# 👤 Instagram Profile Scraper Enhanced (`ayeeyee/instagram-profile-scraper-enhanced`) Actor Rich Instagram profile data with contact extraction, engagement scoring, follower bands, and MCP-ready metadata. Desktop+mobile fallback chain. 3 modes. No paid API keys required. - **URL**: https://apify.com/ayeeyee/instagram-profile-scraper-enhanced.md - **Developed by:** [Virtual Footprint LLC](https://apify.com/ayeeyee) (community) - **Categories:** Social media, Marketing - **Stats:** 1 total users, 0 monthly users, 33.3% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.77 / 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 ## Instagram Profile Scraper Enhanced ![Apify](https://img.shields.io/badge/Apify-Actor-ff6b37?logo=apify) ![Version](https://img.shields.io/badge/version-3.0-blue) ![Pricing](https://img.shields.io/badge/pricing-PAY_PER_EVENT-success) ![Platform](https://img.shields.io/badge/platform-Instagram-E4405F?logo=instagram) ![Modes](https://img.shields.io/badge/modes-fast_lookup%7Cenrich%7Cbatch-orange) ![MCP-ready](https://img.shields.io/badge/MCP-ready-brightgreen) > Creator intelligence from Instagram profiles: bio contact extraction, engagement-rate scoring, follower-band classification, and MCP-ready `providerHealth` metadata. Desktop+mobile fallback chain. No login cookies required. --- ### Why This Actor Is Better This actor is engineered to outperform every Instagram profile scraper on the Apify Store on reliability, creator-intelligence depth, and price. #### Competitor comparison | Feature | **This Actor** | Apify IG Scraper (top free) | IG API Scraper | Influencer analytics tools | |---|:---:|:---:|:---:|:---:| | No login cookies required | ✅ | ❌ | ❌ | ❌ | | Desktop+mobile fallback | ✅ | ❌ | ❌ | ❌ | | Bio email extraction | ✅ | partial | ❌ | ✅ | | Bio-link website email scrape | ✅ | ❌ | ❌ | ✅ | | Engagement-rate score | ✅ | ❌ | ❌ | ✅ | | Follower-band classification | ✅ nano/micro/macro/mega | ❌ | ❌ | ✅ | | Confidence score (0-1) | ✅ every result | ❌ | ❌ | ❌ | | Source attribution | ✅ per-field | ❌ | partial | partial | | MCP-ready metadata | ✅ `providerHealth` | ❌ | ❌ | ❌ | | Optional HypeAuditor fraud check | ✅ user key | ❌ | ❌ | ✅ paid only | | Price / 1K profiles | **$1.77** | free (rate-limited) | ~$2.50 | ~$5-10 | #### Key Features - 🛡️ **Multi-API fallback chain** — Instagram desktop (Playwright) primary with automatic mobile-HTML fallback. Never fails silently. - ✉️ **Contact extraction** — emails/phones from bio + bio-link website (open-source, no paid API). - 📊 **Engagement scoring** — engagement-rate proxy + label (very_high/high/medium/low) on every profile. - 👥 **Follower bands** — automatic classification: starter / nano / micro / macro / mega for influencer marketing. - 🎯 **Confidence scoring** — every result gets a 0.0–1.0 score so you can filter noise. - 🔗 **Source attribution** — know exactly which provider contributed each field (`instagram`, `website_scraper`, `hypeauditor`). - ⚡ **Cache-first mode** — `fast_lookup` hits the Apify KVS cache for <800ms responses (1h TTL — profiles change fast). - 🤖 **MCP-ready** — every result carries `providerHealth{}` so MCP agents can route around failed providers. - 🔌 **Optional paid enrichment** — drop in `HYPEAUDITOR_API_KEY` for fraud detection. Disabled by default. - 💰 **Transparent PPE pricing** — pay only for successful profiles, no subscription. --- ### Architecture ```mermaid flowchart TD A[Input: usernames/URLs + mode] --> B{Cache hit?} B -- yes --> C[Return cached base profile] B -- no --> D[Primary: Instagram desktop Playwright] D -- fails --> E[Fallback: Instagram mobile HTML httpx] D -- ok --> F[Normalize: followers/posts/bio/profile pic] E --> F F --> G[Enrichment layer] G --> G1[Bio email/phone extraction] G --> G2[Bio-link website contact scraper] G --> G3[Engagement-rate + follower-band scoring] G --> G4[Optional: HypeAuditor fraud detection] G1 --> H[Confidence scoring + source attribution] G2 --> H G3 --> H G4 --> H H --> I[Progressive dataset push] I --> J[Webhook + MCP-ready metadata] C --> J ```` **Data flow:** Input → Cache check → Primary scraper (fallback on failure) → Profile normalization → Bio + website contact enrichment → Engagement + follower-band scoring → Optional fraud detection → Confidence scoring → Progressive dataset push → Webhook delivery. Every result includes `providerHealth{}` tracking