A free price comparison website for SaaS dev tools

Hacker News

The calculators are things I’ve researched for my own needs, and I made a site to hopefully help others aswell. Everything is done manually by me at the moment. So far I’ve got calculators for auth, analytics, newsletters, s3, postgresql, images, emails, llm apis and serverless hosting. More is coming! Thinking of adding APIs for accepting new providers and for updating prices for providers.

Visit product View on Hacker NewsDec 22, 2024

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · 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 · Strong signals: saas · Missing: arr, mrr, revenue

20%20% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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