# ๐ŸŒ 500 Global Companies Directory โ€” Global Accelerator Alumni (`nexgendata/500-global-companies-directory`) Actor Scrape 500 Global (500 Startups) portfolio โ€” 2,200+ alumni across HQ + MENA, LatAm, SEA, Korea, Taiwan, Vietnam, Eurasia programs. Batch, stage, country, industry, business model, website, founders. VC sourcing, BD, M\&A. Bloomberg / PitchBook / CB Insights alternative. - **URL**: https://apify.com/nexgendata/500-global-companies-directory.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 500 global companies 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 ## ๐ŸŒ 500 Global Companies Directory โ€” Global Accelerator Alumni Database Pull the **entire 500 Global portfolio** (formerly **500 Startups**) โ€” every batch, every regional program, every alumni since 2010 โ€” straight from the official 500.co portfolio backend. **2,200+ portfolio companies across 9 programs**: Mountain View HQ (Global Accelerator 1 through GA 34 + Distro), 500 MENA (Saudi Arabia, UAE, Egypt, Morocco), 500 LatAm (Mexico, Brazil, Colombia, Chile), 500 SEA (Singapore, Malaysia, Indonesia), 500 Korea, 500 Taiwan, 500 Vietnam, 500 Thailand, and 500 Eurasia (Georgia, Kazakhstan). No login, no flaky CSS selectors, no captchas. Each company arrives as one clean structured row ready for your CRM, lead-gen pipeline, BI warehouse or ML feature store. If you sell to startups, recruit from startups, invest in startups, write about startups, or vendor-to startups โ€” **the 500 Global alumni roster is one of the most expensive datasets in private markets** ($60k+/year on Bloomberg, PitchBook or CB Insights). 500 Global was the most prolific seed investor of the 2010s, has backed **Canva, Credit Karma, Talkdesk, Udemy, Twilio (early), GitLab (early), Grab, Carousell, Intercom, Wildfire (Google), MakerBot (Stratasys)** and another 2,000+ active alumni. This Actor delivers the whole portfolio for **$0.05 per company**. --- ### ๐Ÿ’ฐ Pricing | Event | Price | When | |-------|-------|------| | Actor start | **$0.01** | Once per run, when Actor begins | | 500 Global company record | **$0.05** | Per company pushed to the dataset | **Example runs** | Run size | Cost | |----------|------| | 10-row smoke test | $0.51 | | 1 cohort year (~170 companies) | ~$8.50 | | Top 5 regional programs (~1,400 companies) | ~$70 | | Full alumni dump (2,224 companies) | ~$111 | Compare that to **$24,000+ for one Crunchbase Pro seat**, **$25,000+ for CB Insights**, **$30kโ€“$80k for PitchBook**, or **$24,000+ per Bloomberg Terminal seat**. This Actor pays for itself the first time a single qualified lead converts. --- ### ๐ŸŽฏ Who buys this - **VC associates** sourcing seed and Series A deals from 500 Global graduating classes (this is the #2 most-prolific seed accelerator after Y Combinator, with deeper MENA + LatAm + SEA + Korea coverage than any US-centric source) - **Corporate development / M&A** teams hunting acquisition targets โ€” 500 Global has **675+ exited** portfolio companies (acquired or IPO'd) - **Sales & BD** at startup-tooling vendors (Stripe, Brex, Mercury, Ramp, AWS, Vercel, Datadog, Snowflake competitors) building target lists of 2โ€“10 year-old companies past PMF - **Recruiters** sourcing operators with accelerator pedigree โ€” 500 Global alumni founders are a known strong signal - **Regional VCs and family offices** mapping the MENA / LatAm / SEA / Korea / Taiwan startup ecosystems where 500 Global has unique deal-flow access - **Founders** running competitive analysis or partnership outreach to portfolio peers - **Journalists & analysts** writing accelerator-cohort retrospectives, regional ecosystem reports, exit-rate studies - **Academics & researchers** studying