SaaS waste calculator (most waste –$250/employee/year)

Hacker News

We're building a SaaS license management tool and kept hearing the same story: "We know we're wasting money on unused licenses, but we don't know how much." So we built a dead-simple calculator. One slider (employee count) → three estimates: • Total SaaS apps (~13 per employee) • Annual waste (~$250/employee on ghost accounts, unused seats) • IT time burned on manual tracking The ratios come from industry research + early customer data. Curious if HN folks are seeing similar patterns at their companies - are we close? Way off? (Also: feedback on the calculator UX welcome. Tried to make it ridiculously simple.)

Visit product View on Hacker NewsNov 23, 2025

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TrustMRRFits verified-revenue profile · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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