Launch Intel
Social Fetch

Social Fetch

TrustMRR

Social Fetch is social media scraper API for every major platform. Fetch profiles, posts, comments, videos, transcripts, metrics, and engagement signals from TikTok, Instagram, YouTube, X, LinkedIn, F

Social Fetch is social media scraper API for every major platform. Fetch profiles, posts, comments, videos, transcripts, metrics, and engagement signals from TikTok, Instagram, YouTube, X, LinkedIn, Facebook, Reddit, and more without maintaining scrapers yourself. Built for founders and engineering teams shipping social analytics, creator tools, enrichment workflows, monitoring dashboards, and AI agents. Live data, pay-as-you-go credits and no subscription.

Visit productView on TrustMRRMar 31, 2026Developer ToolsSoftwareB2B
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Actual performance

29customers
$1,241MRR/mo
Made the leaderboard

Traction signals

Domain Rating27
MRR growth 30d+90.0%

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent · Missing: mac, macos, cursor
81%81% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
55%55% 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: subscription · Missing: arr, mrr, revenue
43%43% predicted probability of success on Acquire.com, 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
40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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