# Twitter Tweet Reply Scrape | $0.25/1K Tweets | Pay-Per Result (`kaitoeasyapi/twitter-reply`) Actor 🚀 Instantly scrape Twitter/X replies & conversations with Pay only $0.25/1K tweets. Get complete reply chains, user info & engagement metrics. Perfect for sentiment analysis, market research & competitor tracking. 100% reliable data extraction, no API needed! - **URL**: https://apify.com/kaitoeasyapi/twitter-reply.md - **Developed by:** [Kaito T](https://apify.com/kaitoeasyapi) (community) - **Categories:** Social media, Lead generation, News - **Stats:** 955 total users, 51 monthly users, 100.0% runs succeeded, 34 bookmarks - **User rating**: 3.79 out of 5 stars ## Pricing $0.25 / 1,000 tweets 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 ## Twitter Reply Scraper API - Fast & Affordable Tweet Replies Collection Get all replies to any tweet quickly and affordably with our Twitter Reply Scraper API. At just $0.25 per 1,000 tweets, we offer the most cost-effective solution for gathering Twitter conversation data. ### Key Features - **Fast & Reliable**: - Scrapes 150 tweets per second - 99.9% uptime guarantee - Real-time data access - **Simple to Use**: - Just provide the tweet ID - No Twitter authentication needed - Instant setup and deployment - **Comprehensive Data**: - Captures all replies to a tweet - Includes key metrics like likes, retweets, quotes - Full user profile information - Timestamps and engagement data - Media attachments ### Use Cases - Social media monitoring - Customer feedback analysis - Market research - Sentiment analysis - Competitive intelligence - Academic research ### Why Choose Our API - Most affordable rate: $0.25/1000 tweets - Enterprise-grade reliability - No rate limits - Clean, structured JSON output - 24/7 technical support - Regular updates and maintenance Start collecting Twitter replies data today with our fast, reliable and cost-effective API solution. ### 🎯 Transformative Solutions for Industry Leaders - **Enterprise Decision Makers**: Transform raw data into actionable boardroom insights - **Marketing Strategists**: Leverage behavioral psychology for precision targeting - **Data Scientists & Analysts**: Access comprehensive datasets for predictive modeling - **Research Institutions**: Conduct groundbreaking social behavior studies - **Brand Architects**: Build data-driven brand narratives that resonate ### 🎮 Strategic Applications & ROI Impact #### 📊 Advanced Market Intelligence - **Competitive Edge Analytics** - Deploy AI-powered competitor trajectory mapping - Uncover hidden market opportunities through sentiment analysis - Predict market shifts using behavioral pattern recognition - **Strategic Foresight** - Identify emerging market leaders before mainstream recognition - Leverage psychographic profiling for deeper market understanding - Calculate industry network effects and influence multipliers #### 💡 Growth Engineering - **Influence Optimization** - Utilize network theory to identify key opinion leaders - Apply social proof metrics for authenticity validation - Calculate true ROI through advanced engagement analytics - **Audience Architecture** - Build high-converting audience segments using behavioral clustering - Deploy psychological triggers for maximum engagement - Design viral growth loops based on social network theory #### 🔍 Advanced Analytics Suite - **Network Intelligence** - Map complex relationship matrices using graph theory - Quantify influence paths and information flow - Model viral coefficient potential across networks - **Predictive Insights** - Forecast trend trajectories using machine learning - Apply sentiment analysis for early trend detection - Calculate engagement velocity and acceleration metrics #### 💼 Revenue Acceleration - **Pipeline Generation** - Implement intent-based prospect identification - Calculate customer lifetime value predictions - Deploy automated lead scoring and prioritization - **Partnership Analytics** - Identify synergistic partnership opportunities - Calculate partnership value potential - Design data-driven alliance strategies #### 📱 Engagement Optimization - **Community Architecture** - Apply social psychology principles to community building - Calculate engagement health metrics - Design viral loops for organic growth - **Content Engineering** - Utilize cognitive psychology for content optimization - Deploy machine learning for timing optimization - Calculate content resonance scores #### 🎯 Performance Marketing - **Campaign Intelligence** - Build lookalike audiences using advanced clustering - Calculate customer acquisition cost predictions - Design multi-channel attribution models - **Content ROI** - Apply neuromarketing principles to content strategy - Calculate content performance elasticity - Design automated optimization loops #### 🏢 Enterprise Risk & Opportunity - **Brand Intelligence** - Deploy sentiment analysis for brand health monitoring - Calculate brand equity metrics - Design crisis prediction models - **Risk Mitigation** - Implement early warning systems using AI - Calculate reputation risk scores - Design automated response protocols ### 💫 Success Stories #### Luxury Brand Digital Transformation A global luxury fashion house leveraged our API to decode their audience's psychological patterns and behaviors. By identifying high-net-worth individuals through social signals, they achieved: - 65% reduction in customer acquisition costs - 3.2x increase in conversion rates from targeted campaigns - 89% improvement in customer lifetime value #### Fortune 500 Market Intelligence A leading technology corporation revolutionized their competitive intelligence using our platform's deep analytics: - Predicted 3 major market shifts 6 months ahead of competitors - Identified $2B in new market opportunities - Achieved 47% faster time-to-market for new products - Generated 12x ROI on market research investment #### Global Beauty Brand Success A premium beauty conglomerate transformed their influencer strategy through our AI-powered analytics: - Discovered micro-influencers with 8x higher engagement rates - Reduced marketing costs by 72% while doubling reach - Achieved 95% campaign authenticity score - Generated $4.5M in attributable revenue #### Venture Capital Innovation A top-tier VC firm revolutionized their deal-flow using our social intelligence: - Identified 3 unicorn startups pre-Series A - Reduced due diligence time by 60% - Achieved 4.2x better portfolio performance - Generated $120M in additional portfolio value ### 🛠️ Technical Details #### Input Parameters ```json { "conversation_ids": [ "1846987139428634858" ], "max_items_per_conversation": 200 } ```` #### Sample Output ```json [{ "type": "tweet", "id": "1870089571729584535", "url": "https://x.com/KaitoEasyAPI/status/1870089571729584535", "twitterUrl": "https://twitter.com/KaitoEasyAPI/status/1870089571729584535", "text": "Create value for developer.", "source": "Twitter for iPhone", "retweetCount": 0, "replyCount": 0, "likeCount": 0, "quoteCount": 0, "viewCount": 15, "createdAt": "Fri Dec 20 12:51:13 +0000 2024", "lang": "en", "bookmarkCount": 0, "isReply": true, "inReplyToId": "1846987617864503431", "conversationId": "1846987139428634858", "inReplyToUserId": "1825192371518377984", "inReplyToUsername": "KaitoEasyAPI", "isPinned": false, "author": { "type": "user", "userName": "KaitoEasyAPI", "url": "https://x.com/KaitoEasyAPI", "twitterUrl": "https://twitter.com/KaitoEasyAPI", "id": "1825192371518377984", "name": "Kaito", "isVerified": false, "isBlueVerified": true, "profilePicture": "https://pbs.twimg.com/profile_images/1846385503987552257/5_6H39MO_normal.jpg", "coverPicture": "https://pbs.twimg.com/profile_banners/1825192371518377984/1729047585", "description": "", "location": "San Jose", "followers": 1710, "following": 360, "status": "", "canDm": true, "canMediaTag": true, "createdAt": "Sun Aug 18 15:25:59 +0000 2024", "entities": { "description": { "urls": [] }, "url": {} }, "fastFollowersCount": 0, "favouritesCount": 1071, "hasCustomTimelines": true, "isTranslator": false, "mediaCount": 112, "statusesCount": 831, "withheldInCountries": [], "affiliatesHighlightedLabel": {}, "possiblySensitive": false, "pinnedTweetIds": [ "1846987139428634858" ], "profile_bio": { "description": "Building enterprise-grade APIs for X data collection. Helping developers focus on what matters - your core business. Reliable • Scalable • Cost-effective 🚀", "entities": { "description": {}, "url": { "urls": [{ "display_url": "apify.com/kaitoeasyapi", "expanded_url": "https://apify.com/kaitoeasyapi", "indices": [ 0, 23 ], "url": "https://t.co/iJ8hfzHqRO" }] } } } }, "extendedEntities": {}, "card": null, "place": {}, "entities": {}, "reply_to_user_results": { "rest_id": "1825192371518377984", "result": { "__typename": "User", "rest_id": "1825192371518377984", "core": { "screen_name": "KaitoEasyAPI" } } }, "quoted_tweet_results": null, "quoted_tweet": null, "retweeted_tweet": null, "isConversationControlled": false }] ``` ### 💎 Why Choose Our Scraper? - **Reliable**: Built with enterprise-grade error handling and retry mechanisms - **Compliant**: Respects platform rate limits and terms of service - **Scalable**: Handles both small and large-scale data extraction needs - **Current**: Regular updates to maintain compatibility with X platform changes - **Support**: Dedicated technical support and documentation ### 🚀 Getting Started Create an Apify account - **Try it Free**: Test our API on Apify platform with no commitment - **Write Sample Code**: Experiment with different API calls and parameters. - **Integrate & Deploy**: Seamlessly integrate the API into your project - **Peace of Mind**: Focus on your core business while we ensure API reliability and stability Below is a sample code using curl to call the API. You can relate the [apify api documentations](https://docs.apify.com/api) and find more details. ```bash curl --location 'https://api.apify.com/v2/acts/kaitoeasyapi~twitter-x-data-tweet-scraper-pay-per-result-cheapest/run-sync-get-dataset-items?token=apify_api_xxxxxxxxxxx' \ --header 'Content-Type: application/json' \ --data '{ "twitterContent": "make -\"live laugh love\"", "maxItems": 200, "queryType": "Latest", "lang": "en", "from": "elonmusk" }' ``` ### 📈 Performance Metrics - **Average processing speed**: 50-70 tweets/second - **Success rate**: >99.99% - **API uptime**: real-time ### 🔒 Data Quality Guarantee - **Verified accurate profile information** - **Real-time data extraction** - **Structured, clean JSON output** - **Comprehensive error logging** ### 📈 Comparison with Competitors At Tweet Scraper, we believe in empowering our users through transparency and continuous improvement. While we maintain a humble approach, we regularly analyze the market landscape to ensure we're delivering maximum value to our customers. This comparative analysis reflects our commitment to: - **Empowering Your Success**: Understanding your needs and exceeding expectations - **Continuous Innovation**: Staying at the forefront of technology and capabilities - **Value-Driven Solutions**: Offering enterprise-grade features at accessible prices - **Customer-First Philosophy**: Making data accessibility seamless and affordable Our goal isn't just to compete - it's to elevate the entire industry standard while making powerful data solutions accessible to businesses of all sizes. The following comparison helps illustrate how we strive to deliver exceptional value: | Feature | ![](https://i.ibb.co/VgQCHDm/Kaito-Easy-API.jpg) Kiato Scraper | 🏯 Tweet Scraper | Microworlds Twitter Scraper | Quacker Twitter-scraper | |---------|-----------------|---------------------------|----------------------|----------------------| | Working Twitter Search | ✔️ | ✔️ | ✔️ | ✔️ | | Query Wizard | ✔️ | ✔️ | ❌ | ❌ | | Search Data Range | Unlimited | Unlimited | Unlimited | N/A | | Price Per 100k Results (Tweet URL) | $25 | $40 | $150 | $95.6 | | Price Per 100k Results (Profile) | $10 | $30 | $150 | $22.5 | | Price Per 100k Results (Profile with Filtering) | $10 | $30 | $150 | N/A | | Price Per 100k Results (Search) | $25 | $40 | $150 | N/A | | Tweets Per Second (URL) | 60 | 64 | 24 | 7 | | Tweets Per Second (Profile) |60| 58 | 13 | 15 | | Tweets Per Second (Search) |60 | 49 | 14 | N/A | | Pricing Plan | $0.25 per 1k results | $0.40 per 1k results | $1 per 1k results | Pay for Usage | | Maximum Tweets Per Input Limit | ✔️ | ✔️ | ✔️ | ✔️ | | Maximum Tweets Total Limit | ✔️| ✔️ | ✔️ | ❌ | ✔️ | | Maximum Profiles Scraped Limit | ✔️ | ✔️ | ✔️ | ❌ | ✔️ | | Language Detection | ✔️| ✔️ | ✔️ | ❌ | | Collect Detailed User Info| ✔️ | ✔️ | ✔️ | ✔️ | | Scrape Specific Profiles | ✔️| ✔️ | ✔️ | ✔️ | | Scrape From Tweet URLs | ✔️| ✔️ | ✔️ | ✔️ | | Scrape From Twitter List URLs | ✔️| ✔️| ✔️ | ❌ | ✔️ | | Advanced User Information| ✔️ | ✔️ | ✔️ | ❌ | | Advanced Filtering Options in Interface | ✔️ | ✔️ | ✔️ | ❌ | | Date Range Interface | ✔️ | ✔️ | ✔️ | ✔️ | | Maximum Attempts | ✔️ | ✔️ | ✔️ | ❌ | | No Proxy Required | ✔️ | ✔️ | ✔️ | ❌ | ### 📞 Support & Contact Need help or have special requirements? Contact us at [X](https://x.com/KaitoEasyAPI) for more details.We are always happy to help. Custom solutions - **Enterprise pricing** - **Technical support** - **Feature requests** ### 🌟 Our Product Suite | API Type | Price | Features | Get Started | |---------|------|----------|----------| | Tweet Intelligence API | $0.25/1,000 tweets | • Comprehensive tweet content extraction
• Real-time engagement metrics
• Media and link analysis
• Conversation threading
• Sentiment analysis capabilities | [Start Mining Tweets →](https://apify.com/kaitoeasyapi/twitter-x-data-tweet-scraper-pay-per-result-cheapest) | | User Intelligence API | $0.15/1,000 profiles | • Rich user profile data
