Jina AI Reader is a developer-focused API that transforms any webpage URL into clean, structured Markdown, simplifying data extraction for LLM applications, RAG pipelines, and AI agents.
Jina AI Reader provides a simple REST API that accepts a public URL and returns its core content in a clean Markdown format. The service is designed primarily for developers, data scientists, and AI engineers who need to ingest and process web content for their applications. Its key value proposition is abstracting away the complexity of web scraping, ad removal, and content parsing, providing a reliable and structured input for Large Language Models (LLMs). By simply prefixing a URL, users can instantly get LLM-ready data, which significantly accelerates the development of AI agents and content-based applications. This ensures high-quality, consistently formatted data from diverse web sources without maintaining complex custom parsers.
AI developers, data scientists, and software engineers building applications that need to ingest and process web page content, particularly for Retrieval-Augmented Generation (RAG) and LLM-powered agents.
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2020
Berlin, Germany
Free
Includes 1,000 credits per month. A basic reader request costs 1 credit. For hobbyists and small projects.
Free
Standard
Includes 10,000 credits per month. Enables advanced features like JS rendering (5 credits) and screenshots (10 credits).
$10/mo
Pro
Includes 60,000 credits per month. Provides higher rate limits and priority support for larger applications.
$50/mo
Enterprise
Custom credit volume, SLAs, and dedicated support for large-scale deployments in production environments.
Custom
A direct competitor focused on crawling and converting websites into LLM-ready markdown, but is primarily designed for crawling entire sites rather than single pages.
A better choice for users who need to manage proxies, browsers, and CAPTCHAs for scraping raw HTML at scale, without the content-cleaning aspect.
Offers a far more extensive (and expensive) suite of AI tools to turn unstructured web data into a structured Knowledge Graph, suitable for enterprise-level data extraction projects.
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