APIs, and Larridin connects to the billing sources for AI spend across the company. If you run Weave today, start a pilot on two teams, compare what each product shows you and what Larridin recommends, then decide with real numbers in front of you.
Questions to test in a pilot
- Open the same agent session in both. Check whether each product tells you only what the agent did, or also how the engineer could have done it better.
- Follow agent spend by type of work. Ask each product how much agent spend went to feature work, bug fixing and research last month.
- Ask for total AI spend. Ask each product for the company’s total AI spend this quarter, including teams outside engineering.
- Ask what to change. After 30 days, ask each vendor what it recommends you change and what that would save.
Proof
Larridin data AI Impact in Engineering Report, Q3 2026 For measured findings across Larridin customers, see the AI Impact in Engineering Report, Q3 2026. Read the reportMore benchmarks in the Larridin data hub.
FAQ
Is Larridin a better fit than Weave for AI-native teams?
Yes. If your company is moving to AI-native development, Larridin is the better choice. It measures how well engineers work with agents and what to change, shows what every agent costs by the work it does, and covers AI spend and adoption across the whole company. Weave focuses on engineering teams and their coding tools.[2][3][9][11]
How is Larridin’s Agent Effectiveness different from Weave’s agent observability?
Weave’s agent observability traces each session’s steps, time and cost. Larridin’s Agent Effectiveness also scores how the engineer worked with the agent on six dimensions, with the evidence behind the score, and turns what your best AI users do into guidance for the team.[2][9]
Does Weave track AI spend outside engineering?
Weave’s Token Intelligence and Dev FinOps track spend for AI coding tools, including R&D capitalization reports. Company-wide AI spend, such as cloud models, LLM gateways and agents built by other departments, is Not shown. Larridin tracks AI spend across every source and department.[3][11]
Does Larridin measure AI used by people outside engineering?
Yes. Larridin measures AI adoption, fluency and spend across every department, including desktop agents such as Cowork and agents built by employees outside engineering.[11][13][18]
Do both have a router?
Yes, but they work differently. The Larridin Router runs inside the same platform that measures Engineering Output and spend, so you see the savings next to the quality of the work that shipped. Weave’s Router is a separate product.[5][18]
What data does Larridin Developer Intelligence collect?
Git metadata, coding-agent session data and usage from AI tool APIs. It does not record keystrokes or take screenshots. Security reports and a data flow diagram are available at trust.larridin.com.[15]
Can we switch from Weave to Larridin?
Yes. Larridin connects directly to your Git hosts, coding agents, AI tool APIs and billing sources, so you can start a pilot on two teams without any Weave data.
Sources and method
Written by Larridin using Weave’s public website and documentation and Larridin’s own product pages. This is a documentation review, not a hands-on benchmark of Weave. Where Weave’s pages do not describe something, we write “Not shown” and do not treat it as missing. Weave facts were reviewed on November 9, 2026. To report an error, email support@larridin.com; we correct pages within 3 business days.
- Weave vs Larridin (Weave): weaveos.com/compare/weave-vs-larridin
- Weave agent observability: weaveos.com/agent-observability
- Weave Token Intelligence and Dev FinOps: weaveos.com/products/token-intelligence
- Weave Silk 1 and Code Intelligence: weaveos.com/products/code-intelligence
- Weave Router: weaveos.com/products/router
- Weave Router coding-agent cost evaluation: weaveos.com/router/coding-agent-cost-evaluation
- Weave homepage: weaveos.com
- Weave Enterprise: weaveos.com/enterprise
- 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 platform overview: larridin.com
- 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
- Fortune, “Anthropic’s head of Claude Code on how the tool won over non-coders” (January 24, 2026): fortune.com/2026/01/24/anthropic-boris-cherny-claude-code-non-coders-software-engineers
- Larridin Developer Intelligence: larridin.com/developer-intelligence