Top YouTube Datasets: Bright Data, Oxylabs, Decodo & Grepsr
YouTube has become a primary source for training advanced multimodal AI and large language models (LLMs). However, obtaining YouTube data at scale remains difficult due to anti-bot measures and significant bandwidth requirements.
This review examines key companies in the YouTube data sector: Bright Data, Oxylabs, Decodo, and Grepsr. Each targets a specific market segment, ranging from pre-indexed metadata to large-scale video download solutions.
Pricing comparison of the best YouTube datasets
Detailed review of the top YouTube dataset providers
Bright Data is a leading provider of ready-to-use datasets, offering access to an extensive, pre-indexed YouTube data library. This service is well-suited for enterprise users who require large volumes of clean, structured metadata without coding.
Key features
- Massive scalability: Billions of records support comprehensive historical analysis.
- Format flexibility: Supports JSON, CSV, and Parquet formats for big data workflows.
- Customization: Request specific delta updates or select data points tailored to your project.
Pricing:
- Pricing starts at $2.50 per 1,000 records or $250 for a 100,000-record sample.
- Monthly refreshes offer discounts of up to 80%, providing a cost-effective solution for ongoing monitoring.
Oxylabs provides video data solutions for YouTube, including high-bandwidth proxies, a YouTube API, and pre-scraped datasets. You can choose standard or custom datasets. Standard datasets include transcripts and subtitles in JSON, as well as video formats like mp4 and audio formats like M4A.
With custom datasets, you select your preferred video or audio quality and define the content scope and type. You can get structured media assets in the following formats:
- Transcripts and Subtitles (.json): Delivering these in JSON ensures they are ready to be ingested into vector databases immediately.
- Video Content (.mkv or .mp4): Standardized video formats that are compatible with almost all computer vision frameworks (like OpenCV or PyTorch).
- Audio Assets (.m4a or .mp3): High-quality audio extraction for Speech-to-Text (STT) model training or acoustic analysis.
Pricing:
- Standard datasets start at $5,000 per month.
Decodo is a managed service that helps users collect large amounts of content. It is made for people who already have Video IDs and need to deliver many files to their own servers.
- How it works: You give Decodo a list of YouTube Video IDs and where you want the files sent. Decodo handles downloading, formatting, and delivering the files.
- Technical details: Decodo extracts speech, visuals, and audio from videos. By default, files come in MP4 and MP3 formats, ready to use in machine learning projects.
Pricing:
Pricing is based on the amount of data in terabytes, not the number of files:
- 10 TB Plan: $4,000 per month ($0.40 per GB)
- 50 TB Plan: $6,500 per month ($0.13 per GB)
- 100 TB Plan: $8,000 per month ($0.08 per GB)
Grepsr
Grepsr is a managed scraping service. Users set their target, for example, “All YouTube videos under the ‘Renewable Energy’ category uploaded in the last 30 days.” Grepsr manages proxy rotation and bot detection. It collects standard metadata and engagement metrics, with an emphasis on frequent updates.
- Video data includes the title, URL, duration, upload date, and description.
- Metrics include real-time view counts, likes, and comments. Channel information covers subscriber counts, total video count, and channel description.
Available formats include CSV, JSON, and XML. Data can be delivered directly to Google Drive, Dropbox, Amazon S3, Azure, or via FTP.
Pricing:
- The starter pack for one-time projects starts at $350. It is designed for researchers or companies needing a single, specific snapshot of YouTube data, such as a one-time extraction of 50,000 video records for a particular keyword.
- The growth pack offers custom pricing for ongoing data needs, such as weekly updates on competitor channel performance or trending topics.
What types of data are included in YouTube datasets?
1. Video metadata (structural data)
These data points support efficient indexing and organization of content.
- Video ID & URL: Unique identifiers for each record.
- Title and description: Full text metadata for each video, often used in natural language processing and keyword analysis.
- Duration: The length of the video, provided in seconds or ISO 8601 format.
- Upload date and timestamp: The precise date and time the video was published
- Category and tags: Classifications assigned by users or the platform, such as Education or Gaming.
- License type: Indicates whether the content uses the Standard YouTube License or Creative Commons. Privacy status: Specifies if a video is public, unlisted, or age-restricted.
2. Engagement & performance metrics
- View count: The total number of views at the time of data collection.
- Like count: The number of likes a video has received. Count: Total number of top-level and nested replies.
- Favorite count: When available, shows how many times a video was saved as a favorite.
3. Channel & creator profiles (firmographic data)
This data supports influencer marketing and analysis of the creator economy.
- Channel ID & Handle: Unique channel identifiers.
- Subscriber count: The total number of people subscribed to the channel
- Total video count: The total number of videos in the creator’s library.
- Joined date: The date the channel was created.
- Country and language: The creator’s primary location and language.
- Banner and profile image URLs: Links to the channel’s banner and profile images.
- Verified status: Indicates whether the channel is officially verified by the platform.
4. Comment & interaction data
This data is valuable for sentiment analysis and understanding community feedback.
- Comment text: The content users write in comments.
- Author handle: The unique identifier of the commenter.
- Comment likes: The number of likes a comment has received.
- Reply count: The number of replies within a comment
- Sentiment score: In some datasets, this AI-generated value indicates whether a comment is positive, negative, or neutral.
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