# Walmart Intelligence Scraper (`jdtpnjtp/walmart-intelligence-scraper`) Actor Extract Walmart products, prices and reviews: title, brand, price, rollback, rating, review count, stock, seller, specs and review text. Search, category, bestsellers, deals, product detail and reviews in one Actor. Pay per result. - **URL**: https://apify.com/jdtpnjtp/walmart-intelligence-scraper.md - **Developed by:** [Data Forge](https://apify.com/jdtpnjtp) (community) - **Categories:** E-commerce, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.10 / 1,000 product results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Walmart Scraper Scrape Walmart products, prices and reviews at scale. **Products from $1 / 1,000, reviews from $0.60 / 1,000, full product detail $3.60 / 1,000.** Search the catalog, browse categories, pull best sellers and rollback deals, get full product detail and collect reviews, then export thousands of clean rows in a single run. Pay only for the rows you keep. No setup, no browser, no infrastructure to manage. Pick what you need under **What to scrape**, press **Start**, and get flat tables out: **Products** and **Reviews**. > Prefer a single-purpose tool? Data Forge also ships the focused **Walmart Product Scraper** and **Walmart Reviews Scraper**. ### Why this Actor? One umbrella Actor covers 6 Walmart data surfaces and can combine them in a single run, so you skip wiring separate tools together and joining their output by hand. Winning cells are in bold. | Capability | This Actor | Typical single-purpose Walmart actors | |---|---|---| | Walmart surfaces in one Actor | **6: search, product detail, best sellers, deals, category URLs, reviews** | 1 surface per actor | | Product detail + reviews in one run | **Yes - the Product 360 mode below** | Run 2 actors, then join the output yourself | | Search-to-detail enrichment | **1 toggle - `includeProductDetails`** | Not offered | | Sort orders on search and deals | **5: best match, price low, price high, best seller, newest** | Limited or none | | Best sellers and rollback deals by category | **Both, category-scoped** | Separate actors, or missing | | Combined product + review intelligence | **1 dataset, 6 row types** | 2 datasets to reconcile | | Billing | **Pay per result, $5 free monthly credit** | Often per-run or a subscription | ### What does the Walmart Scraper do? It turns a keyword, a product ID, a category or a deals list into structured Walmart data. Tick 1 or more data types and the Actor pulls exactly that: | Data type | What it returns | |---|---| | 🔎 **Search** | Products matching your keyword, paginated. | | 📦 **Product detail** | A full record for specific products (specs, images, model, brand, seller). | | 🏆 **Best sellers** / 🔥 **Deals** | Best-seller and rollback / clearance lists by category. | | 🗂 **Category** | Browse a Walmart category. | | ⭐ **Reviews** | Product reviews, sorted and paginated. | The dataset opens on ready-made views (**Products**, **Reviews**), so the right columns are in front of you. ### What data can you get from Walmart? **Per product:** item ID, title, brand, price, was-price, rollback flag, rating, review count, in-stock status, seller name, model number, UPC, images and the product URL. **Per review:** reviewer name, star rating, review title, review text, date, helpful votes and verified-purchase flag. Each row also carries the raw source object under a `data` field, so nothing the page exposes is dropped. ### Input modes 4 published example tasks map 1:1 to the common jobs below. Copy a JSON block into the input editor, or open the matching example task and press **Start**. #### 1. Product 360 - detail plus reviews in one run Sellers and product teams: pull a product's full spec sheet and up to 100 recent reviews for the same item in a single run. ```json { "productIds": ["18533160127"], "dataTypes": ["product", "reviews"], "maxReviewsPerProduct": 100 } ```` #### 2. Market snapshot - search enriched with full details Category managers: map a keyword's top 40 listings and enrich each one with a full product-detail record. ```json { "searchQueries": ["air fryer"], "dataTypes": ["search"], "includeProductDetails": true, "maxResultsPerType": 40 } ``` #### 3. Category intelligence with details Merchandisers: walk a Walmart category page and enrich the first 40 items with full detail. ```json { "categoryUrls": ["https://www.walmart.com/browse/electronics/3944"], "dataTypes": ["category"], "includeProductDetails": true, "maxResultsPerType": 40 } ``` #### 4. Best-sellers deep-dive Trend and sourcing analysts: rank a category's best sellers and enrich the top 40 with full detail. ```json { "dataTypes": ["bestsellers"], "bestsellersDealsCategory": "electronics", "includeProductDetails": true, "maxResultsPerType": 40 } ``` ### How to scrape Walmart 1. Under **What to scrape**, tick the data types you want. **Search** is prefilled, so the Actor runs on the first click. 