# Levels.fyi Scraper — Tech Salaries, Compensation & Leveling (`scrapesage/levels-fyi-scraper`) Actor Scrape Levels.fyi tech compensation: company salaries by job family & level (base, stock, bonus, total), individual salary submissions, leveling data, percentiles, titles, and role + location market benchmarks with top-paying companies. Monitoring mode. No API key, no browser. - **URL**: https://apify.com/scrapesage/levels-fyi-scraper.md - **Developed by:** [Scrape Sage](https://apify.com/scrapesage) (community) - **Categories:** Jobs, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 company compensation overviews 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 ## Levels.fyi Scraper — Tech Salaries, Compensation & Leveling Extract **complete Levels.fyi compensation data** — the numbers other salary scrapers skip: **per-level base, stock, bonus and total comp**, **individual salary submissions**, **leveling maps** (skill index, % of workforce, title ladders), **percentiles (p10–p90)**, and **role-by-location market benchmarks** with the top-paying companies. Turn any company or role into a clean, structured compensation dataset. No login, no cookies, no browser — fast JSON extraction from the page's own data, with high reliability. ### Why this Levels.fyi scraper? Most Levels.fyi scrapers dump a flat list of raw salary rows and stop there. This actor ships the **richest compensation dataset in the category** — the aggregates, the leveling, *and* the individual submissions, in one run: | Data | Typical scrapers | This actor | |---|---|---| | Individual salary submissions (base/stock/bonus/total) | ✅ | ✅ | | Per-level aggregates by company & role | ❌ | ✅ | | Leveling map (titles, skill index, % of workforce, bonus target) | ❌ | ✅ | | Percentiles p10 / p25 / p50 / p75 / p90 | ❌ | ✅ | | Company overview (median TC, all roles, highest/lowest-paying) | ❌ | ✅ | | Role × location market (percentiles, histogram, submission count) | ❌ | ✅ | | Top-paying companies & locations per role | ❌ | ✅ | | Tracked titles per role with sample counts | ❌ | ✅ | | Monitor mode — only **new / changed** comp | ❌ | ✅ | | No start fee | charges start fees | ✅ | ### Use cases - **Compensation benchmarking** — build pay bands by company, level, role and location from real, recent data (`baseSalary`, `stockGrantValue`, `bonus`, `totalCompensation`, percentiles). - **Recruiting & sourcing** — know what an offer needs to beat before you make it; compare `medianTotalCompensation` across companies and levels. - **Job seekers & negotiation** — pull every submission for your target company and level, with years of experience and offer dates. - **Market & talent intelligence** — track role × location markets (`roleMarket`), top-paying companies, and submission volume over time. - **Leveling research** — map title ladders across companies (`levels[].levelTitles`, `skillIndex`, `percentWorkforce`) for HR, comp and people-analytics teams. - **Monitoring** — schedule recurring runs and capture only **new** salary submissions and **changed** medians. ### How to use 1. [Sign up for Apify](https://console.apify.com/sign-up) — the free plan is enough to try this actor. 2. Open the **Levels.fyi Scraper**, pick a mode, enter companies (or job families + locations), and click **Start**. 3. Watch results stream into the dataset table. 4. **Export** as JSON, CSV, Excel, XML, or RSS — or pull results programmatically via the [Apify API](https://docs.apify.com/api/v2). ### Input ```json { "mode": "companyComp", "companies": ["Google", "Stripe", "Jane Street"], "jobFamilies": ["Software Engineer", "Product Manager"], "includeSamples": true, "maxSamplesPerRole": 50, "maxJobFamiliesPerCompany": 6, "monitorMode": false } ```` - **mode** — `companyComp` (full comp for the companies you list), `roleComp` (market benchmarks for a job family across locations), or `companyDirectory` (list popular companies + the job-family taxonomy to discover valid slugs). - **companies** — company names or Levels.fyi slugs (`Google`, `Meta`, `stripe`, `Jane Street`). Names are auto-converted to slugs. - **jobFamilies** — limit which roles are detailed per company (empty = the company's most popular roles), or the roles to benchmark in `roleComp`. Examples: `Software Engineer`, `product-manager`, `data-scientist`. - **locations** *(roleComp)* — location slugs: `united-states`, `san-francisco-bay-area`, `new-york-city-area`, `greater-seattle-area`, `united-kingdom`, `india`, `canada`. - **startUrls** — paste Levels.fyi URLs directly; they auto-route (company page, single role, `/t/` market page, or the `/companies/` directory). - **includeSamples** *(default true)* — also output every individual salary submission, not just per-level aggregates. - **maxSamplesPerRole** *(default 50)* — cap individual submissions per company + role. - **monitorMode** *(default false)* — only emit new submissions and changed aggregates across runs (see below). ### Output Records are tagged by `type`. **`company`** (overview), **`role`** (company + job family with per-level comp, leveling and