AppSumo Scraper helps you collect structured information about software, courses, and digital product deals from AppSumo. It simplifies deal discovery and analysis, making it easier to track offers, filter by categories, and monitor new opportunities in one place.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts and organizes deal-related data so users can quickly explore what AppSumo offers without manual browsing. It solves the problem of fragmented deal discovery by returning clean, structured results suitable for analysis or integration. It is built for marketers, founders, researchers, and deal hunters who need fast access to AppSumo deal data.
- Searches deals by keyword with fast response times
- Retrieves large lists of active deals by category or type
- Supports filtering for vetted-only deals
- Designed for automation-ready, repeatable data collection
| Feature | Description |
|---|---|
| Keyword Search | Find up to 10 relevant deals matching a specific search term. |
| Deal Listing | Retrieve up to 1,000 active deals for a selected type or category. |
| Vetted Filter | Optionally return only vetted deals for higher quality results. |
| Structured Output | Returns clean, machine-readable data for easy processing. |
| Scalable Queries | Built to handle frequent runs and large datasets efficiently. |
| Field Name | Field Description |
|---|---|
| deal_id | Unique identifier for the deal. |
| title | Name of the software, course, or product. |
| description | Short summary describing the deal. |
| category | Deal type or product category. |
| price | Current deal price or offer value. |
| original_price | Original price before discount, if available. |
| is_vetted | Indicates whether the deal is vetted. |
| deal_url | Direct link to the deal page. |
| status | Current availability status of the deal. |
AppSumo/
├── src/
│ ├── main.py
│ ├── collectors/
│ │ ├── search_deals.py
│ │ └── list_deals.py
│ ├── parsers/
│ │ └── deal_parser.py
│ ├── utils/
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.json
│ └── outputs.sample.json
├── requirements.txt
└── README.md
- Marketers use it to monitor software deals, so they can identify promotions to share with their audience.
- Startup founders use it to discover discounted tools, so they can reduce operational costs.
- Deal aggregators use it to collect structured deal data, so they can build comparison platforms.
- Researchers use it to analyze deal trends, so they can study pricing and category popularity.
Does this project return all available deals at once? It can return large batches of active deals, with configurable filters such as type or vetted-only status to control volume and relevance.
Can I search for specific tools or keywords? Yes, keyword-based search is supported and returns the most relevant deals for the provided term.
Is the output suitable for analytics or dashboards? Yes, the data is structured and consistent, making it easy to store, analyze, or visualize.
Are there limits on how often it can be run? It is designed for frequent execution, but practical usage should consider network stability and system resources.
Primary Metric: Average retrieval time of keyword-based results under 2 seconds for standard queries.
Reliability Metric: Stable execution with a success rate above 98% across repeated runs.
Efficiency Metric: Capable of processing hundreds of deal records per minute with minimal memory usage.
Quality Metric: High data completeness with consistently populated core fields such as title, price, and deal URL.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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