---

💬 Reddit Comments Scraper [From $0.50💰] — Extract Full Discussion Threads & Replies at Scale

The easiest, most reliable way to extract complete Reddit discussions, reply trees, and user sentiment.
Harvest full discussion threads, nested comment chains, upvotes, and unfiltered customer opinions without API keys, accounts, or developer approvals.

---

⚡ 30-Second Quick Start (No Code Required)

Extract complete comment discussions in 3 quick steps:

1. Paste Target Discussion Link: Copy any Reddit post URL (e.g. https://www.reddit.com/r/technology/comments/1x0b0f5/...) and paste it into startUrls.
2. Choose Comment Limit: Set maxCommentsPerPost (e.g. 100 or 500).
3. Click "Start" & Export: Hit Start. Within 1–2 seconds, click Export to download your conversation tree cleanly in Excel, CSV, or JSON.

💡 Sentiment & Hierarchy Ready: Every row includes explicit parentId and depth indicators so you can instantly reconstruct who replied to whom or filter exclusively for top-level feedback.

---

💡 What You Can Do With This Scraper

• 📊 Customer Voice & Brand Sentiment — Discover what real people say about your products, brand, competitors, or pricing across authentic community discussions.
• 🤖 AI & LLM Training Datasets — Generate high-quality conversational datasets, multi-turn dialogues, Q&A pairs, and debate transcripts ready for fine-tuning.
• 🎯 Product Feedback & Bug Discovery — Find unfiltered user reviews, bug reports, feature requests, and workarounds shared in tech and niche subreddits.
• 📈 Market & Trend Research — Track community reactions to breaking industry news, viral announcements, and policy changes with full context.

---

⭐ Key Advantages & What You Get

• 🌳 Complete Discussion Trees — Extracts every layer of conversation—from top-level thoughts to deep back-and-forth replies (depth=0, 1, 2, 3...) with clear parent-child connections (parentId).
• 📊 Zero-Cleaning Exports (Excel, CSV, JSON) — Clean row-per-comment structure that opens directly in Google Sheets, Microsoft Excel, or Pandas with zero formatting headaches or broken columns.
• ⚡ Lightning-Fast Results — Extracts hundreds of comments in 1–2 seconds, saving you hours of manual copying and waiting.
• 🛡️ 100% Reliable & Non-Stop — Continuous, automated data collection that runs smoothly 24/7 without getting interrupted, rate-limited, or blocked.
• 💰 Fair & Predictable Pricing — From $0.50 per 1,000 comments with zero startup penalties and an unconditional $0.00 guarantee on empty threads.

---

💡 Why Businesses Choose This Over Reddit Official API

• Skip Weeks of Waiting & Strict Approval: Reddit requires comprehensive commercial applications, legal reviews, and NDA signing to access comment streams. UnitBytes lets you pull full threads immediately.
• No Expensive Monthly Contracts: The official API charges $0.24 per 1,000 calls and requires enterprise commitments starting at $1,000+/mo. UnitBytes charges from $0.50 per 1,000 comments on a pure pay-as-you-go basis.
• Full Depth & Nested Hierarchy: While standard API endpoints limit comment depth and require complex pagination loops, UnitBytes harvests the complete conversational hierarchy (depth=0, 1, 2, 3...) in a single lightning-fast run.
• Zero Formatting Headaches: Reddit's API returns deeply nested JSON trees that crash standard spreadsheet tools. UnitBytes flattens discussions into clean, relational rows that open directly in Excel, Google Sheets, or Pandas.

🏆 How It Compares to Other Options

Feature / Capability	UnitBytes Comments Scraper	Official Reddit API	Other Scraping Tools
Setup & Approvals	✅ Instant 1-click start (Zero setup)	🛑 Strict enterprise approvals & weeks of waiting	❌ None needed
Cost per 1,000 Comments	💰 $0.50 – $1.00	💵 $0.24 + Enterprise monthly minimums	💸 $2.00 – $5.00
Discussion Depth	✅ Complete nested reply trees	⚠️ Complex multi-step pagination	⚠️ Surface top 10–20 comments only
Parent-Child Hierarchy	✅ Explicit parentId & depth keys	Complex nested structures	Inconsistent formats
Time to Results	⚡ 1–2 seconds (Instant)	~3–5 seconds	🐢 30–60 seconds (Slow & laggy)
Data Usability	✅ 1-Click Clean CSV, Excel & JSON	Complex nested formats	Broken columns requiring hours of cleanup
Compute Surcharges	✅ $0.00 (No hidden server fees)	N/A	💸 High hourly server compute bills
Empty Result Runs	✅ $0.00 (Guaranteed Zero Charge)	❌ Charged per request	⚠️ Charged start fees ($0.02)

