Larridin aggregates billing and engineering telemetry to show how much teams actually spend on AI coding, how that spend maps to output, and where additional token dollars stop buying meaningful returns.
Larridin groups product capabilities around Spend Intelligence, AI Impact, Developer Intelligence, and a model Router that ties routing decisions to output and quality.
Companies should track their own ROI curves and set review triggers where incremental spend stops improving output, rather than copying another company's budget cap.
B2B companies are spending significant money on AI in 2026 — seats for ChatGPT and Claude, metered token bills for coding agents, and growing line items for always-on agents. Much of that spend is fragmented across invoices, corporate cards, and personal subscriptions, leaving finance and engineering leaders unable to see whether dollars translate to useful output. Larridin connects AI spend to engineering telemetry to answer that question.
Larridin split engineers into cohorts by the share of shipped output that was AI-attributed. All cohorts started with roughly the same baseline spend, which lets the findings isolate skill and fluency effects. Results:
Practical takeaway: more budget only converts to more output when AI fluency exists. Larridin recommends tracking each team's marginal ROI curve and placing review triggers where returns level off.
Larridin scores merged pull requests using model-assessed complexity across five levels, adjusts for low-quality output and missing tests, and scales by code churn. The company is explicit that its relationships are associational: high-output engineers may both ship more and spend more. Larridin flags that its metrics estimate AI-equivalent work and are not direct measures of cash saved or headcount reduction.
Larridin's initial benchmark provides concrete numbers to start conversations inside engineering and finance. The most useful next steps are running the same linkage on your organization's billing and telemetry, and comparing marginal output per dollar across cohorts to set targeted training and spend policies.
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