Top 7 web scraping challenges for AI teams in 2025

From training large language models to powering real-time personalization engines, AI systems depend on fresh, large-scale, high-quality web data to deliver meaningful results. As the demand for such data intensifies, web scraping has become a critical part of the modern AI pipeline.

But despite advancements in scraping tools and proxy infrastructure, reliably collecting web data at scale remains a persistent pain point. If you’re on an AI team, you’ve probably face roadblocks that disrupt workflows, inflate operational costs, or compromise the accuracy of their models. These challenges don’t just affect engineering teams—they directly impact an AI company’s ability to compete.

This article outlines the top 7 web scraping challenges that AI teams face in 2025, with insights into how teams can overcome them using the right tools, strategies, and infrastructure. Each one is grounded in our experience building technical AI products as well as feedback from other engineering AI teams.

Why web data is crucial (and uniquely challenging) for AI

Web data is foundational to the most advanced AI applications in 2025. It’s used it to train large language models, power recommendation engines, fuel competitive intelligence systems, and drive predictive analytics across industries. 

Unlike static datasets, the web offers a dynamic stream of real-world information—from product listings and job postings to news articles and user-generated content. Dynamic data is important if you want to keep AI systems relevant and effective.

But the same qualities that make web data so valuable also make it hard to acquire. 

Websites change frequently, implement anti-bot mechanisms, enforce geo-restrictions, and throttle access at scale. Public data may be fragmented, hidden behind JavaScript, or inconsistently structured. When you need clean, structured, and up-to-date data at massive volumes, these friction points introduce latency, degrade model quality, or can even block key product features.

If you want to successfully operationalize web data for AI, you have to overcome technical, legal, and infrastructure challenges—at scale and under pressure.

Top 7 web scraping challenges for AI in 2025

Here are some of the challenges many AI companies are currently facing.

Challenge 1: Frequent scraping failures and IP blocking

When requests fail or return incomplete content, they cause data gaps, outdated insights, and interrupted model training. This directly undermines the performance of AI systems. 

The impact is even more severe for applications that rely on real-time data feeds, like pricing engines or market trend prediction models.

In 2025, websites are more aggressive than ever in defending against automated scraping. Cloudflare, Akamai, and similar WAF providers continuously evolve their detection systems. They combine IP reputation scoring, browser fingerprinting, rate limiting, and behavior-based anomaly detection. 

Even advanced scrapers can get blocked after a few dozen requests, especially if they lack proper rotation logic or rely on overused proxy pools.

To overcome this, AI companies have to invest in smarter infrastructure. SOAX’s Web Data API is built specifically to handle these scenarios. It automatically bypasses common anti-bot mechanisms—including CAPTCHAs and JavaScript challenges—by simulating real user behavior at the network and browser level. 

If you prefer a more hands-on approach, you can integrate Puppeteer or Playwright with custom stealth plugins and fingerprint randomization.  However, that does demand ongoing engineering resources and tuning. Regardless of the approach, solving the IP blocking problem is foundational to any reliable AI data pipeline.

Challenge 2: Messy, unstructured data and parsing complexity

Even when scraping succeeds, the data it collects is rarely ready for use. Raw web data is often messy, inconsistently formatted, and embedded within layers of dynamic HTML. Before any of it can be used to train models or power inference pipelines, it must be parsed and structured. 

This preprocessing stage is one of the biggest time sinks in the AI data lifecycle. It delays experimentation, adds engineering overhead, and in many cases, degrades model performance.

At the root of the problem is the unpredictable nature of web content. HTML structures vary widely across sites, and often change without warning. There are no standard schemas. Content might be split across deeply nested tags, hidden behind JavaScript rendering, or inconsistently labelled. 

As a result, you end up writing custom parsing logic for every target domain, often reworking it with every minor layout change.

To streamline this, it’s better to tap into a structured scraping solutions. SOAX, for example, offers scraper APIs that return structured, pre-parsed data for specific domains. This reducines the need for manual HTML traversal. 

For example, if you're targeting e-commerce sites or job listings, you can fetch normalized outputs with product names, prices, and other relevant fields directly—saving hours of parsing work per data source.

For teams that prefer full control, libraries like BeautifulSoup and lxml in Python remain go-to tools. Here’s a quick example of how a scraper might extract and clean data:

from bs4 import BeautifulSoup

import requests

import pandas as pd

html = requests.get("https://example.com").text

soup = BeautifulSoup(html, "html.parser")

data = []

for item in soup.select(".product"):

    title = item.select_one(".title").get_text(strip=True)

    price = item.select_one(".price").get_text(strip=True)

    data.append({"title": title, "price": price})

df = pd.DataFrame(data)

df.drop_duplicates(inplace=True)

df.fillna("N/A", inplace=True)

print(df.to_json(orient="records"))

You can embed this kind of logic can be embedded into custom pipelines using Scrapy. You can transform it further with Pandas. You can even pipe it through CLI tools like JQ for lightweight processing in data workflows.

from playwright.sync_api import sync_playwright

with sync_playwright() as p:

    browser = p.chromium.launch(headless=True)

    page = browser.new_page()

    page.goto("https://example.com")

    page.wait_for_load_state("networkidle")  # wait until AJAX content loads

    html = page.content()

    print(html)

    browser.close()

While SOAX doesn’t handle parsing directly, it allows high-reliability access to raw HTML and supports integration with both code-based parsers and no-code or low-code data transformation platforms. 