---

💎 Predictable Pricing & Volume Discounts

We use Apify Pay-Per-Event (PPE) pricing with active Store Discounts. Users on higher subscription tiers automatically unlock substantial volume discounts:

Plan Tier	Apify Discount Tier	Unit Price per Item	Price / 1,000 Comments	Included Monthly Capacity
🟢 Free Plan	FREE	$0.0010	$1.00	Up to 5,000 comments/mo FREE ($5 credit)
🥉 Starter Plan	BRONZE	$0.0008	$0.80 (20% OFF)	Up to 23,750 comments/mo ($19 credit)
🥈 Scale Plan	SILVER	$0.0006	$0.60 (40% OFF)	Up to 331,600 comments/mo ($199 credit)
🥇 Business Plan	GOLD / PLATINUM	$0.0005	$0.50 (50% OFF)	Up to 1,998,000 comments/mo ($999 credit)

💰 What Does This Cost in Real Life?

Extraction Scenario	Comments Harvested	Estimated Cost (Starter / Scale)	Time to Complete
Product Launch Reaction Check	250 Comments	$0.15 – $0.20	~1 second
In-Depth Competitor Review Audit	1,000 Comments & Replies	$0.50 – $0.80	~1–2 seconds
Full Viral Mega-Thread Archive	5,000 Nested Comments	$2.50 – $4.00	~3–5 seconds
AI Dialogue Training Dataset	20,000 Multi-Turn Pairs	$10.00 – $16.00	~15 seconds
Apify Free Plan Users	Up to 5,000 comments/month	$0.00 FREE (Included with $5 credit)	Instant

Fair Billing Guarantee: Zero charges on empty discussion threads or deleted posts. Startup fee is capped at the platform minimum ($0.00005).

---

🚀 3 Ready-to-Run Workflows

Workflow 1: Single Discussion Deep-Dive

Extract every reaction, debate, and expert opinion from a viral Reddit thread.

1. Copy the discussion URL (e.g. https://www.reddit.com/r/technology/comments/1x0b0f5/...).
2. Paste into startUrls.
3. Set maxCommentsPerPost to 100 or 500.
4. Click Start — download clean, sentiment-ready CSV/Excel in seconds.

Workflow 2: Multi-Thread Batch Intelligence

Analyze audience sentiment across multiple competitor announcements or community discussions simultaneously.

1. Add multiple URLs into startUrls (e.g. 5 different product review threads).
2. The scraper processes each thread in sequence and merges all comments into a single unified dataset with clear postId references.

Workflow 3: Conversational AI & LLM Training

Build multi-turn dialogue pairs and Q&A training datasets.

1. Scrape full reply trees from advice, technical support, or debate communities (r/AskReddit, r/explainlikeimfive, r/MachineLearning).
2. Group records by postId and pair each comment with its parentId to reconstruct full conversation flows.

---

📥 Simple Input Parameters

Parameter	Type	Default	Description
startUrls	Array	(Sample URL)	Direct Reddit post URLs you want to extract comments from.
maxCommentsPerPost	Integer	100	Maximum number of comments and replies to extract per post (1 – 5,000).
proxyConfiguration	Object	Default	Recommended proxy settings for high-speed, reliable extraction.
customProxyUrl	String	""	Optional custom proxy URL if you prefer to use your own proxy provider.

