# Hugging Face Spaces Scraper (`automation-lab/huggingface-spaces-scraper`) Actor Scrape Hugging Face Spaces: get space IDs, SDKs, likes, tags, authors, descriptions, and live URLs. Filter by SDK, author, or tag. Sort by likes or trending. - **URL**: https://apify.com/automation-lab/huggingface-spaces-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Hugging Face Spaces Scraper Extract data from Hugging Face Spaces — AI demos, interactive apps, and machine learning tools built with Gradio, Streamlit, Docker, and more. Filter by SDK, author, or tags. Sort by likes, trending score, or recency. No API key required. ### What does it do? This actor scrapes the public [Hugging Face Spaces](https://huggingface.co/spaces) catalog using the official Hugging Face REST API. It extracts metadata about AI demo spaces: who built them, what SDK they use, how many likes they have, their tags, descriptions, and direct URLs — both to the HuggingFace profile page and to the live running space. You can filter results by: - 🔍 **Search query** — keyword matching across space names and descriptions - 🛠 **SDK** — Gradio, Streamlit, Docker, or Static - 👤 **Author** — spaces from a specific user or organization (e.g. `stabilityai`, `google`) - 🏷 **Tag** — any HuggingFace tag (e.g. `language:en`, `task:text-generation`, `license:mit`) - 📊 **Sort** — by likes, trending score, creation date, or last modified date ### Who is it for? #### 🧑‍🔬 AI researchers tracking the ecosystem Scan the Spaces catalog for specific task types or SDKs. Monitor which new demos are trending in your research area — computer vision, NLP, audio, or multimodal AI. #### 🏢 Product teams and competitive analysts Track AI tool releases from competitor organizations. Watch what spaces a specific company (e.g. Meta, Google, Stability AI) is publishing and when they go live. #### 📊 Data scientists building ML registries Populate databases of publicly available AI demos. Build search engines, comparison tools, or leaderboards on top of the extracted space metadata. #### 🤖 Developers building AI-powered pipelines Feed space metadata into LLM workflows, vector databases, or recommendation systems that surface relevant AI tools to end users. #### 🎓 Educators and AI course creators Discover teaching demos and Gradio apps for specific ML tasks. Find spaces with the most likes for a given topic to recommend to students. ### Why use it? - ✅ **No API key needed** — the Hugging Face public API is open - ✅ **Full metadata** — ID, author, SDK, likes, all tags, description, live space URL, dates - ✅ **Flexible filtering** — combine search, SDK, author, and tag filters simultaneously - ✅ **Fast and cheap** — HTTP-only, no browser, low compute cost - ✅ **Pagination handled** — fetch hundreds or thousands of spaces in one run - ✅ **Follows rate limits** — exponential backoff and retry logic built in ### Data extracted | Field | Type | Description | |-------|------|-------------| | `id` | string | Full space ID in `owner/space-name` format | | `author` | string | Author or organization username | | `spaceName` | string | Space name without owner prefix | | `sdk` | string | Framework: `gradio`, `streamlit`, `docker`, or `static` | | `likes` | number | Number of likes on this space | | `tags` | array | All tags (SDK, region, tasks, languages, etc.) | | `description` | string | Short description from the space README card | | `cardTitle` | string | Display title from the README card | | `license` | string | License identifier (e.g. `apache-2.0`, `mit`) | | `createdAt` | string | ISO 8601 creation timestamp | | `lastModified` | string | ISO 8601 last modified timestamp | | `spaceUrl` | string | Live running space URL (e.g. `https://xxx.hf.space`) | | `url` | string | HuggingFace profile page URL | | `scrapedAt` | string | ISO 8601 timestamp of when data was scraped | ### How to use it #### Step 1 — Open the actor Go to [Hugging Face Spaces Scraper](https://apify.com/automation-lab/huggingface-spaces-scraper) on Apify Store and click **Try for free**. #### Step 2 — Configure your input Set your filters in the input form: 1. **Search query** — enter a keyword (e.g. "text to speech") or leave empty to browse all spaces 2. **SDK** — choose a framework or leave as "All SDKs" 3. **Author** — enter an organization name to scrape their spaces (e.g. `facebook`) 4. **Tag** — enter a HuggingFace tag filter (e.g. `language:en`) 5. **Sort by** — choose likes, trending, or date 6. **Max results** — set how many spaces to extract #### Step 3 — Run and export Click **Start**. When the run completes, download results as JSON, CSV, or Excel from the **Dataset** tab. ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `searchQuery` | string | `""` | Keyword to search across space names and descriptions | | `sdk` | string | `""` | Filter by SDK: `gradio`, `streamlit`, `docker`, `static`, or empty for all | | `author` | string | `""` | Filter by author/organization username | | `tag` | string | `""` | Filter by tag (e.g. `license:mit`, `language:fr`) | | `sortBy` | string | `likes` | Sort by: `likes`, `createdAt`, `lastModified`, `trendingScore` | | `maxResults` | integer | `100` | Maximum spaces to extract (use `0` for unlimited) | | `batchSize` | integer | `100` | Items per API request (max 100) | | `maxRetries` | integer | `3` | Retries on failed requests | ### Output example ```json { "id": "stabilityai/stable-diffusion", "author": "stabilityai", "spaceName": "stable-diffusion", "sdk": "gradio", "likes": 8432, "tags": ["gradio", "region:us", "license:creativeml-openrail-m"], "private": false, "description": "Stable Diffusion is a state-of-the-art text-to-image model", "cardTitle": "Stable Diffusion", "license": "creativeml-openrail-m", "createdAt": "2022-08-22T13:00:00.000Z", "lastModified": "2024-01-15T10:32:00.000Z", "spaceUrl": "https://stabilityai-stable-diffusion.hf.space", "url": "https://huggingface.co/spaces/stabilityai/stable-diffusion", "scrapedAt": "2026-04-28T09:00:00.000Z" } ```` ### Tips and tricks - 💡 **Trending spaces** — use `sortBy: trendingScore` to discover what the community is excited about right now - 💡 **Combine filters** — all filters are applied simultaneously (AND logic), so `sdk=gradio` + `author=google` returns only Google's Gradio spaces - 💡 **Scrape an org's whole portfolio** — set `author=huggingface` (or any org) and `maxResults=0` to get all their public spaces - 💡 **Tag syntax** — HuggingFace tags use colon notation: `language:en`, `license:apache-2.0`, `task:image-classification` - 💡 **Monitor new releases** — sort by `createdAt` to see the newest spaces first - 💡 **Find live demos** — the `spaceUrl` field gives you the running app URL you can embed or test directly ### How much does it cost to scrape Hugging Face Spaces? The actor uses pay-per-event (PPE) pricing — you are charged per space extracted, not per run minute. | Plan | Price per space | |------|----------------| | Free | $0.00115 | | Bronze | $0.001 | | Silver | $0.00078 | | Gold | $0.00060 | | Platinum | $0.00040 | | Diamond | $0.00028 | **Estimate:** - 100 spaces → ~$0.10 (Bronze) - 1,000 spaces → ~$1.00 (Bronze) - 10,000 spaces → ~$10 (Bronze) or ~$2.80 (Diamond) There is also a small one-time start fee of $0.005 per run. HuggingFace's public Spaces catalog has 500,000+ spaces. A full catalog scrape at Diamond tier costs approximately $140. **Free plan:** Apify's free plan includes $5 in monthly credits — enough to scrape ~4,300 spaces at Bronze pricing. ### Integrations #### Export to Google Sheets Use the **Export to Google Sheets** integration in Apify Console to automatically write extracted spaces to a spreadsheet. Perfect for tracking an organization's space portfolio or building a weekly trending report. #### Airtable database of AI demos Connect to Airtable using Apify's built-in webhooks. Each run can append new spaces to a base, enabling you to build a curated database of AI tools with custom fields and views. #### Slack notifications on new trending spaces Schedule a daily run filtering by `sortBy: trendingScore` and pipe results to Slack using an Apify webhook → Zapier → Slack flow. Get alerted to new viral demos every morning. #### LLM-powered space categorization Export JSON results and feed to an LLM (Claude, GPT-4) to auto-categorize spaces by use case, assign difficulty ratings, or summarize what each space does — then import back to your own database. #### Vector search over space descriptions Embed the `description` and `cardTitle` fields using an embedding model and store in Pinecone or Weaviate. Enable semantic search over the full HuggingFace Spaces catalog. ### API usage #### Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('automation-lab/huggingface-spaces-scraper').call({ searchQuery: 'text to image', sdk: 'gradio', sortBy: 'likes', maxResults: 100, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Extracted ${items.length} spaces`); console.log(items[0]); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient(token='YOUR_APIFY_TOKEN') run = client.actor('automation-lab/huggingface-spaces-scraper').call(run_input={ 'searchQuery': 'text to image', 'sdk': 'gradio', 'sortBy': 'likes', 'maxResults': 100, }) dataset = client.dataset(run['defaultDatasetId']).list_items() for item in dataset['items']: print(item['id'], item['likes']) ``` #### cURL ```bash ## Start a run curl -X POST "https://api.apify.com/v2/acts/automation-lab~huggingface-spaces-scraper/runs" \ -H "Authorization: Bearer YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"searchQuery":"text to image","sdk":"gradio","maxResults":50}' ## Get results (replace DATASET_ID with run's defaultDatasetId) curl "https://api.apify.com/v2/datasets/DATASET_ID/items?limit=100" \ -H "Authorization: Bearer YOUR_APIFY_TOKEN" ``` ### Use with Claude and MCP You can query this scraper directly from Claude Code, Claude Desktop, Cursor, or VS Code using the Apify MCP server. #### Claude Code (terminal) ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/huggingface-spaces-scraper" ``` #### Claude Desktop / Cursor / VS Code Add to your MCP config: ```json { "mcpServers": { "apify": { "command": "npx", "args": ["-y", "@apify/mcp-server"], "env": { "APIFY_TOKEN": "YOUR_APIFY_TOKEN", "ACTORS": "automation-lab/huggingface-spaces-scraper" } } } } ``` #### Example prompts - *"Find the top 50 most-liked Gradio spaces for image generation"* - *"Get all spaces published by stabilityai and export as CSV"* - *"Show me spaces that were created in the last 7 days, sorted by trending score"* - *"List all Docker-based spaces with more than 1000 likes"* ### Legality — Is it legal to scrape Hugging Face Spaces? This actor uses the official, publicly documented Hugging Face REST API (`https://huggingface.co/api/spaces`) — the same API that powers the HuggingFace website itself. There is no scraping of HTML, no bypassing of rate limits, and no collection of private or authenticated data. Only **public** spaces are accessible (private spaces require authentication which this actor does not use). The extracted metadata is publicly visible to any visitor on huggingface.co. Always review [HuggingFace's Terms of Service](https://huggingface.co/terms-of-service) and their API usage policies before using this data commercially. ### FAQ **Q: Does this require a HuggingFace API key?** A: No. The Spaces API is publicly accessible without authentication. No API key is needed. **Q: Can I scrape private spaces?** A: No. This actor only accesses public spaces. Private spaces require authentication which is not supported. **Q: How many spaces can I scrape?** A: HuggingFace has 500,000+ public spaces. Set `maxResults: 0` for an unlimited run that will fetch all available spaces matching your filters. **Q: Why are some descriptions empty?** A: Not all spaces have a `short_description` in their README card. Many spaces (especially older ones) were created without a card description. The `cardTitle` field is more consistently populated. **Q: I'm getting fewer results than expected — what's wrong?** A: The HuggingFace API may return fewer results when combining strict filters. Try relaxing one filter at a time. Also check that your tag syntax uses HuggingFace's colon format (e.g. `license:mit` not just `mit`). **Q: The actor timed out — what should I do?** A: Increase the timeout in Advanced Settings, or reduce `maxResults` to scrape in smaller batches. For very large runs (10,000+ spaces), consider splitting by SDK type and merging results. **Q: How do I get spaces from a specific task category?** A: Use the `tag` filter with a task tag (e.g. `tag: task:text-generation`). You can find valid tag values by browsing the HuggingFace Spaces UI and checking the filter panel. ### Related scrapers Looking for more HuggingFace data? Check out our other automation-lab scrapers: - 🤖 [Hugging Face Scraper](https://apify.com/automation-lab/huggingface-scraper) — models, datasets, spaces, and papers in one actor - 📄 [Hugging Face Papers Scraper](https://apify.com/automation-lab/huggingface-papers-scraper) — research papers with abstracts, authors, and upvotes - 📦 [Hugging Face Datasets Scraper](https://apify.com/automation-lab/huggingface-datasets-scraper) — ML datasets with downloads, licenses, and metadata # Actor input Schema ## `searchQuery` (type: `string`): Keyword to search within space names and descriptions. Leave empty to browse all public spaces. ## `sdk` (type: `string`): Filter spaces by SDK type. Leave empty to include all SDKs. ## `author` (type: `string`): Filter spaces from a specific author or organization (e.g. 