How to Scrape YouTube Data Without Coding in 2025

Scraping YouTube is challenging due to the frequent changes in the layout and the necessity of dynamically loading data (e.g., comments and view counts). It is important to achieve a balance between efficiency, legality, and cost. There are multiple ways to extract data from YouTube:

• Web scraping APIs for on-demand scraping with no limitations such as fetching live comments or trending videos.
• Proxies for cost efficiency in large-scale YouTube scraping (~$15K per month).
• YouTube API ideal for fetching video statistics at scale but is rate-limited (10,000 units/day).
• YouTube datasets ideal when real-time data isn’t required (e.g., archived video performance).

Web scraping APIs

Web scraping APIs eliminate the need for in-house infrastructure, including development, testing, and maintenance, making them a scalable and cost-efficient alternative. Platforms like YouTube employ rate limiting, and CAPTCHA challenges to detect and block automated scrapers. To avoid IP bans, custom scrapers must implement proxy rotation strategies, but managing proxies and bypassing CAPTCHAs requires significant effort. For instance, maintaining load balancing across multiple IPs adds complexity to scaling operations.

Additionally, API providers may not support all data types, and the depth of collected data can vary. Before selecting a pre-built scraping API, ensure it aligns with your specific data extraction requirements for different page types.

For example, a third-party YouTube scraper API charges $0.0010 to $0.0050 per request. Scraping 10 million pages per month would cost over $10,000 to $50,000 at an average API charge of $0.003 per page.

Proxies

For large-scale YouTube data gathering, proxies can be cost-effective; the per-IP cost decreases, and many proxy providers, such as Bright Data, provide bulk discounts, making it cheaper at scale.

Alternatively, investing in proxies and a custom YouTube parser may be less expensive than using third-party scraping APIs.

For instance, purchase rotating residential proxies in bulk (about $3 per GB for high-volume plans). Scraping 10 million pages at an average size of 400KB per page consumes 4,000 GB of bandwidth. At $3 per gigabyte, the total proxy cost is $9,000 per month. Investing in custom parser building and maintenance, for example, XPath (1–5 hours per update) updates for changing YouTube layouts (5–20 hours/month), might cost between $2,000 and $5,000 per month. Monthly cost is approximately $15,000.

Disclaimer: This is a general estimation; actual costs may vary significantly based on project complexity, unexpected website changes, and infrastructure requirements.

Free YouTube Data API for developers

YouTube Data API v3 offers free access to YouTube data, allowing developers to build apps that interact with YouTube. The API allows you to interface with several types of resources, including activity, channel, playlist, search result, subscription, and thumbnails. Here’s an outline of how to get started using the YouTube Data API:

1. First, you need to gain API access:Go to Google Cloud Console and create a new project.
   Enable the YouTube Data API version 3.
2. Select Your Authentication Method; API key (public data) or OAuth 2.0 (private user data).
3. Choose your client library (Java, PHP, or Python) to make API queries.

Rate limits: YouTube Data API contains rate constraints to ensure that users do not construct apps that unfairly impair service quality or restrict access to other users. API requests have a daily quota of 10,000 units. Quota Consumption:

• Searching for video: 100 units per request
• Obtaining video details: one unit per request.
• Obtaining channel information: 1 unit per request.
• Fetching comments: one unit per request

YouTube datasets

Most web data providers offer datasets that are often updated. Web data providers collect, process, and update datasets at regular intervals. Web scraper APIs enable on-demand scraping. You can combine pre-collected datasets for bulk data with periodic manual scraping for real-time data requirements.

Sponsored:

Bright Data’s YouTube Scraper enables users to automatically scrape public YouTube video data and channel data such as comments, video titles, and descriptions using URS(s) from any location.

What kind of information can be extracted from YouTube?

Below is a detailed breakdown of the different types of data that can be collected from YouTube:

• YouTube video metadata/available via YouTube API & scraping: Video title, duration, video links,tags, publish date, and description.
• Engagement metrics/available via YouTube API & scraping: View count, like/dislike count, comments count and subscriber count for channels.
• YouTube search results page and trending videos/available via third-party APIs & scraping: Trending videos by region, hashtag-based searches, or search results for keywords.
• YouTube channel information: Channel name and description, total videos uploaded, channel creation date.
• Video transcripts and captions/available via third-party APIs & scraping: Auto-generated captions and manually uploaded subtitles.
• YouTube ads and sponsored content/available via third-party APIs & scraping: YouTube Ad formats, brand mentions in video descriptions, or sponsored video listings.
• Historical YouTube data/available via third-party datasets: Archived video performance (views, likes over time), or trending video history.

Is it legal to scrape YouTube?

