Is There an Alternative to Apify for Scraping Reddit?

Reddit is the most useful and least cooperative source in social listening. It is where people describe problems in their own words, which is exactly what you want, and it is also where a keyword search returns a pile of threads that have nothing to do with your keyword.

This guide answers both halves: which endpoints read Reddit if you would rather not run Apify, and why the results come back wrong so often. Monid is the OpenRouter for agent tools, so we resell most of these rather than being one of them.

Fair disclosure: you are on the Monid blog. We also use this data ourselves, and the field list below comes from a pull we actually ran rather than from a documentation page.

Is there an alternative to Apify for scraping Reddit?

Yes, and they split by what you are reading: a subreddit's feed, one post's comments, or a user's history.

TikHub covers all three with per-call endpoints: fetch_subreddit_feed, fetch_post_comments, fetch_user_comments. One request, one price, whatever comes back. That suits a monitoring loop where you poll the same subreddit on a schedule.

Apify actors cover deeper comment extraction: crawlerbros/reddit-comment-scraper pulls full threads, billed per result with a flat fee per run. That suits a one-off deep read of a specific discussion.

Reddit's own API is the fourth option and the one people forget to consider. It is official, it is free at low volume, and it requires an app registration and OAuth. If your use is modest and you are willing to hold credentials, it is the correct answer and no scraper beats free-and-official.

The honest framing: the alternatives to Apify here are not better scrapers. They are different billing shapes and different depths, and picking well is mostly about which of those two you need.

For agents

Grab an API key at app.monid.ai, then paste this to your agent and hand it the key:

set up https://monid.ai/SKILL.md

It learns the whole discover, inspect, run workflow itself. More in the agent quickstart.

For humans

npm install -g @monid-ai/cli
monid keys add -k <your-key> -l main
monid discover -q "reddit posts comments"

discover and inspect are free, so comparing the three shapes before spending costs nothing.

Why does a Reddit scraper return irrelevant posts that do not match my search keywords?

Because the scraper is passing your string to Reddit's own search and inheriting its behaviour. It is rarely the scraper ignoring you.

This is the most-reported complaint about Reddit scraping and the diagnosis matters, because the usual response is to switch tools, which does not help when every tool queries the same search.

Three things are happening.

Reddit's search is relevance-ranked, not filtered. It returns what it judges related, and relatedness on a site of this size is loose. A query about "scraping API" surfaces threads about scraping in general, about APIs in general, and about neither.

Term ambiguity is brutal here. Reddit spans every domain at once, so a term that is unambiguous in your industry is not unambiguous on Reddit. We hit this directly: pulling threads for "linkedin scraping" returned a 488-comment thread about whether to delete your LinkedIn profile before a policy change, which shares one word with the query and nothing else.

Sorting is not filtering. Sorting by relevance or top still returns the whole result set in a different order. If the set is wrong, the order does not save you.

What actually fixes it

Search inside subreddits, not across Reddit. A domain-specific subreddit does the disambiguation for you: the same query in r/webscraping means one thing, and across Reddit it means several. This is the single highest-leverage change.

Filter after retrieval, on your own criteria. Pull wider than you need, then keep rows that match a stricter local rule: required terms, minimum comment count, recency. Retrieval is cheap; judgement is yours.

Require more than one term to match. A single shared word is not a topical match, and treating it as one is exactly how the irrelevant results get in.

We learned the third one by getting it wrong. Our own ranking treated a single-term overlap as a hit, and a busy off-topic thread outranked everything genuinely on-topic because it had 488 comments. The fix was to score by how much of the query a row covers and to scale popularity by that same coverage, so a busy thread only ranks if it is also relevant.

📖 See also why one Google Maps scraper is not enough, which is the same lesson on a different source.

What do you actually get back?

Post title, body, subreddit, score, comment count, author, timestamp and permalink; comments come from a separate call.

We can be specific because we ran this job for our own SEO work: two rounds of pulls across roughly fifteen search terms, deduplicated to 272 posts that we kept as evidence for which questions real people ask. The fields that earned their place:

Title does most of the work. On Reddit the title is usually the question, phrased the way the asker phrases it, which is the thing you cannot get from a keyword tool.

Comment count is the best single quality signal available. A thread with forty comments is a discussion; a thread with zero is a post. When we ranked what to pay attention to, comment count beat score.