percentiles), **`salary`** (individual submission), **`roleMarket`** (job family × location), and free **`jobFamily`** reference rows. A `salary` record: ```json { "type": "salary", "company": "Google", "jobFamily": "Software Engineer", "title": "Software Engineer", "level": "L5", "focusTag": "Full Stack", "yearsOfExperience": 8, "yearsAtCompany": 3, "baseSalary": 230000, "stockGrantValue": 180000, "bonus": 45000, "totalCompensation": 455000, "location": "San Francisco, CA", "offerDate": "2026-05-14T00:00:00.000Z", "submissionId": "57c00b79-9761-41bf-8bb4-56ab9fffe7d7", "scrapedAt": "2026-06-15T12:00:00.000Z" } ``` A `role` record (per company + job family) carries the full level ladder: ```json { "type": "role", "company": "Google", "jobFamily": "Software Engineer", "levelCount": 8, "totalSampleCount": 1840, "medianTotalCompensation": 330000, "percentiles": { "totalCompensation": { "p10": 185000, "p25": 212000, "p50": 330000, "p75": 432500, "p90": 573000 }, "baseSalary": { "p10": 160000, "p50": 200000, "p90": 267000 } }, "levels": [ { "level": "L3", "levelTitles": ["L3", "SWE II"], "percentWorkforce": 20, "skillIndex": 10.2, "sampleCount": 32, "baseSalary": 161594, "stockGrantValue": 34141, "bonus": 16373, "totalCompensation": 212108 } ], "currency": "USD" } ``` A `roleMarket` record (job family × location) carries percentiles, a histogram and the top-paying companies/locations. ### Monitor only new compensation Turn on **monitorMode** and the actor remembers what it has already returned (in a named key-value store) and, on the next run, emits **only new salary submissions** and rows whose **compensation changed**. Combine it with [Schedules](https://docs.apify.com/platform/schedules) to build a fresh-comp feed — e.g. *"every Monday, give me the new Google + Stripe SWE submissions"*. Monitor mode is independent of scheduling, so the two work together with no conflict. ### Automate & schedule Run this actor on autopilot and pull results into your own stack: - **[Apify API](https://docs.apify.com/api/v2)** — start runs, fetch datasets, and manage schedules over REST. - **[apify-client for JavaScript](https://docs.apify.com/api/client/js/)** and **[apify-client for Python](https://docs.apify.com/api/client/python/)** — official SDKs. - **[Schedules](https://docs.apify.com/platform/schedules)** — run it daily/weekly to track new submissions or shifting medians; perfect with monitor mode. - **[Webhooks](https://docs.apify.com/platform/integrations/webhooks)** — trigger downstream actions (warehouse load, Slack alert, dashboard refresh) the moment a run finishes. ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'MY_APIFY_TOKEN' }); const run = await client.actor('scrapesage/levels-fyi-scraper').call({ mode: 'companyComp', companies: ['Google', 'Stripe'], jobFamilies: ['Software Engineer'], includeSamples: true, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Got ${items.length} compensation records`); ``` ### Integrate with any app Connect the dataset to 5,000+ apps — no code required: - **[Make](https://docs.apify.com/platform/integrations/make)** — multi-step automation scenarios. - **[Zapier](https://docs.apify.com/platform/integrations/zapier)** — push new comp data into Sheets, Notion or a database. - **[Slack](https://docs.apify.com/platform/integrations/slack)** — get notified when a monitored company posts new submissions. - **[Google Drive / Sheets](https://docs.apify.com/platform/integrations/drive)** — auto-export every run to a spreadsheet. - **[Airbyte](https://docs.apify.com/platform/integrations/airbyte)** — pipe results into your data warehouse. - **[GitHub](https://docs.apify.com/platform/integrations/github)** — trigger runs from commits or releases. ### Use with AI assistants (MCP) The output is clean, LLM-ready JSON. Call this actor from Claude, ChatGPT, or any agent framework through the **[Apify MCP server](https://docs.apify.com/platform/integrations/mcp)** — ask your assistant to "compare median total comp for software engineers at Google, Meta and Stripe" and let it run this scraper for you. ### More scrapers from scrapesage Build a complete **tech talent & market stack** alongside this compensation data: - **[LinkedIn Jobs Scraper](https://apify.com/scrapesage/linkedin-jobs-scraper)** — job postings as hiring-intent signals, no login. - **[Multi-ATS Job Scraper](https://apify.com/scrapesage/multi-ats-job-scraper)** — jobs from Greenhouse, Lever, Ashby & Workday boards. - **[BuiltIn Jobs Scraper](https://apify.com/scrapesage/builtin-jobs-scraper)** — tech jobs, salaries & companies. - **[Y Combinator Scraper](https://apify.com/scrapesage/ycombinator-scraper)** — startups, founders & jobs. - **[Product Hunt Scraper](https://apify.com/scrapesage/product-hunt-scraper)** — launches, makers & leads. - **[Google Play Store Scraper](https://apify.com/scrapesage/google-play-scraper)** — apps, reviews & developer leads. - **[Apple App Store Scraper](https://apify.com/scrapesage/app-store-scraper)** — apps, reviews & charts. ### Tips - **Find a company's slug**: run `companyDirectory` mode, or just type the name — `Jane Street` → `jane-street`, `JPMorgan Chase` → `jpmorgan-chase`. Slugs are lowercase with hyphens. - **Cheaper test runs**: lower `maxSamplesPerRole` and `maxJobFamiliesPerCompany`, or set `includeSamples` off to get just the rich aggregates. - **Whole-market view**: use `roleComp` with several `locations` to compare the same role across geos. - **Proxies**: the default Apify datacenter proxy is plenty — Levels.fyi has no aggressive anti-bot. ### FAQ **How do I scrape salaries for a specific company?