# levels.fyi Salary Scraper — Tech Compensation & Salary Data API (`herus13/levels-fyi-salary-scraper`) Actor Scrape tech compensation data from levels.fyi: base salary, stock, bonus, and total compensation by company, job family, level, and location. Per-offer records or aggregated percentile bands. - **URL**: https://apify.com/herus13/levels-fyi-salary-scraper.md - **Developed by:** [bootforge](https://apify.com/herus13) (community) - **Categories:** Developer tools, Jobs, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 salary records This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Salary Scraper — Tech Compensation Data levels.fyi Salary Scraper is an Apify actor that extracts tech compensation data from [levels.fyi](https://www.levels.fyi) — per-offer salary records and per-level aggregate/percentile bands — by company, job family, and location. Use it to benchmark comp bands before a negotiation, build a compensation-intelligence dashboard, track how a company's pay moves over time, or feed a recruiting/market-research pipeline — exported to JSON, CSV, or Excel. ### Table of contents - [What the levels.fyi Salary Scraper does](#what-the-levelsfyi-salary-scraper-does) - [How to scrape levels.fyi salary data](#how-to-scrape-levelsfyi-salary-data) - [levels.fyi scraper input](#levelsfyi-scraper-input) - [What data you get](#what-data-you-get) - [Pricing](#pricing) - [Recommended proxies for levels.fyi](#recommended-proxies-for-levelsfyi) - [Why this levels.fyi scraper](#why-this-levelsfyi-scraper) - [FAQ](#faq) - [Rate this actor](#rate-this-actor-) - [Related actors](#related-actors) ### What the levels.fyi Salary Scraper does - 💰 **Per-offer records** — individual self-reported comp entries: base, stock, bonus, total compensation, title, years of experience, and location. - 📊 **Per-level aggregates** — company/level rollups with percentile bands (p10–p90) for total compensation and base salary, plus sample count. - 🏢 **Company + job family targeting** — pass explicit company slugs (`google`, `meta`) and a job family (`software-engineer`, `product-manager`, `data-scientist`), or enable **Discover all companies** to enumerate the whole levels.fyi company index. - 🌍 **Location filters** — narrow to specific levels.fyi location slugs (`united-states`, `india`), or leave empty for the default all-location view. - ⚡ **HTTP-only, no anti-bot** — no login, no CAPTCHA, no browser tier; runs are fast and proxy is optional. ### How to scrape levels.fyi salary data 1. Click **Try for free** and open the actor. 2. Enter one or more `companies` (slugs like `google`, `meta`) **or** turn on `discover_all` to enumerate every company on levels.fyi. 3. Set `job_family` (defaults to `software-engineer`) and, optionally, `locations`. 4. Choose `mode` — **one per run**: `records` (one row per individual offer, the default) **or** `aggregates` (one summary row per level: averages + percentile bands). Each mode yields a clean single-schema dataset; run twice if you want both. 5. Click **Start** and watch results stream into the dataset. 6. Export as **JSON, CSV, or Excel**, or pull from the [Apify API](https://docs.apify.com/api/v2). Individual salary records for two companies (the default): ```json { "companies": ["google", "meta"], "job_family": "software-engineer", "mode": "records" } ```` Aggregated salary bands only, capped discovery run across the whole company index: ```json { "discover_all": true, "max_companies": 50, "mode": "aggregates" } ``` ### levels.fyi scraper input | Field | Type | Default | Description | |---|---|---|---| | `companies` | string\[] | — | Company names or levels.fyi slugs (e.g. `google`, `Meta` — auto-slugified). Provide this or enable `discover_all`. | | `job_family` | string | `software-engineer` | levels.fyi job-family slug, e.g. `software-engineer`, `product-manager`, `data-scientist`. | | `locations` | string\[] | — | Optional levels.fyi location slugs for metro-specific pay. Empty = nationwide. See [Locations](#locations) for common slugs. | | `mode` | enum | `records` | One per run. `records` = one row per individual offer; `aggregates` = one summary row per level (averages + percentile bands). Never mixed, so each run's dataset has a single clean schema. | | `discover_all` | boolean | `false` | Enumerate every company on levels.fyi (ignores `companies`). Use `max_companies` to cap. | | `max_companies` | int | — | Cap on how many companies to enumerate when `discover_all` is on. | | `max_records` | int | — | Cap on per-offer salary records per company/location. | | `transport` | enum | `auto` | HTTP engine: `auto` (curl\_cffi), `curl_cffi`, `httpx`, or `primp`. | | `proxy` | object | — | Optional Apify Proxy configuration. Not required for correctness — see [Recommended proxies](#recommended-proxies-for-levelsfyi). | ### Locations Compensation varies widely by metro (SF Bay Area vs. the rest of the US can differ by tens of thousands). Pass one or more `locations` slugs to scrape metro- or country-specific pay; each is scraped separately and the `location_name` output field tells you which one a row belongs to. Leave `locations` empty for nationwide numbers. **Common US metros:** `san-francisco-bay-area`, `greater-seattle-area`, `new-york-city-area`, `greater-los-angeles-area`, `greater-boston-area`, `greater-chicago-area`, `greater-austin-area`, `greater-dallas-area`, `greater-houston-area`, `atlanta-area`, `greater-san-diego-area`, `raleigh-durham-area`, `greater-detroit-area`, `philadelphia-area`, `phoenix-area` **Countries:** `united-states`, `india`, `united-kingdom`, `canada`, `germany`, `netherlands`, `ireland`, `australia`, `singapore`, `israel`, `france`, `switzerland`, `poland`, `japan`, `brazil`, `mexico`, `spain`, `sweden` **Any other location:** open it on levels.fyi and copy the last path segment of the URL — `…/salaries/software-engineer/locations/`**``**. ### What data you get #### Aggregated salary bands (`mode: aggregates`) One summary row per company/level. Sample from a live run (Google, L3, software-engineer, United States): ```json { "company": "Google", "level": "l3", "job_family": "software-engineer", "level_name": "L3", "scraped_at": "2026-07-17T07:09:42.077676+00:00", "count": 33, "base": 161788, "stock": 34280, "bonus": 8702, "total": 204770, "tc_p10": 176000, "tc_p25": 208000, "tc_p50": 293000, "tc_p75": 403000, "tc_p90": 486500, "base_p10": 151000, "base_p25": 168000, "base_p50": 200000, "base_p75": 218000, "base_p90": 242000, "location_name": "United States" } ``` | Field | Description | |---|---| | `company`, `level`, `job_family` | Company name, level identifier, and job family the aggregate covers. | | `count` | Number of self-reported data points behind this rollup. | | `base`, `stock`, `bonus`, `total` | Average annual base salary, stock grant value, bonus, and total compensation (USD). | | `tc_p10`…`tc_p90` | Total-compensation percentile bands where levels.fyi exposes them; unpublished percentiles are `null`. | | `base_p10`…`base_p90` | Base-salary percentile bands, same null-if-unpublished rule. | | `location_name`, `scraped_at` | Location the aggregate covers and capture timestamp. | #### Individual salary records (`mode: records`, default) One row per self-reported offer. Sample from a live run (Google, L3): ```json { "company": "Google", "level": "L3", "uuid": "b6e2f5b0-7c9a-4b8e-9d1a-2f6a8c3d5e9f", "scraped_at": "2026-07-17T09:12:44.118203+00:00", "title": "Software Engineer", "job_family": "software-engineer", "focus_tag": "DevOps", "years_of_experience": 1, "years_at_company": null, "offer_date": null, "location": "Los Angeles, CA", "country_id": null, "dma_id": null, "base_salary": 140000, "avg_annual_stock_grant_value": 20000, "avg_annual_bonus_value": null, "total_compensation": 160000, "gender": null } ``` | Field | Description | |---|---| | `company`, `level`, `title`, `focus_tag` | Company, level, job title, and specialization tag (e.g. `DevOps`) as self-reported. | | `years_of_experience`, `years_at_company` | Reporter's tenure, when disclosed. | | `location` | Free-text city/region as reported. | | `base_salary`, `avg_annual_stock_grant_value`, `avg_annual_bonus_value`, `total_compensation` | Annualized compensation components (USD). | | `uuid`, `scraped_at` | levels.fyi's own record identifier and capture timestamp. | ### Pricing This actor uses **pay-per-event** pricing — you pay for what you scrape, not for time. Pricing below is **provisional** until Console monetization is finalized (see the Monetization tab for current live pricing). | Event | USD | Per 1,000 | |---|---|---| | Actor start (per run) | $0.001 | — | | Salary record scraped (`salary-record`) | $0.002 | $2 | | Salary aggregate scraped (`salary-aggregate`) | $0.001 | $1 | | Typical run | Cost | |---|---| | 1 company, aggregates (~7 levels) | ~$0.008 | | 1 company, records (~50 offers) | ~$0.101 | | 10 companies, records (~500 offers) | ~$1.001 | ### Recommended proxies for levels.fyi **Proxy is optional.** levels.fyi's public salary pages are server-rendered and require no login or CAPTCHA, so a proxy is not required for correctness — only useful for scale (avoiding shared-IP rate limits on large `discover_all` runs). If you run your own scrapers (inside or outside Apify) and need reliable proxies for scale, we use **[DataImpulse](https://dataimpulse.com/?aff=404588\&utm_source=apify)** — pay-as-you-go IPs with per-country targeting and no monthly minimum: 👉 **[Get DataImpulse proxies](https://dataimpulse.com/?aff=404588\&utm_source=apify)** (referral link) ### Why this levels.fyi scraper - **No anti-bot tax** — HTTP-only, no browser, no CAPTCHA solving; runs are fast and cheap because levels.fyi's public salary pages need none of that. - **Two granularities, one actor** — per-offer records for the raw distribution, or per-level aggregates with percentile bands; pick one per run for a clean single-schema dataset. - **Company discovery built in** — `discover_all` enumerates the full levels.fyi company index instead of requiring you to hand-curate slugs. - **Validated output** — every row is Pydantic-validated before it's pushed; malformed entries are dropped, not shipped with garbage fields. - **Open source** — the underlying `levels-fyi-scraper` Python package ships a Typer CLI and a FastAPI server; the Apify wrapper is a thin layer. ### FAQ **What's the difference between records and aggregates?** Records are individual self-reported offers (one row per person) — pick this (the default) for the raw, granular data. Aggregates are per-level rollups levels.fyi computes from those same reports — one row per level with averages plus percentile bands — pick this for quick benchmarking. You choose one `mode` per run so each dataset stays a single clean schema; run the actor twice if you want both. **Do I need a proxy?** No. levels.fyi's public salary pages have no known anti-bot layer, so a proxy is optional. It only helps at scale on large `discover_all` runs. For your own scrapers, we recommend [DataImpulse](https://dataimpulse.com/?aff=404588\&utm_source=apify). **Why are some percentile fields `null`?** levels.fyi doesn't publish every percentile band (p10/p25/p75, etc.) for every company/level/location combination — only the bands it actually surfaces are populated; the rest are `null` rather than guessed. **What does `discover_all` do, and how do I limit it?** It ignores `companies` and enumerates every company slug on levels.fyi's public index instead. Set `max_companies` to cap how many it processes — useful to control run cost and duration. **Can I scrape multiple job families or locations in one run?** One `job_family` per run by design (it's part of the URL levels.fyi serves). `locations` accepts multiple slugs, and each is fetched per company. **Is scraping levels.fyi legal?** This actor collects only publicly available, aggregated and self-reported compensation data. You are responsible for complying with levels.fyi's terms and applicable laws. Do not use this data to identify or target individual reporters. ### Rate this actor ⭐ If the levels.fyi Salary Scraper saved you time, please **leave a review on its Apify Store page** — ratings help other people find it and tell us what to build next. Hit a bug or missing field? Open an issue or contact us through the actor's **Issues** tab and we'll fix it fast — recency and reliability are what keep this actor ranking. ### Related actors Building a compensation or hiring-intelligence pipeline? Pair this actor with our other scrapers — same proxy config format, same Pydantic-validated output, all open source. - **[Indeed Job Scraper](https://apify.com/herus13/indeed-scraper)** — cross-reference open roles at the same companies you're benchmarking here. - **[LinkedIn Jobs Scraper](https://apify.com/herus13/linkedin-jobs-scraper)** — pull live job postings to pair against comp bands. - **[Google Play App Search & Reviews Scraper](https://apify.com/herus13/google-play-reviews-scraper)** — another HTTP-only, no-anti-bot actor if you're assembling a lightweight-scrape toolkit. # Actor input Schema ## `companies` (type: `array`):

