Best TikTok Scrapers: Scrape Video & Profile Data

A TikTok scraper collects public data from TikTok, including video metadata, profile details, engagement metrics, and comments, without using TikTok’s official API.

We tested Bright Data, Apify, and Decodo by running 500 unique TikTok video URLs per provider. We measured two dimensions: validation success rate and the breadth of available metadata fields.

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TikTok scrapers benchmark results

• Bright Data sits in the most attractive quadrant, with a high success rate (99.6%) and rich metadata (41 fields).
• Decodo returns the most metadata fields (48) but at a lower success rate (94.6%).
• Apify achieves near-identical reliability (99%) with fewer fields (27). Best suited for teams that prioritize uptime over metadata depth.

See our methodology for validation criteria and success rate definitions.

TikTok scrapers review in detail

Bright Data leads on both dimensions in our benchmark. Its dedicated TikTok Scraper API returned 41 structured fields per video, including engagement metrics, video URL, and carousel image data.

Bright Data’s TikTok Scraper API provides three dedicated endpoints for collecting structured TikTok data at scale:

• Profile endpoint: Collect profile data including nickname, biography, is_verified, followers, following, videos_count, likes, and engagement metrics such as awg_engagement_rate, comment_engagement_rate, and like_engagement_rate. Supports two input methods: direct profile URL or discovery via TikTok search URL (filterable by country).

• Posts endpoint: Extract detailed post-level data including description, hashtags, play_count, share_count, collect_count, comment_count, video_duration, video_url, music, and carousel_images, alongside the creator’s profile details. Supports four input methods: direct post URL, by profile URL (with date range and post-count filtering), by keyword or hashtag, and by TikTok discover URL.

• Comments endpoint: Retrieve per-comment data including comment_text, num_likes, num_replies, comment_id, and full commenter details (commenter_user_name, commenter_id, commenter_url), tied back to the source post via post_url, post_id, and post_date_created.

Decodo offers a TikTok post scraper that collects comment threads and search results by country or keyword. The tool returned the highest number of metadata fields (48) in our benchmark, more than either Bright Data or Apify. However, its validation success rate of 94.6% means roughly 1 in 18 requests returns incomplete or inaccurate data. This gap matters at scale: across 10,000 requests, approximately 540 would fail validation.

Apify provides flexible input options for TikTok scraping, including hashtags, profile URLs, keywords, and search URLs. The tool delivers 99% reliability with only 27 fields, though comment-level data is less structured.

• Automatically handles dynamic JavaScript loading and pagination.
• Allows retrieval of engagement metrics, hashtags, and music IDs.
• Works with Python, Node.js, or cURL, supporting multi-language integration.

Nimble’s web scraping API offers proxy rotation and fingerprint evasion, improving the reliability of TikTok scraping. While not TikTok-exclusive, its residential proxy network and anti-bot bypass logic make it a strong choice for accessing public TikTok endpoints from different regions.

Octoparse offers multiple pre-built TikTok scraper templates for collecting post, profile, and comment data directly from TikTok’s public pages.

Unlike API-based tools such as Bright Data or Apify, Octoparse utilizes visual automation that replicates real user interactions through its browser emulator. Each template supports configuration for:

• Batch input (up to 10,000 TikTok URLs)
• Custom page size (50–200 results)
• Export options (Excel, CSV, JSON, or Google Sheets)
• Pricing tiers (Free: $0.4/1,000 lines – $2/1,000 lines for detailed video metadata)

What data can you scrape from TikTok videos?

Note: Music metadata fields (music_title, artist_name) may return empty values in 2026 following TikTok’s dispute with Universal Music Group.

TikTok scraper benchmark methodology

We benchmarked web data scrapers to evaluate their ability to scrape TikTok video data. We executed 500 video URL’s per provider, with each video tested once.

• Dataset: We used a curated list of 500 TikTok video URLs spanning diverse content categories and engagement levels.

• Target: Each provider scraped individual video metadata, including descriptions, creation times, video durations, comment counts, and other engagement metrics.

• Runs: We performed 1 run per video.

Success rates:

We defined three levels of success:

Submission success: We considered a submission successful if the API accepted our initial request (HTTP 200/202) without authentication or rate limit errors.

Execution success: We considered an execution successful if the scraping job completed without timeout or system errors.

Validation success: We applied a set of rules to ensure data quality and usability. We considered a result VALID only if it met at least 60% of the validation criteria below, with at least 3 of 5 criteria passing.

A trial that fails at any earlier stage cannot proceed to later stages and is recorded as a failed trial in the final validation calculation. For example, if a request fails during submission, it receives a validation score of 0. The final validation success rate includes all trials across all stages.

Validation Criteria

We validated five key fields to ensure data accuracy and completeness:

1. URL validation

• Video ID must match exactly between the requested and scraped URLs
• Example: Extract 7557884684533910815 from both URLs and verify match

2. Description validation

• At least 3 common words are required between the ground truth and scraped text
• Skipped if ground truth has fewer than 3 words
• Method: Tokenize (lowercase, alphanumeric only) and count matches

3. Create time validation

• Within ±2 minutes OR ±24 hours
• Accounts for timing discrepancies and timezone differences

4. Video duration validation

• Within ±2 seconds tolerance
• Tight tolerance suitable for TikTok’s typical 15-180 second videos

5. Comment count validation

• Logarithmic + 5% tolerance: max(count × 0.05, log₁₀(count + 1) × 5, 3)
• Wider tolerance for small counts (≤100), tighter for large counts (>100)
• Examples: 2 → [0, 5] | 100 → [90, 110] | 1000 → [950, 1050]

A result is VALID if at least 3 out of 5 non-null criteria pass (60% threshold). Criteria are skipped only when the ground truth is null. If ground truth exists for a criterion but the scraped value is null, that criterion is marked as failed and counted in the validation calculation.

A video scrape result is considered VALID if:

• At least 3 out of 5 criteria pass, OR
• At least 60% of non-null criteria pass

This approach accounts for cases where certain fields may be legitimately unavailable while still requiring majority accuracy across available data points.

Broken URL detection

We automatically skipped videos with broken or unavailable URLs. Detection included:

• HTTP 404 errors
• “Video not found” or “Video removed” messages
• “Video unavailable” or “Content removed” errors
• TikTok-specific errors (e.g., “aweme not found”)

However, there were no broken URLs in our dataset, so we did not need to exclude any videos.

Available metadata

We counted the number of structured data fields returned by each provider, including:

• Core fields: video ID, description, create time, duration, comment count
• Engagement metrics: likes, shares, views, play count
• Author information: username, nickname, follower count
• Additional metadata: hashtags, music info, video quality, captions

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@misc{dogan2026,
  author = {Dogan, Sedat and Şipi, Nazlı},
  title  = {{Best TikTok Scrapers: Scrape Video & Profile Data}},
  year   = {2026},
  month  = jul,
  howpublished    = {\url{https://aimultiple.com/tiktok-scraping}},
  note   = {AIMultiple. Retrieved July 24, 2026}
}

Sedat Dogan

CTO

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Sedat is a technology and information security leader with experience in software development, web data collection and cybersecurity. Sedat:
- Has ⁠20 years of experience as a white-hat hacker and development guru, with extensive expertise in programming languages and server architectures.
- Is an advisor to C-level executives and board members of corporations with high-traffic and mission-critical technology operations like payment infrastructure.
- ⁠Has extensive business acumen alongside his technical expertise.

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Researched by

Nazlı Şipi

AI Researcher

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Nazlı is a data analyst at AIMultiple. She has prior experience in data analysis across various industries, where she worked on transforming complex datasets into actionable insights.

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