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I built a service to help companies reduce AWS spend by 50%+

Hacker News

I built a service to help companies reduce AWS spend by 50%+

Hey HN: Kaveh here, the founder of https://www.usage.ai/ Thanks to the support of the HN community, we've made massive improvements to our savings product from the last post. Since our last post, we've helped companies like imgur.com save 50% on their EC2 bill. We help companies drive down AWS EC2 spend. Why? Because the way it's done now is a pain. DevOps and Software Engineers end up spending time managing costs rather than focusing on business problems. 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 brought in by a DevOps manager to cut AWS EC2 costs. The app is entirely self-service and the savings are generated automatically, typically we do this live on a call. On average, we reduce AWS EC2 spend by 50% for 5 minutes of work. To reduce by 50%+, we don't touch the instances, require any code change, or change the performance of your instances. We buy Reserved Instances on your behalf (a billing layer change only) and bundle them with guaranteed buyback. So you get the steep 57% savings of 3-year no-upfront RIs with none of the commitment (you can sell them back to us anytime after 30 days). We make money off of a 20% Savings Fee. Happy to chat directly kaveh@usage.ai Have you experienced any issues with managing your company or organization's AWS expenses? We'd love to hear your feedback and ideas!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
78%78% 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 HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
46%46% 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
33%33% 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
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, make money · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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