YouTube Channel & Video Scraper

Extract Channel Statistics and Video Metadata at Scale

Turn any YouTube channel or video URL into structured data in minutes. This scraper collects subscriber counts, view metrics, video metadata, tags, descriptions, and publishing history -- everything you need for competitive analysis, influencer vetting, or content research, without manual data entry.

Whether you are benchmarking competitor channels, auditing creator performance for influencer partnerships, or building a dataset of trending content in your niche, this actor delivers clean, export-ready data at a fraction of the cost of commercial analytics tools.

What data can you extract?

Channel data

Field	Type	Description
dataType	string	Record type identifier (channel)
channelName	string	Display name of the YouTube channel
channelUrl	string	Full URL to the channel page
subscriberCount	string	Formatted subscriber count (e.g., "300M subscribers")
totalViews	string	Total lifetime views across all videos
videoCount	integer	Number of public videos uploaded to the channel
subscriberCountNumeric	integer	Subscriber count parsed into a plain number (e.g., 300000000)
channelDescription	string	Channel's About section text
joinDate	string	Date the channel was created
country	string	Country associated with the channel
thumbnailUrl	string	URL of the channel's profile image
scrapedAt	string	ISO 8601 timestamp of when the data was collected

Video data

Field	Type	Description
dataType	string	Record type identifier (video)
channelName	string	Name of the channel that uploaded the video
channelUrl	string	URL of the channel
videoTitle	string	Title of the video
videoUrl	string	Direct link to the video
viewCount	string	Number of views
viewCountNumeric	integer	View count parsed into a plain number
likeCount	string	Number of likes
commentCount	string	Number of comments
publishDate	string	Date the video was published
duration	string	Video length (e.g., "12:34")
videoDescription	string	Full video description text (up to 2,000 characters)
tags	array	List of tags and keywords assigned to the video
thumbnailUrl	string	URL of the video thumbnail image
scrapedAt	string	ISO 8601 timestamp of when the data was collected

Use cases

• Competitor benchmarking -- Track rival channels over time to understand their growth trajectory, posting frequency, and top-performing content formats.
• Influencer vetting -- Pull real subscriber counts, view averages, and engagement data before committing budget to a creator partnership.
• Content strategy research -- Analyze which topics, titles, thumbnails, and video lengths generate the most views in your niche.
• Market trend analysis -- Build datasets of trending content across multiple channels to identify emerging topics before they peak.
• Academic and media research -- Collect structured YouTube data for studies on media consumption, algorithmic reach, or creator economics.

Two modes, and which fields each one returns

This is the thing to get right before your first run, because it decides what lands in your export.

channelUrls — the inventory pass. Give it channels and it walks their uploads. Every row carries channelName, subscriberCount, videoTitle, videoUrl, viewCount, publishDate and duration. It does not open each video, so likeCount, commentCount, videoDescription and tags stay empty. That is the trade: one request per channel instead of one per video.

videoUrls — the detail pass. Give it specific videos and each one is opened, so you get the full record: likeCount ("19M"), commentCount ("2.4M"), the complete videoDescription, and the tags array the creator set.

The efficient pattern is both, in order: run channelUrls to find the videos worth studying, sort by viewCount, then feed the top URLs back through videoUrls. You pay for depth only on the videos that earned it.

Scraping channel statistics in bulk

channelUrls takes a list, so a competitive set is one run. Every row repeats channelName and subscriberCount alongside the video data, which means the export is already joined — no separate channel table to merge.

subscriberCount arrives as the displayed string and subscriberCountNumeric as a number, so sorting and filtering work without parsing "12.4M" yourself. Same for views: viewCount and viewCountNumeric.

Use maxVideosPerChannel to control depth. Twenty is usually enough to see a channel's current direction; fifty covers a year for most creators.

Exporting video metadata to a spreadsheet

The dataset is flat, so CSV and Excel exports open with no nested columns to flatten.

For a content audit, the columns that carry the analysis are publishDate, duration and viewCountNumeric. Group by month to see cadence, and plot duration against views to find the length that actually performs for that channel — the answer is rarely the one the creator assumes.

tags (from the videoUrls mode) is the one field that comes back as an array. Most spreadsheet tools join it with commas automatically; in pandas it stays a list you can explode.

Finding creators for sponsorship outreach

Sponsorship qualification is a ratio, not a subscriber count.

Pull a set of channels with channelUrls, then compare viewCountNumeric on recent uploads against subscriberCountNumeric. A channel whose typical video reaches a healthy share of its subscriber base has an audience that shows up. One with a large following and thin views is renting attention it no longer has — the exact deal that looks good in a media kit and underperforms.

publishDate is the second filter: a creator who has not uploaded in four months is not a campaign partner, whatever the subscriber number says.

One honest limit: this Actor returns what YouTube shows publicly. It does not extract creator email addresses — those live on the channel's about page behind a captcha, or in a media kit. Use the channel URL as your starting point for contact.

