# PayScale Salary Scraper (`parseforge/payscale-scraper`) Actor Scrape salary data from PayScale.com. Extract base pay, bonuses, total compensation, and breakdowns by experience, location, skills, employer, and gender. Get median pay, hourly rates, profit sharing, and related jobs for any job title. Up to 40 fields per record. - **URL**: https://apify.com/parseforge/payscale-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Jobs, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $8.00 / 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. 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 ![ParseForge Banner](https://raw.githubusercontent.com/ParseForge/apify-assets/main/banner.jpg) ## ๐Ÿ’ฐ PayScale Salary Scraper > ๐Ÿš€ **Extract salary data, compensation breakdowns, and career insights from PayScale for any job title across 9 countries, with up to 40 fields per record.** > ๐Ÿ•’ Last updated: 2026-04-16 Whether you need salary benchmarks for HR planning or compensation research for recruiting, the PayScale Salary Scraper pulls real-time pay data straight from PayScale in a structured format. Enter job titles or browse entire categories and get median salaries, bonus data, experience breakdowns, location comparisons, and skill-based pay insights. No coding, no manual lookups. Just configure your job titles, pick a country, and click Start. Get results as JSON, CSV, or Excel in seconds. | ๐ŸŽฏ Target | PayScale salary research pages | |---|---| | ๐Ÿ”Ž Best for | HR benchmarking, recruiting, career research, compensation analysis | --- ### ๐Ÿ“‹ What it does - Collects median base salary, bonus, total compensation, hourly rates, profit sharing, and commission data for any job title - Returns experience-level and location-level salary breakdowns so you can compare pay across career stages and cities - Extracts skill-based pay insights, employer breakdowns, gender pay data, related jobs, and job satisfaction scores - Supports 9 countries (US, CA, GB, AU, IN, DE, FR, NL, SG) and full job category browsing --- ### ๐ŸŽฌ Demo ๐Ÿšง Coming soon --- ### โš™๏ธ Input
FieldTypeDescription
Job Titlesstring[]Job titles to look up salary data for (e.g. "Software Engineer", "Data Scientist"). Overrides Start URL.
Job CategoryselectBrowse an entire category (e.g. Information Technology, Healthcare, Sales). Used when no job titles or Start URL provided.
Start URLstringDirect PayScale research URL for a specific job or category page.
CountrystringCountry code for salary data. Supported: US, CA, GB, AU, IN, DE, FR, NL, SG. Default: US.
Max ItemsintegerFree users: limited to 10 items. Paid users: up to 1,000,000.
Request Delay (ms)integerMilliseconds to wait between batches. Increase if rate-limited. Default: 500.
**Example 1: Search by job titles** ```json { "jobTitles": ["Software Engineer", "Data Scientist", "Product Manager"], "country": "US", "maxItems": 10 } ```` **Example 2: Browse a job category** ```json { "category": "Information-Technology", "country": "US", "maxItems": 50 } ``` > โš ๏ธ **Good to Know**: Free users are limited to 10 items per run. The scraper supports 29 job categories including Healthcare, Legal, Construction, Education, and more. Minute-level data depends on sample sizes available on PayScale. *** ### ๐Ÿ“Š Output #### ๐Ÿงพ Schema Each job title returns up to 40 data fields including salary percentiles (P10-P90), bonus and commission medians, experience and location breakdowns, skills, related jobs, and job satisfaction scores.
