How to Collect Reddit Data for AI Training Datasets

To collect Reddit data for AI training, you have three viable paths: pay Reddit's official Data API (starts at $0.24 per 1,000 API calls with usage caps), use the Pushshift archive (now restricted to moderators), or run a managed scraper like Reddit API Lite at $5 per 1,000 items with no login required. After the October 2025 Reddit vs Perplexity lawsuit, the licensing question matters as much as the technical one — unlicensed bulk scraping for commercial LLM training is now active litigation territory.

Quick Answer

The cheapest reliable way to collect Reddit data for AI training is a pay-per-result Apify actor that pulls posts, comments, and user threads without authentication, typically $2–$5 per 1,000 items. For research and non-commercial fine-tuning, this is straightforward. For commercial LLM pretraining, Reddit's October 2025 lawsuit against Perplexity signals you should either license directly from Reddit's Data API or limit usage to fair-use evaluation sets. Always log timestamps, subreddit sources, and author handles so you can filter deleted content and respect user removals later.

Why is Reddit data valuable for training AI models?

Reddit gives models something most web data does not: threaded human dialogue with upvote signals. A single AskHistorians thread can contain 40 expert-cited replies; r/AskScience threads average 12–18 substantive comments. That conversational structure is why GPT-3, LLaMA, and most open-source instruction-tuned models historically used Reddit as a backbone — the Pushshift Reddit dump alone contributed roughly 1.7TB of training tokens to early LLaMA versions.

Specific use cases where Reddit data outperforms generic web crawls:

• Instruction tuning: Question-comment pairs from r/explainlikeimfive, r/AskReddit, r/NoStupidQuestions
• Domain adaptation: r/MachineLearning, r/legaladvice, r/medicine for technical vocabulary
• Preference modeling (RLHF): Upvote/downvote ratios as weak reward signals
• Sentiment and slang: Real-time language drift that Common Crawl misses by 6–12 months

The catch: as of October 2025, Reddit is actively suing Perplexity for using scraped Reddit data to train commercial models without a license. This changes the calculus.

What does the Reddit vs Perplexity lawsuit mean for AI training data?

Filed October 22, 2025, Reddit's complaint alleges Perplexity bypassed Reddit's API paywall by scraping Google search results that contained Reddit content, then used that data for commercial AI training. The lawsuit follows Reddit's earlier $60M/year licensing deal with Google and a similar settlement with OpenAI.

Practical takeaways for anyone building a training set today:

1. Non-commercial research and academic work remain on safe ground under fair use precedent.
2. Internal evaluation datasets (under ~100K items, not redistributed) carry low legal risk.
3. Commercial pretraining corpora without a Reddit license are now in active litigation territory.
4. Republishing scraped Reddit data as a public dataset (HuggingFace, etc.) is the highest-risk category — Reddit has DMCA'd several since 2024.

The lawsuit does not make scraping technically illegal — it makes commercial use of scraped data legally contested. Collect carefully and document your purpose.

What are the best methods to scrape Reddit data at scale?

There are four practical methods. Here's how they compare for a 1M-item dataset:

For most teams building a fine-tuning or evaluation set under 5M items, the Apify route wins on developer time. Reddit API Lite runs at $5 per 1,000 results, handles proxy rotation, and returns posts, comments, user profiles, and subreddit metadata as JSON, CSV, or Parquet.

A typical input config looks like:

{
  "searches": ["machine learning", "diffusion models"],
  "subreddits": ["MachineLearning", "LocalLLaMA"],
  "maxItems": 50000,
  "type": "posts_and_comments",
  "sort": "top",
  "time": "year"
}

That run costs about $250 and finishes in 2–4 hours depending on subreddit size.

How do I clean Reddit data for AI training?

