# Warehouse Email Scraper (`contacts-api/warehouse-email-scraper`) Actor Warehouse email scraper to extract verified warehouse and distribution center emails from logistics websites and business directories πŸ“§πŸ’ Perfect for supply chain outreach, partnerships, and logistics industry lead generation. - **URL**: https://apify.com/contacts-api/warehouse-email-scraper.md - **Developed by:** [Lead Heaven](https://apify.com/contacts-api) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.99 / 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 ### 🏭 Warehouse Email Scraper β€” Find Verified Leads In Minutes > The fastest way to build a targeted warehouse email pipeline and a clean warehouse list. This actor searches Google Maps for warehouses, fulfillment centers, 3PLs, storage providers, and logistics hubs in any location, crawls their websites, and extracts verified emails, phone numbers, and social media profiles β€” all in one export-ready dataset of warehouse leads. --- #### πŸš€ Quick Start 1. Enter your search term β€” e.g., "Warehouse", "3PL", "Fulfillment Center" 2. Add one or more target locations β€” e.g., "New York", "Dallas, Texas" 3. Set your desired number of businesses with emails 4. Hit Run and get your results in minutes No coding required. Export to CSV, JSON, or Excel instantly. --- #### πŸ’‘ What Is The Warehouse Email Scraper? The Warehouse Email Scraper is a specialized prospecting and contact discovery tool for: - 3PL sales teams and logistics SaaS companies - Warehouse equipment and supplies vendors - Freight brokers and transportation services - Real estate and industrial leasing teams - Recruiters hiring for operations and logistics This warehouse email scraper blends Google Maps discovery with deep website crawling to extract public contact info β€” building a deduplicated stream of warehouse leads ready for outreach. Use it as your warehouse lead finder, warehouse lead scraper, and warehouse email finder all in one. It also functions as a warehouse contact finder and warehouse contact scraper to surface key channels beyond email (phones and social links), and doubles as a lightweight warehouse data scraper for structured, CRM-ready results. --- #### πŸ“¦ What Data Does It Extract? | Field | Description | |---|---| | Business Name | Company or facility name (from Google Maps) | | Website | Official domain or listing URL | | Phone | Primary phone from the listing or website | | Full Address | Complete address string as listed | | City / State / ZIP | Parsed location components where available | | Country Code | ISO country code (if detected) | | Emails | All public emails found on the domain | | Additional Phones | Extra phone numbers discovered during crawl | | Social Media Links | Facebook, Instagram, LinkedIn, Twitter/X, and others | | Email Count | Number of unique emails per business | | Pages Scraped | Approximate number of pages crawled per domain | | Rating | Average Google Maps star rating | | Reviews | Total number of Google reviews | | Latitude / Longitude | Geo-coordinates for mapping and filtering | | Place ID | Google Maps place identifier | | Scrape Status | Per-record status: success, partial, or error | --- #### βš™οΈ Input Configuration ##### Minimal Example ```json { "googleMapsSearchTerm": "Warehouse", "googleMapsLocation": ["New York"], "maxBusinesses": 50 } ```` ##### Full Example With All Options ```json { "googleMapsSearchTerm": "Warehouse", "googleMapsLocation": [ "New York", "Dallas, Texas", "Chicago, Illinois" ], "maxBusinesses": 200, "scrapeMaxBusinessesPerLocation": true, "proxyConfiguration": { "useApifyProxy": true } } ``` ##### Input Parameters | Parameter | Type | Required | Description | |---|---|---|---| | `googleMapsSearchTerm` | String | βœ… Yes | Business type to search β€” e.g., "Warehouse", "3PL", "cold storage" | | `googleMapsLocation` | Array | βœ… Yes | One or more locations β€” e.g., \["New York", "Houston, Texas"] | | `maxBusinesses` | Number | ❌ No | Max businesses with emails to return (1–1,000). The run stops at this target | | `scrapeMaxBusinessesPerLocation` | Boolean | ❌ No | If true, applies the cap