pushsaas.com

TrustMRR

PushSaaS is a done-for-you SaaS directory submission service designed to help founders increase visibility, build high-quality backlinks, and improve Domain Rating (DR) fast. Instead of spending hours researching and submitting to directories manually, PushSaaS handles everything for you — starting with 50 free manual submissions to high-DR directories, with no credit card required. The platform focuses on quality and measurable SEO impact. Every submission is done manually (no bots), categoriz

Visit product View on TrustMRRJan 27, 2026Marketing Services B2B

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating44

MRR growth 30d-100.0%

Launch Intel predictions

Analyze your own launch →

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

83%83% 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.

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

62%62% 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

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

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

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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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