We

We built a product to help companies reduce AWS/GCP/Azure spend by 50%

Hacker News

We built a product to help companies reduce AWS/GCP/Azure spend by 50%

Hey HN: Kaveh here, founder of https://www.usage.ai/ We help companies drive down AWS, GCP, and Azure 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. I have been building Usage AI for almost 4 years now (4 year anniversary in 1 month from now!) with an incredible group of founding people. We started as a product just to help lower AWS EC2 costs, and now we do all major AWS services (such as RDS, OpenSearch, ElastiCache, and Redshift with more on the way) and other clouds: Azure and Google Cloud. Here's how it works: We are typically brought in by a DevOps manager to cut AWS/GCP/Azure 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 Cloud spend by 30-50%. To reduce by 30-50%, we don't touch the instances, require any code change, or change the performance of your instances. We buy Reserved Instances and Savings Plans on your behalf (a billing layer change only) and bundle them with guaranteed buyback. So you get the steep 50% savings of 3-year no-upfront SPs with none of the commitment (we reimburse you for any underutilization). We make money off of a % 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
74%74% 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: google, using, code · Missing: mac, agents, macos
72%72% 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, 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.
TrustMRRLess likely to generate early MRR · Strong signals: month, google, way · Missing: mobile apps, ios, personal
47%47% 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
31%31% 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.

Incorrect prediction on native model

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