per location. If false, uses a combined total across locations | | `proxyConfiguration` | Object | ❌ No | Proxy settings for stable, large-scale runs | *** #### πŸ“€ Sample Output ```json [ { "name": "Metro Storage & Logistics", "website": "https://www.metrostoragelogistics.com", "phone": "+1 212-555-0147", "full_address": "121 Industrial Blvd, New York, NY 10001, United States", "city": "New York", "state": "NY", "zip": "10001", "country_code": "US", "scraped_emails": [ "info@metrostoragelogistics.com", "operations@metrostoragelogistics.com" ], "scraped_phones": ["+1 212-555-0147", "+1 212-555-0198"], "scraped_social_media": [ "linkedin:https://www.linkedin.com/company/metro-storage-logistics", "facebook:https://www.facebook.com/metrostoragelogistics" ], "emails_found": 2, "pages_scraped": 11, "avg_rating": 4.6, "total_reviews": 76, "lat": 40.7512, "long": -73.9985, "place_id": "ChIJ-EXAMPLE-PID-001", "scrape_status": "success" }, { "name": "Hudson River Fulfillment Warehouse", "website": "https://www.hudsonriverfulfillment.com", "phone": "+1 646-555-0202", "full_address": "455 Pier Ave, New York, NY 10013, United States", "city": "New York", "state": "NY", "zip": "10013", "country_code": "US", "scraped_emails": [ "sales@hudsonriverfulfillment.com" ], "scraped_phones": ["+1 646-555-0202"], "scraped_social_media": [ "linkedin:https://www.linkedin.com/company/hudson-river-fulfillment" ], "emails_found": 1, "pages_scraped": 7, "avg_rating": 4.4, "total_reviews": 53, "lat": 40.7239, "long": -74.0104, "place_id": "ChIJ-EXAMPLE-PID-002", "scrape_status": "success" } ] ``` *** #### πŸ”§ How It Works β€” Under The Hood Step 1 β†’ Search Google Maps using your term + location\ Step 2 β†’ Collect business listings (name, address, phone, website)\ Step 3 β†’ Visit each website and crawl key pages (Home, Contact, About, etc.)\ Step 4 β†’ Extract public emails, phone numbers, and social media links\ Step 5 β†’ Deduplicate and normalize all contact data\ Step 6 β†’ Return structured results, ready for export or API consumption Designed to operate as both a discovery engine and an extraction pipeline, the warehouse email scraper finds the right companies and surfaces actionable contact channels in one run. *** #### ✨ Key Features ##### 🎯 Precise Location Targeting Target any city, state, or region to build geo-specific warehouse leads you can action right away. ##### πŸ” Deep Website Crawl Goes beyond the listing to scan contact pages, about pages, and footers to uncover public emails and phones β€” acting as a dependable warehouse email extractor. ##### 🌎 Multi-Location Scaling Run across dozens of cities simultaneously with either a combined cap or per-location limits β€” ideal for building a diversified warehouse list. ##### 🧹 Smart De-Duplication Automatically deduplicates emails per domain, keeping your exports clean and reliable for CRM import. ##### πŸ“Š Structured, CRM-Ready Output Normalized fields with consistent naming make it easy to push data into HubSpot, Salesforce, Apollo, or any outreach stack β€” a practical warehouse data scraper for operations teams. ##### πŸ›‘οΈ Proxy-Ready For Reliability Built-in proxy support enables stable, large-scale runs without rate limits β€” perfect for agency and enterprise workloads. ##### ⏱️ Stop At Your Target Stops as soon as your `maxBusinesses` threshold is reached β€” compute-efficient and predictable. ##### πŸ”„ Schedulable & Repeatable Schedule daily, weekly, or monthly runs to continuously refresh your warehouse leads and maintain always-on pipelines. *** #### 🎯 Use Cases ##### πŸ’Ό B2B Outreach & Sales Generate highly targeted warehouse leads for 3PL services, WMS software, industrial equipment, or racking systems β€” powered by your warehouse lead finder and warehouse lead scraper workflow. ##### 🚚 Freight & Brokerage Find new storage and cross-dock partners in specific metros. Use the warehouse contact scraper to enrich routes with emails and phones. ##### 🏭 Vendor & Supplier Prospecting Identify warehouses that need forklifts, pallets, automation, maintenance, and safety gear β€” export a clean warehouse list for SDR teams. ##### πŸ§‘β€πŸ’Ό