Steam Game Data Scraper — Global Pricing, Reviews & Metadata

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Scrape Steam game data across 60 countries in a single run — regional pricing, user reviews, Metacritic scores, genres, and metadata. An affordable, export-first alternative to SteamDB and Gamalytic — pipe results straight into your spreadsheet, database, or BI tool.

✨ What does this scraper do?

This Actor returns Steam game listings filtered by tag (genre), enriched with regional pricing across up to 60 countries — delivered as a flat table ready for Google Sheets, BigQuery, or any CSV tool.

Zero-config start

Click Start with default settings — you'll immediately get the Top 100 Roguelike games in the US, fully enriched with pricing, reviews, and metadata.

💰 Cost comparison

Plus, this Actor delivers multi-country pricing across 60 regions in a single run — the same global coverage as Gamalytic and SteamDB, without their subscription or web-only limitations.

For users who need structured, exportable Steam data — not real-time sales estimates or revenue models — this Actor delivers catalog, pricing, and review data without a seat-based subscription, starting from ~$0.30 per run.

🎯 Use cases

• Indie developers — Track competitor pricing, discount patterns, and review momentum across your genre to price your own game confidently.
• Publishers — Benchmark regional pricing strategies (US vs JP vs BR) for localization and launch planning.
• Market researchers — Analyze genre trends, review distributions, and price tiers with structured, timestamped data.
• Steam key resellers & deal sites — Monitor discount rotations and currency-adjusted pricing across markets.
• Data journalists — Build stories on platform economics (price parity, regional discounting, indie vs AAA review gaps).

📖 How to use

Step 1: Configure input

1. Tags — Pick one or more genres (multiple = AND filter). Default: Roguelike.
2. Countries — Choose regions for pricing. Default: US. Each country adds one row per game.
3. Max games — 1 to 10,000. Default: 100.
4. Sort by (optional) — Relevance / Release Date / User Reviews / Top Sellers / Name.
5. Enriched fields (optional) — Toggle on for developer, publisher, Metacritic, genres, description, screenshots.
6. Filters (optional) — Minimum reviews, Max price (USD).

Step 2: Run

Click Start. Runtime scales with games × countries and enrichment toggle.

Step 3: Export

Download as CSV, JSON, Excel, or auto-sync to Google Sheets / BigQuery / a webhook via Apify integrations.

Input example: Compare Roguelike pricing across JP / US / BR

{
"tags":["Roguelike"],
"countryCodes":["us","jp","br"],
"maxResults":50,
"sortBy":"Top Sellers",
"includeEnrichedFields":true,
"minReviews":500
}

This produces: 50 games × 3 countries = 150 rows with full enrichment — ready to analyze regional discount spreads and pricing parity.

📊 Output

Each row represents one game in one country. The flat structure is ideal for pivot tables, time-series tracking, and direct import into analytical tools.

Enriched fields (marked (enriched)) only populate when Include enriched fields is ON.

Output example (Roguelike, Japan, on sale)

{
"appId":646570,
"name":"Slay the Spire",
"headerImage":"https://cdn.cloudflare.steamstatic.com/steam/apps/646570/header.jpg",
"storeUrl":"https://store.steampowered.com/app/646570/",
"countryCode":"jp",
"price":"¥1,650",
"priceRaw":165000,
"priceOriginal":"¥3,300",
"priceOriginalRaw":330000,
"currency":"JPY",
"discountPercent":50,
"reviewScoreDesc":"Overwhelmingly Positive",
"reviewScorePercent":96,
"totalReviews":108742,
"releaseDate":"2019-01-23",
"platforms":{"windows":true,"mac":true,"linux":true},
"tagIds":[1716,3859],
"tagNames":["Roguelike","Deckbuilding"],
"developer":"Mega Crit",
"publisher":"Mega Crit",
"genres":["Indie","Strategy"],
"metacriticScore":89,
"shortDescription":"We fused card games and roguelikes together to make the best single player deckbuilder we could.",
"screenshots":["https://cdn.cloudflare.steamstatic.com/..."],
"scrapedAt":"2026-04-15T12:00:00.000Z"
}

📅 Daily scheduling for trend tracking

This Actor is optimized for daily scheduled runs. Set up an Apify Schedule to build a historical pricing and review dataset.

Recommended workflow:

1. Configure your target tags, countries, and filters
2. Set a daily schedule (e.g. 09:00 UTC)
3. Connect a Google Sheets or BigQuery integration
4. Track discount cycles, review momentum, and regional pricing shifts over weeks and months

💵 Pricing

This Actor uses pay-per-event pricing — you pay only for the rows you extract.

Cost examples

With Apify's free tier ($5 credit), you can run the Standard daily schedule for ~5 days — or the Light setup for ~16 days — completely free.

🏷️ Supported tags (27)

Multiple tags apply an AND filter — a game must match all selected tags to appear.

🌍 Supported countries (60)

💡 Notes & tips

• Regional result differences: Steam ranks tag-search results per country, so selecting multiple countries may return partially different game sets — a title popular in Japan may not appear in the US top results. Raise Max games to improve overlap.
• Metacritic sparseness: Metacritic scores only populate for titles actually reviewed by Metacritic (mostly AAA). Indie titles will show metacriticScore: null — this is not a scraper issue; Steam has no Metacritic data for those games.
• Free-to-play: price displays "Free" and priceRaw is 0.
• No discount: priceOriginal equals price (not empty) — easy to compare without null handling.
• No reviews yet: reviewScoreDesc shows "No user reviews" instead of null.
• Max price filter: Applied per game using the base USD price (not per country). If a game passes the filter, rows for all selected countries are returned — regardless of each country's local price.
• No price data: For some titles (unreleased, region-locked, or delisted) Steam returns no price. In those rows price, priceRaw, priceOriginal, and priceOriginalRaw are all null.

⚙️ Technical details

📬 Feedback & support

Have a feature request, need another tag or country added, or hit an issue? Open an issue on the Actor's page in Apify Console — we'd love to hear from you.