When you combine well-planned schema design and normalization strategies, you can convert chaotic web data into model-ready inputs with far less friction.

Challenge 3: Scaling infrastructure and maintenance overhead

Scaling a scraping operation isn’t as simple as spinning up more scrapers. The underlying infrastructure—proxies, rotation logic, scheduling systems, adaptive parsers, and storage pipelines—becomes increasingly complex and expensive to manage. For AI teams focused on building and improving models, this overhead is a costly distraction.

Maintaining a high-volume web scraping stack in-house often means dealing with proxy churn, adapting to constantly changing website structures, handling anti-bot defenses, and writing custom retry logic. As more targets are added or scraping frequency increases, the effort scales linearly—if not worse. Engineering teams quickly find themselves maintaining infrastructure rather than shipping AI features.

This is where managed solutions become essential. With SOAX’s Web Data API, you don’t need to worry about proxy rotation, CAPTCHA handling, browser fingerprinting, or dynamic response parsing. It handles all of it under the hood—providing consistent, high-success scraping at scale. 

For teams that still want flexibility and control over scheduling and orchestration, SOAX proxies can be integrated into existing workflows using tools like Apache Airflow, AWS Lambda, or serverless scraping architectures. This lets teams run crawlers on-demand, scale based on events, and reduce compute costs while avoiding manual management of IP pools and bot evasion tactics.

By offloading the most painful parts of scraping, you can shift focus back to your real priority: building better models, not fighting infrastructure fires.

Challenge 4: Handling dynamic content (JavaScript-heavy websites)

A growing number of websites in 2025 rely on JavaScript to load key content, often through single-page application (SPA) frameworks like React, Vue, or Angular. For AI teams depending on scraped web data, this creates a major challenge. Traditional scrapers that fetch raw HTML frequently miss large portions of page content, leading to incomplete or misleading datasets.

This is especially a problem for AI models that rely on full-page context, whether it’s product metadata, user-generated content, or nested data structures revealed only after JavaScript execution. Since much of the content is rendered client-side via AJAX calls or dynamic event triggers, simply sending an HTTP request to the target URL is no longer enough.

To overcome this, you can use headless browsers like Puppeteer and Playwright  to programmatically render full web pages, wait for content to load, and return the final DOM. Here’s a basic example using Playwright in Python:

from playwright.sync_api import sync_playwright

with sync_playwright() as p:

    browser = p.chromium.launch(headless=True)

    page = browser.new_page()

    page.goto("https://example.com")

    page.wait_for_load_state("networkidle")  # wait until AJAX content loads

    html = page.content()

    print(html)

    browser.close()

While effective, browser automation comes with trade-offs—namely, increased compute cost and slower scraping speeds. It also requires careful tuning for each site.

SOAX simplifies this drastically with Web Data API, which includes real browser rendering as part of its scraping stack. By combining browser-level rendering with residential or mobile proxies, SOAX makes sure that AI teams get the full picture, not just partial data. The result is higher model accuracy, fewer data quality issues, and reduced engineering effort.

Challenge 5: Ensuring data quality, consistency, and freshness

In AI, the quality of your outputs is limited by the quality of your inputs. If web data is inconsistent, outdated, or duplicated, it directly compromises the accuracy and reliability of your models. 

This is especially critical for applications like real-time market analysis, news monitoring, or recommendation systems, where even a few hours’ delay or a skewed dataset can result in poor predictions or missed opportunities.

Data quality issues often stem from scraper failures, parsing errors, or inconsistent website structures. Without robust validation logic, bad data can silently propagate through pipelines and degrade model performance. 

The key to solving this is twofold:

• Create reliable access to target websites 

• Implement strong data hygiene practices

On the pipeline side, validation and deduplication logic can be implemented using tools like Pandas or Apache Spark. Here’s a simple example using Pandas to remove duplicates and check for nulls:

import pandas as pd

df = pd.read_json("scraped_data.json")

df.drop_duplicates(inplace=True)

df.dropna(subset=["title", "price"], inplace=True)  # critical fields must be present

# Ensure date freshness

df['timestamp'] = pd.to_datetime(df['timestamp'])

fresh_df = df[df['timestamp'] > pd.Timestamp.now() - pd.Timedelta(days=1)]

It’s also best practice to schedule regular crawls, track changes using data versioning systems (like DVC or LakeFS), and include freshness checks as part of automated workflows.

