I

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

Hacker News

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

Hey HN: I'm Kaveh, the founder of Usage (https://www.usage.ai/) We help companies drive down AWS costs. Why? Because the way it's done now is a pain. Stakeholders, especially engineers, are required to spend unnecessary time manually finding underutilized or overly expensive EC2s. We believe the optimization process should be done automatically through a series of sophisticated algorithms. At the moment, there are over 70,000 AWS EC2 prices - doing that manually just won't scale at most organizations. My background is in software engineering. Previous to founding Usage, I worked on high-performance computing research at JP Morgan Chase and as a software engineer at a number of smaller startups. Here's how it works: We are typically hired by the head of engineering or the CFO. Usage analyzes your cloud usage at 5-minute increments to find cost reduction opportunities. You'll see up-front savings within minutes, and then we'll continue to email you additional savings we find that you can approve or deny. You can register for free, then charge 20% of savings. Have you experienced any issues with managing your company or organization's AWS expenses? We'd love to hear your feedback and ideas! Here's a demo video https://www.loom.com/share/79f88ab43f58484181d86dc6b918813a

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: organizations · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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.

Incorrect prediction on native model

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