Collect Web Data for AI & LLMs at Scale

AI training requires large volumes of data, disqualifying the traditional hands-on data acquisition methods. Here are the four ways you can source AI training data for machine learning:

• Scraping web data with automated means from public websites.

• Acquiring AI training datasets from third-party providers.

• Generating synthetic training data using graphics engines.

• Partnering with businesses willing to share their proprietary data.

See this article about the main public data sources for LLM training to learn more on this topic as well as take a look into what is LLM. You can also use already pre-trained LLMs, such as GPT-4.5 and similar, allowing you to save time and resources. To build your own AI agents, connect them to live web data with Web API, and check out our tutorial on how to scrape Google AI Mode.

The actual data volumes for machine learning are highly dependent on the specific use case. The best approach is to start with existing benchmarks and gradually scale up as necessary.

Generally, you can try to predict your AI training data needs.

Small models (simple tasks): 100s to 1,000s of examples:

• Spam filters

• Website guidance (recommendations)

• Voice assistants

Medium models (moderately complex tasks): 10,000s to 100,000s of examples:

• Natural language processing (chatbots)

• Facial recognition for smartphones

• Translators

Large models (highly complex tasks): millions of examples:

• Generative AI

• Autonomous driving

• Robotics

With Oxylabs solutions, you get collected data in either structured JSON or raw HTML format. For datasets, we provide AI training data in the format of your choice.

To learn how this data is used to teach AI models, see our guide on how to train AI models or how to build an AI scraper with DeepSeek and Crawl4AI.