With the advent of large language models (LLMs) and vision models, AI has been increasingly used across all aspects of computation, including data extraction and web automation. While AI offers significant advantages for web scraping, it also introduces a number of challenges.
What is AI Web Scraping?
AI web scraping is the process of extracting structured data from text, HTML or images (web page screenshots) using a language or vision model. For example, AI models like GPT-4o, Gemini 2.0 Flash, Claude 3.5 Sonnet and others can extract data from text, HTML or screenshots of web pages into a table/spreadsheet format.
Advantages of AI Web Scrapers
1. Easy to build
As with most AI related tasks, all you need is a prompt to build a scraper that extracts data from web pages. No coding is required since the model 'understands' the content (text, html or image) and allows the user to query the required data using natural-language prompts (for example: scrape the latest stock prices of S&P 500 companies from finance.yahoo.com)
2. Handles unstructured pages
AI can handle pages with little or no internal HTML structure to identify repeating data that can be saved to a table or spreadsheet. This is because the language model can semantically understand the content, unlike traditional web scraping tools, and perform the extraction accordingly.
3. Easy to extract data from images
AI can read data from images. For example, AI vision models can understand graphs, convert the data displayed in images to textual tables, interpret diagrams etc. AI also handles data extraction from PDFs better than traditional web scrapers.
Disadvantages of AI Web Scrapers
1. Inaccurate Data
The number one problem with AI web scraping is inaccurate or hallucinated data. When scraping thousands of rows of data from multiple pages and when 100% accuracy is required, AI cannot be fully trusted. Errors can creep in. Without human verification, you cannot be certain that the extracted data matches what appears on the website. But manual verification will defeat the purpose and advantage of using AI for web scraping.
Traditional web scraping tools work in a deterministic way and accurately extract data from web pages. They parse the HTML code in a strict and precise manner, without hallucinations or interpretations.
2. Higher Costs
For one-off data extraction requirements, it is economical to use AI for scraping. It is even cheaper (often free) compared to traditional scraping tools, since you can scrape the data within the daily free usage limits of many AI models like ChatGPT or Gemini. But once you need to scrape large amounts of data, when you need to scale up your scraping framework, the costs can increase exponentially.
Locally installable web scraping tools like WebHarvy comes with a one-time purchase cost which does not increase with usage. So scaling your scraping infrastructure does not result in higher expenses.
3. Slow
Compared to traditional scraping tools, AI scraping is slow because AI takes time to 'understand' data. AI models always try to extract the data semantically (the way a human would do) rather than automatically parsing it (the way a program is supposed to do). This is a use case where we need the software to act like a computer, not like a human.
Traditional scrapers run faster since they parse data from page HTML, using predefined algorithms, which does not involve any 'thinking time'.
4. Difficulty in handling page navigation, anti-bot systems, login etc.
When using AI for web scraping, it is often necessary to rely on a third party API or MCP server to read the HTML source or text of a web page, since AI is not good at navigating web pages or extracting the underlying source. In many cases, a full browser instance is required to load modern web pages correctly and obtain their text or HTML. Users may also need to follow links, handle pagination, submit forms, etc. Performing these tasks in a deterministic manner is much easier with traditional web scraping tools than with AI.
When to use AI Web Scraping?
AI web scraping is best suited for one-off data extraction tasks where ease of use is more important than accuracy, speed or cost. It is also useful when scraping unstructured pages or extracting data from images or PDFs. For large scale, recurring web scraping tasks that require high accuracy and speed, traditional web scraping tools are a better choice.