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The 7 Best Reddit Scrapers in 2026 (Free & Paid, Tested)

#webscraping #api #datascience #python

Looking for the best way to scrape Reddit posts and comments in 2026? Here's an honest, hands-on comparison of the top Reddit scrapers — including the free API route, no-code tools, and the historical-archive options most guides forget.

TL;DR: If you want fresh posts and full comment threads with no code, use a hosted Reddit scraper like the Reddit Scraper on Apify. If you need years of history (more than the ~1,000 posts Reddit's API will give you), you need an archive-based tool like the Reddit Archive Scraper. If you're a Python developer doing a one-off, PRAW + the official API is fine.

What changed with Reddit scraping in 2024–2026

Two things make scraping Reddit harder than it used to be:

  1. The anonymous .json endpoints are now challenge-walled. The old trick of appending .json to any Reddit URL increasingly returns a "please wait" verification page, on datacenter and residential IPs.
  2. Listings are hard-capped at ~1,000 items. Reddit's API will not paginate a subreddit's new/top/hot feed beyond roughly 1,000 posts. For an active subreddit that's only a few weeks of history — no matter which tool you use.

Any honest comparison has to separate "fresh data" tools from "historical archive" tools, because no single approach does both well.

What to look for in a Reddit scraper

  • Auth handling — does it deal with Reddit's OAuth/blocking for you, or will you be debugging 403s?
  • Comment depth — does it expand "load more comments" and deep threads, or stop at the first ~50?
  • History limit — can it go past Reddit's 1,000-post cap?
  • Output format — JSON/CSV, and ideally clean Markdown for AI/RAG pipelines.
  • Cost model — per-result vs. per-month vs. free-but-DIY.

1. Reddit Scraper (Apify) — best no-code option for fresh data

A hosted, pay-per-result scraper that pulls posts, comments, and user data and returns them as JSON or AI-ready Markdown. It uses Reddit's official app OAuth under the hood, so you don't deal with blocking or API keys, and it expands hidden "load more" comment stubs so the scraped comment count actually matches the thread.

Pros

  • No code, no API key, no proxy setup
  • Full threaded comments (expands "load more" and continue-thread links)
  • Markdown output is handy for RAG/LLM ingestion
  • Pay per result, so small jobs are cheap

Cons

  • Bound by Reddit's ~1,000-post listing cap (same as every API-based tool)
  • Comment-heavy jobs cost more (comments dominate the row count)

Best for: marketers, researchers, and builders who want clean, fresh Reddit data without writing or maintaining code.

➡️ Reddit Scraper on Apify

2. Reddit Archive Scraper (Apify) — best for years of history

This is the tool for the job that breaks every other scraper: pulling months or years of a subreddit's history. It reads from the PullPush archive (the public Pushshift successor) instead of the live API, so it sails past the 1,000-post cap. Filter by subreddit(s), date range, and keyword; optionally include archived comments.

Pros

  • Goes far beyond the 1,000-post limit — true historical backfill
  • Date-range and keyword filtering across multiple subreddits
  • Posts + comments, clean flat JSON, great for datasets
  • Pay per result

Cons

  • Archive freshness depends on PullPush (use the live scraper for the last few days)
  • Not for real-time monitoring

Best for: researchers, data scientists, and anyone building a historical or sentiment dataset.

➡️ Reddit Archive Scraper on Apify

3. PRAW (Python Reddit API Wrapper) — best for developers

The official-API Python library. Free, well-documented, and the right call if you're comfortable writing code and your needs fit inside the API limits.

Pros

  • Free and official
  • Total control in your own code

Cons

  • You build and maintain everything (auth, pagination, retries, comment expansion)
  • Still capped at ~1,000 posts per listing
  • No hosting, scheduling, or export — that's on you

Best for: developers doing a contained, one-off pull.

4. PullPush / Arctic Shift (raw archives) — best free historical source

Public archives of historical Reddit data you can query directly via HTTP. Free and deep, but raw — you handle pagination, rate limits, and data cleaning yourself.

Pros

  • Free, with years of history
  • Good for bulk research dumps

Cons

  • Raw JSON, no UI, no scheduling
  • Coverage/freshness varies; you do the plumbing

Best for: technical users who want raw historical data and don't mind scripting. (Prefer it hosted with filtering and exports? That's exactly what #2 wraps.)

5. Pushshift (mod-only) — historical, now restricted

Once the go-to for historical Reddit data, Pushshift is now limited to subreddit moderators. Worth knowing about, but no longer a general option — which is why archive mirrors like PullPush matter.

6. Official Reddit Data API — best for licensed, large-scale use

Reddit's official paid data API. The right path if you need licensed data at scale and can absorb the cost and approval process.

Pros

  • Official, compliant, high volume

Cons

  • Paid and gated; overkill for most projects

7. Generic web-scraping APIs (ScraperAPI, Bright Data, etc.)

General-purpose scraping/proxy products you can point at Reddit. They solve proxies but not Reddit-specifics (comment expansion, the 1,000 cap, parsing).

Pros

  • Strong proxy infrastructure

Cons

  • You still write the Reddit parsing logic
  • No Reddit-specific output

Quick comparison

Tool No code Fresh data Years of history Comments Cost model
Reddit Scraper (Apify) Yes Yes No (API cap) Full Per result
Reddit Archive Scraper (Apify) Yes Recent gap Yes Yes Per result
PRAW No Yes No DIY Free
PullPush / Arctic Shift No No Yes Yes Free
Pushshift No No Yes Yes Mod-only
Official Reddit API No Yes Limited Yes Paid
Generic scraping APIs No Yes No DIY Paid

How to scrape a subreddit in under a minute (no code)

  1. Open the Reddit Scraper.
  2. Set mode to subreddit, enter the subreddit name, and sort (e.g. new).
  3. Toggle includeComments on if you need comment text; set maxComments.
  4. Run it, then export the dataset as JSON, CSV, or Markdown.

For ongoing monitoring, schedule it with sort=new + sinceDate so each run only pulls new posts — cheap and fast. For a year of back-data, use the Archive Scraper with a date range.

Conclusion

There's no single "best" Reddit scraper — it depends on whether you need fresh or historical data:

Pairing the live scraper (for ongoing updates) with the archive scraper (for backfill) covers essentially every Reddit data use case in 2026.

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