How much does Bulk Wikipedia Fetch: Get Page Data by Title cost?

$0.001 per page — $1 per 1,000. Billing is pay-per-result on your own Apify account: no subscription, and a run that returns nothing costs nothing.

What do I need to provide to run Bulk Wikipedia Fetch: Get Page Data by Title?

Nothing is mandatory — every field has a working default. The settings you will usually change are Search query, Page titles (instead of search), Fetch full article text, Wikipedia language, Max items.

Do I need to code to use Bulk Wikipedia Fetch: Get Page Data by Title?

No. It is a ready-to-run cloud tool: open it on Apify, fill in the form, click Run, and download the results. Everything runs on Apify's servers, so there is nothing to install or host.

Bulk Wikipedia Fetch: Get Page Data by Title

Q: What data does Bulk Wikipedia Fetch: Get Page Data by Title return?

A structured dataset with 10 fields per record, including mode, pageid, size, snippet, timestamp, title, url, wordcount, extract, thumbnail. Export it as JSON, CSV or Excel, or send it straight to your app, a database or an AI agent.

Pass up to 50 page titles and get each Wikipedia entry's intro, thumbnail, and categories back as clean JSON for datasets and enrichment.

$0.001 per page ($1 / 1,000)

Run this use case nowRun on Apify →

How it works

1
Open it on Apify
Hit Run on Apify — it opens the tool in the cloud, no install.
2
Set the inputs
Adjust searchQuery, pageTitles, fullText (sensible defaults are pre-filled).
3
Click Run
The tool runs on Apify’s cloud and collects the data for you.
4
Export the results
Download as JSON, CSV or Excel, or pipe straight into your app, Google Sheets, or an AI agent.

Pricing

$0.001 per page = $1 per 1,000

You are charged for	When	Price
Page returned	Charged per page/result returned.	$0.001

Pay-per-event pricing: you are billed per result, not per subscription — a run that returns nothing costs nothing beyond the start fee. Billing is handled by Apify on your own account. These are the live Apify store prices, in effect since 2026-06-12, and they are what you are actually charged.

Inputs

Field	What it does	Type
`searchQuery`	Keywords to search Wikipedia for (e.g. "machine learning"). Returns matching pages with snippet, word count, and URL. Leave empty if you instead provide exact Page titles below.	string
`pageTitles`	Exact Wikipedia article titles to fetch full data for (plain-text extract, thumbnail, categories, URL). Batched 50 at a time. Use this OR a search query.	array
`fullText`	Only applies in Page titles mode. When on, returns the whole article as plain text instead of just the intro paragraph(s).	boolean
`language`	Wikipedia language edition code, e.g. en, fr, de, es, ja. Picks the host {lang}.wikipedia.org.	string
`maxItems`	Maximum number of pages to return. In search mode the actor paginates until it reaches this. In page-titles mode it caps how many titles are fetched.	integer
`notionConnector`	Optional. Write each page as a page into your Notion when the run finishes. Authorize a Notion connector once in Settings → API & Integrations → MCP connectors, then pick it here. Leave empty to skip (default) — results are always saved to the dataset regardless.	string
`notionParentId`	Optional. The Notion data source ID of the database to write into (only used if a Notion connector is set). Leave empty to create the pages privately in your workspace instead.	string

What you get

A structured dataset — each result includes fields like:

modepageidsizesnippettimestamptitleurlwordcountextractthumbnail

Export every run as JSON, CSV or Excel, or send it to your app, a database, Google Sheets, or an AI agent.

More use cases for Wikipedia Scraper

Wikipedia Search API: Find Pages by Keyword

Search Wikipedia by keyword and rank matching pages by relevance, with snippets and word counts so researchers can decide what to read first.

Full Wikipedia Article Text Scraper (Plain Text)

Need the complete body of Wikipedia articles? This task returns full plain-text content by title, ready for NLP, summarization, and text analysis.

Built on Wikipedia Scraper — see every input, output field and price on the tool page.

Where this tool sits

Categories: Developer & Research Tools
Platforms: Wikipedia