# Reddit Scraper (`alwaysprimedev/reddit-scraper`) Actor Scrape Reddit posts, threads, and comments from any subreddit, search, or user โ€” clean structured JSON, fast. - **URL**: https://apify.com/alwaysprimedev/reddit-scraper.md - **Developed by:** [Always Prime](https://apify.com/alwaysprimedev) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 2 total users, 1 monthly users, 80.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.50 / 1,000 posts 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 ## ๐Ÿš€ Reddit Scraper โ€” every post, comment & thread, as clean JSON [![Apify](https://img.shields.io/badge/Run%20on-Apify-00b96b?logo=apify&logoColor=white)](https://apify.com/) [![Python](https://img.shields.io/badge/Built%20with-Python%203.11-3776AB?logo=python&logoColor=white)](https://www.python.org/) [![Output: JSON ยท CSV ยท Excel](https://img.shields.io/badge/Output-JSON%20%7C%20CSV%20%7C%20Excel-orange)](#-sample-output) > Pull **structured Reddit data at speed** โ€” posts, comments, scores, flairs, awards, media, timestamps. No login. No code. No babysitting. ๐Ÿ  Subreddits ยท ๐Ÿ” Keyword search ยท ๐Ÿ‘ค User submissions/comments ยท ๐Ÿ”— Custom URLs โ€” all four sources, one input form. --- ### โšก๏ธ Why this scraper - **๐ŸŽฏ 50+ fields per post** โ€” full title and body, score breakdown, upvote ratio, flair, awards, removal status, media URLs, edit timestamps. Nothing dropped on the floor. - **๐Ÿ’ฌ Comment threads on demand** โ€” flip one switch and get the full comment tree per post, threaded via `parent_id` and `depth`. - **๐Ÿš„ Fast** โ€” ~3 posts/second steady-state on default settings; ~250ms median per detail fetch. - **๐Ÿง  Smart pagination** โ€” stops the moment your `Max items` budget is reached. Never over-fetches, never wastes Apify Compute Units. - **๐Ÿ” Incremental mode** โ€” pass a `since` timestamp and only get posts newer than your last run. Perfect for daily monitoring jobs. - **๐Ÿ›ก๏ธ Built-in failure budget** โ€” if Reddit starts pushing back (challenges, hard 4xx), the actor aborts cleanly instead of burning through your CU on a broken extractor. - **๐Ÿ“Š Three export formats out of the box** โ€” JSON, CSV, Excel. Direct download links from the run page. --- ### ๐Ÿš€ Quick start 1. Click **Try for free** (top-right). No code, no API key. 2. Pick a search type โ€” Subreddit, Search, User, or paste your own URLs. 3. Hit **Start** and let it run. 4. Download as **JSON / CSV / Excel** from the run page. --- ### ๐Ÿ“ฅ Input | Field | Type | Description | |---|---|---| | **What to scrape** (`searchType`) | enum | `subreddit` ยท `search` ยท `user` ยท `urls` | | **Subreddits** (`subreddits`) | string list | e.g. `python`, `programming` (no `r/` prefix) | | **Search query** (`query`) | string | Keywords. Reddit operators work: `author:`, `subreddit:`, `self:yes`, `flair:`. | | **Users** (`users`) | string list | Usernames to scrape (no `u/` prefix) | | **User content type** (`userContent`) | enum | `submitted` (posts) or `comments` | | **Sort by** (`sortBy`) | enum | `hot` ยท `new` ยท `top` ยท `rising` ยท `controversial` ยท `relevance` ยท `comments` | | **Time window** (`time`) | enum | `hour` ยท `day` ยท `week` ยท `month` ยท `year` ยท `all` (only matters for `top`/`controversial`) | | **Max items** (`maxItems`) | int | Stop after N posts. `0` = unlimited. Default `50`. | | **Scrape comments** (`scrapeComments`) | bool | Fetch the comment tree for every post. Default off (cheaper for indexing). | | **Max comments per post** (`commentDepth`) | int | Cap on comments per post (BFS). Default `200`. | | **Only posts newer than** (`since`) | datetime | ISO 8601 cutoff for incremental runs. | | **Concurrency** (`concurrency`) | int | Parallel fetches. Default `5`, max `25`. | | **Start URLs** (`startUrls`) | string list | Advanced override โ€” paste any reddit URLs and ignore the search-type builder. | --- ### ๐Ÿ“ฆ Sample output One record per post โ€” flat, JSON-friendly, ready to load into BigQuery / Postgres / pandas. ```json { "id": "1t3x7ba", "fullname": "t3_1t3x7ba", "url": "https://www.reddit.com/r/Python/comments/1t3x7ba/whos_going_to_pycon_us_next_week/", "subreddit": "Python", "subreddit_prefixed": "r/Python", "subreddit_id": "t5_2qh0y", "title": "Who's going to PyCon US next week?", "selftext": "Me โœ‹ I hope