Company names or levels.fyi slugs, e.g. google, Meta. Leave empty and enable Discover all companies to enumerate every company.

## `job_family` (type: `string`): levels.fyi job-family slug, e.g. software-engineer, product-manager, data-scientist. ## `locations` (type: `array`):

Optional levels.fyi location slugs for metro-specific pay (comp varies a lot by area — e.g. SF Bay Area vs. the rest of the US). Each location is scraped separately and the output location_name field tells you which one a row is for. Empty = nationwide.

Common US metros: san-francisco-bay-area, greater-seattle-area, new-york-city-area, greater-los-angeles-area, greater-boston-area, greater-chicago-area, greater-austin-area, greater-dallas-area, greater-houston-area, atlanta-area, greater-san-diego-area, raleigh-durham-area, greater-detroit-area, philadelphia-area, phoenix-area.

Countries: united-states, india, united-kingdom, canada, germany, netherlands, ireland, australia, singapore, israel, france, switzerland, poland, japan, brazil, mexico, spain, sweden.

Any other location: open it on levels.fyi and copy the last path segment of the URL (…/salaries/software-engineer/locations/<slug>).

## `mode` (type: `string`):

Pick ONE per run — each produces a clean, single-schema dataset (mixing the two would make the CSV/Excel export full of empty cells).

