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I built a service to help companies save 35%+ on their AWS bills

Hacker News

I built a service to help companies save 35%+ on their AWS bills

Hi everyone, I've been working on a side project over quarantine called Usage.ai and I finally feel comfortable enough to launch it. We're a service that plugs directly into AWS, automatically finds savings, and applies those savings at the press of a confirmation button all without ever needing to go to an AWS console. I'd love to get HN's thoughts on it! Demo: https://www.loom.com/share/2a6f1c8e4c214914a1cdd88c6fdec4ac Link: https://www.usage.ai/

Visit product View on Hacker NewsMay 14, 2020

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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