We help companies take back money from AWS

Hacker News

Hi HN community, my name's Kaveh; I founded www.usage.ai to help companies reduce their AWS bills. Before I started working on Usage, I saw this problem first-hand at the last job I was at. Our team was overpaying by thousands of dollars per month on AWS, and I found out that other teams within the company were overspending by a similar amount. I then dug deeper and after some time, set up budgeting controls and saved our teams thousands of dollars per month. We had an AWS optimization company that wasn't able to find these savings and AWS's trusted advisor only found marginal savings. So I decided to start a new kind of optimization platform. We're driven by data (we ingest hundreds of thousands of data points from CloudWatch) and our dashboards are completely automated. We support optimization for EC2 and RDS. We do our optimization in two main ways: we search for the best combination of RIs and Savings Plans, then we bundle our RIs with guaranteed buyback so you can sell your RI early. All the savings with nearly none of the risk. Next, we found that many of our customers have non-prod EC2s and RDS instances that sit idle on weekends. We automatically create policies to shut them down to save you money. Every Friday, we send an email with the savings our system found and all you need to do is hit accept or reject. We just helped a company save 30% ($8.5k/year) in less than 30 minutes (the time to register for an account + action savings). What do you think HN community? URL: www.usage.ai p.s. we have an upcoming AWS Cost Optimization Immersion Day coming up next Friday, December 18th from 1:00 pm to 2:00 pm EST. Here's an invite! https://docs.google.com/forms/d/e/1FAIpQLSdhELeO1VS42hLXri0OqqsDCm_lyRPoJh_Co3WxljmLQeRm_A/viewform

Visit product View on Hacker NewsDec 7, 2020

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, email · Missing: mac, agents, macos

79%79% 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

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.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, google, way · 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: platform · Missing: plus, intuitive, reviews

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: margin · 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

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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