2. Fill the matching input: keywords for Search, item IDs or product URLs for Product detail / Reviews, a category URL for Category. Product input is adaptive: an item ID or a product URL both resolve. 3. Set the sort, deals category and reviews options in the collapsible sections. 4. Press **Start**. Export to JSON, CSV, Excel or Google Sheets, or pull from the Apify API. #### Input example ```json { "dataTypes": ["search"], "searchQueries": ["air fryer"], "sort": "best_seller", "maxResultsPerType": 40, "includeProductDetails": true } ``` ### Output Each dataset item has a `row_type` field, so the 6 surfaces stay easy to filter and split. | `row_type` | What it is | Key fields | |---|---|---| | `search_result` | 1 listing from a keyword search | name, brand, price, rating, seller\_name, in\_stock, url | | `category_result` | 1 listing from a category browse | name, brand, price, rating, in\_stock, url | | `product_detail` | Full detail record for 1 product | name, brand, price, model\_number, upc, rating, review\_count, image\_url | | `bestseller` | 1 item from a category best-sellers list | name, brand, price, rating, url | | `deal` | 1 rollback / clearance item | name, brand, price, original\_price, url | | `review` | 1 product review | author, rating, title, text, date, verified\_purchase, helpful\_count | #### Output examples Search-result row: ```json { "query": "air fryer", "row_type": "search_result", "us_item_id": "967557625", "name": "Ninja AF101 Air Fryer, 4 Qt", "brand": "Ninja", "price": 89.0, "original_price": 119.0, "rating": 4.8, "review_count": 38400, "seller_name": "Walmart.com", "in_stock": true, "url": "https://www.walmart.com/ip/967557625" } ``` Product-detail row: ```json { "query": "18533160127", "row_type": "product_detail", "us_item_id": "18533160127", "name": "Ninja AF101 Air Fryer, 4 Qt, Black and Grey", "brand": "Ninja", "price": 89.0, "original_price": 119.0, "rating": 4.8, "review_count": 38400, "model_number": "AF101", "upc": "622356561105", "url": "https://www.walmart.com/ip/18533160127" } ``` Review row: ```json { "query": "18533160127", "row_type": "review", "review_id": "a1b2c3d4", "author": "Jamie R.", "rating": 5, "title": "Works great", "text": "Crispy results each time, easy to clean.", "date": "2026-05-22", "verified_purchase": true, "helpful_count": 12 } ``` ### How much does it cost to scrape Walmart? You pay per result, billed by Apify, with no subscription. With the **$5 free monthly credit** you can pull, for example: - **~8,300 reviews** (reviews from $0.60 / 1,000), or - **~5,000 product listings** (products from $1 / 1,000), or - **~1,380 full product details** ($3.60 / 1,000). Detail fan-out is opt-in, so a default search run stays cheap. Failed items (a product that no longer exists, an out-of-stock region) are returned for visibility and are **never charged**. ### What can you use Walmart data for? - **Price and assortment monitoring** - track price, rollback status and availability across a catalog. - **Competitor and market research** - compare brands, ratings and price bands in a category. - **Best-seller and deal tracking** - watch what is rising and discounted by category. - **Product intelligence** - specs, images, model and seller for catalog enrichment. - **Review mining and sentiment** - feed product reviews into NLP for CX insight. - **Reseller and arbitrage sourcing** - spot clearance and rollback opportunities at scale. - **MAP and pricing compliance** - monitor seller prices against policy. ### Is it legal to scrape Walmart? Scraping publicly available Walmart data is legal in most jurisdictions when you collect only public information and respect personal-data laws such as GDPR and CCPA. This Actor returns only data a visitor can see on Walmart.com. You are responsible for how you use the output, so consult a lawyer for your specific case. ### FAQ **Can I run just 1 data type?