Beyond APIs: Why 65% of enterprises now scrape data for AI training

Just 5 years ago, a talk about getting data for enterprise came to the one term – API. The vendor provides an endpoint, you sign a deal, pay for calls, and get structured, neat data flow. It seemed like an all-time solution to the problem of access to data. 

The development of generative AI rewrote that rule entirely. According to different data, 65% of enterprises that develop or train AI models already use web scraping as a main or aiding source of data, together with APIs or even replacing them. And this is not a temporary anomaly nor a detour for those who cannot find a suitable API vendor. This is a fundamental change in how companies see data as assets. 

If your company builds an ML pipeline, trains an LLM using their own domain data, or supports RAG systems using up-to-date, publicly available information, the question changes from “Do we need scraping?” to “How do we scale up our scraping activities, keep them legal and stable?” Here, the infrastructure reigns everything. 

Why APIs do not meet AI teams’ needs anymore 

APIs are perfect when the data vendor knows exactly what you need and agrees to provide you with that data. However, the training of modern models requires an altogether different approach. 

• Limited data volume – the majority of APIs return only the amount of data the vendor decided to provide. Historical data, niche pages, images with the original resolution, page markup – all those details are often excluded from API results. 
• Rate limits are designed for products, not for ML. Originally, API limits were set based on the needs of app-to-app integrations, which require hundreds to thousands of calls per day. Getting data for ML model training calls for millions of requests. Scales just won’t match. 
• Delay between the event and data availability – for some models, it is crucial to understand the current content – competitors’ prices, news, social media trends. The value of data decreases with every hour of delay. And APIs refresh according to the vendor’s schedule, not real-time shifts or your needs. 
• Costs – enterprise-level API plans, designed for high-volume load, are often several times more expensive than building your own data pipeline, especially when multimodal data (images, videos, PDF files, etc.) is in question. Besides, APIs just do not provide raw data for those. 
• The limitations of data sources available – a huge part of the WEB – local forums, lower-grade marketplaces, regional news sites, niche-specific sources – do not have their own APIs for data extraction. At the same time, they are all valuable data sources that you cannot exclude. Web scraping is the only way with them. 

What do AI teams really look for on the Web

The talk about “data for AI” sounds vague until you know exactly what data enterprise teams look for:

• Domain-specific corpora for LLM training – legal papers, medical publications, niche-specific technical documentation, where generic models show poor results.
• Data regarding competitors’ prices and variety of goods – for dynamic price intelligence models training.
• Multimodal datasets – images of goods, premises blueprints, cover texts for computer vision models.
• Trends and mood shifts from forums, reviews, and social media for models that foresee demand or reputational risks. 
• Alt-data for financial models – job postings, storage images, employee feedback – everything that gives a several-hour advantage compared to traditional sources. 

All those cases have something in common – data that is scattered across domains and  refreshed every second. And no API can cover it all. 

What are the problems of enterprise-level scraping 

Your scraping pipeline may perform perfectly while a pilot project with 500 requests, but after tests are successfully completed and you move to the real scraping with millions of requests,  it may suddenly break. The thing is that the scale shows issues that are impossible to spot without the high load. 

• IP denials 

Websites detect abnormal activity coming from a single IP in a matter of minutes. Datacenter IPs from which requests come en masse are blacklisted faster than a data collection process could end. 

• Geo-specific content 

Prices, availability, region-targeted ads – all that data is nonexistent if your request originates from the “wrong” place. If a model has to understand the Brazilian, German, or Japanese market, traffic has to look like it originates from there. 

• CAPTCHA and browser fingerprinting 

Modern bot protection systems do not rely solely on IP analysis – they also consider behavioral patterns, TLS fingerprints, request headers, and other signals. Generally, detection happens across 5 layers. If even one of those signals is suspicious, the scraper is likely to be blocked, and the whole pipeline becomes as helpless as beached fish – before a developer fixes it. 

• Data quality 

In order to be effective? Your model needs accurate data for training. When your scraper gets blocked repeatedly, collects partial or compromised data, or gets caught within honey pots and other traps for scrapers, the model learns wrong signals – and its assistance is questionable. The worst is that you would find that out after already investing loads of money into scraping and training. 

• Legal and compliance risks 

Enterprise teams can not allow themselves to scrape and see what will happen. They need an infrastructure that allows them to regulate request frequency, work along with robots.txt, and document the sources for audits – especially in strictly regulated niches and countries. 

Separately, each of those problems can be solved. However, they do not come separately – they are a package deal. Solving them is another job, not a one-time setting. 

When infrastructure decides

The best scraper is useless if it is blocked in the second minute by the target platform. Code is important – but the system around it also not to be underrated. 

• IP rotation – proxy pool allows for distributing requests in a way that each IP won’t get close to detection. This is the imitation of natural, user-typical traffic patterns. In a way, your scraper is also a user who tries to figure out a vast amount of data. 
• Precise geo-targeting – requests have to originate from a place that this model will later make forecasts and work with. If you intend to use a model for price prediction in Japan, but use data from Europe, the model will later give answers that are irrelevant for the target location, and it will happen at a production stage, when it is the most expensive to fix. 
• Reliability – SLA at uptime and proxy response time defines how much time the developer team spends on fixing the pipeline instead of training and upgrading the model itself. When it comes to enterprise, downtime costs delayed releases. 
• Session control – when you need to stick to the same session without getting an IP blocked, even the most sophisticated scraper alone won’t cut it without proxies.
• Compliance – enterprise-level proxy vendors that offer ethically sourced IP pools and stick to the local regulations, take over a part of the legal load from AI teams, which otherwise goes to compliance teams. 

To sum up 

Companies that only start developing their own models often treat a pilot project for a hundred requests and a full-scale project for a million requests as the same thing, just scaled up. In reality, they are two different things that require different approaches, as for a production-level system, the weak point is not the scraper itself, but the necessity to get training data legally, fast, and stably. So, the question is not “Do we really need to switch from APIs to scraping data, and do we need proxies for that?” but “How much does it cost us to lose another week working with the limited data APIs provide while our competitors release new models and scale data collection?”

Frequently Asked Questions