# Coupang Eats Scraper (`amit123/coupang-eats-scraper`) Actor Extract restaurants, menus, prices, ratings, and delivery info from **Coupang Eats (쿠팡이츠)** — South Korea's #2 food delivery platform and one half of a fast-tightening duopoly that now controls ~88% of the Korean delivery market. - **URL**: https://apify.com/amit123/coupang-eats-scraper.md - **Developed by:** [Amit](https://apify.com/amit123) (community) - **Categories:** E-commerce, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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. 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 ## 🍱 Coupang Eats Crawler Extract restaurants, menus, prices, ratings, and delivery info from **Coupang Eats (쿠팡이츠)** — South Korea's #2 food delivery platform and one half of a fast-tightening duopoly that now controls ~88% of the Korean delivery market. --- ### 🌏 What is Coupang Eats? Coupang Eats is the food-delivery arm of **Coupang**, Korea's largest e-commerce company (the "Amazon of Korea", NYSE-listed CPNG). Coupang Eats launched in 2019 as a third-place challenger to Baemin (Baedal Minjok) and Yogiyo. As of May 2026 it has overtaken Yogiyo and become the clear #2: | Platform | Weekly Active Users (May 2026) | Market position | |---|---|---| | **Baemin** (Baedal Minjok) | ~12M | #1 | | **Coupang Eats** | **8.45M** | **#2 — fast growing** | | Yogiyo | ~2M | distant #3 | Baemin + Coupang Eats together hold **88.3% of Korea's delivery app market** — the highest concentration on record. Korea is now effectively a duopoly, and Coupang Eats is the side that's still gaining share, largely because every Coupang Wow membership (Coupang's Amazon-Prime equivalent, ~15M subscribers paying ₩7,890/month) bundles free Eats delivery into the subscription. The platform is **address-gated**: every search, category browse, and store ranking is computed relative to a delivery point (latitude + longitude). The same restaurant can rank #1 in Gangnam and not appear at all in Hongdae 8 km away. #### 🇺🇸 US equivalent - **DoorDash** — closest by market structure (the dominant scale player whose growth was unlocked by the DashPass subscription bundle). - **Uber Eats** — closest by parent-company logic (a delivery service riding on top of a bigger consumer platform's logistics + payments + identity). --- ### 🚀 What does this actor do? Extracts **restaurant listings and menus** from Coupang Eats. You can: - Provide one or more **delivery addresses** (`addresses`) — required for search/category browsing because Coupang Eats results depend on the delivery location. - Provide **search queries** (`searchQueries`) — Korean *or* English. Use Korean (`치킨`, `피자`, `떡볶이`) for the broadest results; English (`Starbucks`, `McDonald's`, `pizza`) works for global chains and cafes with English names. - Provide **category IDs** (`categoryIds`) — browses each category at each address. - Provide **direct store IDs** (`storeIds`) — fetches each store; no address required. - Mix any combination in a single run. You give it inputs, it returns the dataset. --- ### 🍗 Why this data matters (buyer personas) Food-delivery data has more independent buyer personas than almost any other vertical — that's what makes it a high-margin scraping target. | Buyer | What they use the data for | Refresh cadence | |---|---|---| | **Hedge fund / alt-data desks** | "How many restaurants did Coupang Eats add this quarter? Is Baemin pushing harder on discounting? Use weekly catalog deltas as a leading indicator on CPNG and on the privately held Baemin parent (Woowa/DH)." | Daily / weekly | | **Restaurant brands & franchises** | "Where is BBQ Chicken under-distributed? What's our menu pricing vs Kyochon and bhc, per district?" | Daily | | **Brand-protection / IP teams** | "Find restaurants illegally selling our trademarked menu items or impersonating our brand." | Daily | | **F&B market-research firms** | "What's the average delivery fee in Gangnam vs Jeju? How often do new openings stick after 90 days?" | Weekly / monthly | | **Restaurant SaaS (POS, ad tools)** | "Cold-call lists of independents on Coupang Eats sorted by review count — they're the ones doing volume." | Monthly | | **Cross-border quick-commerce expansion teams** | DoorDash / Uber / Wolt strategy desks studying the Korean duopoly playbook (Wow-membership