Recruiting & Staffing Source operations managers and warehouse contacts for hiring outreach using the warehouse contact finder to fill missing details. ##### 🏒 Commercial & Industrial Real Estate Map competitors or prospect tenants by submarket; enrich your records with contact info via the warehouse email finder. *** #### πŸ“ˆ How Many Results Can You Get? Scale from a small prospecting run to a multi-region database. Output depends on: - Number of locations included - Breadth of your search terms ("warehouse", "cold storage", "3PL") - Email visibility on websites in each market - `maxBusinesses` setting and per-location vs. combined limits - Proxy configuration and run timeout ##### Typical Output Ranges | Campaign Size | Businesses With Emails | |---|---| | Small (1–3 cities) | 20 – 100 | | Medium (5–15 cities) | 200 – 600 | | Large (20+ cities / states) | 800 – 1,000+ per run | Run multiple campaigns across regions to steadily expand your warehouse list and maintain a fresh pipeline. *** #### πŸ”Œ Integrations & Export Options Once your run completes, you can: | Option | Details | |---|---| | JSON | Full structured data download | | CSV / Excel | Import directly into spreadsheets | | Apify API | Stream results into your apps or workflows | | Zapier / Make | Trigger automations when new contacts are found | | Webhooks | Push results to your CRM or data pipelines on run completion | Works seamlessly with Instantly, Smartlead, Apollo, HubSpot, Salesforce, or any platform that accepts CSV/JSON. *** #### πŸ› οΈ Step-By-Step Usage Guide Step 1 β€” Log In To Apify\ Create a free account or sign in at apify.com. Step 2 β€” Open The Actor\ Search for "Warehouse Email Scraper" in the Apify Store and click Try for free. Step 3 β€” Set Your Search Term\ Enter "Warehouse" or variants like "3PL", "fulfillment center", or "cold storage". Step 4 β€” Add Target Locations\ Enter one or more city/state combos β€” e.g., "New York", "Atlanta, Georgia". Step 5 β€” Set Your Limit\ Choose how many businesses with emails you want β€” 20 for a quick test, up to 1,000 for a full campaign. Step 6 β€” Choose Scaling Strategy - Combined cap: The total across all locations combined - Per-location cap: Applies your limit independently to each city Step 7 β€” Enable Proxies\ Turn on Apify Proxy for stable, uninterrupted crawling at scale. Step 8 β€” Run πŸš€\ Click Start and let the Warehouse Email Scraper do the work. Step 9 β€” Export\ Download as JSON, CSV, or Excel β€” or pull via API straight into your stack. *** #### βš–οΈ Legal & Compliance This actor collects only publicly available business contact information β€” emails and phone numbers published openly on websites. Best practices to stay compliant: - Only use publicly visible contact data - Do not bypass login walls or paywalls - Respect each website’s robots.txt and Terms of Service - Include an unsubscribe option in all outreach - Follow applicable laws in your jurisdiction Relevant regulations to consider: - GDPR (European Union) - CAN-SPAM Act (United States) - CASL (Canada) - PECR (United Kingdom) This tool is intended for ethical business prospecting only. You are responsible for how you use the data collected. *** #### ❓ Frequently Asked Questions Q: Does this scrape private or personal emails?\ A: No. It only extracts emails that are publicly visible on business websites. Think of it as a warehouse email extractor for public data. Q: Can I search multiple cities in one run?\ A: Yes. Add as many locations as needed. Use per-location capping if you want even geographic distribution of warehouse leads. Q: Does it deduplicate results?\ A: Yes. Emails are deduplicated per domain to keep your outreach lists clean. Q: Can I schedule recurring runs?\ A: Absolutely. Use Apify’s scheduler to run daily, weekly, or monthly. Q: What niches does the search term support?\ A: Any Google Maps searchable term β€” try "warehouse", "3PL", "cold storage", "fulfillment center", or "distribution center". Q: Are proxies required?\ A: Not for very small runs, but strongly recommended for 200+ business runs to avoid rate limits. Q: What export formats are available?