to see a good number of you all in Long Beach, too! ...", "is_self": true, "domain": "self.Python", "post_hint": "self", "link_url": null, "author": "Loren-PSF", "author_fullname": "t2_so0s40st", "author_flair_text": ":pythonLogo: Python Software Foundation Staff", "distinguished": null, "score": 46, "ups": 46, "upvote_ratio": 0.91, "num_comments": 35, "num_crossposts": 0, "total_awards_received": 0, "gilded": 0, "over_18": false, "spoiler": false, "locked": false, "stickied": true, "archived": false, "is_video": false, "is_original_content": false, "link_flair_text": "Discussion", "link_flair_css_class": "discussion", "link_flair_background_color": "#f50057", "thumbnail": null, "preview_image_url": "https://external-preview.redd.it/FBtD3iI-OdRHdmfJbVushiwzLeMcmgTx-Ff3FnwUUg0.jpeg", "video_url": null, "removed_by_category": null, "removal_reason": null, "created_at": "2026-05-04T22:40:29+00:00", "edited_at": null, "scraped_at": "2026-05-09T13:43:47+00:00", "comments": [ { "id": "myz2pn1", "parent_id": "t3_1t3x7ba", "depth": 0, "author": "vintagegeek", "body": "I'll be there with bells on. Looking forward to meeting people!", "score": 19, "is_submitter": false, "stickied": false, "permalink": "https://www.reddit.com/r/Python/comments/1t3x7ba/.../myz2pn1/", "created_at": "2026-05-04T23:01:14+00:00", "edited_at": null } ], "comments_count_scraped": 35 } ```` *** ### ๐Ÿ’ก Use cases | Who | What for | |---|---| | ๐Ÿ“ˆ **Market researchers** | Track sentiment, competitor mentions and product feedback across niche subreddits. | | ๐Ÿค– **AI / ML teams** | Build training corpora from focused subreddits โ€” clean text, threading preserved. | | ๐Ÿ“ฐ **Journalists & analysts** | Monitor breaking-story subreddits and surface trending discussions for coverage. | | ๐Ÿ’ผ **Brand / community managers** | Find unanswered support questions about your product across Reddit, on a daily cron. | | ๐Ÿท๏ธ **Recruiters & talent intel** | Pull discussions in tech-job subreddits to track skill demand and salary chatter. | | ๐Ÿง‘โ€๐Ÿ”ฌ **Academic researchers** | Public-discourse datasets for sociolinguistics, network analysis, opinion mining. | *** ### ๐Ÿงฐ Tips & tricks - ๐Ÿชถ **Index-first, hydrate later.** Run with `scrapeComments: false` and `maxItems: 0` to cheaply enumerate everything. Then a second run with `startUrls` and `scrapeComments: true` only on the posts you care about. - โฑ๏ธ **Daily diffs.** Save the timestamp of your last successful run, then pass it as `since` next time. The actor short-circuits old posts before fetching them. - ๐ŸŽ›๏ธ **Subreddit-scoped search.** Set `searchType: search`, fill `query`, and add subreddits to `subreddits` โ€” the actor automatically scopes search to those subreddits. - ๐Ÿ”— **Mix custom URLs.** Drop any `reddit.com/...` URL into `startUrls` (a thread, a multireddit, a sort variant) โ€” the actor strips/appends `.json` itself. *** ### โ“ FAQ **Does it need a Reddit account?** No. **What about the new Reddit API limits?** This actor doesn't use Reddit's Data API, so the post-2023 commercial pricing tiers don't apply. **Can I scrape NSFW subreddits?** Yes. NSFW posts are returned with `over_18: true` so you can filter downstream. **Will it get all comments on a huge thread?** Up to your `commentDepth` cap (default 200, max 5000), breadth-first across the tree. For Reddit's truly massive megathreads (>10K comments), Reddit itself paginates and not every comment is reachable in one fetch โ€” that's a Reddit limitation, not the scraper's. **What if a post is deleted while scraping?** Deleted posts come through with `author: "[deleted]"`, `selftext: "[deleted]"`, and `removed_by_category: "deleted"`. They're not skipped โ€” you get the metadata Reddit still surfaces. **How fresh is the data?