** Use `companyComp` mode and put the company in `companies` (name or slug). Add `jobFamilies` to focus on specific roles, or leave it empty to get the company's most popular roles. **Can I get individual salary submissions, not just averages?** Yes — keep `includeSamples` on (default). Each role includes its individual submissions with base, stock, bonus, total, level, years of experience, location and offer date. **Can I compare a role across locations?** Yes — use `roleComp` with a `jobFamilies` entry and several `locations` (e.g. `united-states`, `san-francisco-bay-area`, `united-kingdom`). **Can I export to Google Sheets, CSV, or Excel?** Yes — one click in the dataset view, or automatically on every run via the [Google Drive integration](https://docs.apify.com/platform/integrations/drive). **How do I monitor new compensation automatically?** Turn on `monitorMode` and create a [Schedule](https://docs.apify.com/platform/schedules); each run returns only new submissions and changed medians. **A field is null — why?** Some submissions genuinely omit bonus, focus, gender or other details, and some companies don't track every role. Fields are `null` only when the data doesn't exist, not because the scraper skipped them. **Is scraping Levels.fyi legal?** This actor collects publicly available data only. You are responsible for using the data in compliance with applicable laws and Levels.fyi's terms. ### Need help? Open an issue on the actor's **Issues** tab, or visit the [Apify help center](https://help.apify.com/). Feature requests are welcome — this actor is actively maintained. # Actor input Schema ## `mode` (type: `string`): What to scrape. companyComp = full compensation for the companies you list. roleComp = market benchmarks for a job family in given locations. companyDirectory = list popular companies + the job-family taxonomy (to discover valid slugs). startUrls override the mode. ## `companies` (type: `array`): Company names or Levels.fyi slugs for companyComp mode, e.g. "Google", "Meta", "stripe", "Jane Street". Names are auto-converted to slugs. Use the companyDirectory mode if you are unsure of a slug. ## `jobFamilies` (type: `array`): Job-family names or slugs. In companyComp it limits which roles are detailed per company (empty = the company's most popular roles). In roleComp it is the roles to benchmark. Examples: "Software Engineer", "product-manager", "data-scientist". ## `locations` (type: `array`): Location slugs for roleComp market benchmarks. Examples: "united-states", "san-francisco-bay-area", "new-york-city-area", "greater-seattle-area", "united-kingdom", "india", "canada". Defaults to united-states. ## `startUrls` (type: `array`): Levels.fyi URLs to scrape directly (auto-routed). Supports company pages (/companies//salaries), a single role (/companies//salaries/), market pages (/t//locations/) and the /companies/ directory. ## `maxJobFamiliesPerCompany` (type: `integer`): In companyComp mode, how many of a company's roles to pull detailed compensation for (ordered by popularity) when no job families are specified. ## `includeSamples` (type: `boolean`): Also output each individual salary submission (base, stock, bonus, total, level, years of experience, location, offer date) embedded in every role, not just the per-level aggregates. ## `maxSamplesPerRole` (type: `integer`): Cap on individual salary submissions emitted per company + job family. Lower it for quick or cheap runs. ## `monitorMode` (type: `boolean`): Remember what was scraped in previous runs (in a named key-value store) and emit only NEW salary submissions and rows whose compensation changed. Pair with Apify Schedules to track fresh comp data over time. Does not conflict with scheduling. ## `monitorStoreName` (type: `string`): Named key-value store that holds the 'already seen' keys for monitor mode. Use a distinct name per saved task so their histories stay separate. ## `maxConcurrency` (type: `integer`): Maximum number of pages fetched in parallel. ## `proxyConfiguration` (type: `object`): Proxy settings. Levels.fyi has no aggressive anti-bot, so the default Apify datacenter proxy is plenty. ## Actor input object example ```json { "mode": "companyComp", "companies": [ "Google", "Stripe" ], "locations": [ "united-states" ], "maxJobFamiliesPerCompany": 6, "includeSamples": true, "maxSamplesPerRole": 50, "monitorMode": false, "monitorStoreName": "levels-fyi-monitor", "maxConcurrency": 5, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `results` (type: `string`): All scraped company, role, salary-submission, market and job-family