\ A: JSON, CSV, and Excel. You can also consume results via the Apify API or webhooks. Q: How is this different from manual research?\ A: Manual work is slow and inconsistent. This warehouse lead scraper automates discovery and extraction at scale β€” saving hours per campaign. Q: Can agencies white-label or resell this?\ A: Yes. Many use it as an internal warehouse lead finder and warehouse contact scraper to deliver ongoing lead programs for clients. *** #### πŸ§‘β€πŸ’» Support & Custom Solutions Have a question, feature request, or need a custom build? - Custom input/output configurations - Niche-specific adaptations (cold storage, cross-dock, 3PL) - Bulk or enterprise run support - Integration assistance (CRM, Zapier/Make, webhooks) Email: scrapermindapi@gmail.com For any help or custom solution, contact via this mail: scrapermindapi@gmail.com # Actor input Schema ## `googleMapsSearchTerm` (type: `string`): Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists'). ## `googleMapsLocation` (type: `array`): Target geographic location for the email scraper (e.g., 'Miami, Florida'). ## `maxBusinesses` (type: `integer`): Target number of businesses to find (1-1000). The scraper will stop when this target is reached. ## `scrapeMaxBusinessesPerLocation` (type: `boolean`): If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit. ## `proxyConfiguration` (type: `object`): Proxy settings for scraping. Recommended for large-scale scraping. ## Actor input object example ```json { "googleMapsSearchTerm": "Warehouse", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "googleMapsSearchTerm": "Warehouse", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("contacts-api/warehouse-email-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "googleMapsSearchTerm": "Warehouse", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("contacts-api/warehouse-email-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "googleMapsSearchTerm": "Warehouse", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }' | apify call contacts-api/warehouse-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=contacts-api/warehouse-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Warehouse Email Scraper", "description": "Warehouse email scraper to extract verified warehouse and distribution center emails from logistics websites and business directories πŸ“§πŸ’ Perfect for supply chain outreach, partnerships, and logistics industry lead generation.", "version": "1.0", "x-build-id": "AR1sTXaFJQ77Zp4nJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/contacts-api~warehouse-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-contacts-api-warehouse-email-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/contacts-api~warehouse-email-scraper/runs": { "post": { "operationId": "runs-sync-contacts-api-warehouse-email-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/contacts-api~warehouse-email-scraper/run-sync": { "post": { "operationId": "run-sync-contacts-api-warehouse-email-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", "required": [ "googleMapsLocation", "googleMapsSearchTerm" ], "properties": { "googleMapsSearchTerm": { "title": "Search Term", "type": "string", "description": "Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists').", "default": "Warehouse" }, "googleMapsLocation": { "title": "Location", "type": "array", "description": "Target geographic location for the email scraper (e.g., 'Miami, Florida').", "default": [ "New York" ], "items": { "type": "string" } }, "maxBusinesses": { "title": "Maximum Businesses With Emails", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Target number of businesses to find (1-1000). The scraper will stop when this target is reached.", "default": 5 }, "scrapeMaxBusinessesPerLocation": { "title": "Scrape Max Businesses Per Location", "type": "boolean", "description": "If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit.", "default": false }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for scraping. Recommended for large-scale scraping." } } }, "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 } } } } } } } } } } ```