---

📤 Sample Output Datasets

Every extracted comment is delivered as a clean, flat record ready for immediate analysis:

1. Top-Level Comment (depth=0)

{
  "itemType": "comment",
  "id": "t1_m3k0x1y",
  "postId": "t3_1x0b0f5",
  "parentId": "t3_1x0b0f5",
  "author": "tech_enthusiast",
  "body": "Streaming platforms need stricter auditing mechanisms. 10k bots running 24/7 is an insane amount of synthetic streams.",
  "score": 342,
  "depth": 0,
  "createdAt": "2026-10-11T12:30:15Z",
  "permalink": "https://www.reddit.com/r/technology/comments/1x0b0f5/comment/m3k0x1y/",
  "scrapedAt": "2026-10-11T16:01:26Z"
}

2. Nested Reply (depth=1)

{
  "itemType": "comment",
  "id": "t1_m3k1a2z",
  "postId": "t3_1x0b0f5",
  "parentId": "t1_m3k0x1y",
  "author": "audio_engineer_99",
  "body": "Agreed. What makes it worse is that royalty pools are zero-sum, so this fraud directly steals payouts from legitimate indie musicians.",
  "score": 128,
  "depth": 1,
  "createdAt": "2026-10-11T12:45:00Z",
  "permalink": "https://www.reddit.com/r/technology/comments/1x0b0f5/comment/m3k1a2z/",
  "scrapedAt": "2026-10-11T16:01:26Z"
}

📋 Field Reference Table

Field Name	Type	Description
itemType	String	Always "comment" for comment records (or "post" if root post info is included).
id	String	Unique Reddit comment ID (e.g. t1_m3k0x1y).
postId	String	The parent discussion post ID (e.g. t3_1x0b0f5).
parentId	String	Direct parent reference: links to postId for root comments, or parent comment id for replies.
author	String	Username of the comment author ([deleted] if removed).
body	String	Full comment text in clean markdown.
score	Integer	Net community upvote score (upvotes minus downvotes).
depth	Integer	Conversation level: 0 = root comment, 1 = direct reply, 2 = reply to reply, etc.
createdAt	String	ISO 8601 UTC timestamp of comment creation.
permalink	String	Direct link to view the specific comment on Reddit.
scrapedAt	String	Timestamp when the comment was extracted.

---

📊 Instant Analysis in Excel, Google Sheets & Python

Because the data is delivered in a clean flat layout, you can export it with 1 click:

• In Google Sheets / Excel: Download the dataset as CSV or Excel. Use depth = 0 to filter for main comments, or sort by score to see the most popular opinions.
• In Python / Pandas:

  import pandas as pd
  from apify_client import ApifyClient
  
  # 1. Fetch dataset from your Apify run
  client = ApifyClient("YOUR_APIFY_TOKEN")
  items = client.dataset("DATASET_ID").list_items().items
  df = pd.DataFrame(items)
  
  # 2. Find high-impact discussions
  top_comments = df[(df["depth"] == 0) & (df["score"] >= 50)]
  print(f"Found {len(top_comments)} top-level discussions.")
  
  # 3. View all replies to any comment
  def find_replies(comment_id):
      return df[df["parentId"] == comment_id]
  

---

❓ Frequently Asked Questions (FAQ)

Do I need a Reddit account or API key?

No. You can run this actor immediately with zero API keys, developer tokens, or Reddit accounts.

How do I open the results in Excel or Google Sheets?

Once your run finishes, click Export on the Apify dataset tab and choose CSV or Excel. The data is pre-flattened so every column loads cleanly with zero formatting errors.

Can I reconstruct who replied to whom?

Yes. Every record has a parentId field. If parentId equals postId, it is a top-level comment. If parentId starts with t1_, it points directly to the comment it is replying to.

How many comments can I extract from a post?

You can extract anywhere from 1 up to 5,000 comments and nested replies per post by adjusting maxCommentsPerPost.

What happens if a post is deleted or has no comments?

The actor finishes cleanly with zero errors, delivers an empty dataset, and charges $0.00. You only pay for real comments successfully scraped.

---

⚖️ Compliance & Privacy

This scraper extracts only publicly available discussions that Reddit serves to web visitors. It does not access private communities, user direct messages, or restricted subreddits. Please ensure your data collection complies with applicable regulations (such as GDPR / CCPA) and respects Reddit terms.