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AWS Savings CoPilot to save up to 50%

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AWS Savings CoPilot to save up to 50%

Hi HN community, My name is Kaveh, I'm the founder and CEO of www.usage.ai I launched on HN last year with the first version of Usage AI, we used Standard RIs and the RI marketplace to give customers the ability to buy 3-year no-upfront RIs which yielded 57% savings with none of the commitment. In conjunction with AWS, we have since moved to using Convertible Reserved Instances which are the most misunderstood but powerful savings instrument on AWS. As long as you have servers in any of the popular AWS regions, you can achieve up to 50% savings with no code change and the flexibility to scale your workloads up and down using Usage.AI's CRI automation. We blend CRIs with SRIs (that are non-discounted since they can still be traded) and CSPs to maximize a customer's savings rate net of our fees. This is what we call AWS Savings CoPilot. This has especially helped us assist some of the largest AWS customers with their spend since the volume of RIs can be adjusted up and down in real time at scale. In addition, for anyone who signs up for Usage.AI Savings CoPilot, we are also including ClearCost, a multi-cloud visibility, budgeting, and anomaly detection tool for AWS, GCP, Azure, Snowflake, Databricks, Datadog, and Kubernetes. Pricing: You get all of this for a % of what we save you on your AWS bill. Unlike other providers, we do not charge a % of spend. Are you tasked with managing your AWS bill at your organization? Let me know what you think! You can also reach me directly at kaveh@usage.ai

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Indie HackersFits the IH revenue-focused audience · Strong signals: including, maximize · 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: using, code · Missing: mac, agents, macos
62%62% 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, including · Missing: https docs, excited, just released
51%51% 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 · Missing: mobile apps, ios, personal
41%41% 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
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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