Us

Usage, Cut your AWS Bill by 50%+ in 5 Minutes

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Usage, Cut your AWS Bill by 50%+ in 5 Minutes

Hi HN community, [Direct Link: www.usage.ai] I’m Kaveh, founder and CEO of Usage, and am excited to show you Usage, an app that helps you slash your AWS EC2 bill by 50% in ~5min by trading reservations. As of today, Usage is in General Availability and any AWS user can use it. It works by creating a limited-access IAM role (ReadOnly + Ability to Manage Reservations) into your AWS account(s). The AWS console interface has made it hard for companies to optimize their AWS spend. After years of working for different companies that use AWS, I still find it difficult to understand how much money I’m spending on AWS. I don’t know who owns what instances, how our commitments are saving us money (RIs, SPs, EDPs), and what instances can be sized down (or switched to spot). At Usage, we are building a web-based app that keeps you in charge of your AWS while minimizing your bill. No code change, no moving your AWS account or instances around, and no downtime. We’ve built: 1) Real-Time RI/SP Recommendations: See which instances are uncovered by your SPs and/or RIs and get them covered with a single button tap. Instant savings. 2) RI Sell Recommendations: RIs that are no longer utilized are highlighted and sold instantly. No more worrying about unutilized RIs and no more needing to forecast your compute needs. 3) Consolidated View: View your EC2 instances and RI/SPs across all your AWS accounts in a single space. No more switching between AWS accounts. 4) Teams and Audit Log: Add as many users as you’d like to your Usage dashboard, and see who approved which recommendations. We built Usage in ReactJS, Python, Java– and along the way we built our own internal accounting system to keep track of customer savings. We have plans to eventually release an open-source version of Usage. Our business model is 20% of the savings we find you. We only make money when we save you money. We bill monthly and have longer-term enterprise plans available. We take privacy extremely seriously. Your data is always protected both at-rest and in-transit. Additionally, Usage never collects or stores sensitive information. Usage only collects meta-data such as CPU utilization, launch time, instance configuration, region, etc. You can read our full privacy policy here: www.usage.ai/policy/ We are confident we can deliver a better AWS cost savings experience that is meaningfully better than other tools. If you use AWS, please give it a shot at www.usage.ai and let us know. Let me know what you think! Ask me anything!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, single · Missing: mac, agents, macos
89%89% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
67%67% 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 · Strong signals: month, monthly, users · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive
22%22% 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
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: make money · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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