startup outcomes by program, geography, business model --- ### ๐Ÿ“Š What every row contains ```json { "id": 3270, "name": "9fin", "legal_name": "9FIN Limited", "slug": "9fin", "description": "Developer of a data analytics platform designed to provide AI-powered financial data...", "batch": "GA 17", "batches": ["GA 17"], "program": "global", "programs": ["global"], "cohort_year": 2017, "first_investment_date": "2017-05-02T00:00:00.000Z", "industry": "Real Estate", "industries": ["Real Estate", "FinTech"], "sector": "Real Estate, FinTech", "business_model": "B2B", "stage": "Series C", "funding_status": "Series C", "country": "United Kingdom", "region": "Europe", "location": "United Kingdom, Europe", "team_size": null, "founders": ["Steven Hunter", "Huss EL-Sheikh"], "logo_url": "https://founder-hub-public.s3.us-west-1.amazonaws.com/...", "website": "https://9fin.com", "linkedin_url": null, "500global_url": "https://500.co/portfolio", "data_source": "500.co/api/startups" } ```` *** ### ๐Ÿ› ๏ธ Input filters All optional โ€” leave empty to dump the full alumni roster. | Field | Type | Example | Effect | |-------|------|---------|--------| | `programs` | list\[str] | `["mena","korea"]` | Tenant slug match: global / mena / latam / sea / korea / taiwan / vietnam / thailand / ee | | `batches` | list\[str] | `["GA 25","Mena 1"]` | Substring match against batch brand names (GA 1โ€“34, Distro 1, Mena 1โ€“9, Korea 1โ€“12, Vietnam 1, etc.) | | `cohortYears` | list\[str] | `["2023","2024"]` | Earliest-investment-year filter | | `industries` | list\[str] | `["FinTech","AI"]` | Substring match against industry tags | | `countries` | list\[str] | `["United States","Saudi Arabia"]` | Country-of-operation match | | `fundingStatus` | list\[str] | `["Exited"]` or `["Series B","Series C"]` | Pre-Seed / Seed / Series Aโ€“D+ / Exited / Active / Writeoff / Dissolved | | `businessModel` | list\[str] | `["B2B"]` | B2B / B2C / B2A / D2C / Fund | | `limit` | int | `100` | Max companies to return (1โ€“3000) | *** ### ๐Ÿ†š vs. Bloomberg / Crunchbase Pro / PitchBook / CB Insights | Capability | This Actor | Bloomberg | Crunchbase Pro | PitchBook | CB Insights | |------------|:----------:|:---------:|:--------------:|:---------:|:-----------:| | 500 Global-specific filtering | โœ… native | โš ๏ธ tags only | โš ๏ธ tags only | โš ๏ธ tags only | โš ๏ธ tags only | | Cohort-year + batch granularity | โœ… exact | โŒ | โš ๏ธ | โš ๏ธ | โš ๏ธ | | Regional program slicing (MENA, LatAm, SEA, Korea, Taiwan...) | โœ… native | โŒ | โŒ | โŒ | โŒ | | Founder names per row | โœ… | โš ๏ธ extra add-on | โš ๏ธ limited | โš ๏ธ paid add-on | โš ๏ธ paid add-on | | Business-model + stage filter | โœ… both | โš ๏ธ stage only | โš ๏ธ stage only | โš ๏ธ stage only | โš ๏ธ stage only | | Pay-per-record pricing | โœ… $0.05 | โŒ $24k+/yr | โŒ $24k+/yr | โŒ $30โ€“80k/yr | โŒ $25k+/yr | | Annual minimum | โŒ none | $24k+ | $7k+ | $30k+ | $25k+ | | Self-service (no sales call) | โœ… | โŒ | โš ๏ธ | โŒ | โŒ | | Raw structured JSON export | โœ… | โš ๏ธ Excel only | โš ๏ธ limited | โš ๏ธ paid add-on | โš ๏ธ paid add-on | | API access | โœ… Apify API | $$$ extra | $$$ extra | $$$ extra | $$$ extra | | Free trial credit | โœ… | โŒ | โŒ | โŒ | โŒ | For one-shot 500 Global sourcing this Actor is **100ร—โ€“1000ร— cheaper** than the incumbents and exposes the batch + program + business-model metadata that the big platforms bury under generic "accelerator backed" tags. MENA / LatAm / SEA / Korea / Taiwan coverage is particularly thin on US-centric incumbents โ€” 500 Global is the canonical source for those ecosystems. *** ### โšก Performance - **No anti-bot friction.