I analysed 5.2M social media API requests over 91 days. This is how 400+ teams monitor multiple platforms

I run a social media data API company, so I get a slightly unusual view of how teams monitor platforms (X, Instagram, TikTok, YouTube, Reddit etc..) in production.

I recently analysed 91 days of anonymised usage: 5.2 million requests across 400+ teams. 82% monitored at least 2 platforms, with an average of 5.3 platforms and 19 endpoints each.

The higher-volume users weren’t asking AI to monitor everything. They built collection pipelines and used a language model near the end.

There are 4 major pipelines I've noticed:

1. Tracking affiliate and creator performance

Teams keep a list of creators and check their latest posts on a schedule. One team monitors around 200 creators on Instagram+TikTok+YouTube by pulling the latest 20 non-pinned contents from each account. They save the post link, follower count, views, likes, and comments, then match those numbers with their own campaign or sales data.

2. Finding new creators and content

Teams search hashtags, keywords, and reels to find creators they don’t already know. One user checked 23,116 public handles on Instagram/YouTube in a day and filtered them by location. AI can remove duplicates, group similar accounts, and rank creators by audience fit and typical engagement. A person still checks the original profile before reaching out to anyone.

3. Following brand and keyword conversations

Teams search the same brand name or topic across Reddit, YouTube, TikTok, Instagram, X, and LinkedIn. They save the post, author, date, link, and engagement numbers. AI then groups the results into themes such as praise, complaints, customer questions, and competitor mentions. Every summary should link back to the original posts so someone can check whether the AI got it right.

4. Monitoring brand sentiment

Teams collect their own captions and replies from different platforms, then give AI examples of what “on-brand” writing sounds like. The AI flags content that sounds different, quotes the exact wording that triggered the warning, and suggests a rewrite. A human is always on loop to make the final decision because tone is subjective.

Curious what other teams are monitoring and what your AI workflow looks like!

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