** Real-time. Each record carries a `scraped_at` UTC timestamp. *** ### ๐Ÿ“… Changelog #### 0.1 (initial release) - Subreddit, search, user, and start-URL modes - Configurable comment-tree scraping with depth cap - Incremental `since` filter, `maxItems` cap, dedup, failure budget - JSON / CSV / Excel exports *** ### โš–๏ธ Legal This scraper accesses Reddit through public, non-authenticated requests. Reddit's robots.txt disallows automated crawling, and Reddit's User Agreement and Public Content Policy restrict automated/commercial use of Reddit content. By using this scraper you take on responsibility for the legality of your specific use case in your jurisdiction (including GDPR / CCPA where applicable). The scraper does not bypass authentication, paywalls, or technical access controls. Use it for research, journalism, internal analytics, ML/AI training datasets, or other lawful purposes โ€” and confirm that those purposes are compatible with Reddit's policies and any applicable law before running large-scale jobs. Personal data scraped from Reddit (usernames, comment bodies, flair) may constitute PII under GDPR even though usernames are pseudonymous; treat the output dataset accordingly. # Actor input Schema ## `searchType` (type: `string`): Choose the source of posts: a subreddit's listing, a keyword search, a user's submissions/comments, or a custom list of Reddit URLs. ## `subreddits` (type: `array`): List of subreddit names to scrape (without the r/ prefix). Used when search type is 'subreddit', or to scope a search to specific subreddits. ## `query` (type: `string`): Keywords to search for. Required when search type is 'search'. Reddit's search supports operators like author:USERNAME, subreddit:NAME, self:yes/no, flair:NAME. ## `users` (type: `array`): Reddit usernames to scrape (without the u/ prefix). Used when search type is 'user'. ## `userContent` (type: `string`): When scraping a user, choose whether to fetch their submitted posts or their comments. ## `sortBy` (type: `string`): How Reddit should order results before scraping. Available options depend on search type: subreddits use hot/new/top/rising/controversial; search adds 'relevance' and 'comments'; users use hot/new/top/controversial. ## `time` (type: `string`): Time range filter โ€” only matters when sort is 'top' or 'controversial'. Otherwise ignored by Reddit. ## `maxItems` (type: `integer`): Stop after this many posts have been scraped. Set to 0 for unlimited (the scraper will paginate until Reddit runs out of results). ## `scrapeComments` (type: `boolean`): If on, fetches each post's full comment thread (one extra request per post โ€” slower and ~2ร— the cost). If off, you get post metadata only, which is much cheaper for large indexing runs. ## `commentDepth` (type: `integer`): Cap on how many comments to keep per post (counted breadth-first across the comment tree). Only applies when 'Scrape comments' is on. ## `since` (type: `string`): Optional ISO 8601 cutoff (e.g. 2026-01-01T00:00:00Z). Posts created before this timestamp are skipped โ€” perfect for incremental/diff runs that only pull what's new since last time. ## `concurrency` (type: `integer`): How many simultaneous detail fetches to run. Higher is faster but burns Reddit's per-IP rate budget โ€” leave at 5 unless you've configured a proxy pool. ## `startUrls` (type: `array`): Bypass the searchType-based URL builder and scrape exactly these Reddit URLs. Any reddit.com or old.reddit.com URL works โ€” the scraper appends .json automatically. ## Actor input object example ```json { "searchType": "subreddit", "subreddits": [ "python", "programming" ], "query": "fastapi", "users": [ "spez" ], "userContent": "submitted", "sortBy": "hot", "time": "all", "maxItems": 50, "scrapeComments": false, "commentDepth": 200, "since": "2026-01-01T00:00:00Z", "concurrency": 5, "startUrls": [ "https://www.reddit.com/r/python/top/?t=week" ] } ``` # Actor output Schema ## `posts` (type: `string`): All scraped posts as a JSON array. ## `postsCsv` (type: `string`): All scraped posts as a CSV โ€” opens directly in Excel or Google Sheets. ## `postsXlsx` (type: `string`): All scraped posts as an XLSX workbook. ## `consoleView` (type: `string`): Browse, filter and re-export results in the Apify web console. # 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 = { "subreddits": [ "python", "programming" ] }; // Run the Actor and wait for it to finish const run = await client.actor("alwaysprimedev/reddit-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 = { "subreddits": [ "python", "programming", ] } # Run the Actor and wait for it to finish run = client.actor("alwaysprimedev/reddit-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 '{ "subreddits": [ "python", "programming" ] }' | apify call