Please note that this section is for informational purposes and should not be taken as legal advice. For your scraping projects, you are advised to get specific legal advice.

Most data on YouTube is publicly accessible. Scraping public data from YouTube is legal as long as your scraping activities do not harm the scraped website’s operations. It is important not to collect personally identifiable information (PII), and make sure that collected data is stored securely.

Top 3 business use cases of YouTube data

1. Opinion mining using YouTube scraped data 

Opinion mining, also known as sentiment analysis, is a machine learning technique used by businesses to automatically extract sentiment (positive, negative, neutral, etc.) and valuable insight from text data. People use social media to express their thoughts on brands’ products and services. 

YouTube video details such as likes and comments are a great way for businesses to get insights about their customers. Understanding customers’ preferences and needs enable businesses to improve and customize their products and services. 

Web scrapers allow companies to gather video comments from YouTube. They can use opinion mining or sentiment analysis to analyze and understand how people think and feel about their brands, products, and services.

For instance, Youtube allows its users to leave likes on video content. The simplest way to understand your audience’s feelings is to compare the number of likes and dislikes. However, it provides limited insight into the audience’s thoughts about the content. You cannot identify what consumers’ pain points are. 

1. You first need to gather your audience’s comments on your YouTube videos. 
2. When data scraping is completed, the scraped data must be cleaned. The collected comments may be opinions, suggestions, complaints, or spam. You have to remove unrelated items from your database.
   • Delete duplicate data. 
   • Remove items that do not convey meaning, such as and, the, etc. 
   • Remove all punctuation and emojis from the text.
3. There are several approaches to sentiment analysis; choose one to analyze the extracted YouTube data. If you scrape a mass amount of data, you can
   • use a data annotation tool to annotate and classify words as negative or positive. 
   • split a particular part of extracted data to label manually. You can train a machine learning model to recognize these patterns in new data after manually labeling each review as positive or negative.

2. Expand customer base by using YouTube data

One of the most effective ways to generate leads is through referral marketing (word-of-mouth recommendations). People use social media to express their opinions about a brand, its products and services. One example of a product review is a YouTube unboxing video concept. You can use web scraping to collect customer feedback on your YouTube videos. 

Web scrapers gather comments with reviewer contact information. It enables businesses to respond to complaints or check whether customers are satisfied with their products or services. You can gain their long-term loyalty. Here is a quick guide to help you understand how to scrape data from YouTube videos: 

• Let’s search for “Sony WH-1000xm4” on YouTube to see what people think and feel about the product. You can visit the brand’s own YouTube video post or other product review videos published by end users. 
• Choose which video URL you want to scrape data from. I chose one of the most popular product review videos at random. 
• There are approximately 700 reviews under the video. 
• Copy the video’s URL and paste it into the web scraper’s input field. The bot will collect all comments along with the contact information for their reviewers. 
• Remove fake reviews and decide which ones are worth reaching out to. The feedback could be a complaint about the product or a question. You can assist your customers in resolving their issues.

3. Keep up with the emerging trends

Youtube algorithm ranks videos based on the number of viewers. Understanding what works best for your target audience is essential to increase your online presence on YouTube. 

Assume you work in the software industry. Your company’s YouTube channel contains a variety of video concepts, such as demo videos (which explain how a website or product works), explainer videos, how-to videos, and Q&A videos.

YouTube Studio enables you to monitor and analyze the performance of each video. Maybe your video content is doing well compared to other posts on your YouTube channel. This is good indeed, but not enough to rank high on YouTube search results. You must also understand how your competitors are performing and your current market positioning. 

External factors such as trending topic(s)/keyword(s) and competition influence your videos’ performance as well. You cannot fully comprehend which keywords are trending upwards or downwards.

Enter the search keyword, say “application software,” into the search box. YouTube will show you all of the most popular topics that have been viewed for your entered keyword/topic. A large number of video results will appear.

Manually collecting and analyzing video data is inefficient and time-consuming. Web scraping allows you to crawl YouTube search results and extract data such as video hashtags, titles, descriptions, video ID, channel ID, the number of views, etc. Scraping video data from competitors enables businesses to:

• Identify competitive keywords.
• Understand target audience behaviors and which pages they interact with the most. 

Quick tip: Do not overlook the advantages of long tail keywords. Broad keywords have aggressive competition. It isn’t easy to rank for broad terms with just one video content.

Suppose you want to create content on big data for your YouTube channel. You need to publish a number of video content on big data with different concepts such as how-to, informing, or Q&A. Otherwise, YouTube algorithm will have difficulty recognizing and ranking your content in this competition.

Another option is to narrow the keyword, such as big data for healthcare. Long tail keywords have low traffic and competition. However, ranking high on YouTube is much easier.