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Best YouTube Scrapers in 2026: Comparing Every Apify Actor

#webscraping #python #apify #youtube

YouTube is the world's second-largest search engine, and scraping it is one of the most common data extraction tasks. Whether you need competitor analytics, influencer research, or content trend tracking, you'll eventually need a reliable YouTube scraper.

The Apify Store has several YouTube scraping actors, each with different approaches, feature sets, and reliability levels. In this article, I'll compare the most popular options and introduce a new actor built on yt-dlp — arguably the most battle-tested YouTube metadata extraction tool available today.

Why Scrape YouTube Programmatically?

The official YouTube Data API v3 has strict quota limits: 10,000 units per day by default. A single search request costs 100 units, meaning you can run only 100 searches per day. For any serious data collection — monitoring competitors, tracking influencers across hundreds of channels, or building content databases — you'll hit those limits within minutes.

That's where scrapers come in. They extract the same data (and often more) without requiring API keys or dealing with quota restrictions.

The Current Landscape on Apify

Let's look at the most established YouTube scraping actors on the Apify Store.

1. Streamers' YouTube Scraper

This is the most popular YouTube scraper on Apify, with tens of thousands of users and a strong rating. It positions itself as an "alternative YouTube API with no limits or quotas."

What it does well:

  • Extracts channel names, view counts, likes, and subscriber counts
  • Handles video and channel scraping
  • Large user base means well-tested edge cases

Limitations:

  • Relies on scraping YouTube's web interface directly
  • YouTube frequently changes its frontend, which can break scrapers
  • Rate limiting and anti-bot measures can cause intermittent failures

2. Bernardo's YouTube Scraper

Another established option that focuses on extracting video metadata and channel information.

What it does well:

  • Solid video metadata extraction
  • Good documentation
  • Reasonable pricing on Apify's platform

Limitations:

  • Similar web-scraping approach, meaning similar fragility when YouTube updates its interface
  • May lag behind on supporting new YouTube features or layout changes

3. Channel-Specific Scrapers

Several actors focus on specific use cases — channel scraping only, comment extraction, or search result scraping. These can be useful if you have a narrow need, but you often end up combining multiple actors (and paying for each) to get a complete picture.

The Core Problem: Web Scraping vs. Extraction Tools

Here's something most users don't realize: the majority of YouTube scrapers on Apify work by scraping YouTube's web pages directly. They load the page (or make requests that mimic a browser), parse the HTML/JSON responses, and extract data from the page structure.

This approach has a fundamental weakness: YouTube changes its frontend constantly. Google's engineering teams push updates to YouTube's interface weekly, sometimes daily. Every change risks breaking scrapers that depend on specific CSS selectors, JSON structures, or DOM layouts.

When a scraper breaks, you get one of three outcomes:

  1. Empty results — the scraper runs but finds nothing
  2. Partial data — some fields extract correctly, others return null
  3. Stale data — the scraper falls back to cached patterns that return outdated information

You often don't notice the problem until you've already consumed compute credits on failed runs.

A Different Approach: yt-dlp Under the Hood

YouTube Scraper by CryptoSignals takes a fundamentally different approach. Instead of scraping YouTube's web interface, it uses yt-dlp as its extraction engine.

If you're not familiar with yt-dlp: it's the most actively maintained YouTube metadata extraction tool in the open-source ecosystem, with over 85,000 stars on GitHub and hundreds of contributors. It's the successor to youtube-dl, and it's used by millions of developers worldwide.

Why yt-dlp Matters

yt-dlp doesn't scrape YouTube's web pages. It uses YouTube's internal APIs and data endpoints — the same ones YouTube's own apps use. This means:

  1. It's more resilient to frontend changes. When YouTube redesigns its web interface, yt-dlp keeps working because it doesn't depend on the frontend.

  2. It's maintained by a massive community. When YouTube does change something that affects extraction, yt-dlp's 1,000+ contributors typically push a fix within hours, not days or weeks.

  3. It extracts richer metadata. Because it taps into YouTube's internal data structures, yt-dlp can pull fields that web scrapers often miss: exact upload timestamps, detailed category information, chapter markers, and more.

Feature Comparison

Feature Web-Based Scrapers CryptoSignals (yt-dlp)
Video metadata ✅ ✅
Channel info ✅ ✅
Search results Varies ✅
Subscriber counts ✅ ✅
Resilience to YouTube updates ⚠️ Fragile ✅ Robust
Underlying tool maintenance Individual developer 85K+ star OSS project
Requires API keys ❌ ❌
Anti-bot detection risk ⚠️ High ✅ Lower

Quick Start: Using the Actor

Here's how to call the actor via Apify's API with Python:

import requests

API_TOKEN = "your_apify_token"
ACTOR_ID = "cryptosignals/youtube-scraper"

run_input = {
    "urls": [
        "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
        "https://www.youtube.com/@MrBeast"
    ]
}

# Start the actor run
response = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR_ID}/runs",
    params={"token": API_TOKEN},
    json=run_input
)

run_id = response.json()["data"]["id"]
print(f"Run started: {run_id}")

# Fetch results (after run completes)
results = requests.get(
    f"https://api.apify.com/v2/actor-runs/{run_id}/dataset/items",
    params={"token": API_TOKEN}
)

for item in results.json():
    print(f"{item.get('title')} — {item.get('viewCount')} views")
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Or with curl:

curl -X POST "https://api.apify.com/v2/acts/cryptosignals~youtube-scraper/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "urls": ["https://www.youtube.com/@MrBeast"]
  }'
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Pricing Comparison

All actors on Apify use compute units (CUs) for pricing. The exact cost per run depends on the amount of data you're extracting, but the key differentiator is reliability: a scraper that fails 20% of the time effectively costs you 20% more because you're paying for failed runs.

yt-dlp-based extraction tends to have higher success rates, which translates to better cost efficiency even if the per-run compute cost is similar.

When to Choose What

Choose a web-based scraper if:

  • You have a very specific, narrow extraction need that a specialized actor handles perfectly
  • You're already integrated with one and it's working reliably for your use case
  • You need a feature that only a specific actor provides

Choose the yt-dlp-based scraper if:

  • You need reliable, long-term data collection
  • You're tired of scrapers breaking after YouTube updates
  • You want richer metadata extraction
  • You're building a pipeline that needs to work unattended

The Bottom Line

The YouTube scraping landscape on Apify has been dominated by web-scraping approaches for years. They work — until they don't. The yt-dlp approach represents a fundamental improvement in reliability and data quality.

If you're starting a new YouTube data project in 2026, I'd recommend starting with YouTube Scraper by CryptoSignals and evaluating whether it meets your needs before falling back to traditional web scrapers.

→ Try it on the Apify Store

What's your experience with YouTube scraping? Have you dealt with scrapers breaking after YouTube updates? Share your story in the comments.

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