Challenge 6: Geo-restrictions and localization for AI models

For AI models that need to understand local markets, behavior, or sentiment, access to region-specific data isn’t optional—it’s essential. Whether you're fine-tuning a recommendation engine for a specific country or performing localized pricing intelligence, failing to gather accurate data from the right regions leads to blind spots and weaker model performance.

The challenge is that many websites serve different content depending on the visitor’s location. Some restrict access entirely based on geography, while others dynamically adjust pricing, product availability, or even language based on IP-based geolocation. 

This makes it difficult for AI teams to collect the localized data they need, especially if their scrapers are running from a single region or cloud provider.

To solve this, you need to route requests through IPs that appear to originate from specific regions. You need high-quality proxies.

Challenge 7: Legal and ethical compliance in data collection

In a regulatory environment that’s tightening worldwide, legal and ethical compliance in web scraping is no longer a nice-to-have; it’s a non-negotiable. For AI companies, non-compliance can result in hefty fines, lawsuits, and long-term reputational damage. But beyond the legal risks, scraping practices that disregard user privacy or website terms can undermine public trust in AI systems and the organizations behind them.

The challenge is that legal frameworks for web scraping—like GDPR, CCPA, and others—are still evolving and often ambiguous. What's permitted in one jurisdiction may be restricted in another. Scraping personal or sensitive information without proper controls, or ignoring signals like robots.txt, can quickly cross the line from aggressive data collection to unethical behavior.

AI teams must take a compliance-first approach to web scraping. That means respecting robots.txt directives where applicable. It also means avoiding the scraping of login-gated or explicitly private content. You also need to anonymize data where possible, and implement clear data retention and usage policies. Scraped datasets should be audited for personal information. Safeguards must be in place to make sure that collection practices align with regional laws.

SOAX supports this approach by providing ethical data collection tools by design. Our platform includes built-in rate limiting to avoid aggressive behavior that could overwhelm servers. It also includes sourcing controls so you can make sure proxies are from legitimate, consenting users. These features can give your AI company the confidence that your data infrastructure is aligned with responsible and lawful standards.

Overcoming challenges with SOAX's comprehensive solutions

Today, every enterprise AI team need a robust, reliable, and scalable data acquisition partner. SOAX offers a comprehensive suite of tools designed to address the exact challenges outlined in this article. We empower AI teams to move faster, stay compliant, and maintain data quality at scale.

SOAX’s Web Data API lets you reliably access even the most protected websites. It intelligently bypasses anti-bot measures—including rate limits, CAPTCHAs, and browser fingerprinting—while leveraging real browser rendering to retrieve complete content from JavaScript-heavy and SPA-based websites. Combined with a massive pool of residential, mobile, and ISP proxies with granular geo-targeting, you can maintain uninterrupted, high-fidelity data flows across virtually any domain or region.

When it comes to parsing flexibility, SOAX doesn’t dictate how teams structure or process their data. Instead, it guarantees consistent, dependable delivery of raw HTML—creating a solid foundation for integration with third-party or in-house parsers. Whether you're using BeautifulSoup, Scrapy, Spark, or low-code tools, SOAX makes sure the content is always there when you need it.

For teams looking to skip parsing altogether, SOAX provides scraper APIs that return structured, pre-cleaned data from select domains. Our APIs eliminate the need for custom extraction logic. They deliver JSON outputs that are ready to feed directly into AI workflows with minimal transformation.

In terms of scalability and maintenance, SOAX offloads the operational burden of managing proxies, adapting scrapers, or fighting anti-bot systems. That means AI teams no longer need to scale infrastructure manually or build workarounds for every site update. It frees them to focus on experimentation, modeling, and deployment.

SOAX proxies are ethically sourced with transparent opt-in mechanisms. We have a compliance-first mindset. Our built-in rate limiting helps make sure scrapers operate responsibly. Our platform architecture aligns with best practices under GDPR, CCPA, and other regulatory frameworks.

The solutions

AI teams face a complex set of challenges—from IP blocking and JavaScript-heavy websites to data inconsistency, localization gaps, and compliance risks. These obstacles don’t just slow down development—they directly impact the performance and reliability of AI models.

To solve these problems you need more than ad-hoc scripts and patchwork proxies. You need a robust, scalable, and ethical approach to web scraping—one that provides consistent access, clean inputs, and minimal operational overhead.

That’s where SOAX comes in. With products like Web Data API, scraper APIs, and a diverse network of residential, mobile, and ISP proxies, SOAX empowers your AI company to streamline data pipelines, scale intelligently, and stay compliant. And when paired with flexible parsing libraries, data validation logic, and transformation tools, the result is a scraping stack that’s purpose-built for modern AI workflows.

It’s time to make the change today and get the SOAX enterprise plan for your AI team!