records as JSON items in the default dataset. # 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 = { "companies": [ "Google", "Stripe" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapesage/levels-fyi-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 = { "companies": [ "Google", "Stripe", ] } # Run the Actor and wait for it to finish run = client.actor("scrapesage/levels-fyi-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 '{ "companies": [ "Google", "Stripe" ] }' | apify call scrapesage/levels-fyi-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapesage/levels-fyi-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Levels.fyi Scraper — Tech Salaries, Compensation & Leveling", "description": "Scrape Levels.fyi tech compensation: company salaries by job family & level (base, stock, bonus, total), individual salary submissions, leveling data, percentiles, titles, and role + location market benchmarks with top-paying companies. Monitoring mode. No API key, no browser.", "version": "0.1", "x-build-id": "3qJwdvp5k8zdd8d3t" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapesage~levels-fyi-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapesage-levels-fyi-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapesage~levels-fyi-scraper/runs": { "post": { "operationId": "runs-sync-scrapesage-levels-fyi-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapesage~levels-fyi-scraper/run-sync": { "post": { "operationId": "run-sync-scrapesage-levels-fyi-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "mode": { "title": "Mode", "enum": [ "companyComp", "roleComp", "companyDirectory" ], "type": "string", "description": "What to scrape. companyComp = full compensation for the companies you list. roleComp = market benchmarks for a job family in given locations. companyDirectory = list popular companies + the job-family taxonomy (to discover valid slugs). startUrls override the mode.", "default": "companyComp" }, "companies": { "title": "Companies", "type": "array", "description": "Company names or Levels.fyi slugs for companyComp mode, e.g. \"Google\", \"Meta\", \"stripe\", \"Jane Street\". Names are auto-converted to slugs. Use the companyDirectory mode if you are unsure of a slug.", "items": { "type": "string" } }, "jobFamilies": { "title": "Job families", "type": "array", "description": "Job-family names or slugs. In companyComp it limits which roles are detailed per company (empty = the company's most popular roles). In roleComp it is the roles to benchmark. Examples: \"Software Engineer\", \"product-manager\", \"data-scientist\".", "items": { "type": "string" } }, "locations": { "title": "Locations (roleComp)", "type": "array", "description": "Location slugs for roleComp market benchmarks. Examples: \"united-states\", \"san-francisco-bay-area\", \"new-york-city-area\", \"greater-seattle-area\", \"united-kingdom\", \"india\", \"canada\". Defaults to united-states.", "items": { "type": "string" }, "default": [ "united-states" ] }, "startUrls": { "title": "Start URLs", "type": "array", "description": "Levels.fyi URLs to scrape directly (auto-routed). Supports company pages (/companies//salaries), a single role (/companies//salaries/), market pages (/t//locations/) and the /companies/ directory.", "items": { "type": "string" } }, "maxJobFamiliesPerCompany": { "title": "Max job families per company", "minimum": 1, "maximum": 80, "type": "integer", "description": "In companyComp mode, how many of a company's roles to pull detailed compensation for (ordered by popularity) when no job families are specified.", "default": 6 }, "includeSamples": { "title": "Include individual salary submissions", "type": "boolean", "description": "Also output each individual salary submission (base, stock, bonus, total, level, years of experience, location, offer date) embedded in every role, not just the per-level aggregates.", "default": true }, "maxSamplesPerRole": { "title": "Max submissions per role", "minimum": 0, "maximum": 1000, "type": "integer", "description": "Cap on individual salary submissions emitted per company + job family. Lower it for quick or cheap runs.", "default": 50 }, "monitorMode": { "title": "Monitor mode (only new / changed data)", "type": "boolean", "description": "Remember what was scraped in previous runs (in a named key-value store) and emit only NEW salary submissions and rows whose compensation changed. Pair with Apify Schedules to track fresh comp data over time. Does not conflict with scheduling.", "default": false }, "monitorStoreName": { "title": "Monitor store name", "type": "string", "description": "Named key-value store that holds the 'already seen' keys for monitor mode. Use a distinct name per saved task so their histories stay separate.", "default": "levels-fyi-monitor" }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of pages fetched in parallel.", "default": 5 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Proxy settings. Levels.fyi has no aggressive anti-bot, so the default Apify datacenter proxy is plenty.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```