๐Ÿ“ฆ Sample output 1 - Software Engineer (US) ```json { "jobTitle": "Software Engineer", "country": "US", "currency": "USD", "salaryMedian": 95000, "salaryP10": 65000, "salaryP25": 77000, "salaryP75": 118000, "salaryP90": 145000, "bonusMedian": 8000, "totalPayMedian": 103000, "hourlyRateMedian": 45.67, "profileCount": 15234, "experienceBreakdown": [ {"level": "Entry Level (0-1 year)", "salary": 72000}, {"level": "Mid Career (5-9 years)", "salary": 105000}, {"level": "Experienced (10-19 years)", "salary": 125000} ], "locationBreakdown": [ {"city": "San Francisco, CA", "salary": 130000}, {"city": "New York, NY", "salary": 115000}, {"city": "Austin, TX", "salary": 98000} ], "skillsList": ["Python", "Java", "JavaScript", "AWS", "SQL"], "relatedJobs": ["Senior Software Engineer", "Full Stack Developer", "DevOps Engineer"], "jobSatisfactionScore": 3.8, "scrapedAt": "2026-04-16T12:00:00.000Z" } ```
๐Ÿ“ฆ Sample output 2 - Registered Nurse (US) ```json { "jobTitle": "Registered Nurse (RN)", "country": "US", "currency": "USD", "salaryMedian": 78000, "salaryP10": 58000, "salaryP25": 66000, "salaryP75": 92000, "salaryP90": 108000, "bonusMedian": 2500, "totalPayMedian": 80500, "hourlyRateMedian": 35.50, "profileCount": 42000, "experienceBreakdown": [ {"level": "Entry Level (0-1 year)", "salary": 60000}, {"level": "Mid Career (5-9 years)", "salary": 82000} ], "locationBreakdown": [ {"city": "San Francisco, CA", "salary": 120000}, {"city": "Dallas, TX", "salary": 74000} ], "skillsList": ["Patient Care", "Critical Care", "IV Therapy"], "relatedJobs": ["Nurse Practitioner", "ICU Nurse", "Travel Nurse"], "scrapedAt": "2026-04-16T12:00:00.000Z" } ```
๐Ÿ“ฆ Sample output 3 - Data Scientist (GB) ```json { "jobTitle": "Data Scientist", "country": "GB", "currency": "GBP", "salaryMedian": 47000, "salaryP10": 30000, "salaryP25": 37000, "salaryP75": 58000, "salaryP90": 72000, "bonusMedian": 3200, "totalPayMedian": 50200, "profileCount": 3500, "experienceBreakdown": [ {"level": "Entry Level (0-1 year)", "salary": 32000}, {"level": "Experienced (10-19 years)", "salary": 65000} ], "locationBreakdown": [ {"city": "London", "salary": 55000}, {"city": "Manchester", "salary": 42000} ], "skillsList": ["Python", "Machine Learning", "R", "SQL", "TensorFlow"], "relatedJobs": ["Machine Learning Engineer", "Data Analyst", "AI Engineer"], "scrapedAt": "2026-04-16T12:00:00.000Z" } ```
*** ### โœจ Why choose this scraper | Feature | Details | |---|---| | Up to 40 fields per job title | Salary percentiles, bonuses, total pay, hourly rates, profit sharing, commissions | | Experience-level breakdowns | Compare entry-level to senior compensation | | Geographic salary comparison | See pay differences across cities and states | | Skill-based pay insights | Identify which skills boost earning potential | | 9 countries supported | US, CA, GB, AU, IN, DE, FR, NL, SG | | 29 job categories | Browse full categories like IT, Healthcare, Legal, Construction | | Export as JSON, CSV, or Excel | Download structured data in the format you need | *** ### ๐Ÿ“ˆ How it compares | Capability | This scraper | Manual research | |---|---|---| | Batch job title lookup | Hundreds of titles in one run | One title at a time | | Structured salary percentiles | P10 through P90 in every record | Read charts manually | | Experience breakdowns | Included per title | Navigate sub-pages | | Location breakdowns | Included per title | Switch filters repeatedly | | Skill and employer data | Parsed and structured | Scroll through sections | | Export formats | JSON, CSV, Excel | Copy and paste from web | | Scheduling | Automated daily, weekly, or monthly | Manual visits | *** ### ๐Ÿš€ How to use 1. [Create a free Apify account](https://console.apify.com/sign-up?fpr=vmoqkp) (includes $5 credit) 2. Search for **PayScale Salary Scraper** in the Apify Store 3. Enter your job titles or select a job category, pick a country 4. Click **Start** and wait for your results 5. Download your data as JSON, CSV, or Excel *** ### ๐Ÿ’ผ Business cases | Use case | How it helps | |---|---| | HR compensation benchmarking | Validate salary bands against market data for any role | | Recruiting and offer planning | Build competitive offers using median pay and bonus data by location | | Career advisory services | Advise clients on earning potential across cities and experience levels | | Market research reports | Track salary trends over time for published compensation studies | *** ### ๐Ÿ”Œ Automating runs Schedule the PayScale Salary Scraper to run automatically on any interval. Set up daily, weekly, or monthly runs from the Apify console or connect with Make, Zapier, or webhooks to trigger runs based on external events. Pair with Google Sheets or Slack to deliver fresh salary data to your team without manual effort. *** ### โ“ FAQ
How does the scraper collect salary data? It accesses PayScale research pages and extracts salary statistics, breakdowns, and related job information from each page.
