Practise vocabulary for platform team ROI, developer productivity ROI, time to first deployment, platform adoption metrics, cost avoided framing, and cost per deployed feature.
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"Time to first deployment" as a platform metric measures:
"Time to first deployment" measures onboarding friction: does a new engineer deploy in 2 hours or 2 weeks? A good internal developer platform reduces this from weeks (manual environment setup, ticket-based provisioning) to hours (self-serve, golden path). This metric captures the compounded value of platform investment across every new hire and every new team.
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"Cost avoided" framing in platform ROI means:
"Cost avoided" captures prevention value, which is otherwise invisible: "The platform's automated dependency scanning prevented 3 critical vulnerabilities from reaching production last quarter. Average cost of a production security incident in our industry: $2.1M. Estimated cost avoided: $6.3M." This is powerful for justifying platform investment because it makes the dog-not-barking value explicit.
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"Platform adoption rate" as a metric tracks:
Adoption rate reveals whether the platform delivers value: "72% of engineering teams are using the self-service deployment pipeline; 28% are still using ad-hoc scripts." Low adoption despite mandates indicates poor developer experience or missing features. High voluntary adoption indicates genuine value delivery. Track: active users/month, % services onboarded, golden path usage vs. custom solutions.
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"Developer productivity ROI" in platform economics is typically quantified by:
Calculating developer productivity ROI: (1) estimate time saved per engineer per week via surveys or time-tracking, (2) multiply by fully-loaded engineer cost (salary + benefits + overhead, typically 1.3-1.5× salary), (3) multiply by number of engineers. "4 hours/week saved × $150/hour × 200 engineers = $6.24M/year." Compare to platform team headcount cost to calculate net ROI.
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"Cost per deployed feature" as a platform economics metric expresses:
"Cost per deployed feature" combines deployment pipeline cost (CI/CD compute, testing environments) and operational cost (production infrastructure per feature). A mature platform reduces both: faster pipelines cost less to run; well-designed abstractions prevent over-provisioning. Tracking this over time demonstrates platform efficiency improvements: "cost per deployed feature decreased 40% year-over-year as the platform matured."