bundle). | Quarterly | | **Logistics & last-mile** | Delivery time benchmarks per district + time-of-day; courier-supply intelligence. | Weekly | | **Consumer-trend / agencies** | What cuisines are trending? Which delivery times are demanded most? Promotion-frequency analysis. | Weekly | These personas pay separately, which lets you stack the same dataset into multiple revenue streams instead of competing on a single buyer. --- ### 🗾️ Example input **Search by keyword at a single address (most common) — works in Korean *or* English:** ```json { "addresses": [ { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam, Seoul" } ], "searchQueries": ["치킨", "피자", "Starbucks"], "maxPagesPerQuery": 1 } ```` **Browse categories across multiple districts:** ```json { "addresses": [ { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam" }, { "latitude": 37.5563, "longitude": 126.9236, "label": "Hongdae" }, { "latitude": 35.1796, "longitude": 129.0756, "label": "Busan Seomyeon" } ], "categoryIds": ["1", "2", "3"], "maxPagesPerQuery": 3 } ``` **Direct store fetch by ID (no address required):** ```json { "storeIds": ["461101", "837003"], "fetchMenu": true } ``` **Mix everything:** ```json { "addresses": [ { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam" } ], "searchQueries": ["치킨", "pizza"], "categoryIds": ["1"], "storeIds": ["461101"], "fetchMenu": true, "maxPagesPerQuery": 2 } ``` *** ### 📦 What data do you get? Every restaurant is returned with the following fields: ```json { "store_id": "461101", "name": "BBQ치킨 강남점", "name_en": null, "categories": ["치킨", "한식"], "category_id": null, "rating_avg": 4.7, "rating_count": 1234, "review_count": 1234, "address": "서울 강남구 테헤란로 152", "address_road": "테헤란로 152", "lat": 37.4979, "lng": 127.0276, "phone": null, "delivery_fee_min": 0, "delivery_fee_max": 3000, "delivery_fee_text": "0원 ~ 3,000원", "delivery_time_min": 25, "delivery_time_max": 35, "minimum_order_amount": 15000, "currency": "KRW", "is_coupang_delivery": true, "is_wow_free_delivery": true, "is_open": true, "is_new": false, "is_promoted": false, "discount_badge": "20% 할인", "promotion_text": "신규 가입 5,000원 쿠폰", "thumbnail": "https://image.coupangcdn.com/.../bbq.jpg", "images": [], "hours": null, "menu_count": 12, "menu": [ { "menu_item_id": "...", "name": "황금올리브 치킨", "price": 23000, "original_price": null, "description": "바삭한 후라이드 치킨", "thumbnail": "https://...", "is_signature": true, "is_popular": true, "options": [] } ], "search_query": "치킨", "search_rank": 1, "search_address_label": "Gangnam", "search_lat": 37.4979, "search_lng": 127.0276, "store_url": "https://web.coupangeats.com/share?storeId=461101", "scraped_at": "2026-06-09T13:42:24.433Z" } ``` #### Field reference | Field | Type | Notes | |---|---|---| | `store_id` | string | Coupang Eats numeric store ID (the `storeId` in the share URL) | | `name`, `name_en` | string | Restaurant name (Korean; English when available) | | `categories` | array | Cuisine categories (치킨, 한식, 일식, etc.) | | `category_id` | string | Set when the store was found via a category browse | | `rating_avg` | number | Average review rating (0–5) | | `rating_count` / `review_count` | number | Total review count | | `address`, `address_road` | string | Full address and road-name address (도로명 주소) | | `lat`, `lng` | number | Restaurant coordinates | | `phone` | string | Phone (often null — many stores hide this) | | `delivery_fee_min`, `delivery_fee_max` | number | Delivery fee range in KRW (varies by distance / promotions) | | `delivery_fee_text` | string | Original humanised fee string when not numeric | | `delivery_time_min`, `delivery_time_max` | number | Estimated delivery time in minutes | | `minimum_order_amount` | number | Minimum order in KRW | | `currency` | string | Always `"KRW"` | | `is_coupang_delivery` | bool | True when delivered by Coupang's own fleet (쿠팡배달), not the restaurant's | | `is_wow_free_delivery` | bool | True when the store qualifies for free delivery under a Coupang Wow membership | | `is_open` | bool | Currently accepting orders | | `is_new` | bool | Recently opened on the platform | | `is_promoted` | bool | Paid placement (Coupang Eats ads) | | `discount_badge` | string | E.g. `"20% 할인"` | | `promotion_text` | string | Free-form promo text | | `thumbnail` | string | Primary restaurant image