How accurate is the data? Data accuracy depends on the sample size (profile count) for each role. More profiles mean more reliable data. The profile count is included in every record.
Can I schedule runs automatically? Yes. Set up scheduled runs in the Apify console for daily, weekly, monthly, or any custom interval.
What countries are supported? US, CA, GB, AU, IN, DE, FR, NL, and SG. Each country returns salary data in the local currency.
What job categories can I browse? 29 categories including Information Technology, Healthcare, Legal, Construction, Education, Sales, Marketing, Finance, and more.
What happens if a job title is not found? The scraper will skip that title and continue with the remaining ones. No errors are charged.
How long does a run take? Scraping a single job title takes about 5 to 10 seconds. A batch of 10 titles finishes in about 1 to 2 minutes.
Are there any limits for free users? Free users can collect up to 10 results per run. Paid users can access up to 1,000,000 results per run.
Can I filter by specific skills or experience levels? Experience and skill breakdowns are returned as part of each job title record. You can filter the output data after collection.
Is the data returned in real-time? Yes. The scraper fetches current data from PayScale at run time, so you always get the latest available compensation figures.
What export formats are available? JSON, CSV, and Excel. You can download directly from the Apify console or use integrations for automatic delivery.
Can I use the PayScale Scraper with my own tools? Yes. Use the Apify API to trigger runs and retrieve results programmatically. Works with any language or platform.
*** ### ๐Ÿ”Œ Integrate with your tools - [Make](https://docs.apify.com/platform/integrations/make) - Automate salary data workflows - [Zapier](https://docs.apify.com/platform/integrations/zapier) - Connect with 5,000+ apps - [Slack](https://docs.apify.com/platform/integrations/slack) - Get salary reports in your channel - [Google Sheets](https://docs.apify.com/platform/integrations/drive) - Export directly to spreadsheets - [Airbyte](https://docs.apify.com/platform/integrations/airbyte) - Build data pipelines - [Webhooks](https://docs.apify.com/platform/integrations/webhooks) - Trigger workflows on run completion *** ### ๐Ÿ”— Recommended ParseForge actors | Actor | What it does | |---|---| | [FRED Economic Data Scraper](https://apify.com/parseforge/fred-scraper) | Collect economic time series from the Federal Reserve | | [World Bank Scraper](https://apify.com/parseforge/world-bank-scraper) | Gather global development indicators for 200+ countries | | [LinkedIn Jobs Scraper](https://apify.com/parseforge/linkedin-jobs-scraper) | Extract job postings with salaries and company data | | [Glassdoor Scraper](https://apify.com/parseforge/glassdoor-scraper) | Collect company reviews and ratings | | [Indeed Scraper](https://apify.com/parseforge/indeed-scraper) | Gather job listings and salary information | *** ### ๐Ÿ†˜ Need help? [Contact us through our support form](https://tally.so/r/BzdKgA) to request a new scraper, report an issue, or propose a custom project. *** > **Disclaimer:** This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by PayScale or any of its subsidiaries. All trademarks mentioned are the property of their respective owners. # Actor input Schema ## `jobTitles` (type: `array`): List of job titles to look up salary data for. Example: \["Software Engineer", "Data Scientist", "Product Manager"]. If provided, startUrl is ignored. ## `category` (type: `string`): Browse an entire job category to get salary data for all jobs in it. If no job titles or start URL are provided, this category will be used. ## `startUrl` (type: `string`): PayScale research URL to scrape directly. Supports job URLs (e.g. /research/US/Job=Software\_Engineer/Salary) and category URLs (e.g. /research/US/Job/Information-Technology). ## `country` (type: `string`): Country code to look up salaries for. Default: US. Supported: US, CA, GB, AU, IN, DE, FR, NL, SG. ## `maxItems` (type: `integer`): Free users: Limited to 10 items. Paid