Brilliant Directories

AppSumo

Customers have praised Brilliant Directories for its user-friendly interface, robust features, and responsive customer support. While some users have mentioned minor limitations in design templates and storage space, the platform has garnered an impressive 4.9 overall rating from 911 reviews. It is considered a solid investment for building and monetizing membership websites and directories. Additionally, with a 7-day free trial and a 60-day money-back guarantee, it's worth giving it a try.

Visit product View on AppSumoJun 7, 2026

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Actual performance

911reviews

Made the leaderboard

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: platform, reviews, friendly · Missing: plus, intuitive, host

90%90% predicted probability of success on AppSumo, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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