URL | | `images` | array | Additional images | | `hours` | object | Operating hours per day-of-week (when available) | | `menu_count` | number | Number of items in the menu (populated when `fetchMenu=true` or when listings inline the menu) | | `menu` | array | Menu items with name, price, description, thumbnail, options | | `search_query` | string | Set when the store was found via a search | | `search_rank` | number | Position in the listing | | `search_address_label`, `search_lat`, `search_lng` | string/number | The address slice this result was collected under | | `store_url` | string | Public share URL — `https://web.coupangeats.com/share?storeId={store_id}` | | `scraped_at` | string | ISO timestamp when the page was extracted | *** ### 🦖 How to use 1. Enter at least one input source — `addresses` + `searchQueries` / `categoryIds`, OR `storeIds`. 2. Optionally set `maxPagesPerQuery` (default `1`) and `fetchMenu` (default `false`). 3. Run the actor. 4. Download the results from the **Dataset** tab or fetch them via the **API**. > **Note:** A run with 1 address × 3 queries × 1 page completes in under 10 seconds. *** ### 🔍 Key features - **Fast** — most runs complete in seconds, not minutes. - **Geo-aware search** — every search/category is anchored to a real delivery address, so rankings match what a Gangnam customer would actually see. - **Three input modes** — keyword search, category browsing, and direct store fetch — mixable in one run. - **Search in Korean or English** — Korean returns more results, English works great for global chains. - **Structured output** — 40+ fields per restaurant, plus optional embedded menu array. - **Automatic retries** — transient errors are retried with exponential backoff. *** ### 📋 Input parameters | Parameter | Type | Default | Description | |---|---|---|---| | `addresses` | array of `{latitude, longitude, label}` | `[]` | Delivery addresses to browse from. Required when using `searchQueries` or `categoryIds`. | | `searchQueries` | array of strings | `[]` | Search keywords — Korean or English. Korean returns the broadest results; English works for global chains and English-named cafes. | | `categoryIds` | array of strings | `[]` | Numeric Coupang Eats category IDs (e.g. `1` for chicken). | | `storeIds` | array of strings | `[]` | Direct numeric store IDs — no address required. | | `maxPagesPerQuery` | integer | `1` | Pages to walk per search/category at each address. Each page ≈ 20 stores. | | `fetchMenu` | boolean | `false` | Attach the full menu and additional store fields to each discovered store. | | `proxyConfiguration` | object | none | Optional. Not required for normal runs. | #### Common address coordinates (for testing) | District | Latitude | Longitude | |---|---|---| | Seoul — Gangnam Station | 37.4979 | 127.0276 | | Seoul — Hongdae | 37.5563 | 126.9236 | | Seoul — Itaewon | 37.5347 | 126.9947 | | Seoul — Jongno | 37.5703 | 126.9824 | | Busan — Seomyeon | 35.1796 | 129.0756 | | Daegu — Dongseong-ro | 35.8693 | 128.5953 | | Incheon — Songdo | 37.3831 | 126.6562 | | Jeju City | 33.4996 | 126.5312 | *** ### 📩 Feedback Found a bug or have ideas? Open an issue on the actor's Apify page — happy to improve it. # Actor input Schema ## `addresses` (type: `array`): Delivery addresses to browse from. Coupang Eats results depend on the delivery location — every search/category browse needs a delivery point. Each item: { latitude, longitude, label }. Example Seoul Gangnam: { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam" }. ## `searchQueries` (type: `array`): Search keywords — Korean or English. Korean (치킨, 피자, 떡볶이) returns the broadest results. English (Starbucks, McDonald's, pizza) works great for global chains and English-named cafes. Each query runs at each address. ## `categoryIds` (type: `array`): Numeric Coupang Eats category IDs to browse at each address. Common starter: 1 (chicken). ## `storeIds` (type: `array`): Numeric Coupang Eats store IDs. Returns the full store + menu. No address required. ## `maxPagesPerQuery` (type: `integer`): Maximum number of result pages to collect per search/category at each address. Each page is roughly 20 stores. Default 1. ## `fetchMenu` (type: `boolean`): If true, the actor attaches the full menu and additional store fields to each discovered restaurant. Slower