Web scraping in 2025 sits at an interesting crossroads. Traditional tools are still widely used and capable, but maintaining large scraping pipelines has become more demanding. Layouts change frequently, defensive systems are being improved, and HTML adjustments that break parsers are implemented faster than before.
At the same time, AI-driven techniques are maturing. Large language models (LLM) don’t replace the fundamentals of crawling, but they do change how we interpret page content and handle structured extraction. According to a 2025 McKinsey research, companies adopting generative AI jumped from 33% to 71% in a single year. Scraping is one of the areas where this shift was expected. More teams explore web scraping LLM methods and AI data scraping to reduce manual maintenance.
In this article we discuss a breakdown of the main AI-powered approaches based on scientific research, what is AI data scraping, and how they compare when tested on real webpages. It assumes that the reader already knows the answer to the question how does scraping work in general.
Where LLMs excel
GenAI gives scrapers three advantages:
Resilience to layout changes: LLMs can be trained to not rely on CSS selectors. They “understand” patterns in HTML or screenshots, making them far more tolerant of renamed classes, reordered and other elements, slight design changes.
Natural language extraction: Instead of writing parser logic, developers can roughly request: “Extract product title, price, rating, and features.” And the model returns structured JSON.
Understanding unstructured content: LLMs prompts for web scraping with ChatGPT, Gemini, DeepSeek and other models can be used to analyze sentiment, tone, classification, and semantic grouping. Dynamic content and JavaScript rendering become less of an obstacle.
Where LLMs fall short
Despite the impressive results, AI-powered approaches have clear pitfalls:
- Costs scale with LLM calls.
- Latency is higher than local parsing.
- Validation is mandatory.
- Vision models may hallucinate details.
- “URL-driven” method is statistically unreliable.
Where web scraping with LLMs fit into the workflow
Instead of manually defining extraction rules, engineers can use models to interpret HTML or screenshots directly. Depending on the method, the effort involved can range from “just run the code the model wrote for you” to “send cleaned HTML and let it return a JSON structure.”
There are three dominant approaches today.
1. AI-generated code from snippets
With an HTML snippet, an LLM can infer the structure of the page and produce a functional scraper. This is a practical example of AI data scraping in day-to-day workflows.
Typical workflow:
- Provide a small HTML sample from the target site.
- Describe the extraction task in natural language.
- The LLM writes the scraper code.
- You review the output and adjust where needed.
If the target website changes, the script can be regenerated or patched with another short prompt. This method doesn’t eliminate per-site customization, but it makes the process faster.
Benefits:
- Accuracy is close to a manual scraper for specific websites' structures. It's one of the best scraping tools for experienced teams.
- Code is generated once (or more for maintenance) and then LLM doesn't create additional costs.
- Generated code is customizable and can be improved manually.
Trade-offs:
- Must be maintained for each specific website, same as traditional methods.
- High maintenance costs for websites that frequently change CSS classes or layout.
2. Structured data extraction from HTML
Instead of generating code, it’s possible to send raw or cleaned HTML of a page directly to an LLM for web scraping and ask it to produce structured output.
Preprocessing and cleaning the code helps reduce costs in data extraction pipelines: removing navigation, boilerplate, scripts, and irrelevant sections can cut token usage by orders of magnitude.
Benefits:
- A single LLM web scraping prompt with ChatGPT can parse pages with different layouts.
- No XPath or CSS selectors required.
- The model identifies patterns for automated data parsing directly from the HTML.
Trade-offs:
- Token usage grows with page size.
- Throughput depends on API latency.
- Very large pages must be cleaned aggressively.
3. Vision-based extraction using screenshots (computer vision)
A newer but rapidly improving technique that uses screenshots of a rendered page instead of LLM prompts for web scraping. Vision-based LLMs can interpret text, layout, and visual patterns. This approach is especially useful for websites that rely heavily on JavaScript or use markup obfuscation.
Benefits:
- Extracts exactly what a user sees.
- Can handle dynamic elements, banners, overlays.
- Costs are predictable (fixed number of screenshots).
Trade-offs:
- Vision models have a higher chance of hallucinations.
- Heavier computations.
- Sensitive to small visual ambiguities.
4. URL-driven extraction
Some LLMs can browse the web and fetch data directly via a URL passed in the prompt. Its ease of use is appealing for people who don't have a technical background to learn how does scraping work. That said, the method is not yet stable enough for production use.
In testing across thousands of pages, the accuracy fluctuated dramatically. With the same URL, same model, and same prompt, results varied anywhere from 0% to 100% correct. This unpredictability makes URL-to-LLM scraping unsuitable for real-world workloads.
Performance comparison of the methods
Research from McGill University (3,000 pages: Amazon, Cars.com, Upwork) provides a clear comparison across key metrics. There is no clear answer on what are the best scraping tools, but some have an advantage in cost, speed, or accuracy.
The future of AI data scraping
Emerging models, particularly in OCR and 2D layout understanding, show that compressed visual representations can sometimes be more efficient than raw HTML. As these technologies evolve, vision-based extraction may become a standard component of scraping pipelines.
There is also an interesting aspect of URL-driven extraction: while it's too unstable today, the LLM evolution can make it viable for AI data scraping. It's unlikely to happen soon, but it's something to look for in the future, especially with a widespread use of AI agents.
It's unclear what AI data scraping will look like in 2026 and beyond, but even today we look at a tangible improvement in:
- Resilience to layout changes.
- Development speed.
- Cross-site generalization.
- Handling unstructured content.
In terms of market forecast, analysts from Technavio predict the AI-based web scraping market will rise to USD 3.16 billion by 2029, growing at a 39.4% CAGR from 2024.
Final thoughts
AI does not eliminate the need for traditional scraping techniques, but it meaningfully enhances them. Automatic code generation, HTML parsing, and screenshot-based extraction each provide reliable ways to interpret complex webpage data with minimal manual logic.
There is no single best scraping tool. Instead, the right choice depends on the complexity of the target site, the acceptable latency, and operational cost constraints. By combining traditional tools like Playwright, Selenium, and Beautiful Soup with modern LLM-based solutions, engineers can improve workflows.
The combination of LLMs for web scraping and rendering tools signals the beginning of more autonomous extraction systems that maintain accuracy while reducing the ongoing maintenance burden that has traditionally dominated web scraping work.
Citations
[1] “Generative AI for Data Scraping", Maxime C. Cohen, McGill University (2025)
[2] “Software Architecture for Improving Scraping Systems Using Artificial Intelligence", Bogdan-Stefan Posedaru, Bucharest University of Economic Studies (2024)
[3] “The state of AI in 2025: Agents, innovation, and transformation”, McKinsey (2025)
[4] “AI Driven Web Scraping Market Analysis”, Technavio (2025)
[5] “AI-Powered Web Scraping in 2025: Best Practices & Use Cases”, Expert Beacon (2023)
[6] “From Manual to Machine: How AI is Redefining Web Scraping for Superior Efficiency”, Enrique Ayuso, CCIS (2025)
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