alwaysprimedev/reddit-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=alwaysprimedev/reddit-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Scraper", "description": "Scrape Reddit posts, threads, and comments from any subreddit, search, or user โ€” clean structured JSON, fast.", "version": "0.1", "x-build-id": "r7cAxLOfrKbPze0pR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/alwaysprimedev~reddit-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-alwaysprimedev-reddit-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/alwaysprimedev~reddit-scraper/runs": { "post": { "operationId": "runs-sync-alwaysprimedev-reddit-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/alwaysprimedev~reddit-scraper/run-sync": { "post": { "operationId": "run-sync-alwaysprimedev-reddit-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchType": { "title": "What to scrape", "enum": [ "subreddit", "search", "user", "urls" ], "type": "string", "description": "Choose the source of posts: a subreddit's listing, a keyword search, a user's submissions/comments, or a custom list of Reddit URLs.", "default": "subreddit" }, "subreddits": { "title": "Subreddits", "uniqueItems": true, "type": "array", "description": "List of subreddit names to scrape (without the r/ prefix). Used when search type is 'subreddit', or to scope a search to specific subreddits.", "items": { "type": "string" } }, "query": { "title": "Search query", "type": "string", "description": "Keywords to search for. Required when search type is 'search'. Reddit's search supports operators like author:USERNAME, subreddit:NAME, self:yes/no, flair:NAME." }, "users": { "title": "Users", "uniqueItems": true, "type": "array", "description": "Reddit usernames to scrape (without the u/ prefix). Used when search type is 'user'.", "items": { "type": "string" } }, "userContent": { "title": "User content type", "enum": [ "submitted", "comments" ], "type": "string", "description": "When scraping a user, choose whether to fetch their submitted posts or their comments.", "default": "submitted" }, "sortBy": { "title": "Sort by", "enum": [ "hot", "new", "top", "rising", "controversial", "relevance", "comments" ], "type": "string", "description": "How Reddit should order results before scraping. Available options depend on search type: subreddits use hot/new/top/rising/controversial; search adds 'relevance' and 'comments'; users use hot/new/top/controversial.", "default": "hot" }, "time": { "title": "Time window", "enum": [ "hour", "day", "week", "month", "year", "all" ], "type": "string", "description": "Time range filter โ€” only matters when sort is 'top' or 'controversial'. Otherwise ignored by Reddit.", "default": "all" }, "maxItems": { "title": "Max items", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Stop after this many posts have been scraped. Set to 0 for unlimited (the scraper will paginate until Reddit runs out of results).", "default": 50 }, "scrapeComments": { "title": "Scrape comments", "type": "boolean", "description": "If on, fetches each post's full comment thread (one extra request per post โ€” slower and ~2ร— the cost). If off, you get post metadata only, which is much cheaper for large indexing runs.", "default": false }, "commentDepth": { "title": "Max comments per post", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Cap on how many comments to keep per post (counted breadth-first across the comment tree). Only applies when 'Scrape comments' is on.", "default": 200 }, "since": { "title": "Only posts newer than", "type": "string", "description": "Optional ISO 8601 cutoff (e.g. 2026-01-01T00:00:00Z). Posts created before this timestamp are skipped โ€” perfect for incremental/diff runs that only pull what's new since last time." }, "concurrency": { "title": "Concurrency", "minimum": 1, "maximum": 25, "type": "integer", "description": "How many simultaneous detail fetches to run. Higher is faster but burns Reddit's per-IP rate budget โ€” leave at 5 unless you've configured a proxy pool.", "default": 5 }, "startUrls": { "title": "Start URLs (advanced)", "uniqueItems": true, "type": "array", "description": "Bypass the searchType-based URL builder and scrape exactly these Reddit URLs. Any reddit.com or old.reddit.com URL works โ€” the scraper appends .json automatically.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```