but turns the dataset into a complete catalog. ## `proxyConfiguration` (type: `object`): Optional. Not required for normal runs. ## Actor input object example ```json { "addresses": [ { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam, Seoul" } ], "searchQueries": [ "치킨", "Starbucks" ], "maxPagesPerQuery": 1, "fetchMenu": false, "proxyConfiguration": { "useApifyProxy": false } } ``` # Actor output Schema ## `restaurants` (type: `string`): Flat dataset of restaurants. One row per restaurant per slice (address × search query / category). Includes name, address, GPS, rating, delivery fee, ETA, minimum order, and optional embedded menu when fetchMenu=true. ## `rawSamples` (type: `string`): First-page raw JSON response of every search/category/store source. Useful for debugging or discovering category IDs. # 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 = { "addresses": [ { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam, Seoul" } ], "searchQueries": [ "치킨", "Starbucks" ] }; // Run the Actor and wait for it to finish const run = await client.actor("amit123/coupang-eats-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 = { "addresses": [{ "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam, Seoul", }], "searchQueries": [ "치킨", "Starbucks", ], } # Run the Actor and wait for it to finish run = client.actor("amit123/coupang-eats-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 '{ "addresses": [ { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam, Seoul" } ], "searchQueries": [ "치킨", "Starbucks" ] }' | apify call amit123/coupang-eats-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=amit123/coupang-eats-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Coupang Eats Scraper", "description": "Extract restaurants, menus, prices, ratings, and delivery info from **Coupang Eats (쿠팡이츠)** — South Korea's #2 food delivery platform and one half of a fast-tightening duopoly that now controls ~88% of the Korean delivery market.", "version": "0.0", "x-build-id": "iTI5jwSSX2fr40hnc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/amit123~coupang-eats-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-amit123-coupang-eats-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/amit123~coupang-eats-scraper/runs": { "post": { "operationId": "runs-sync-amit123-coupang-eats-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/amit123~coupang-eats-scraper/run-sync": { "post": { "operationId": "run-sync-amit123-coupang-eats-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "addresses": { "title": "Addresses (lat/lng)", "type": "array", "description": "Delivery addresses to browse from. Coupang Eats results depend on the delivery location — every search/category browse needs a delivery point. Each item: { latitude, longitude, label }. Example Seoul Gangnam: { \"latitude\": 37.4979, \"longitude\": 127.0276, \"label\": \"Gangnam\" }." }, "searchQueries": { "title": "Search Queries", "type": "array", "description": "Search keywords — Korean or English. Korean (치킨, 피자, 떡볶이) returns the broadest results. English (Starbucks, McDonald's, pizza) works great for global chains and English-named cafes. Each query runs at each address.", "items": { "type": "string" } }, "categoryIds": { "title": "Category IDs", "type": "array", "description": "Numeric Coupang Eats category IDs to browse at each address. Common starter: 1 (chicken).", "items": { "type": "string" } }, "storeIds": { "title": "Direct Store IDs", "type": "array", "description": "Numeric Coupang Eats store IDs. Returns the full store + menu. No address required.", "items": { "type": "string" } }, "maxPagesPerQuery": { "title": "Max Pages Per Query", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of result pages to collect per search/category at each address. Each page is roughly 20 stores. Default 1.", "default": 1 }, "fetchMenu": { "title": "Fetch full menu per store", "type": "boolean", "description": "If true, the actor attaches the full menu and additional store fields to each discovered restaurant. Slower but turns the dataset into a complete catalog.", "default": false }, "proxyConfiguration": { "title": "Proxy configuration (optional)", "type": "object", "description": "Optional. Not required for normal runs.", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```