** The /api/startups endpoint serves the same JSON the public portfolio page renders โ€” no token, no rate limits beyond polite request pacing. - **Full dump in <30 seconds.** 2,224 companies arrive in a single HTTP response (~2.4 MB JSON). - **Idempotent.** Re-runs are safe; the Actor de-duplicates by `company_id` within a run. - **Daily refresh ready.** Schedule a daily run via Apify Schedules โ€” 500 Global updates the portfolio as new investments close, exits announce, and cohorts onboard. *** ### ๐Ÿงช Quick-start examples **Just the 2024 cohort (~109 companies, ~$5.46):** ```json { "cohortYears": ["2024"], "limit": 500 } ``` **Every 500 Global exit (~675 companies, ~$33.76):** ```json { "fundingStatus": ["Exited"], "limit": 1000 } ``` **MENA fintech alumni (~50 companies, ~$2.51):** ```json { "programs": ["mena"], "industries": ["FinTech"], "limit": 200 } ``` **Saudi Arabia + UAE + Egypt founders, all years (~187 companies, ~$9.36):** ```json { "countries": ["Saudi Arabia","United Arab Emirates","Egypt"], "limit": 500 } ``` **Korea + Taiwan B2B SaaS (~150 companies, ~$7.51):** ```json { "programs": ["korea","taiwan"], "businessModel": ["B2B"], "industries": ["SaaS","Developer Tools"], "limit": 500 } ``` **Series Bโ€“C growth stage globally (~200 companies, ~$10.01):** ```json { "fundingStatus": ["Series B","Series C"], "limit": 500 } ``` *** ### ๐Ÿงฑ Schema notes - `program` (tenant slug) buckets each company into one of nine 500 Global units: **global** (Mountain View HQ, the Global Accelerator and Distro batches), **mena** (Riyadh / Cairo / Dubai), **latam** (Mexico City / Sรฃo Paulo), **sea** (Singapore), **korea** (Seoul), **taiwan** (Taipei), **vietnam** (HCMC), **thailand** (Bangkok), **ee** (Eurasia โ€” Tbilisi / Almaty). A handful of companies pass through multiple programs and will show all tenant slugs in `programs[]`. - `batch` is the cohort brand name โ€” most common values are `GA 1` through `GA 34` (Global Accelerator at HQ), `Distro 1` (Distribution series), `Mena 1` through `Mena 9`, `Korea 1` through `Korea 12`, `Vietnam 1`, etc. - `cohort_year` is derived from the **earliest** `initialInvestDate` across all investments. The raw API has some companies with a 1970 epoch sentinel โ€” those are filtered out. - `stage` reflects the latest known stage label. `funding_status` is a tidy bucket: `Exited`, `Dead` (Writeoff / Dissolved), `Active`, or the literal stage name (`Series A`, `Seed`, etc.). - `business_model` is 500 Global's own tag: **B2B** (~54%), **B2C** (~36%), **B2A** (B2G / B2-Admin), **D2C**, **Fund**. - `founders[]` is parsed from the org `positions[]` list โ€” each entry is a `firstName lastName` string. - `website` is normalized to include the `https://` scheme. *** ### ๐Ÿ”— Sister actors in the NexGenData fleet If you bought 500 Global alumni, you'll likely want these adjacent datasets: - **[YC Companies Directory Scraper](https://apify.com/nexgendata/yc-companies-directory-scraper)** โ€” Y Combinator alumni, every batch since S05 (5,000+ companies). The other tier-1 accelerator alumni database. - **[Techstars Companies Directory](https://apify.com/nexgendata/techstars-companies-directory)** โ€” Techstars portfolio: 5,500+ alumni from 128 city- and vertical-specific accelerators (Boulder, NYC, London, Berlin, Tel Aviv, plus Music, Space, Sports, Climate). - **[Startup Funding Tracker](https://apify.com/nexgendata/startup-funding-tracker)** โ€” round-by-round funding events, lead investors, valuations from SEC, TechCrunch, YC, and Crunchbase signals. - **[SEC Form 13F Holdings Tracker](https://apify.com/nexgendata/sec-form-13f-holdings-tracker)** โ€” quarterly institutional holdings disclosures from SEC EDGAR; track which VCs are public-equity adjacent. - **[Hacker