Subreddit is context that changes meaning. The same question in r/n8n and r/OSINT is two different questions with two different right answers.

Permalink is what makes the row auditable later. A finding you cannot trace back to its thread is not evidence.

What you do not get is anything from a private or removed thread, and deleted comments stay deleted. That is correct behaviour rather than a coverage gap.

What the distribution actually looked like

Numbers from that pull, because the shape of a Reddit result set is the part that surprises people who have only read the docs.

39 of the 272 posts had zero comments. Not deleted, not broken, just posted and ignored. That is one in seven, and it is the strongest argument for treating comment count as a filter rather than a display field: a seventh of any Reddit result set is noise by this measure alone.

The median post had five comments. Not fifty. The threads that feel representative when you browse Reddit are the tail, and a pull weighted by relevance returns mostly the middle, which is quiet.

One subreddit supplied 32 of the 272. r/API_Finder, which turned out to be largely vendors posting their own listings, and those listings are why we learned that every LinkedIn scraper on the market leads with "No Cookies" in its title. Useful, but not what we were looking for, and it would have skewed any analysis that treated all 272 rows as equally independent evidence. The next four were r/n8n and r/apify at 13 each, r/SideProject at 10, and r/scrapingtheweb at 5.

That last one is the practical warning. A keyword pull concentrates in whichever subreddit happens to use your vocabulary most, and that subreddit's culture then dominates your conclusions. Check the distribution by source before you read anything into the aggregate. We nearly drew a conclusion about what buyers ask from what was in fact a wall of vendor self-promotion.

Which endpoint should I use for which job?

Verified present on 2026-08-14 with monid discover. The billing column gives the shape rather than a figure; monid inspect prints the current figure for free.

The shape decides your architecture here more than usual. Per call suits a schedule: polling one subreddit hourly costs the same whether it is busy or quiet. Per result suits a burst: one deep read of a big thread, capped by a limit you set deliberately.

Why the title is the whole asset

One more thing that pull taught us, and it changes what you build.

On most sources the body text is the data and the title is a label. On Reddit the title is usually the entire question, phrased by the person who has the problem, and the body often adds context you did not need. That inverts the usual pipeline: you can rank, filter and cluster on titles alone, and only fetch bodies and comments for the rows that survive.

That matters for cost and for quality. Fetching comments for every row is the expensive mistake, and it is unnecessary when a title-only pass already removes the seventh of rows with no discussion and the ones that share a word with your query and nothing else.

It also means the useful output of a Reddit pull is often a list of sentences rather than a dataset. Ours became exactly that: a question bank we now write from, where each row is a real person's phrasing and a link back to where they said it.

When should you not use Monid?

Your volume is low and you can hold credentials. Reddit's own API is free and official at modest volume. If you are building one integration for your own app and OAuth is acceptable, use it. Free and official beats metered when the coverage overlaps.

You need historical data at scale. Live endpoints read what is there now. Deep historical archives are a different product with different terms, and no amount of scraping substitutes for one.

You are doing academic research with a compliance requirement. Reddit's own programmes exist for that and come with the paperwork a review board will want.

And the caution about us. Measuring our own catalogue this month, we found an endpoint whose real charge did not match its stated billing shape, at fifty times the quoted unit, and published it. monid inspect tells you what an endpoint claims. Only a small run tells you what it charges. Do one before any batch.

Conclusion

There are alternatives to Apify for Reddit, and choosing among them is mostly about billing shape and depth rather than quality. Per call for a monitoring loop, per result for a deep read, and Reddit's own free API when your volume is low enough to justify holding credentials.

The harder problem is the one people blame on scrapers. Irrelevant results come from Reddit's own relevance-ranked search and from the fact that a site spanning every domain makes almost every term ambiguous. Searching inside subreddits fixes more of it than switching vendors ever will, and filtering after retrieval on your own criteria fixes most of the rest.

The general form is worth keeping: retrieval is cheap and judgement is not transferable. Pull wider than you need, then decide locally what counts as a match, and require more than one term before you call something relevant.

Start with the free part: monid discover -q "reddit" shows what exists and monid inspect prints the schema and price without spending. Begin at monid.ai.

FAQ

Last updated August 2026.