** Yes. Tick only what you need under **What to scrape**; the rest stays idle and uncharged. **Can I get product detail and reviews for the same item in 1 run?** Yes - that is the Product 360 mode. Pass `productIds` and set `dataTypes` to `["product", "reviews"]`; both surfaces land in the same dataset, keyed by `us_item_id`. **When should I use this Actor vs the split actors?** Use this umbrella when you want 2 or more surfaces, or a product + review combo, in a single run. Reach for **Walmart Product Scraper** or **Walmart Reviews Scraper** when you only need 1 surface at high volume and want the leanest input form. **What happens with a bad product ID or a dead listing?** The Actor writes a free error row (with `error_code` and `error_message`), then keeps going with the next item. You are not charged for error rows, and 1 bad input does not stop the run. **How fresh is the data?** Rows are fetched live from Walmart on each run, so prices, stock and ratings reflect the moment the Actor runs. **How many reviews can I get per product?** Up to the `maxReviewsPerProduct` cap you set (1 to 1,000), in your chosen sort order. **Can I enrich a search or category with full detail?** Yes. Turn on `includeProductDetails` and each listing row is enriched with a product-detail record (adds a product-detail charge per enriched row). **Can I call it from code?** Yes. Run it via the Apify API, the Apify client SDKs, or an MCP server, and read results from the dataset. ### Related actors Part of the Data Forge Walmart and marketplace fleet: - **[Walmart Product Scraper](https://apify.com/jdtpnjtp/walmart-product-scraper)** - focused product search, category, best sellers, deals and detail. - **[Walmart Reviews Scraper](https://apify.com/jdtpnjtp/walmart-reviews-scraper)** - focused, high-volume review extraction. - **[Amazon Scraper](https://apify.com/jdtpnjtp/amazon)** - products, prices and reviews across Amazon marketplaces. *** ### Support Built and maintained by **Data Forge**. Need custom fields, higher volume or a quick answer? [![Telegram](https://img.shields.io/badge/Telegram-Chat-26A5E4?style=for-the-badge\&logo=telegram\&logoColor=white)](https://t.me/j4dtpnj2tp) [![WhatsApp](https://img.shields.io/badge/WhatsApp-Message-25D366?style=for-the-badge\&logo=whatsapp\&logoColor=white)](https://wa.me/380686031542) [![Email](https://img.shields.io/badge/Email-Contact-EA4335?style=for-the-badge\&logo=gmail\&logoColor=white)](mailto:jdtpnjtp@gmail.com) # Actor input Schema ## `dataTypes` (type: `array`): Which verticals to run. Search needs queries, Product/Reviews need product ids, Category needs URLs. Bestsellers / Deals run only when explicitly selected. ## `searchQueries` (type: `array`): Keyword searches (e.g. `laptop`, `4k tv`). ## `productIds` (type: `array`): Walmart products by ID (catalog id or numeric item id) or product URL. Used by Product detail and Reviews. ## `categoryUrls` (type: `array`): Walmart category paths or URLs (e.g. `/browse/electronics/3944`). ## `maxResultsPerType` (type: `integer`): Upper bound on results per vertical / input item (1-500). ## `includeProductDetails` (type: `boolean`): When on, every listing row is enriched with a full product-detail call (adds a product-detail charge per result). ## `sort` (type: `string`): Order of search and deals results. ## `bestsellersDealsCategory` (type: `string`): Optional Walmart category ID to scope bestsellers and deals. ## `reviewsSort` (type: `string`): Sort order for the Reviews vertical. ## `maxReviewsPerProduct` (type: `integer`): Cap on reviews per product for the Reviews vertical (1-1000). ## Actor input object example ```json { "dataTypes": [ "search" ], "searchQueries": [ "laptop" ], "productIds": [], "categoryUrls": [], "maxResultsPerType": 20, "includeProductDetails": false, "sort": "best_match", "reviewsSort": "relevancy", "maxReviewsPerProduct": 40 } ``` # Actor output Schema ## `dataset` (type: `string`): Default dataset; row\_type discriminates search\_result / category\_result / bestseller / deal / product\_detail / review. Full payload under data. Views: Overview / Products / Reviews. ## `summary` (type: `string`): OUTPUT key: data\_types, product\_results, product\_details, reviews, errors, total\_rows, estimated\_cost\_usd, limit\_reached, actor\_version. # 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 = { "dataTypes": [ "search" ], "searchQueries": [ "laptop" ], "productIds": [], "categoryUrls": [], "maxResultsPerType": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("jdtpnjtp/walmart-intelligence-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 = { "dataTypes": ["search"], "searchQueries": ["laptop"], "productIds": [], "categoryUrls": [], "maxResultsPerType": 20, } # Run the Actor and wait for it to finish run = client.actor("jdtpnjtp/walmart-intelligence-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 '{ "dataTypes": [ "search" ], "searchQueries": [ "laptop" ], "productIds": [], "categoryUrls": [], "maxResultsPerType": 20 }' | apify call jdtpnjtp/walmart-intelligence-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jdtpnjtp/walmart-intelligence-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Walmart Intelligence Scraper", "description": "Extract Walmart products, prices and reviews: title, brand, price, rollback, rating, review count, stock, seller, specs and review text. Search, category, bestsellers, deals, product detail and reviews in one Actor. Pay per result.", "version": "1.0", "x-build-id": "pIjuceSj14AUEcMXa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jdtpnjtp~walmart-intelligence-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jdtpnjtp-walmart-intelligence-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jdtpnjtp~walmart-intelligence-scraper/runs": { "post": { "operationId": "runs-sync-jdtpnjtp-walmart-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jdtpnjtp~walmart-intelligence-scraper/run-sync": { "post": { "operationId": "run-sync-jdtpnjtp-walmart-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "dataTypes": { "title": "Data types to scrape", "uniqueItems": true, "type": "array", "description": "Which verticals to run. Search needs queries, Product/Reviews need product ids, Category needs URLs. Bestsellers / Deals run only when explicitly selected.", "items": { "type": "string", "enum": [ "search", "product", "bestsellers", "deals", "category", "reviews" ], "enumTitles": [ "🔎 Search", "📦 Product detail (by id)", "🏆 Bestsellers", "🔥 Deals", "🗂 Category browse", "⭐ Reviews" ] }, "default": [ "search", "product", "bestsellers", "deals", "category", "reviews" ] }, "searchQueries": { "title": "Search queries", "uniqueItems": true, "type": "array", "description": "Keyword searches (e.g. `laptop`, `4k tv`).", "items": { "type": "string" } }, "productIds": { "title": "Product IDs or URLs", "uniqueItems": true, "type": "array", "description": "Walmart products by ID (catalog id or numeric item id) or product URL. Used by Product detail and Reviews.", "items": { "type": "string" } }, "categoryUrls": { "title": "Category URLs", "uniqueItems": true, "type": "array", "description": "Walmart category paths or URLs (e.g. `/browse/electronics/3944`).", "items": { "type": "string" } }, "maxResultsPerType": { "title": "Max results per type", "minimum": 1, "maximum": 500, "type": "integer", "description": "Upper bound on results per vertical / input item (1-500).", "default": 40 }, "includeProductDetails": { "title": "Fan out listings to full product detail", "type": "boolean", "description": "When on, every listing row is enriched with a full product-detail call (adds a product-detail charge per result).", "default": false }, "sort": { "title": "Search / deals sort", "enum": [ "best_match", "price_low", "price_high", "best_seller", "new" ], "type": "string", "description": "Order of search and deals results.", "default": "best_match" }, "bestsellersDealsCategory": { "title": "Bestsellers / deals category ID", "type": "string", "description": "Optional Walmart category ID to scope bestsellers and deals." }, "reviewsSort": { "title": "Reviews sort", "enum": [ "relevancy", "submission-desc", "submission-asc", "rating-desc", "rating-asc" ], "type": "string", "description": "Sort order for the Reviews vertical.", "default": "relevancy" }, "maxReviewsPerProduct": { "title": "Max reviews per product", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Cap on reviews per product for the Reviews vertical (1-1000).", "default": 40 } } }, "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 } } } } } } } } } } ```