## `discover_all` (type: `boolean`): Enumerate every company on levels.fyi (ignores the Companies list). Use Max companies to cap. ## `max_companies` (type: `integer`): Cap on how many companies to enumerate when Discover all companies is on. ## `max_records` (type: `integer`): Cap on per-offer salary records per company/location. ## `transport` (type: `string`): HTTP engine used to fetch pages. Auto uses curl\_cffi. ## `proxy` (type: `object`): Proxy settings. Apify Proxy recommended. ## Actor input object example ```json { "companies": [ "google", "meta" ], "job_family": "software-engineer", "locations": [ "san-francisco-bay-area" ], "mode": "records", "discover_all": false, "transport": "auto" } ``` # Actor output Schema ## `results` (type: `string`): Per-offer salary records and/or per-level aggregates for each requested company/job-family/location # 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", "meta" ], "job_family": "software-engineer", "locations": [ "san-francisco-bay-area" ] }; // Run the Actor and wait for it to finish const run = await client.actor("herus13/levels-fyi-salary-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", "meta", ], "job_family": "software-engineer", "locations": ["san-francisco-bay-area"], } # Run the Actor and wait for it to finish run = client.actor("herus13/levels-fyi-salary-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", "meta" ], "job_family": "software-engineer", "locations": [ "san-francisco-bay-area" ] }' | apify call herus13/levels-fyi-salary-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=herus13/levels-fyi-salary-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "levels.fyi Salary Scraper — Tech Compensation & Salary Data API", "description": "Scrape tech compensation data from levels.fyi: base salary, stock, bonus, and total compensation by company, job family, level, and location. Per-offer records or aggregated percentile bands.", "version": "0.1", "x-build-id": "2ziGFj7bCkArKhC2h" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/herus13~levels-fyi-salary-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-herus13-levels-fyi-salary-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/herus13~levels-fyi-salary-scraper/runs": { "post": { "operationId": "runs-sync-herus13-levels-fyi-salary-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/herus13~levels-fyi-salary-scraper/run-sync": { "post": { "operationId": "run-sync-herus13-levels-fyi-salary-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": { "companies": { "title": "Companies", "type": "array", "description": "

Company names or levels.fyi slugs, e.g. google, Meta. Leave empty and enable Discover all companies to enumerate every company.

", "items": { "type": "string" } }, "job_family": { "title": "Job family", "type": "string", "description": "levels.fyi job-family slug, e.g. software-engineer, product-manager, data-scientist." }, "locations": { "title": "Locations (optional)", "type": "array", "description": "

Optional levels.fyi location slugs for metro-specific pay (comp varies a lot by area — e.g. SF Bay Area vs. the rest of the US). Each location is scraped separately and the output location_name field tells you which one a row is for. Empty = nationwide.

Common US metros: san-francisco-bay-area, greater-seattle-area, new-york-city-area, greater-los-angeles-area, greater-boston-area, greater-chicago-area, greater-austin-area, greater-dallas-area, greater-houston-area, atlanta-area, greater-san-diego-area, raleigh-durham-area, greater-detroit-area, philadelphia-area, phoenix-area.

Countries: united-states, india, united-kingdom, canada, germany, netherlands, ireland, australia, singapore, israel, france, switzerland, poland, japan, brazil, mexico, spain, sweden.

Any other location: open it on levels.fyi and copy the last path segment of the URL (…/salaries/software-engineer/locations/<slug>).

", "items": { "type": "string" } }, "mode": { "title": "What to scrape", "enum": [ "records", "aggregates" ], "type": "string", "description": "

Pick ONE per run — each produces a clean, single-schema dataset (mixing the two would make the CSV/Excel export full of empty cells).

  • Individual salary records — one row per submitted offer: level, focus/specialization, years of experience, exact base + stock + bonus + total comp, and city. Best for granular analysis, negotiation, or building your own stats.
  • Aggregated salary bands — one summary row per level (e.g. L3, L4, L5): headcount, average base/stock/bonus/total, and total-comp & base percentiles (p10/p25/p50/p75/p90). Best for quick benchmarking — \"what does an L5 make here?\" — without crunching raw offers.
", "default": "records" }, "discover_all": { "title": "Discover all companies", "type": "boolean", "description": "Enumerate every company on levels.fyi (ignores the Companies list). Use Max companies to cap.", "default": false }, "max_companies": { "title": "Max companies", "minimum": 1, "type": "integer", "description": "Cap on how many companies to enumerate when Discover all companies is on." }, "max_records": { "title": "Max records per target", "minimum": 1, "type": "integer", "description": "Cap on per-offer salary records per company/location." }, "transport": { "title": "Transport", "enum": [ "auto", "curl_cffi", "httpx", "primp" ], "type": "string", "description": "HTTP engine used to fetch pages. Auto uses curl_cffi.", "default": "auto" }, "proxy": { "title": "Proxy configuration", "type": "object", "description": "Proxy settings. Apify Proxy recommended." } } }, "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 } } } } } } } } } } ```