How to Monitor Subreddits for Leads at Scale

Where manual monitoring breaks down

Reddit's own interface is built for browsing, not for systematic monitoring. There is no dashboard that tells you "here are the new posts across your 15 approved subreddits that mention a competitor," and refreshing each community's new or hot listing by hand simply does not scale past a handful of subreddits before something important gets missed.

The fix is not more discipline — it is treating subreddit monitoring as a background job that runs continuously, whether or not anyone is actively looking.

Poll subreddits, don't just search once

MentionSpot runs a durable poll job per approved subreddit, pulling listings — hot, new, rising, top, or controversial — through redditapis.com's read-only endpoints, up to 100 posts per call. Each poll job runs independently and on its own schedule, so a burst of activity in one community does not delay checking the others.

Because jobs are durable and queued rather than fire-and-forget, a temporary API hiccup or a worker restart does not silently drop a scheduled check — the job simply runs when a worker is available.

Adaptive frequency instead of a fixed schedule

Not every subreddit needs the same check-in frequency. A high-traffic community can bury a relevant post within an hour; a smaller niche subreddit might go a full day without a new thread. Polling everything on the same fixed timer wastes API budget on quiet subreddits and checks busy ones too rarely.

MentionSpot adjusts polling frequency per subreddit based on observed activity, and ties overall check frequency to your plan — roughly every two hours on every plan — so budget goes where the activity actually is.

Dedupe before anything else happens

Cross-posting is common on Reddit, and the same thread can legitimately appear in two or three of your monitored subreddits. Posts are deduplicated by their Reddit fullname as soon as they are ingested, so you see one signal per underlying conversation instead of the same lead three times in your queue.

Pre-filter before you spend LLM budget

Running every single post through an LLM for relevance and intent scoring would be accurate but wasteful — most posts in a general subreddit have nothing to do with your product. A keyword pre-filter, built from your product context, narrows the volume down to a manageable set of candidates before the more expensive scoring step runs.

This two-stage design — cheap filter, then LLM judgment on the survivors — is what keeps monitoring affordable even at 40 or 100 subreddits, where the raw post volume would otherwise be unmanageable.

Aggregate across the whole set, not just per post

Monitoring at scale is valuable beyond individual leads once you can see patterns across the full set of subreddits. Aggregation jobs build pain-point clusters from recurring complaints and track competitor mentions across every monitored community, turning individual signals into a picture of where the market is actually frustrated.

That aggregate view is often what a founder or product lead checks first — not the newest single post, but what's trending across the whole watchlist this week.

Scaling from 10 subreddits to 100

Going from a Solo plan's single product to Growth's three is not a matter of running the same process more often — it changes what you monitor for. At higher subreddit counts, AI subreddit discovery, pain-point radar, and competitor intelligence do more of the work of deciding what deserves a rep's attention, because no person is realistically reading every new thread across 100 communities themselves.

The architecture — durable poll jobs, adaptive frequency, dedup, pre-filter, then LLM scoring — stays the same at every scale. Only the number of subreddits and the size of the resulting queue changes.