users: Optional, max 1,000,000. ## `requestDelayMs` (type: `integer`): Milliseconds to wait between batches. Increase if the site rate-limits you. ## Actor input object example ```json { "jobTitles": [ "Software Engineer", "Data Scientist", "Registered Nurse (RN)" ], "category": "Information-Technology", "country": "US", "maxItems": 10, "requestDelayMs": 500 } ``` # Actor output Schema ## `results` (type: `string`): Dataset with all scraped items # 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 = { "jobTitles": [ "Software Engineer", "Data Scientist", "Registered Nurse (RN)" ], "category": "Information-Technology", "country": "US", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/payscale-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "jobTitles": [ "Software Engineer", "Data Scientist", "Registered Nurse (RN)", ], "category": "Information-Technology", "country": "US", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("parseforge/payscale-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "jobTitles": [ "Software Engineer", "Data Scientist", "Registered Nurse (RN)" ], "category": "Information-Technology", "country": "US", "maxItems": 10 }' | apify call parseforge/payscale-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/payscale-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PayScale Salary Scraper", "description": "Scrape salary data from PayScale.com. Extract base pay, bonuses, total compensation, and breakdowns by experience, location, skills, employer, and gender. Get median pay, hourly rates, profit sharing, and related jobs for any job title. Up to 40 fields per record.", "version": "1.0", "x-build-id": "Pj2FK8j9kljNJJfKh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~payscale-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-payscale-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/parseforge~payscale-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-payscale-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/parseforge~payscale-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-payscale-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": { "jobTitles": { "title": "Job Titles", "type": "array", "description": "List of job titles to look up salary data for. Example: [\"Software Engineer\", \"Data Scientist\", \"Product Manager\"]. If provided, startUrl is ignored.", "items": { "type": "string" } }, "category": { "title": "Job Category", "enum": [ "Accounting-and-Finance", "Administrative-and-Clerical", "Agriculture-and-Forestry", "Architecture-and-Engineering", "Art-and-Design", "Business-Operations", "Construction", "Education", "Food-Service-and-Restaurant", "General-Managers-and-Executives", "Healthcare-Practitioners-and-Technical", "Healthcare-Support", "Human-Resources", "Information-Technology", "Installation-Maintenance-and-Repair", "Legal", "Maintenance", "Manufacturing-and-Production", "Marketing-and-Advertising", "Media-and-Publishing", "Military", "Miscellaneous", "Personal-Care", "Protective-Services", "Retail", "Sales", "Science-and-Biotech", "Social-Service", "Transportation" ], "type": "string", "description": "Browse an entire job category to get salary data for all jobs in it. If no job titles or start URL are provided, this category will be used." }, "startUrl": { "title": "Start URL", "type": "string", "description": "PayScale research URL to scrape directly. Supports job URLs (e.g. /research/US/Job=Software_Engineer/Salary) and category URLs (e.g. /research/US/Job/Information-Technology)." }, "country": { "title": "Country", "type": "string", "description": "Country code to look up salaries for. Default: US. Supported: US, CA, GB, AU, IN, DE, FR, NL, SG.", "default": "US" }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Free users: Limited to 10 items. Paid users: Optional, max 1,000,000." }, "requestDelayMs": { "title": "Request Delay (ms)", "minimum": 0, "type": "integer", "description": "Milliseconds to wait between batches. Increase if the site rate-limits you.", "default": 500 } } }, "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 } } } } } } } } } } ```