Raw Reddit dumps are 60–70% noise for most training objectives. A clean pipeline removes:

1. [deleted] and [removed] content — typically 8–15% of any subreddit. These are mandatory removals; users and moderators deleted them for a reason.
2. Bot accounts — AutoModerator, RemindMeBot, etc. Filter by known username list (~200 bots cover 95% of cases).
3. Score thresholding — drop comments with score ≤ 1 for quality signal. This typically cuts dataset size by 40% but raises perplexity-evaluated quality by 20–30%.
4. PII scrubbing — emails, phone numbers, full names. Use Microsoft Presidio or scrubadub before storage.
5. Duplicate threads — crossposts and reposts. Hash by post title + first 200 chars of body.
6. NSFW and shock content — flag by over_18 field, then content-classify with a small toxicity model.

For instruction tuning specifically, you want question-answer pairs. Filter to top-level comments on posts ending with "?" that have score ≥ 10. From r/AskHistorians alone, this yields about 180K high-quality QA pairs across the full archive.

What format should training data be in?

Most modern training pipelines expect JSONL with these fields per row:

{
  "id": "t3_abc123",
  "subreddit": "MachineLearning",
  "title": "Question about transformer attention",
  "selftext": "...",
  "score": 47,
  "created_utc": 1729536000,
  "comments": [
    {"author_hash": "a1b2c3", "body": "...", "score": 23}
  ]
}

Hash usernames with SHA-256 before storage — you keep author consistency for thread reconstruction without retaining PII. Store the original created_utc so you can re-check deletion status against Reddit later (GDPR compliance for EU users requires this).

For RLHF preference data, pair high-scoring and low-scoring sibling comments on the same parent:

{"prompt": "<parent comment>", "chosen": "<+50 reply>", "rejected": "<-3 reply>"}

A 100K-pair preference dataset from r/ChangeMyView typically takes 30K item credits to assemble — roughly $150 via pay-per-result scraping.

How much does it cost to build a Reddit training dataset?

Realistic budgets for common dataset sizes using Reddit API Lite:

• Evaluation set (10K items): ~$50, runs in 20 minutes
• Domain fine-tune (250K items): ~$1,250, runs over 8–12 hours
• Instruction tuning corpus (1M items): ~$5,000
• Pretraining-scale (50M+ items): ~$250,000 — at this scale, license directly from Reddit instead

Hidden costs to budget separately:

• Storage: 1M Reddit items ≈ 4–6 GB compressed JSONL
• Cleaning compute: 1 vCPU-hour per ~500K items for dedup and PII scrubbing
• Re-fetching for GDPR: 5–10% of items per quarter to confirm non-deletion

Compared to Reddit's commercial Data API tier (reported at $0.24/1K calls with a 100-calls-per-minute cap, requiring 6,944 hours = 9.5 months to fetch 1M items at max rate), Apify is faster and roughly 6–10x cheaper for one-off collection.

FAQ

Q: Is it legal to use scraped Reddit data to train AI models? For academic research, personal projects, and internal evaluation, fair use precedent generally applies. For commercial pretraining of LLMs you plan to sell or release, the October 2025 Reddit vs Perplexity lawsuit signals you should obtain a license or limit use to fair-use evaluation. Consult a lawyer for anything commercial.

Q: Can I get Reddit data without logging in or having an API key? Yes. Managed actors like Reddit API Lite scrape public Reddit pages without OAuth, returning posts, comments, user profiles, and subreddit data. This avoids Reddit's API rate limits and developer registration but does not exempt you from terms-of-service considerations for commercial use.

Q: How fast can I collect 100,000 Reddit posts and comments? Using a managed pay-per-result actor, 100K items typically take 1–3 hours and cost around $500. Using PRAW with a single OAuth token, the same volume takes 28+ hours due to the 60-queries-per-minute rate limit and 1,000-item listing cap per endpoint.

Q: Should I include deleted comments in my training data? No. Comments marked [deleted] (user-removed) or [removed] (moderator-removed) should be excluded both for ethical reasons and to avoid GDPR/CCPA exposure. Re-check your dataset against Reddit every 3–6 months and drop newly deleted items if you retain the dataset long-term.

Q: What's the best subreddit for instruction tuning data? r/explainlikeimfive yields the cleanest question-answer structure with simplified explanations. r/AskHistorians and r/AskScience give expert-grade answers with citations. r/ChangeMyView is the strongest source for preference and debate data. Combining these four typically produces a 400–600K QA-pair corpus after quality filtering.