'stabilityai', 'facebook'). ## `tag` (type: `string`): Filter by a specific HuggingFace tag (e.g. 'language:en', 'task:text-generation', 'license:mit'). ## `sortBy` (type: `string`): Sort spaces by this field. 'likes' returns most popular spaces first. ## `maxResults` (type: `integer`): Maximum number of spaces to extract. Use 0 for all available results. ## `batchSize` (type: `integer`): Number of spaces to fetch per API request (max 100). ## `maxRetries` (type: `integer`): Number of retry attempts for failed API requests. ## Actor input object example ```json { "searchQuery": "image generation", "sortBy": "likes", "maxResults": 20, "batchSize": 100, "maxRetries": 3 } ``` # Actor output Schema ## `overview` (type: `string`): No description # 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 = { "searchQuery": "image generation", "sdk": "", "author": "", "tag": "", "sortBy": "likes", "maxResults": 20, "maxRetries": 3 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/huggingface-spaces-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 = { "searchQuery": "image generation", "sdk": "", "author": "", "tag": "", "sortBy": "likes", "maxResults": 20, "maxRetries": 3, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/huggingface-spaces-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 '{ "searchQuery": "image generation", "sdk": "", "author": "", "tag": "", "sortBy": "likes", "maxResults": 20, "maxRetries": 3 }' | apify call automation-lab/huggingface-spaces-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/huggingface-spaces-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hugging Face Spaces Scraper", "description": "Scrape Hugging Face Spaces: get space IDs, SDKs, likes, tags, authors, descriptions, and live URLs. Filter by SDK, author, or tag. Sort by likes or trending.", "version": "0.1", "x-build-id": "ZdjKCduWI0HEJoslI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~huggingface-spaces-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-huggingface-spaces-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~huggingface-spaces-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-huggingface-spaces-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~huggingface-spaces-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-huggingface-spaces-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQuery": { "title": "🔍 Search query", "type": "string", "description": "Keyword to search within space names and descriptions. Leave empty to browse all public spaces." }, "sdk": { "title": "🛠 SDK / framework", "enum": [ "", "gradio", "streamlit", "docker", "static" ], "type": "string", "description": "Filter spaces by SDK type. Leave empty to include all SDKs." }, "author": { "title": "👤 Author / organization", "type": "string", "description": "Filter spaces from a specific author or organization (e.g. 'stabilityai', 'facebook')." }, "tag": { "title": "🏷 Tag filter", "type": "string", "description": "Filter by a specific HuggingFace tag (e.g. 'language:en', 'task:text-generation', 'license:mit')." }, "sortBy": { "title": "📊 Sort by", "enum": [ "likes", "createdAt", "lastModified", "trendingScore" ], "type": "string", "description": "Sort spaces by this field. 'likes' returns most popular spaces first." }, "maxResults": { "title": "📦 Max results", "minimum": 0, "type": "integer", "description": "Maximum number of spaces to extract. Use 0 for all available results.", "default": 100 }, "batchSize": { "title": "Batch size", "minimum": 1, "maximum": 100, "type": "integer", "description": "Number of spaces to fetch per API request (max 100).", "default": 100 }, "maxRetries": { "title": "Max retries", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of retry attempts for failed API requests.", "default": 3 } } }, "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 } } } } } } } } } } ```