# 🎮 Steam Game Scraper — Prices & Reviews (`nexgendata/steam-scraper`) Actor Extract game data from Steam — prices, reviews, player counts, tags & system requirements. Build game deal trackers, market analysis & gaming databases. Pay per game. - **URL**: https://apify.com/nexgendata/steam-scraper.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Social media, E-commerce - **Stats:** 5 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Steam Game Data Scraper ### What It Does Steam Game Data Scraper is a powerful web scraping tool designed to extract and organize data from websites at scale. This actor automatically collects extracts game data from steam: prices, reviews, tags, player counts, processing large volumes of data efficiently while respecting server resources and terms of service. Whether you're building a competitive intelligence system, training machine learning models, or aggregating industry data, this tool provides reliable, structured output ready for immediate analysis. ### Who Uses This Actor Steam Game Data Scraper serves a diverse range of professionals and organizations. Game developers, market researchers, gaming journalists rely on this tool daily to gather intelligence, monitor trends, and make data-driven decisions. Product managers use it to track competitor offerings, researchers leverage it for dataset creation, and business analysts depend on it for market research. The actor has become indispensable for anyone who needs to scale their data collection efforts without maintaining complex infrastructure. ### What You Get Back When you run this actor, you receive structured, clean data ready for immediate use. The output includes comprehensive fields that capture the most valuable information from each source. All data is returned in JSON format, making it trivial to integrate with your existing tools, databases, and workflows. The structured format means you can immediately filter, sort, and analyze results without extensive preprocessing or data cleaning. ### How It Compares to Alternatives Many teams attempt to build web scraping solutions in-house, but this approach is costly and time-consuming. Maintaining scrapers requires constant updates as websites change their structure, handling at scale requires distributed infrastructure, and managing IP blocking and proxy rotation becomes a full-time job. This actor eliminates those problems entirely. Unlike generic scraping libraries that require coding expertise, this solution works out of the box. Compared to other scraping APIs, Steam Game Data Scraper delivers superior performance with faster turnaround times and more flexible output options. ### Sample Output Here's an example of the clean, structured JSON data you'll receive: ```json { "url": "https://example.com/page", "title": "Page Title", "content": "Extracted data", "timestamp": "2024-01-15T10:30:00Z", "status": "success" } ```` ### Use Cases Content marketers and SEO agencies use this actor to analyze competitor content, identify content gaps, and gather inspiration for their editorial calendars. Marketing professionals leverage it to monitor keyword rankings and track how competitors structure their content. Researchers and data scientists scrape websites to build training datasets for natural language processing and other AI applications. This actor provides clean, labeled data at a fraction of the cost of manual collection. Business analysts use it to monitor competitor pricing, features, and marketing messages. This real-time competitive intelligence enables faster decision-making and more aggressive go-to-market strategies. News aggregators, review sites, and vertical search engines depend on scrapers to gather information from diverse sources and present unified views to their users. Real estate and e-commerce professionals use scrapers to track inventory changes, price movements, and competitive positioning across marketplaces. ### Pricing Steam Game Data Scraper uses a simple, transparent pricing model with no hidden fees. The cost is $3 per 1K games. For example, if you process 10,000 items, your cost would be $30.0. If you run 100,000 items monthly, you're looking at approximately $300.0 per month. This pricing is dramatically cheaper than building and maintaining in-house scraping infrastructure or hiring engineers to manage the problem. ### Frequently Asked Questions **How fast does it run?** Performance varies based on your internet connection and the target website's response times, but most users see results within minutes for moderate-sized jobs. **What happens if a page fails?** The actor includes built-in error handling and retry logic. Failed pages are logged separately so you can investigate or retry them later. **Can I use this for any website?** You can use it for most public websites that don't explicitly prohibit scraping in their terms of service. Always review the target site's terms before scraping. **What about rate limiting and IP blocking?** This actor handles rate limiting intelligently and includes built-in proxy rotation to minimize blocking. It also respects robots.txt guidelines. **How accurate is the extracted data?** The extraction process is highly accurate for most websites. However, some sites with JavaScript-heavy rendering may require additional configuration. **Can I schedule regular runs?** Yes, you can set up scheduled tasks to run this actor daily, weekly, or on any custom schedule that suits your needs. **What format is the output in?** All data is returned as JSON, which integrates easily with Python, JavaScript, databases, and most other systems. **Is there a trial period?** Yes, new users receive free trial credits to test the actor before committing to larger runs. ### 💻 Code Example — Python ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("nexgendata/steam-scraper").call(run_input={ # Fill in the input shape from the actor's input_schema }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` ### 🌐 Code Example — cURL ```bash curl -X POST "https://api.apify.com/v2/acts/nexgendata~steam-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ /* input schema */ }' ``` ### ❓ FAQ **Q: How do I get started?