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AWS usage optimizer project – currently seeing 35% billing reductions

Hacker News

AWS usage optimizer project – currently seeing 35% billing reductions

Hey, I've spent the coronavirus time hacking together a billing optimizer for AWS. My first tests have shown 35% reduction in billing by careful selection of dynamic and static instances. I'm looking for 10 companies to analyze their logs to see if these savings will generalize. If you are interested, please email me at setup@usage.ai Link: www.usage.ai Demo: https://www.loom.com/share/2a6f1c8e4c214914a1cdd88c6fdec4ac

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Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos
73%73% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% 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
18%18% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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