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Bill My Cloud, a simple no-nonsense AWS cost estimator

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Bill My Cloud, a simple no-nonsense AWS cost estimator

I built Bill My Cloud because I wanted a faster way to estimate AWS costs during early planning, without navigating the confusing official calculator or recording on spreadsheets. This started as a simple calculator, but I’ve been expanding it based on real usage needs and AWS services. What it does - Estimate AWS costs with minimal inputs and sensible defaults - Instant PDF export of the estimate (useful for sharing or approvals) - Suggestions for cheaper configurations or cost-saving changes - Shareable config links so others can view the same setup - Optional cloud save for configurations so you can revisit and tweak later - Commonly used currency converted pricing (conversion rates auto-updated every 12 hours) No signup required to try it! How I expect it to be used - Early-stage planning and sanity checks - Comparing instance types, regions, and storage choices - Sharing rough estimates with teammates or clients - Learning how small config changes affect cloud cost What it’s not - It’s NOT meant to replace final AWS billing or enterprise forecasting - Numbers are estimates based on public pricing, not invoices Roadmap - Addition of other major cloud platforms - Commonly used stack templates (Lamp, Lemp etc) - Export compatibility with terraform Pricing data comes from public AWS pricing. Will be actively improving it to ensure accuracy. It's currently in Alpha. Bugs are to be expected. Please report them via comments! Feedbacks appreciated!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
54%54% 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 HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
35%35% 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: way · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

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