News Show HN Tracker](https://apify.com/nexgendata/hn-show-hn-tracker)** โ€” early-signal indie product launches from HN's "Show HN" stream; many YC and 500 Global founders post here pre-batch. - **[Finance MCP Server](https://apify.com/nexgendata/finance-mcp-server)** โ€” Claude / ChatGPT MCP server bundling the entire NexGenData finance fleet for natural-language sourcing queries. Buy them together and you have the same coverage as a six-figure-per-year Bloomberg / PitchBook / CB Insights stack โ€” for pennies per record. The YC + Techstars + 500 Global tri-pack is the canonical "global accelerator alumni universe" and unlocks ~13,000 founders sourced from the three highest-pedigree programs in the world. *** ### ๐Ÿงญ Why 500 Global specifically? 500 Global is the most globally distributed major accelerator on Earth. While Y Combinator is overwhelmingly US-centric and Techstars is US + Europe heavy, 500 Global has spent fifteen years building deep deal-flow networks in: - **MENA**: Saudi Arabia, UAE, Egypt, Morocco, Jordan, Kuwait โ€” the only major US accelerator with consistent Riyadh / Dubai / Cairo coverage - **LatAm**: Mexico, Brazil, Colombia, Chile, Argentina โ€” anchored by 500 LatAm in Mexico City - **SEA**: Singapore, Malaysia, Indonesia, Vietnam, Thailand, Philippines - **Korea**: dedicated Korea fund since 2015, 86+ portfolio companies - **Taiwan**: dedicated Taiwan fund, 809+ portfolio companies (largest regional program) - **Eurasia**: Georgia, Kazakhstan, Kyrgyzstan โ€” frontier markets others ignore If your thesis touches non-US founders, 500 Global is the canonical proprietary dataset. Bloomberg and Crunchbase have spotty coverage of regional accelerator programs in MENA / SEA / LatAm โ€” this Actor surfaces them with native batch + tenant metadata. *** ### ๐Ÿค Affiliate / support Built and maintained by **NexGenData** โ€” drop a star, a review, or share a use case on Apify Console and we'll credit your account. **Need a custom slice (500 Global + LinkedIn enrichment + founder emails + SEC filings + recent funding rounds)?** Hit `scrapers@thenextgennexus.com` โ€” bulk pricing and white-label feeds available. [**โ–ถ๏ธ Try this Actor โ†’**](https://apify.com/nexgendata/500-global-companies-directory) โ€” first 10 rows on us, $0.05 per record after. *** ### About NexGenData NexGenData publishes 220+ buyer-intent actors covering SEC filings, YC alumni, Delaware DOC, global stock screeners across 30+ exchanges, IPO calendars, IP and patent intelligence, FDA approvals, B2B lead generation, and more. Every actor is pay-per-result with no seat licensing. #### Apify affiliate program โ€” free credits + 30% off Sign up to Apify via our referral link and you'll get: - **Free starter credits** to test this actor and the rest of our 220+ actor fleet - **30% off platform fees** for the life of your account **[Browse the full NexGenData catalog and sign up here](https://apify.com/nexgendata?fpr=2ayu9b)** โ€” same Apify, same actors, just cheaper for you. *Built and maintained by NexGenData.* # Actor input Schema ## `programs` (type: `array`): Filter by 500 Global program/tenant. Each company is tagged with one or more tenants: 'global' (Mountain View HQ โ€” Global Accelerator / GA batches and Distro), 'mena', 'latam', 'sea' (Southeast Asia), 'korea', 'taiwan', 'vietnam', 'thailand', 'ee' (Eurasia). Case-insensitive substring match. Examples: \['mena'], \['korea','taiwan']. Empty = ALL 9 programs. ## `batches` (type: `array`): Substring match against batch brand names. 