BGBuster ultra-low-cost Background Removal API built for devs

Hacker News

Hi HN I built BGBuster ( https://bgbuster.com ), a background removal API made for developers who were tired of paying €1+ per image. Why I built it Existing services are expensive and complicated. I wanted something simple, transparent, and affordable. What this does - Upload a file or pass a URL → get a transparent PNG or WebP - Pay once for credits (credits never expire) - Simple API: single POST, returns URL or raw image - Dashboard shows usage, success rate, and recent API calls

Visit product View on Hacker NewsOct 23, 2025

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

77%77% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing · Missing: https docs, excited, just released

42%42% 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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: single · Missing: mac, agents, macos

27%27% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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