Input parameters

Parameter	Type	Required	Default	Description
channelUrls	array	No	[]	List of YouTube channel URLs to scrape (e.g., https://www.youtube.com/@MrBeast)
videoUrls	array	No	[]	List of individual video URLs to extract metadata from
maxVideosPerChannel	integer	No	50	Maximum number of videos to collect per channel (1--500)
maxConcurrency	integer	No	3	Number of browser pages to run in parallel (1--10)

At least one channel URL or video URL must be provided.

Example output

{
    "dataType": "channel",
    "channelName": "MrBeast",
    "channelUrl": "https://www.youtube.com/@MrBeast",
    "subscriberCount": "300M subscribers",
    "totalViews": "50.2B views",
    "videoCount": 812,
    "channelDescription": "New videos every Saturday. I do a lot of crazy stuff, but it's all for a good cause.",
    "joinDate": "Feb 20, 2012",
    "country": "United States",
    "thumbnailUrl": "https://yt3.googleusercontent.com/ytc/AIdro...",
    "scrapedAt": "2026-03-01T12:00:00.000Z"
}

{
    "dataType": "video",
    "channelName": "MrBeast",
    "channelUrl": "https://www.youtube.com/@MrBeast",
    "videoTitle": "$1 vs $1,000,000 Hotel Room!",
    "videoUrl": "https://www.youtube.com/watch?v=iogcY_4xGjo",
    "viewCount": "284M views",
    "likeCount": "8.2M",
    "commentCount": "142K",
    "publishDate": "Jan 14, 2026",
    "duration": "18:42",
    "videoDescription": "I stayed in hotels ranging from $1 to $1,000,000...",
    "tags": ["mrbeast", "hotel", "luxury", "challenge", "expensive"],
    "thumbnailUrl": "https://i.ytimg.com/vi/iogcY_4xGjo/maxresdefault.jpg",
    "scrapedAt": "2026-03-01T12:00:00.000Z"
}

How much does it cost?

This actor uses a pay-per-result pricing model. You are charged for each data record (channel or video) that is successfully extracted.

• $0.0015 per result -- that is $1.50 per 1,000 records.
• Scraping one channel profile plus its 50 most recent videos costs approximately $0.077 (51 results total).
• Scraping 10 channels with 100 videos each (1,010 results) would cost roughly $1.52.

There are no monthly subscriptions or minimum commitments. You only pay for the data you extract.

Tips and tricks

• Start with a small run. Test with one channel and 10 videos to verify the output format meets your needs before scaling up.
• Use video URLs for detailed data. When you scrape individual video URLs directly, the actor extracts richer metadata including full descriptions, like counts, comment counts, and tags. Videos discovered through a channel listing contain title, views, date, and duration.
• Shorts are included. YouTube Shorts appear in channel video listings alongside regular uploads, so you get a complete picture of a channel's output.
• Schedule recurring runs. Set up a daily or weekly schedule on Apify to track channel growth and new video performance over time. Export results to Google Sheets or a database via Apify integrations.

FAQ

How do I scrape YouTube channels and video metadata? Add one or more channel URLs (like https://www.youtube.com/@MrBeast) or individual video URLs, set how many videos you want per channel, and click Start. You get subscriber counts, view metrics, titles, descriptions, tags, and publish dates as a clean dataset you can export to JSON, CSV, or Excel.

Do I need an API key or Google login to use this YouTube scraper? No. There is no YouTube Data API key, Google Cloud project, or login required. Provide channel or video URLs and the actor returns the public data directly.

Why use this instead of the official YouTube Data API? The official YouTube Data API has a daily quota that large channel scrapes burn through quickly, requires a Google Cloud project and API key, and limits how much historical and tag data you can pull. This scraper has no quota key to manage and lets you collect channel stats plus video metadata across many channels in one run.

What is a good YouTube Data API alternative for competitor research? This actor is a no-key YouTube Data API alternative: instead of provisioning a Google Cloud project and rationing quota units, you immediately get subscriber counts, view and like counts, descriptions, and tags ready for benchmarking and influencer vetting.

How fresh is the scraped YouTube data and can I track channels over time? Every run reads YouTube live, so data is current at run time. Schedule daily or weekly runs to track subscriber growth, new uploads, and video performance, building a time series for competitor benchmarking.

Is scraping public YouTube data legal? This tool collects only publicly available channel and video data. You are responsible for complying with YouTube's terms, applicable laws such as GDPR and CCPA, and using the data ethically. Do not collect personal data without a lawful basis.

Automate it

Add this YouTube scraper to an Apify Schedule to track competitor channels, new uploads, and video performance automatically every day or week — ideal for ongoing competitor benchmarking and influencer monitoring. Connect the output to Make, n8n, Zapier, Google Sheets, Slack, or a webhook through Apify integrations so fresh channel and video data flows straight into your dashboards, client reports, or alerts. Recurring runs turn one-off snapshots into a growth time series.

Using AI to write your outreach / posts / replies?

If you use this scraped data to inform AI-generated cold emails, LinkedIn posts, Reddit replies, etc., modern detectors are catching on. Em-dashes, "delve", parallel bullets, and 9 other patterns get accounts flagged or callout-replied. Built aitells.vercel.app after my own reddit account got 2 "all AI generated" callouts in one day. Free detector + $19 lifetime rewriter that matches your voice.

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