500 Global batches include 'GA 1' through 'GA 34' (Global Accelerator at Mountain View HQ), 'Distro 1', 'Mena 1' through 'Mena 9', 'Korea 1' through 'Korea 12', 'Vietnam 1', 'Taiwan' batches, 'LatAm', etc. Examples: \['GA 25'], \['Mena','Korea']. Empty = ALL batches. ## `cohortYears` (type: `array`): Filter by year of initial 500 Global investment. 500 Global has been investing since 2010. Examples: \['2024'], \['2022','2023','2024']. Empty = ALL years. ## `industries` (type: `array`): Substring match against 500 Global industry tags (case-insensitive). Top verticals: 'FinTech', 'Developer Tools', 'AI/Machine Learning', 'Data & Analytics', 'HR Tech', 'Real Estate', 'SaaS', 'Education', 'Healthcare', 'Consumer', 'Social Impact', 'Supply Chain', 'Fashion', 'Ecommerce', 'Content', 'Marketplace', 'Mobility'. Empty = no industry filter. ## `countries` (type: `array`): Filter to specific countries (matched against countryOfOperation, case-insensitive). Top markets: 'United States', 'Singapore', 'Mexico', 'Saudi Arabia', 'South Korea', 'UAE', 'United Arab Emirates', 'Canada', 'Malaysia', 'United Kingdom', 'India', 'Egypt', 'Indonesia', 'Thailand', 'Japan', 'Vietnam'. Examples: \['United States'], \['Saudi Arabia','UAE','Egypt']. Empty = ALL countries. ## `fundingStatus` (type: `array`): Restrict by company lifecycle stage. Allowed values (case-insensitive): 'Pre-Seed', 'Seed', 'Series A', 'Series B', 'Series C', 'Series D & Beyond', 'Exited' (acquired or IPO), 'Active', 'Secondary Sale', 'Writeoff', 'Dissolved'. Examples: \['Exited'] for M\&A targets, \['Series B','Series C'] for growth-stage. Empty = no stage filter (returns all). ## `businessModel` (type: `array`): Filter by business model classification. Allowed values: 'B2B', 'B2C', 'B2A' (business-to-government/admin), 'D2C', 'Fund'. Examples: \['B2B'] to target enterprise SaaS. Empty = ALL business models. ## `limit` (type: `integer`): Maximum number of companies to return (1โ€“3000). Each company is one dataset row. The full 500 Global alumni database currently holds 2,224 entries. ## Actor input object example ```json { "programs": [], "batches": [], "cohortYears": [], "industries": [], "countries": [], "fundingStatus": [], "businessModel": [], "limit": 10 } ``` # 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 = { "programs": [], "batches": [], "cohortYears": [], "industries": [], "countries": [], "fundingStatus": [], "businessModel": [], "limit": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/500-global-companies-directory").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 = { "programs": [], "batches": [], "cohortYears": [], "industries": [], "countries": [], "fundingStatus": [], "businessModel": [], "limit": 10, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/500-global-companies-directory").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 '{ "programs": [], "batches": [], "cohortYears": [], "industries": [], "countries": [], "fundingStatus": [], "businessModel": [], "limit": 10 }' | apify call nexgendata/500-global-companies-directory --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/500-global-companies-directory", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "๐ŸŒ 500 Global Companies Directory โ€” Global Accelerator Alumni", "description": "Scrape 500 Global (500 Startups) portfolio โ€” 2,200+ alumni across HQ + MENA, LatAm, SEA, Korea, Taiwan, Vietnam, Eurasia programs. Batch, stage, country, industry, business model, website, founders. VC sourcing, BD, M&A. Bloomberg / PitchBook / CB Insights