• Engagement statistics
• Account verification details
• Professional metrics
• Historical activity analysis | [Access User Intelligence →](https://apify.com/kaitoeasyapi/premium-twitter-user-scraper-pay-per-result) | | Network Intelligence API | $0.10/1,000 connections | • Follower/Following relationship mapping
• Network influence scoring
• Audience overlap analysis
• Growth pattern tracking
• Community detection | [Explore Network Data →](https://apify.com/kaitoeasyapi/cheapest-x-follower-scraper-following-data-extractor) | ### ⚠️ Important Notes Our API pricing is based on the volume of data returned. However, to ensure we can cover our costs on the Apify platform, we have a minimum charge of $X per API call,even if the response contains no results.Thus, we returned N pieces of mock data. We will monitor and adjust the size of N based on the infrastructure costs incurred by Apify.This helps offset the infrastructure costs we incur for each API request, regardless of the data returned. We want to be upfront about this policy so you understand the pricing structure when using our service.Despite adding mock data when no results are found, we're still barely breaking even. Our prices are already very reasonable, but we're committed to maintaining this service to support the growth of your project.Please let us know if you have any other questions. *** Start leveraging the X Platform User Intelligence API to mine valuable social data - your trusted enterprise-grade social data analytics partner powering your business growth. # Actor input Schema ## `conversation_ids` (type: `array`): Enter the tweet ids you want to scrape. Case1: 1846987139428634858,1858743654778892784. Note: conversation\_ids is the tweet id of the tweet you want to scrape. ## `max_items_per_conversation` (type: `integer`): Maximum number of items that you want as output. When conversation\_ids is set, this field means the maximum number of items for each conversation. ## Actor input object example ```json { "conversation_ids": [ "1846987139428634858" ], "max_items_per_conversation": 200 } ``` # 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 = { "conversation_ids": [ "1846987139428634858" ] }; // Run the Actor and wait for it to finish const run = await client.actor("kaitoeasyapi/twitter-reply").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 = { "conversation_ids": ["1846987139428634858"] } # Run the Actor and wait for it to finish run = client.actor("kaitoeasyapi/twitter-reply").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 '{ "conversation_ids": [ "1846987139428634858" ] }' | apify call kaitoeasyapi/twitter-reply --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kaitoeasyapi/twitter-reply", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Twitter Tweet Reply Scrape | $0.25/1K Tweets | Pay-Per Result", "description": "🚀 Instantly scrape Twitter/X replies & conversations with Pay only $0.25/1K tweets. Get complete reply chains, user info & engagement metrics. Perfect for sentiment analysis, market research & competitor tracking. 100% reliable data extraction, no API needed!", "version": "0.0", "x-build-id": "xv9PAx6Vml2YlqrzV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kaitoeasyapi~twitter-reply/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kaitoeasyapi-twitter-reply", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/kaitoeasyapi~twitter-reply/runs": { "post": { "operationId": "runs-sync-kaitoeasyapi-twitter-reply", "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/kaitoeasyapi~twitter-reply/run-sync": { "post": { "operationId": "run-sync-kaitoeasyapi-twitter-reply", "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", "required": [ "conversation_ids", "max_items_per_conversation" ], "properties": { "conversation_ids": { "title": "Conversation IDs", "type": "array", "description": "Enter the tweet ids you want to scrape. Case1: 1846987139428634858,1858743654778892784. Note: conversation_ids is the tweet id of the tweet you want to scrape.", "items": { "type": "string" } }, "max_items_per_conversation": { "title": "Maximum number of reply items per conversation", "minimum": 20, "maximum": 1000000, "type": "integer", "description": "Maximum number of items that you want as output. When conversation_ids is set, this field means the maximum number of items for each conversation.", "default": 200 } } }, "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 } } } } } } } } } } ```