per-provider status, latency, and errors — making this actor safe to call from MCP agents that need to route around failures. *** ### Modes | Mode | Description | Target latency | Use case | |---|---|---|---| | `fast_lookup` | Cache-first, base profile data only | <800ms cached | Quick lookups, deduplication | | `enrich` | Full enrichment: contacts + engagement + bands | ~2-4s/profile | Influencer prospecting, lead gen | | `batch` | Queue-based, full enrichment, per-item isolation | varies | Large username lists (100+) | *** ### Input | Parameter | Type | Required | Default | Description | |---|---|:---:|---|---| | `mode` | string | — | `enrich` | `fast_lookup` | `enrich` | `batch` | | `queries` | array | ✅ | `["cristiano"]` | Usernames, profile URLs, or search queries | | `maxResults` | integer | — | `25` | Max profiles per query (1–1000) | | `webhookUrl` | string | — | — | Webhook for completion notification | #### Example input ```json { "mode": "enrich", "queries": ["cristiano", "leomessi", "instagram"], "maxResults": 50, "webhookUrl": "https://your-app.com/webhook" } ``` *** ### Output Results are stored in the Apify dataset as structured JSON. See `.actor/output_schema.json` for the canonical schema. | Field | Type | Description | |---|---|---| | `query` | string | Input query that produced this result | | `username` | string | Instagram handle | | `name` | string | Display name | | `url` | string | Profile URL | | `bio` | string | Bio text | | `profilePic` | string | Profile picture URL | | `followers` | string | Follower count (as displayed, e.g. "1.2M") | | `following` | string | Following count | | `posts` | string | Post count | | `followerCount` | integer | Parsed follower count (numeric) | | `followerBand` | string | `starter` | `nano` | `micro` | `macro` | `mega` | | `engagementRate` | number | Engagement-rate proxy (0-100) | | `engagementLabel` | string | `very_high` | `high` | `medium` | `low` | `unknown` | | `recentPosts` | array | Recent post URLs (up to 12) | | `website` | string | null | External link from bio | | `emails` | array | Extracted email addresses (bio + website) | | `phones` | array | Extracted phone numbers | | `fraudAnalysis` | object | null | HypeAuditor data (if key provided) | | `confidenceScore` | number | 0.0–1.0 reliability score | | `sources` | array | Provider attribution (`instagram`, `website_scraper`, `hypeauditor`) | | `providerHealth` | object | Per-provider status/latency/error for MCP routing | | `cacheStatus` | string | `hit` | `miss` | `degraded` | | `mode` | string | Execution mode used | | `extractedAt` | string | ISO timestamp | #### Example output ```json { "query": "cristiano", "username": "cristiano", "name": "Cristiano Ronaldo", "url": "https://instagram.com/cristiano", "bio": "...", "profilePic": "https://...", "followers": "642M", "following": "580", "posts": "3,600", "followerCount": 642000000, "followerBand": "mega", "engagementRate": 0.56, "engagementLabel": "low", "recentPosts": ["https://instagram.com/p/..."], "website": "https://cr7.com", "emails": ["contact@cr7.com"], "phones": [], "confidenceScore": 0.85, "sources": ["instagram", "website_scraper"], "providerHealth": { "instagram_desktop": {"status": "ok", "latency_ms": 2100, "error": null}, "website_scraper": {"status": "ok", "latency_ms": 1400, "error": null}, "engagement_scorer": {"status": "ok", "latency_ms": 0, "error": null} }, "cacheStatus": "miss", "mode": "enrich", "extractedAt": "2026-06-28T23:45:00.000Z" } ``` *** ### Pricing Transparent pay-per-event (PPE) pricing. You only pay for successful profiles. | Plan | Price per 1K profiles | Savings vs. top competitor | |---|---|---| | Leading competitors | ~$2.50/1K | — | | **This actor (≤10K/mo)** | **$1.77/1K** | **29% cheaper** | | **This actor (10K–100K/mo)** | **$1.50/1K** | **40% cheaper** | | **This actor (100K+/mo)** | **$1.25/1K** | **50% cheaper** | Volume discounts apply automatically based on monthly usage. A small one-time Actor Start fee ($0.00005) applies per run. Optional enrichment events: | Event | Price | When charged | |---|---|---| | `contact_found` | $0.50/1K | Per profile with an extracted email or phone | No monthly subscription required. No paid API keys required for core extraction. *** ### Use Cases - **Influencer prospecting** — find creators by audience size (micro/macro/mega) with verified contact emails - **Creator outreach** — build email lists of Instagram creators for brand partnerships - **Engagement benchmarking** — compare engagement rates across a creator shortlist - **Lead generation** — extract contact info from business Instagram bios and their websites - **MCP agent workflows** — call from LLM agents; `providerHealth` lets agents route around failures - **Market research** — map creator density by niche and follower band - **CRM enrichment** — append Instagram follower counts and engagement to existing contact records - **Fraud screening** — optional HypeAuditor integration to flag fake-follower accounts before outreach *** ### Integration Examples #### Python (Apify SDK) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("ayeeyee/instagram-profile-scraper-enhanced").call(run_input={ "mode": "enrich", "queries": ["cristiano", "leomessi", "instagram"], "maxResults": 50, }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(f"@{item['username']}: {item['followerBand']} band, " f"engagement {item['engagementLabel']}, emails {item['emails']}") ``` #### cURL ```bash curl -X POST "https://api.apify.com/v2/acts/ayeeyee~instagram-profile-scraper-enhanced/runs?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"mode":"enrich","queries":["cristiano","leomessi"],"maxResults":25}' ``` #### Zapier / n8n / Make.com 1. Add an **HTTP Request** or **Apify** module. 2. Use actor ID `ayeeyee/instagram-profile-scraper-enhanced`. 3. Map your trigger output to the `queries` input array. 4. Consume the dataset items in the next step. #### MCP (Model Context Protocol) This actor is MCP-ready. Register it via `@apify/actors-mcp-server`: ```bash npx -y @apify/actors-mcp-server --tools actors,ayeeyee/instagram-profile-scraper-enhanced ``` Agents can then call `call-actor` with the input above and use `providerHealth` + `confidenceScore` + `followerBand` to route around failed providers and filter creator quality. *** ### FAQ **Q: Do I need Instagram login cookies?** No. This actor scrapes public profile data via meta tags and JSON-LD — no login or session cookies required. Private profiles return base data only. **Q: How does the desktop+mobile fallback work?** If the primary Playwright desktop scrape fails (blocked, timeout, login wall), the actor automatically falls back to a lightweight mobile HTML fetch (httpx). The `providerHealth` field on every result shows which providers succeeded/failed and their latency. **Q: Where do the emails come from?** Two sources: (1) emails found directly in the Instagram bio, (2) emails scraped from the website linked in the bio. No paid email-finder API is required. For higher match rates, provide a HypeAuditor key. **Q: What is the engagement rate?** A posts-to-follower ratio proxy expressed as a percentage. Real engagement (likes+comments per post) would require scraping each post — left as optional enrichment. The label (`very_high`/`high`/`medium`/`low`) lets you filter creators quickly. **Q: What are follower bands?** Automatic classification by audience size: `starter` (<1K), `nano` (1K-10K), `micro` (10K-100K), `macro` (100K-1M), `mega` (1M+). Standard influencer-marketing tiers. **Q: Can I call this from an LLM agent?** Yes. The actor is MCP-ready. Every result includes `providerHealth{}`, `confidenceScore`, and `followerBand` so agents can make routing and filtering decisions. Register it via `@apify/actors-mcp-server`. **Q: What is the cache TTL?** 1 hour. Cached results in `fast_lookup` mode return in <800ms. Enrichment results are not cached (always fresh). Profiles change faster than business listings, so the TTL is shorter than directory scrapers. **Q: Is this legal?** Yes. This actor scrapes publicly available Instagram profile data. See the Legal & Compliance section below. *** ### Legal & Compliance This actor scrapes **publicly available** Instagram profile data via meta tags and JSON-LD. It does not access private data, bypass authentication, or store credentials. Users are responsible for complying with applicable data protection laws (GDPR, CCPA, etc.) and Instagram's Terms of Service. This actor is intended for legitimate research, analysis, and creator-outreach use cases. It must not be used for spam, harassment, or unlawful activity. *** ### AI-DLC / Data Lifecycle This actor follows AI-DLC principles for ethical data handling: - **Collection** — Public data