alternative.", "version": "0.0", "x-build-id": "gKnui4Tx1CVGxLmG8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~500-global-companies-directory/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-500-global-companies-directory", "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/nexgendata~500-global-companies-directory/runs": { "post": { "operationId": "runs-sync-nexgendata-500-global-companies-directory", "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/nexgendata~500-global-companies-directory/run-sync": { "post": { "operationId": "run-sync-nexgendata-500-global-companies-directory", "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": { "programs": { "title": "Program / tenant filter (optional)", "type": "array", "description": "Filter by 500 Global program/tenant. Each company is tagged with one or more tenants: 'global' (Mountain View HQ โ€” Global Accelerator / GA batches and Distro), 'mena', 'latam', 'sea' (Southeast Asia), 'korea', 'taiwan', 'vietnam', 'thailand', 'ee' (Eurasia). Case-insensitive substring match. Examples: ['mena'], ['korea','taiwan']. Empty = ALL 9 programs.", "items": { "type": "string" } }, "batches": { "title": "Batch / cohort name filter (optional)", "type": "array", "description": "Substring match against batch brand names. 500 Global batches include 'GA 1' through 'GA 34' (Global Accelerator at Mountain View HQ), 'Distro 1', 'Mena 1' through 'Mena 9', 'Korea 1' through 'Korea 12', 'Vietnam 1', 'Taiwan' batches, 'LatAm', etc. Examples: ['GA 25'], ['Mena','Korea']. Empty = ALL batches.", "items": { "type": "string" } }, "cohortYears": { "title": "Cohort years (optional)", "type": "array", "description": "Filter by year of initial 500 Global investment. 500 Global has been investing since 2010. Examples: ['2024'], ['2022','2023','2024']. Empty = ALL years.", "items": { "type": "string" } }, "industries": { "title": "Industry verticals (optional)", "type": "array", "description": "Substring match against 500 Global industry tags (case-insensitive). Top verticals: 'FinTech', 'Developer Tools', 'AI/Machine Learning', 'Data & Analytics', 'HR Tech', 'Real Estate', 'SaaS', 'Education', 'Healthcare', 'Consumer', 'Social Impact', 'Supply Chain', 'Fashion', 'Ecommerce', 'Content', 'Marketplace', 'Mobility'. Empty = no industry filter.", "items": { "type": "string" } }, "countries": { "title": "Country / region filter (optional)", "type": "array", "description": "Filter to specific countries (matched against countryOfOperation, case-insensitive). Top markets: 'United States', 'Singapore', 'Mexico', 'Saudi Arabia', 'South Korea', 'UAE', 'United Arab Emirates', 'Canada', 'Malaysia', 'United Kingdom', 'India', 'Egypt', 'Indonesia', 'Thailand', 'Japan', 'Vietnam'. Examples: ['United States'], ['Saudi Arabia','UAE','Egypt']. Empty = ALL countries.", "items": { "type": "string" } }, "fundingStatus": { "title": "Funding stage / status (optional)", "type": "array", "description": "Restrict by company lifecycle stage. Allowed values (case-insensitive): 'Pre-Seed', 'Seed', 'Series A', 'Series B', 'Series C', 'Series D & Beyond', 'Exited' (acquired or IPO), 'Active', 'Secondary Sale', 'Writeoff', 'Dissolved'. Examples: ['Exited'] for M&A targets, ['Series B','Series C'] for growth-stage. Empty = no stage filter (returns all).", "items": { "type": "string" } }, "businessModel": { "title": "Business model filter (optional)", "type": "array", "description": "Filter by business model classification. Allowed values: 'B2B', 'B2C', 'B2A' (business-to-government/admin), 'D2C', 'Fund'. Examples: ['B2B'] to target enterprise SaaS. Empty = ALL business models.", "items": { "type": "string" } }, "limit": { "title": "Max companies", "minimum": 1, "maximum": 3000, "type": "integer", "description": "Maximum number of companies to return (1โ€“3000). Each company is one dataset row. The full 500 Global alumni database currently holds 2,224 entries.", "default": 100 } } }, "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 } } } } } } } } } } ```