Substack Scraper — Newsletter Posts & Content avatar

Substack Scraper — Newsletter Posts & Content

Pricing

from $10.00 / 1,000 substack posts

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Substack Scraper — Newsletter Posts & Content

Substack Scraper — Newsletter Posts & Content

Extract Substack newsletter posts, titles, subtitles, publication dates, engagement metrics, and optional full text. Useful for media monitoring, creator research, market intelligence, and AI/RAG datasets.

Pricing

from $10.00 / 1,000 substack posts

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Developer

Fast API

Fast API

Maintained by Community

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2

Total users

1

Monthly active users

6 days ago

Last modified

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Extract clean, structured JSON from Substack API + HTML fallback. This Actor is built for media monitoring, creator research, market intelligence, newsletter datasets.

What you can do with it

  • Media monitoring
  • Creator research
  • Market intelligence
  • Newsletter datasets

Features

  • Newsletter archive extraction
  • Titles, subtitles, URLs, dates, and engagement metrics
  • Optional full text extraction
  • Useful for media/RAG datasets

Example input

{
"newsletterUrl": "stratechery",
"maxPosts": 20,
"includeContent": true
}

Example output

{
"title": "Example Newsletter Post",
"subtitle": "A post subtitle",
"url": "https://example.substack.com/p/post",
"publishDate": "2026-07-01",
"reactionCount": 42,
"wordCount": 1800
}

Output

Results are saved to the default Apify dataset as structured JSON. The actor includes an output schema so results are easy to preview, export, and consume from API clients.

Pricing

This Actor is configured for pay-per-result monetization using Apify's pay-per-event model. Users pay for dataset items/results rather than a large fixed upfront fee.

Common use cases

  • AI/RAG dataset creation
  • Market research and competitive intelligence
  • Trend monitoring
  • Data enrichment pipelines
  • Scheduled data extraction

Keywords

substack scraper, newsletter scraper, creator economy data, media monitoring, newsletter dataset

Notes

This Actor focuses on practical structured data extraction with clean defaults and low overhead. For large runs, start with a small maxItems value, verify the output, then scale up.