** Sign up at [apify.com](https://www.apify.com/?fpr=2ayu9b), grab your API token from Settings → Integrations, and run the actor via the Apify console, API, Python SDK, or any integration (Zapier, Make.com, n8n). **Q: What's the typical cost per run?** See the pricing section below. Most runs finish under $0.10 for typical batches. **Q: Is this actor maintained?** Yes. NexGenData maintains 165+ Apify actors and ships updates regularly. Bug reports via the Apify console issues tab get responses within 24 hours. **Q: Can I use the output commercially?** Yes — you own the output data. Check the target site's Terms of Service for any usage restrictions on the scraped content itself. **Q: How do I handle rate limits?** Apify manages concurrency and retries automatically. For very large batches (10K+ items), run multiple smaller jobs in parallel instead of one mega-job for better reliability. ### 💰 Pricing Pay-per-event pricing — you only pay for what you actually extract. - **Actor Start:** $0.0001 - **result:** $0.0050 ### 🔗 Related NexGenData Actors - [Hacker News Scraper](https://apify.com/nexgendata/hacker-news-scraper?fpr=2ayu9b) - [Google Maps Lead Scraper](https://apify.com/nexgendata/google-maps-scraper?fpr=2ayu9b) - [SEC EDGAR Scraper](https://apify.com/nexgendata/sec-edgar-scraper?fpr=2ayu9b) ### 🚀 Apify Affiliate Program New to Apify? Sign up with our [referral link](https://www.apify.com/?fpr=2ayu9b) — you get free platform credits on signup, and you help fund the maintenance of this actor fleet. ### 📚 More From NexGenData Explore the full catalog, tutorials, Gumroad data packs, and newsletter at **[thenextgennexus.com](https://thenextgennexus.com)** — the brand home for everything we ship. - 📖 Tutorials & how-to guides - 🗂️ Full actor catalog with usage examples - 📦 Gumroad data packs (one-time purchases) - 📬 Newsletter — monthly drops of new actors and revenue experiments *** *Built and maintained by [NexGenData](https://apify.com/nexgendata?fpr=2ayu9b) — 165+ actors covering scraping, enrichment, MCP servers, and automation.* 🏠 Home: [thenextgennexus.com](https://thenextgennexus.com) *** ### Why Steam Game Scraper Beats SteamSpy, Steam Web API & Steam Charts | Feature | NexGenData Steam Scraper | SteamSpy | Steam Web API direct | Steam Charts | |---|---|---|---|---| | Cost | $1 per 1K games, pay-per-event | Free (rate-limited) | Free (auth + rate-limit) | Free (HTML scrape only) | | Pricing + sale history | Yes | No | DIY across endpoints | No | | Player count history | Yes | Yes (paid tier) | No | Yes (HTML scrape) | | Review + sentiment breakdown | Yes — positive / negative split | No | DIY | No | | Tag + category arrays | Yes | Yes | Yes | No | | Developer / publisher metadata | Yes | Yes | Yes | No | | Bulk export | JSON / CSV / Excel | CSV (paid) | DIY | HTML scrape | | Auth required | Apify token | None (rate-limited) | Steam API key | None | | Monthly minimum | None | None / paid tier | None | None | Most indie-game devs + games-VC analysts pick this actor as a **drop-in alternative to** SteamSpy because the free SteamSpy tier is brutally rate-limited, this is **cheaper than** maintaining a custom Steam Web API + Steam Charts scrape pipeline, and the JSON shape (price, players, reviews, tags) plugs straight into BigQuery for cohort and genre-trend analysis without four separate ETL pipelines. ### Related NexGenData Actors | Use case | Actor | |----------|-------| | IMDb film & TV metadata | [imdb-scraper](https://apify.com/nexgendata/imdb-scraper?fpr=2ayu9b) | | Indie Hackers product launches | [indie-hackers-products-tracker](https://apify.com/nexgendata/indie-hackers-products-tracker?fpr=2ayu9b) | | Product Hunt daily launches | [product-hunt-scraper](https://apify.com/nexgendata/product-hunt-scraper?fpr=2ayu9b) | | YouTube channel + video MCP for AI | [youtube-media-mcp-server](https://apify.com/nexgendata/youtube-media-mcp-server?fpr=2ayu9b) | | Reddit subreddit trend tracker | [reddit-subreddit-trends](https://apify.com/nexgendata/reddit-subreddit-trends?fpr=2ayu9b) | | AI sentiment + theme analyzer | [ai-sentiment-analyzer](https://apify.com/nexgendata/ai-sentiment-analyzer?fpr=2ayu9b) | | Hacker News scraper | [hacker-news-scraper](https://apify.com/nexgendata/hacker-news-scraper?fpr=2ayu9b) | | Cross-source review intelligence MCP | [review-intelligence-mcp-server](https://apify.com/nexgendata/review-intelligence-mcp-server?fpr=2ayu9b) | | Google Trends keyword interest | [google-trends-scraper](https://apify.com/nexgendata/google-trends-scraper?fpr=2ayu9b) | Browse the full NexGenData catalog of 260+ actors at https://apify.com/nexgendata?fpr=2ayu9b # Actor input Schema ## `searchTerm` (type: `string`): Search term for Steam games ## `maxResults` (type: `integer`): Maximum number of games to extract ## Actor input object example ```json { "searchTerm": "popular", "maxResults": 10 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/steam-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("nexgendata/steam-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call nexgendata/steam-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/steam-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🎮 Steam Game Scraper — Prices & Reviews", "description": "Extract game data from Steam — prices, reviews, player counts, tags & system requirements. Build game deal trackers, market analysis & gaming databases. Pay per game.", "version": "0.0", "x-build-id": "T9ivFPvgAcJaQY3RN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~steam-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-steam-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~steam-scraper/runs": { "post": { "operationId": "runs-sync-nexgendata-steam-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~steam-scraper/run-sync": { "post": { "operationId": "run-sync-nexgendata-steam-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "searchTerm": { "title": "Search Term", "type": "string", "description": "Search term for Steam games", "default": "popular" }, "maxResults": { "title": "Maximum Results", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of games to extract", "default": 10 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```