only; respects robots.txt and rate limits. - **Processing** — In-memory normalization; no PII logging. - **Storage** — Results are pushed to the user's Apify dataset, not retained by the actor. - **Usage** — Designed for analysis, enrichment, and legitimate creator outreach. - **Disposal** — No long-term caching of user data between runs (cache TTL 1h for base results only). *** ### Enhancement Roadmap (API / MCP Integrations) - **HypeAuditor fraud-detection MCP** — flag fake-follower accounts before outreach (optional, user key) - **Google Vision image tagging MCP** — auto-tag profile pictures and recent posts for niche classification - **OpenAI sentiment MCP** — analyze bio sentiment and brand-fit scoring - **LangGraph conditional workflow** — multi-step: IG profile → website → LinkedIn → AI scoring (see `multi-api-orchestration-spec.md`) - **Vector store integration** — semantic creator deduplication across runs *** ### Changelog - **v3.0** — Multi-API orchestration edition: added desktop+mobile fallback chain, MCP-ready `providerHealth` metadata, engagement-rate + follower-band scoring, optional HypeAuditor fraud detection, expanded FAQ, integration examples, volume pricing tiers. - **v2.0** — Premium README, full output schema, AI-DLC documentation, confidence scoring, source attribution, bio-link website enrichment. - **v1.0** — Initial release with core Playwright scraping and structured output. *** ### Links - **Apify Store:** https://apify.com/ayeeyee/instagram-profile-scraper-enhanced - **Actor ID:** `2NiTjgPTRTmO5aYiI` - **Documentation:** See this README - **Support:** Open an issue on the actor's Apify Store page # Actor input Schema ## `mode` (type: `string`): fast\_lookup: cache-first. enrich: multi-provider enrichment. batch: queue-based. ## `queries` (type: `array`): Search queries, profile URLs, or usernames ## `maxResults` (type: `integer`): Maximum results per query (1-1000) ## `webhookUrl` (type: `string`): Optional webhook URL for completion notification ## Actor input object example ```json { "mode": "enrich", "queries": [ "cristiano" ], "maxResults": 25, "webhookUrl": "" } ``` # Actor output Schema ## `results` (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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("ayeeyee/instagram-profile-scraper-enhanced").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 = {} # Run the Actor and wait for it to finish run = client.actor("ayeeyee/instagram-profile-scraper-enhanced").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 '{}' | apify call ayeeyee/instagram-profile-scraper-enhanced --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ayeeyee/instagram-profile-scraper-enhanced", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "👤 Instagram Profile Scraper Enhanced", "description": "Rich Instagram profile data with contact extraction, engagement scoring, follower bands, and MCP-ready metadata. Desktop+mobile fallback chain. 3 modes. No paid API keys required.", "version": "3.0", "x-build-id": "2wQWt3REAeNlKaV5R" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ayeeyee~instagram-profile-scraper-enhanced/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ayeeyee-instagram-profile-scraper-enhanced", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ayeeyee~instagram-profile-scraper-enhanced/runs": { "post": { "operationId": "runs-sync-ayeeyee-instagram-profile-scraper-enhanced", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ayeeyee~instagram-profile-scraper-enhanced/run-sync": { "post": { "operationId": "run-sync-ayeeyee-instagram-profile-scraper-enhanced", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "mode": { "title": "Execution Mode", "enum": [ "fast_lookup", "enrich", "batch" ], "type": "string", "description": "fast_lookup: cache-first. enrich: multi-provider enrichment. batch: queue-based.", "default": "enrich" }, "queries": { "title": "Search Queries", "type": "array", "description": "Search queries, profile URLs, or usernames", "default": [ "cristiano" ] }, "maxResults": { "title": "Max Results per Query", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum results per query (1-1000)", "default": 25 }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Optional webhook URL for completion notification", "default": "" } } }, "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 } } } } } } } } } } ```