# Startup Promo Deals & Codes Tracker for Subscriptions (`raptorlabs/promo-deals-tracker`) Actor

Aggregates publicly-announced startup credits, free-tier programs, and SaaS discounts from official 'for startups' pages. Outputs a clean, validated dataset of deal records — not a coupon scraper.

- **URL**: https://apify.com/raptorlabs/promo-deals-tracker.md
- **Developed by:** [Bob X](https://apify.com/raptorlabs) (community)
- **Categories:** AI, Developer tools, Open source
- **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Startup Deals Tracker

Structured intelligence feed of publicly-announced **startup credits, free-tier programs, and SaaS discounts** — aggregated from 18 official "for startups" pages and curated aggregators.

This is **not a coupon scraper**. It produces clean, schema-validated deal records — including provider, dollar value, eligibility, expiry, source URLs, and a confidence score — so you can rank, filter, and integrate them into founder-facing tools.

### Why this exists

Founders waste hours hunting credits across vendor pages, aggregator sites, and Twitter threads. Most of what's out there is either dead, gated behind accelerators, or tagged with stale expiry dates. This actor gives you a single, filterable, freshly-verified feed.

### What you get

A validated dataset where each row matches a strict Pydantic schema:

- **Identity** — `provider`, `program_name`, stable `id` slug
- **Substance** — `deal_type` (credits / free\_period / percent\_off / lifetime\_deal / ...), `headline`, `description`
- **Quantification** — `credit_value_usd`, `discount_percent`, `free_period_months`, `estimated_total_value_usd`
- **Eligibility** — `eligibility_tags` (e.g. `startup_seed`, `oss_maintainer`, `student`), `funding_cap_usd`, `requires_partner`, `geographic_restrictions`
- **Claim** — `application_url`, public `promo_code` only, `requires_application`, `expected_response_time_days`
- **Time** — `starts_at`, `expires_at`, `is_recurring`
- **Provenance** — `source_urls`, `source_type`, `first_seen_at`, `last_verified_at`, `verification_status`, `verification_method`
- **Categorization** — `confidence_score`, `category`, `tags`

### Sources

**Official programs (12):** Vercel for Startups, Vercel OSS, Render, Notion, Linear, AWS Activate, Microsoft for Startups Founders Hub, Google for Startups Cloud, NVIDIA Inception, Anthropic for Startups, MongoDB for Startups, GitHub for Startups.

**Aggregators (4):** CreditsGull, StartupPerks, AI Perks, AppSumo SaaS.

**Newsletter / community (2):** Lenny's Product Pass status, Product Hunt Stories.

### How it works

1. Each source is fetched (fast HTTP for static pages, headless Chromium for JS-heavy sources).
2. Crawl4AI's BM25 content filter strips boilerplate and keeps only deal-relevant content.
3. Anthropic Claude Haiku extracts deals via a forced tool call producing structured JSON.
4. Every deal is validated against a strict Pydantic v2 schema; invalid records are discarded with a warning.
5. A 7-day content-hash cache skips the LLM call when a source hasn't changed (the main cost optimization).
6. Optional `verify_freshness` re-checks each source URL for "applications closed", "sold out", "waitlist" patterns.

### Input

| Field | Type | Default | Description |
| --- | --- | --- | --- |
| `categories` | `string[]` | `[]` | Filter by category (`ai_tools`, `hosting`, `database`, ...). Empty = no filter. |
| `eligibility_filter` | `string[]` | `[]` | Filter by eligibility tag (`startup_seed`, `oss_maintainer`, ...). Empty = no filter. |
| `verify_freshness` | `boolean` | `false` | Re-verify each source URL. Charges per `verification-check`. |
| `min_confidence` | `number` | `0.7` | Discard deals below this LLM confidence score. |
| `force_refresh` | `boolean` | `false` | Bypass the 7-day source content cache. |
| `max_deals` | `integer` | `500` | Hard cap on returned deals. |

### Pricing (pay-per-event)

| Event | Price | When charged |
| --- | --- | --- |
| `deal-record` | $0.005 | One per validated deal pushed to the dataset |
| `verification-check` | $0.001 | Per deal re-verified when `verify_freshness=true` |

### Notes

- Personalized promo codes are explicitly never extracted — only public terms.
- The actor tolerates source-level failures: if one source breaks, the others continue.
- Output is the default Apify dataset; consume via the standard Apify dataset API.

### Development

For build, deploy, and local-run instructions, see [`DEVELOPMENT.md`](./DEVELOPMENT.md).

### Author

Built and maintained by Bob @ CyberLink Security.

# Actor input Schema

## `categories` (type: `array`):

Optional category whitelist. Empty = no category filter.

## `eligibility_filter` (type: `array`):

Only return deals matching at least one of these eligibility tags. Empty = no eligibility filter.

## `verify_freshness` (type: `boolean`):

Re-fetch each source URL to verify the deal is still live. Charges one verification-check event per deal verified.

## `min_confidence` (type: `number`):

Discard deals below this LLM-reported confidence (0-1).

## `force_refresh` (type: `boolean`):

Bypass the 7-day source content cache and re-extract everything via LLM.

## `max_deals` (type: `integer`):

Hard cap on the total number of deals returned (and charged for).

## Actor input object example

```json
{
  "categories": [],
  "eligibility_filter": [],
  "verify_freshness": false,
  "min_confidence": 0.7,
  "force_refresh": false,
  "max_deals": 500
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("raptorlabs/promo-deals-tracker").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("raptorlabs/promo-deals-tracker").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 raptorlabs/promo-deals-tracker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=raptorlabs/promo-deals-tracker",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/TSKD66lYfeojSX2KZ/builds/i29xetGPhT287J2Pt/openapi.json