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.
This is the 2nd launch from Social Fetch. View more
Social Fetch
Launching today
Social media scraping API for public profiles, posts, comments, videos, transcripts, and metrics from TikTok, Instagram, YouTube, X, LinkedIn, and more. Pay-as-you-go credits, 100 free to start.
Free Options
Launch Team / Built With
How does the pay-as-you-go credit system actually work in practice, like how many requests do typical profile fetches cost and is there a way to estimate usage before running a large batch?
How does the pay-as-you-go pricing actually work in practice, like is it one credit per profile fetch or does it scale with the depth of data you pull back?
How does the pay-as-you-go pricing actually work in practice, like is it per profile fetched, per post returned, or something else? Trying to figure out if it stays cost-effective for monitoring a lot of accounts at once.
How do you prevent missing posts during platform updates. A clear retry process would build stronger trust with developers.
Finally tried Social Fetch for pulling TikTok comments and the latency was way better than what I was getting from a self-hosted scraper. Credits-based pricing makes it easy to experiment without committing to another monthly bill.
Finally got around to testing Social Fetch and the TikTok transcript endpoint was the standout for me, it returned clean captions way faster than the DIY scraper I was patching together. Pay-as-you-go credits also make it painless to spin up for a side project without committing to a monthly plan.
Gave the API a quick spin and was impressed that the transcript endpoint came back with timestamps already cleaned up, which saved me a chunk of preprocessing. Pay-as-you-go credits feel fair for testing too.