CloudRunr Cross Cloud Pricing Calculator and Cloud Intel Platform

Hacker News

Over the last 7 months, we've been hard at work building a Cross-Cloud Pricing Calculator and Cloud Intelligence platform. I would greatly appreciate feedback from the community on what is good or what sucks. CloudRunr aims to be a transparent and objective evaluation of AWS, Azure, and Google Cloud. We automatically fetch your monthly usage data, including reservation and compute savings plan usage, using a read-only IAM role / we can ingest your on-premises usage as an excel. CloudRunr maps usage to equivalent VMs or services across clouds, and calculates 'closest-match' pricing estimates across clouds, considering reservations and savings plans. It highlights gaps and caveats in services for the target cloud, such as flagging unavailable instance types in specific regions. We have had interest from Cloud partners (and their pre-sales teams) who use it in their sales conversations. Please share your feedback here! Thanks a ton.

Visit product View on Hacker NewsOct 18, 2024

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

60%60% 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: lua, ide, io · Missing: https docs, excited, just released

53%53% 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: including · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, google, monthly · 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

15%15% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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