LLM Training Data Collected From the Public Web

Turn entire websites into training-ready content without building or maintaining your own crawling infrastructure. Decodo's /crawl endpoint automatically discovers and collects up to 10K pages per job, returning content in clean Markdown or HTML. Built-in proxy management, anti-bot handling, and optional JavaScript retries help you access even challenging websites, while customizable crawl parameters let you focus on the sections that matter. Spend less time managing crawlers and more time building better models.

Get cleaner, more structured content from the start with Web Data API's Markdown output. Instead of processing HTML, you receive readable text that preserves useful document structure while reducing unnecessary markup and page clutter. This simplifies downstream preprocessing, so you can focus on removing remaining boilerplate, filtering low-quality content, and eliminating duplicate records. The result is a more consistent dataset with less preprocessing overhead before training begins.

Prepare collected content for your LLM training pipeline using structured, easy-to-process Markdown output from Web Data API. Split documents into manageable chunks, attach metadata such as source URLs and collection timestamps, and convert the results into your preferred training format.

Whether you're preparing data for pre-training, fine-tuning, or model evaluation, Decodo helps simplify the collection stage so you can move from web content to usable datasets faster.

Websites may rate-limit automated requests, trigger CAPTCHAs, or block IP addresses, disrupting large-scale data collection. Decodo's Web Data API handles proxy rotation, retries, and anti-bot challenges using a network of 125M+ IPs. With /crawl, failed pages can also receive an automatic retry with JavaScript rendering, improving data retrieval without maintaining custom unblocking infrastructure.

JavaScript-heavy websites often load important content dynamically, leaving traditional HTTP scrapers with incomplete HTML. Web Data API supports JavaScript rendering to retrieve dynamically generated content, while /crawl can automatically retry failed pages with rendering enabled. This reduces the need to manage headless browsers and separate extraction workflows for modern web applications.

Build preprocessing rules to evaluate collected pages by language, content length, relevance, and text quality before adding them to your training corpus. Web Data API provides clean Markdown output to simplify extraction and downstream processing. Apply your own quality scoring, spam detection, and filtering logic to exclude irrelevant or low-value documents from LLM training datasets.

Review website terms, robots.txt directives, content licenses, and applicable AI training restrictions before collecting data. Use the Web Data API to target approved public sources and configure crawl parameters to limit collection to relevant sections. Maintain source URLs, collection timestamps, and permission records in your dataset metadata to support traceability and compliance audits.

Public web content can contain names, email addresses, and other personally identifiable information (PII). Integrate PII detection and masking into your preprocessing pipeline before using collected content for LLM training. Limit collection to authorized sources, exclude unnecessary personal data, and apply retention and access controls to meet applicable privacy requirements. And when in doubt, consult a legal professional regarding your data collection use case.